diff --git a/models/saprot_vh_vl/README.md b/models/saprot_vh_vl/README.md
new file mode 100644
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--- /dev/null
+++ b/models/saprot_vh_vl/README.md
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+# Saprot_VH_VL Baseline
+
+Ridge regression on embeddings from the **SaProt** protein language model on VH (variable heavy) and VL (variable light) sequences with two-chain encoding.
+
+## Description
+
+SaProt (Structure-aware Protein Language Model) generates predictions for protein properties using sequence and structure information. 
+
+This baseline uses locally computed SaProt embeddings(SaProt_35M_AF2) to generate fixed-length embeddings for VH and VL chains using their sequences and structures, then concatenates these embeddings(VH + VL) and trains simple Ridge regression models on top to predict antibody developability properties.
+
+The rationale behind this joint representation is same as that of the ESM2 case(no token contamination and learning from features independently).
+
+Note: At the time of writing, there were two choices to fetch structures from - MOE and ABB3. This implementation concerns itself only with the MOE structures
+
+## Method
+
+### 1. Separate Chain Embedding
+
+For each antibody, we embed the heavy and light chains independently:
+
+**VH Embedding:**
+```
+Complexed .pdb files from MOE → Extract VH pdb  → FoldSeek 3di Descriptors → Interleaved with VH_seq → SaProt Tokenizer → Last Hidden State → Mean Pool → vh_embedding
+```
+
+**VL Embedding:**
+```
+Complexed .pdb files from MOE → Extract VL pdb   → FoldSeek 3di Descriptors → Interleaved with VL_seq → SaProt Tokenizer → Last Hidden State → Mean Pool → vl_embedding
+```
+### 2. Feature Concatenation
+
+After generating embeddings for both chains, we concatenate them:
+
+```
+combined_embedding = np.concatenate([vh_embed, vl_embed])
+```
+
+For SaProt_35M_AF2, the embedding dimension is 480, so:
+- VH embedding: 480D
+- VL embedding: 480D  
+- Combined: 960D
+
+
+## Requirements
+
+- The Complexed(VH+VL) PDB structures for training are in `../../data/structures/MOE_structures/GDPa1/` and in the format of `{antibody_name}.csv`
+- The Complexed(VH+VL) PDB structures for the heldout data is in `../../data/structures/MOE_structures/heldout_test/` and in the format of `{antibody_name}.csv`
+- The Heavy chains are labelled by 'B' and the light chains are labelled by 'A'
+- foldseek installed
+- BioPython installed
+
+Note: While SaProt embeddings can be calculated from the sequence and structure information, in the absence of structure information, it defaults to calculating embeddings with sequence information only.
+
+Also Note: Current implementation works around abdev-core via hard-coding the size of heldout data. This is not good practice, and is only a temporary fix
+
+### Train
+
+From the repository root:
+
+```bash
+cd model/saprot_vh_vl
+pixi install
+
+# Train on GDPa1 dataset
+pixi run python -m saprot_vh_vl train \
+  --data ../../data/GDPa1_v1.2_20250814.csv \
+  --run-dir ./runs/my_run
+```
+
+### Predict
+
+```bash
+# Predict on training data
+pixi run python -m saprot_vh_vl predict \
+  --data ../../data/GDPa1_v1.2_20250814.csv \
+  --run-dir ./runs/my_run
+```
+
+### Full Workflow via Orchestrator
+
+From repository root:
+
+```bash
+pixi run all
+```
+
+This automatically discovers and runs all models, including SaProt_VH_VL, with 5-fold cross-validation.
+
+## Citation
+
+Saprot: Su J, et al. (2023). "SaProt: Protein Language Modeling with Structure-aware Vocabulary." bioRxiv.
+
+# Code References
+
+SaProt - https://github.com/westlake-repl/SaProt
+Foldseek - https://github.com/steineggerlab/foldseek
+
diff --git a/models/saprot_vh_vl/outputs/heldout/predictions.csv b/models/saprot_vh_vl/outputs/heldout/predictions.csv
new file mode 100644
index 0000000..aded43c
--- /dev/null
+++ b/models/saprot_vh_vl/outputs/heldout/predictions.csv
@@ -0,0 +1,247 @@
+antibody_name,vh_protein_sequence,vl_protein_sequence,HIC,Tm2,Titer,PR_CHO,AC-SINS_pH7.4
+abagovomab,QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLDWIGAIYPGDGNTRYTHKFKGKATLTADKSSSTAYMQLSSLASEDSGVYYCARGEGNYAWFAYWGQGTTVTVSS,DIELTQSPASLSASVGETVTITCQASENIYSYLAWHQQKQGKSPQLLVYNAKTLAGGVSSRFSGSGSGTHFSLKIKSLQPEDFGIYYCQHHYGILPTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+abituzumab,QVQLQQSGGELAKPGASVKVSCKASGYTFSSFWMHWVRQAPGQGLEWIGYINPRSGYTEYNEIFRDKATMTTDTSTSTAYMELSSLRSEDTAVYYCASFLGRGAMDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISNYLAWYQQKPGKAPKLLIYYTSKIHSGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQGNTFPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+abrezekimab,QVTLKESGPVLVKPTETLTLTCTVSGFSLTNYHVQWIRQPPGKALEWLGVMWSDGDTSFNSVLKSRLTISRDTSKSQVVLTMTNMDPVDTATYYCARDGTIAAMDYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCLASEDISNYLAWYQQKPGKAPKLLIYHTSRLQDGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGYRFPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+abrilumab,QVQLVQSGAEVKKPGASVKVSCKVSGYTLSDLSIHWVRQAPGKGLEWMGGFDPQDGETIYAQKFQGRVTMTEDTSTDTAYMELSSLKSEDTAVYYCATGSSSSWFDPWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYGASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFANYYCQQANSFPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+adalimumab,EVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQAPGKGLEWVSAITWNSGHIDYADSVEGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAKVSYLSTASSLDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGIRNYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCQRYNRAPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+aducanumab,QVQLVESGGGVVQPGRSLRLSCAASGFAFSSYGMHWVRQAPGKGLEWVAVIWFDGTKKYYTDSVKGRFTISRDNSKNTLYLQMNTLRAEDTAVYYCARDRGIGARRGPYYMDVWGKGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSTPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+alemtuzumab,QVQLQESGPGLVRPSQTLSLTCTVSGFTFTDFYMNWVRQPPGRGLEWIGFIRDKAKGYTTEYNPSVKGRVTMLVDTSKNQFSLRLSSVTAADTAVYYCAREGHTAAPFDYWGQGSLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQNIDKYLNWYQQKPGKAPKLLIYNTNNLQTGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCLQHISRPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+alirocumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFNNYAMNWVRQAPGKGLDWVSTISGSGGTTNYADSVKGRFIISRDSSKHTLYLQMNSLRAEDTAVYYCAKDSNWGNFDLWGRGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSVLYRSNNRNFLGWYQQKPGQPPNLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQYYTTPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+amatuximab,QVQLQQSGPELEKPGASVKISCKASGYSFTGYTMNWVKQSHGKSLEWIGLITPYNGASSYNQKFRGKATLTVDKSSSTAYMDLLSLTSEDSAVYFCARGGYDGRGFDYWGSGTPVTVSS,DIELTQSPAIMSASPGEKVTMTCSASSSVSYMHWYQQKSGTSPKRWIYDTSKLASGVPGRFSGSGSGNSYSLTISSVEAEDDATYYCQQWSKHPLTFGSGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+andecaliximab,QVQLQESGPGLVKPSETLSLTCTVSGFSLLSYGVHWVRQPPGKGLEWLGVIWTGGTTNYNSALMSRFTISKDDSKNTVYLKMNSLKTEDTAIYYCARYYYGMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVRNTVAWYQQKPGKAPKLLIYSSSYRNTGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQHYITPYTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+anetumab,QVELVQSGAEVKKPGESLKISCKGSGYSFTSYWIGWVRQAPGKGLEWMGIIDPGDSRTRYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARGQLYGGTYMDGWGQGTLVTVSS,DIALTQPASVSGSPGQSITISCTGTSSDIGGYNSVSWYQQHPGKAPKLMIYGVNNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCSSYDIESATPVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+anifrolumab,EVQLVQSGAEVKKPGESLKISCKGSGYIFTNYWIAWVRQMPGKGLESMGIIYPGDSDIRYSPSFQGQVTISADKSITTAYLQWSSLKASDTAMYYCARHDIEGFDYWGRGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSFFAWYQQKPGQAPRLLIYGASSRATGIPDRLSGSGSGTDFTLTITRLEPEDFAVYYCQQYDSSAITFGQGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+anrukinzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFISYAMSWVRQAPGKGLEWVASISSGGNTYYPDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARLDGYYFGFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASESVDNYGKSLMHWYQQKPGKAPKLLIYRASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSNEDPWTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+atezolizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLEWVAWISPYGGSTYYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARRHWPGGFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLYHPATFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+avelumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYIMMWVRQAPGKGLEWVSSIYPSGGITFYADTVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARIKLGTVTTVDYWGQGTLVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGGYNYVSWYQQHPGKAPKLMIYDVSNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCSSYTSSSTRVFGTGTKVTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bapineuzumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNYGMSWVRQAPGKGLEWVASIRSGGGRTYYSDNVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCVRYDHYSGSSDYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCKSSQSLLDSDGKTYLNWLLQKPGQSPQRLIYLVSKLDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCWQGTHFPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+basiliximab,QVQLQQSGTVLARPGASVKMSCKASGYSFTRYWMHWIKQRPGQGLEWIGAIYPGNSDTSYNQKFEGKAKLTAVTSASTAYMELSSLTHEDSAVYYCSRDYGYYFDFWGQGTTLTVSS,QIVSTQSPAIMSASPGEKVTMTCSASSSRSYMQWYQQKPGTSPKRWIYDTSKLASGVPARFSGSGSGTSYSLTISSMEAEDAATYYCHQRSSYTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bavituximab,EVQLQQSGPELEKPGASVKLSCKASGYSFTGYNMNWVKQSHGKSLEWIGHIDPYYGDTSYNQKFRGKATLTVDKSSSTAYMQLKSLTSEDSAVYYCVKGGYYGHWYFDVWGAGTTVTVSS,DIQMTQSPSSLSASLGERVSLTCRASQDIGSSLNWLQQGPDGTIKRLIYATSSLDSGVPKRFSGSRSGSDYSLTISSLESEDFVDYYCLQYVSSPPTFGAGTKLELK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+belantamab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSNYWMHWVRQAPGQGLEWMGATYRGHSDTYYNQKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARGAIYDGYDVLDNWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKLLIYYTSNLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYRKLPWTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+belimumab,QVQLQQSGAEVKKPGSSVRVSCKASGGTFNNNAINWVRQAPGQGLEWMGGIIPMFGTAKYSQNFQGRVAITADESTGTASMELSSLRSEDTAVYYCARSRDLLLFPHHALSPWGRGTMVTVSS,SSELTQDPAVSVALGQTVRVTCQGDSLRSYYASWYQQKPGQAPVLVIYGKNNRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCSSRDSSGNHWVFGGGTELTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bemarituzumab,QVQLVQSGAEVKKPGSSVKVSCKASGYIFTTYNVHWVRQAPGQGLEWIGSIYPDNGDTSYNQNFKGRATITADKSTSTAYMELSSLRSEDTAVYYCARGDFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQGVSNDVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQHSTTPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+benralizumab,EVQLVQSGAEVKKPGASVKVSCKASGYTFTSYVIHWVRQRPGQGLAWMGYINPYNDGTKYNERFKGKVTITSDRSTSTVYMELSSLRSEDTAVYLCGREGIRYYGLLGDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCGTSEDIINYLNWYQQKPGKAPKLLIYHTSRLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGYTLPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bevacizumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFTNYGMNWVRQAPGKGLEWVGWINTYTGEPTYAADFKRRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCAKYPHYYGSSHWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKVLIYFTSSLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSTVPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bezlotoxumab,EVQLVQSGAEVKKSGESLKISCKGSGYSFTSYWIGWVRQMPGKGLEWMGIFYPGDSSTRYSPSFQGQVTISADKSVNTAYLQWSSLKASDTAMYYCARRRNWGNAFDIWGQGTMVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSTWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bimagrumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSSYINWVRQAPGQGLEWMGTINPVSGSTSYAQKFQGRVTMTRDTSISTAYMELSRLRSDDTAVYYCARGGWFDYWGQGTLVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGSYNYVNWYQQHPGKAPKLMIYGVSKRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCGTFAGGSYYGVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bimekizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYNMAWVRQAPGKGLEWVATITYEGRNTYYRDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCASPPQYYEGSIYRLWFAHWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRADESVRTLMHWYQQKPGKAPKLLIYLVSNSEIGVPDRFSGSGSGTDFRLTISSLQPEDFATYYCQQTWSDPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bleselumab,QLQLQESGPGLLKPSETLSLTCTVSGGSISSPGYYGGWIRQPPGKGLEWIGSIYKSGSTYHNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCTRPVVRYFGWFDPWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+blosozumab,QVQLVQSGAEVKKPGASVKVSCKVSGFPIKDTFQHWVRQAPGKGLEWMGWSDPEIGDTEYASKFQGRVTMTEDTSTDTAYMELSSLRSEDTAVYYCATGDTTYKFDFWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVHTAVAWYQQKPGKAPKLLIYWASTRWTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSDYPWTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+bococizumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYYMHWVRQAPGQGLEWMGEISPFGGRTNYNEKFKSRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARERPLYASDLWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQRYSLWRTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+brazikumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVIWYDGSNEYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGYTSSWYPDAFDIWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSSSNTGAGYDVHWYQQVPGTAPKLLIYGSGNRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDSSLSGWVFGGGTRLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+brentuximab,QIQLQQSGPEVVKPGASVKISCKASGYTFTDYYITWVKQKPGQGLEWIGWIYPGSGNTKYNEKFKGKATLTVDTSSSTAFMQLSSLTSEDTAVYFCANYGNYWFAYWGQGTQVTVSA,DIVLTQSPASLAVSLGQRATISCKASQSVDFDGDSYMNWYQQKPGQPPKVLIYAASNLESGIPARFSGSGSGTDFTLNIHPVEEEDAATYYCQQSNEDPWTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+briakinumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAFIRYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCKTHGSHDNWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCSGSRSNIGSNTVKWYQQLPGTAPKLLIYYNDQRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDRYTHPALLFGTGTKVTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+brodalumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTRYGISWVRQAPGQGLEWMGWISTYSGNTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARRQLYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVSSNLAWFQQKPGQAPRPLIYDASTRATGVPARFSGSGSGTDFTLTISSLQSEDFAVYYCQQYDNWPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+brontictuzumab,QVQLVQSGAEVKKPGASVKISCKVSGYTLRGYWIEWVRQAPGKGLEWIGQILPGTGRTNYNEKFKGRVTMTADTSTDTAYMELSSLRSEDTAVYYCARFDGNYGYYAMDYWGQGTTVTVSS,QAVVTQEPSLTVSPGGTVTLTCRSSTGAVTTSNYANWFQQKPGQAPRTLIGGTNNRAPGVPARFSGSLLGGKAALTLSGAQPEDEAEYYCALWYSNHWVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+budigalimab,EIQLVQSGAEVKKPGSSVKVSCKASGYTFTHYGMNWVRQAPGQGLEWVGWVNTYTGEPTYADDFKGRLTFTLDTSTSTAYMELSSLRSEDTAVYYCTREGEGLGFGDWGQGTTVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSIVHSHGDTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHIPVTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+burosumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNHYMHWVRQAPGQGLEWMGIINPISGSTSNAQKFQGRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARDIVDAFDFWGQGTMVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALVWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNDYFTFGPGTKVDIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+cabiralizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDNYMIWVRQAPGQGLEWMGDINPYNGGTTFNQKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARESPYFSNLYVMDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCKASQSVDYDGDNYMNWYQQKPGQAPRLLIYAASNLESGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCHLSNEDLSTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+camidanlumab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSRYIINWVRQAPGQGLEWMGRIIPILGVENYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARKDWFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+camrelizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYMMSWVRQAPGKGLEWVATISGGGANTYYPDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARQLYYFDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCLASQTIGTWLTWYQQKPGKAPKLLIYTATSLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQVYSIPWTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+carlumab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYGISWVRQAPGQGLEWMGGIIPIFGTANYAQKFQGRVTITADESTSTAYMELSSLRSEDTAVYYCARYDGIYGELDFWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSDAYLAWYQQKPGQAPRLLIYDASSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCHQYIQLHSFTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+cemiplimab,EVQLLESGGVLVQPGGSLRLSCAASGFTFSNFGMTWVRQAPGKGLEWVSGISGGGRDTYFADSVKGRFTISRDNSKNTLYLQMNSLKGEDTAVYYCVKWGNIYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDSITITCRASLSINTFLNWYQQKPGKAPNLLIYAASSLHGGVPSRFSGSGSGTDFTLTIRTLQPEDFATYYCQQSSNTPFTFGPGTVVDFR,2.8211362,82.16446,240.63281,0.17229764,6.423554
+certolizumab,EVQLVESGGGLVQPGGSLRLSCAASGYVFTDYGMNWVRQAPGKGLEWMGWINTYIGEPIYADSVKGRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCARGYRSYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQNVGTNVAWYQQKPGKAPKALIYSASFLYSGVPYRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNIYPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+cetrelimab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGGIIPIFDTANYAQKFQGRVTITADESTSTAYMELSSLRSEDTAVYYCARPGLAAAYDTGSLDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVRSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRNYWPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+cetuximab,QVQLKQSGPGLVQPSQSLSITCTVSGFSLTNYGVHWVRQSPGKGLEWLGVIWSGGNTDYNTPFTSRLSINKDNSKSQVFFKMNSLQSNDTAIYYCARALTYYDYEFAYWGQGTLVTVSA,DILLTQSPVILSVSPGERVSFSCRASQSIGTNIHWYQQRTNGSPRLLIKYASESISGIPSRFSGSGSGTDFTLSINSVESEDIADYYCQQNNNWPTTFGAGTKLELK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+cixutumumab,EVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGGIIPIFGTANYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARAPLRFLEWSTQDHYYYYYMDVWGKGTTVTVSS,SSELTQDPAVSVALGQTVRITCQGDSLRSYYATWYQQKPGQAPILVIYGENKRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCKSRDGSGQHLVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+clazakizumab,EVQLVESGGGLVQPGGSLRLSCAASGFSLSNYYVTWVRQAPGKGLEWVGIIYGSDETAYATSAIGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDDSSDWDAKFNLWGQGTLVTVSS,AIQMTQSPSSLSASVGDRVTITCQASQSINNELSWYQQKPGKAPKLLIYRASTLASGVPSRFSGSGSGTDFTLTISSLQPDDFATYYCQQGYSLRNIDNAFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+codrituzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTDYEMHWVRQAPGQGLEWMGALDPKTGDTAYSQKFKGRVTLTADKSTSTAYMELSSLTSEDTAVYYCTRFYSYTYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLVHSNRNTYLHWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQNTHVPPTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+coltuximab,QVQLVQPGAEVVKPGASVKLSCKTSGYTFTSNWMHWVKQAPGQGLEWIGEIDPSDSYTNYNQNFQGKAKLTVDKSTSTAYMEVSSLRSDDTAVYYCARGSNPYYYAMDYWGQGTSVTVSS,EIVLTQSPAIMSASPGERVTMTCSASSGVNYMHWYQQKPGTSPRRWIYDTSKLASGVPARFSGSGSGTDYSLTISSMEPEDAATYYCHQRGSYTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+concizumab,EVQLVESGGGLVKPGGSLRLSCAASGFTFSNYAMSWVRQTPEKRLEWVATISRSGSYSYFPDSVQGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARLGGYDEGDAMDSWGQGTTVTVSS,DIVMTQTPLSLSVTPGQPASISCKSSQSLLESDGKTYLNWYLQKPGQSPQLLIYLVSILDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQATHFPQTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+crenezumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYGMSWVRQAPGKGLELVASINSNGGSTYYPDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCASGDYWGQGTTVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSLVYSNGDTYLHWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHVPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+crizanlizumab,QVQLVQSGAEVKKPGASVKVSCKVSGYTFTSYDINWVRQAPGKGLEWMGWIYPGDGSIKYNEKFKGRVTMTVDKSTDTAYMELSSLRSEDTAVYYCARRGEYGNYEGAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQSVDYDGHSYMNWYQQKPGKAPKLLIYAASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSDENPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+cusatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSVYYMNWVRQAPGKGLEWVSDINNEGGTTYYADSVKGRFTISRDNSKNSLYLQMNSLRAEDTAVYYCARDAGYSNHVPIFDSWGQGTLVTVSS,QAVVTQEPSLTVSPGGTVTLTCGLKSGSVTSDNFPTWYQQTPGQAPRLLIYNTNTRHSGVPDRFSGSILGNKAALTITGAQADDEAEYFCALFISNPSVEFGGGTQLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+dacetuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYSFTGYYIHWVRQAPGKGLEWVARVIPNAGGTSYNQKFKGRFTLSVDNSKNTAYLQMNSLRAEDTAVYYCAREGIYWWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSLVHSNGNTFLHWYQQKPGKAPKLLIYTVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYFCSQTTHVPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+daclizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTSYRMHWVRQAPGQGLEWIGYINPSTGYTEYNQKFKDKATITADESTNTAYMELSSLRSEDTAVYYCARGGGVFDYWGQGTLVTVSS,DIQMTQSPSTLSASVGDRVTITCSASSSISYMHWYQQKPGKAPKLLIYTTSNLASGVPARFSGSGSGTEFTLTISSLQPDDFATYYCHQRSTYPLTFGQGTKVEVK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+dalotuzumab,QVQLQESGPGLVKPSETLSLTCTVSGYSITGGYLWNWIRQPPGKGLEWIGYISYDGTNNYKPSLKDRVTISRDTSKNQFSLKLSSVTAADTAVYYCARYGRVFFDYWGQGTLVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSIVHSNGNTYLQWYLQKPGQSPQLLIYKVSNRLYGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+daratumumab,EVQLLESGGGLVQPGGSLRLSCAVSGFTFNSFAMSWVRQAPGKGLEWVSAISGSGGGTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCAKDKILWFGEPVFDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+denosumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSGITGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDPGTTVIMSWFDPWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVRGRYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVFYCQQYGSSPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+depatuxizumab,QVQLQESGPGLVKPSQTLSLTCTVSGYSISSDFAWNWIRQPPGKGLEWMGYISYSGNTRYQPSLKSRITISRDTSKNQFFLKLNSVTAADTATYYCVTAGRGFPYWGQGTLVTVSS,DIQMTQSPSSMSVSVGDRVTITCHSSQDINSNIGWLQQKPGKSFKGLIYHGTNLDDGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCVQYAQFPWTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+dinutuximab,EVQLLQSGPELEKPGASVMISCKASGSSFTGYNMNWVRQNIGKSLEWIGAIDPYYGGTSYNQKFKGRATLTVDKSSSTAYMHLKSLTSEDSAVYYCVSGMEYWGQGTSVTVSS,EIVMTQSPATLSVSPGERATLSCRSSQSLVHRNGNTYLHWYLQKPGQSPKLLIHKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPPLTFGAGTKLELK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+domagrozumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSTISSGGSYTSYPDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKQDYAMNYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVSTAVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQHYSTPWTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+dostarlimab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYDMSWVRQAPGKGLEWVSTISGGGSYTYYQDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCASPYYAMDYWGQGTTVTVSS,DIQLTQSPSFLSAYVGDRVTITCKASQDVGTAVAWYQQKPGKAPKLLIYWASTLHTGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCQHYSSYPWTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+duligotuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTLSGDWIHWVRQAPGKGLEWVGEISAAGGYTDYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARESRVSFEAAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQNIATDVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSEPEPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+dupilumab,EVQLVESGGGLEQPGGSLRLSCAGSGFTFRDYAMTWVRQAPGKGLEWVSSISGSGGNTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDRLSITIRPRYYGLDVWGQGTTVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSLLYSIGYNYLDWYLQKSGQSPQLLIYLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGFYYCMQALQTPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+durvalumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRYWMSWVRQAPGKGLEWVANIKQDGSEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGGWFGELAFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQRVSSSYLAWYQQKPGQAPRLLIYDASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSLPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+eculizumab,QVQLVQSGAEVKKPGASVKVSCKASGYIFSNYWIQWVRQAPGQGLEWMGEILPGSGSTEYTENFKDRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARYFFGSSPNWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCGASENIYGALNWYQQKPGKAPKLLIYGATNLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQNVLNTPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+efalizumab,EVQLVESGGGLVQPGGSLRLSCAASGYSFTGHWMNWVRQAPGKGLEWVGMIHPSDSETRYNQKFKDRFTISVDKSKNTLYLQMNSLRAEDTAVYYCARGIYFYGTTYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASKTISKYLAWYQQKPGKAPKLLIYSGSTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQHNEYPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+eldelumab,QMQLVESGGGVVQPGRSLRLSCTASGFTFSNNGMHWVRQAPGKGLEWVAVIWFDGMNKFYVDSVKGRFTISRDNSKNTLYLEMNSLRAEDTAIYYCAREGDGSGIYYYYGMDVWGQGTTVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPIFTFGPGTKVDIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+elezanumab,EVQLVQSGAEVKKPGASVKVSCKASGYTFTSHGISWVRQAPGQGLDWMGWISPYSGNTNYAQKLQGRVTMTTDTSTSTAYMELSSLRSEDTAVYYCARVGSGPYYYMDVWGQGTLVTVSS,QSALTQPRSVSGSPGQSVTISCTGTSSSVGDSIYVSWYQQHPGKAPKLMLYDVTKRPSGVPDRFSGSKSGNTASLTISGLQAEDEADYYCYSYAGTDTLFGGGTKVTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+elotuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFDFSRYWMSWVRQAPGKGLEWIGEINPDSSTINYAPSLKDKFIISRDNAKNSLYLQMNSLRAEDTAVYYCARPDGNYWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVGIAVAWYQQKPGKVPKLLIYWASTRHTGVPDRFSGSGSGTDFTLTISSLQPEDVATYYCQQYSSYPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+emactuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYDISWVRQAPGQGLEWMGVIWTDGGTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARDQRLYFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASEDVNTYVSWYQQKPGKAPKLLIYAASNRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSFSYPTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+emapalumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDGSSGWYVPHWFDPWGQGTLVTVSS,NFMLTQPHSVSESPGKTVTISCTRSSGSIASNYVQWYQQRPGSSPTTVIYEDNQRPSGVPDRFSGSIDSSSNSASLTISGLKTEDEADYYCQSYDGSNRWMFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+emibetuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTDYYMHWVRQAPGQGLEWMGRVNPNRRGTTYNQKFEGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARANWLDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCSVSSSVSSIYLHWYQQKPGKAPKLLIYSTSNLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQVYSGYPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+enavatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYWMSWVRQAPGKGLEWVAEIRLKSDNYATHYAESVKGRFTISRDDSKNSLYLQMNSLRAEDTAVYYCTGYYADAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSVSTSSYSYMHWYQQKPGKAPKLLIKYASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHSWEIPYTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+enokizumab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSYYWIEWVRQAPGQGLEWMGEILPGSGTTNPNEKFKGRVTITADESTSTAYMELSSLRSEDTAVYYCARADYYGSDYVKFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQHVITHVTWYQQKPGKAPKLLIYGTSYSYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFYEYPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+epratuzumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTSYWLHWVRQAPGQGLEWIGYINPRNDYTEYNQNFKDKATITADESTNTAYMELSSLRSEDTAFYFCARRDITTFYWGQGTTVTVSS,DIQLTQSPSSLSASVGDRVTMSCKSSQSVLYSANHKNYLAWYQQKPGKAPKLLIYWASTRESGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCHQYLSSWTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+eptinezumab,EVQLVESGGGLVQPGGSLRLSCAVSGIDLSGYYMNWVRQAPGKGLEWVGVIGINGATYYASWAKGRFTISRDNSKTTVYLQMNSLRAEDTAVYFCARGDIWGQGTLVTVSS,QVLTQSPSSLSASVGDRVTINCQASQSVYHNTYLAWYQQKPGKVPKQLIYDASTLASGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCLGSYDCTNGDCFVFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+erenumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSFGMHWVRQAPGKGLEWVAVISFDGSIKYSVDSVKGRFTISRDNSKNTLFLQMNSLRAEDTAVYYCARDRLNYYDSSGYYHYKYYGMAVWGQGTTVTVSS,QSVLTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKLLIYDNNKRPSGIPDRFSGSKSGTSTTLGITGLQTGDEADYYCGTWDSRLSAVVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+etaracizumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYDMSWVRQAPGKGLEWVAKVSSGGGSTYYLDTVQGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARHLHGSFASWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCQASQSISNFLHWYQQRPGQAPRLLIRYRSQSISGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQSGSWPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+etrolizumab,EVQLVESGGGLVQPGGSLRLSCAASGFFITNNYWGWVRQAPGKGLEWVGYISYSGSTSYNPSLKSRFTISRDTSKNTFYLQMNSLRAEDTAVYYCARTGSSGYFDFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASESVDDLLHWYQQKPGKAPKLLIKYASQSISGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGNSLPNTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+evinacumab,EVQLVESGGGVIQPGGSLRLSCAASGFTFDDYAMNWVRQGPGKGLEWVSAISGDGGSTYYADSVKGRFTISRDNSKNSLYLQMNSLRAEDTAFFYCAKDLRNTIFGVVIPDAFDIWGQGTMVTVSS,DIQMTQSPSTLSASVGDRVTITCRASQSIRSWLAWYQQKPGKAPKLLIYKASSLESGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQYNSYSYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+evolocumab,EVQLVQSGAEVKKPGASVKVSCKASGYTLTSYGISWVRQAPGQGLEWMGWVSFYNGNTNYAQKLQGRGTMTTDPSTSTAYMELRSLRSDDTAVYYCARGYGMDVWGQGTTVTVSS,ESALTQPASVSGSPGQSITISCTGTSSDVGGYNSVSWYQQHPGKAPKLMIYEVSNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCNSYTSTSMVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+farletuzumab,EVQLVESGGGVVQPGRSLRLSCSASGFTFSGYGLSWVRQAPGKGLEWVAMISSGGSYTYYADSVKGRFAISRDNAKNTLFLQMDSLRPEDTGVYFCARHGDDPAWFAYWGQGTPVTVSS,DIQLTQSPSSLSASVGDRVTITCSVSSSISSNNLHWYQQKPGKAPKPWIYGTSNLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSYPYMYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+fasinumab,QVQLVQSGAEVKKPGASVKVSCKVSGFTLTELSIHWVRQAPGKGLEWMGGFDPEDGETIYAQKFQGRVTMTEDTSTDTAYMELTSLRSEDTAVYYCSTIFGVVTNFDNWGQGTLVTVSS,DIQMTQSPSSLSASAGDRVTITCRASQAIRNDLGWYQQKPGKAPKRLIYAAFNLQSGVPSRFSGSGSGTEFTLTISSLQPEDLASYYCQQYNRYPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+fezakinumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYYMHWVRQAPGQGLEWVGWINPYTGSAFYAQKFRGRVTMTRDTSISTAYMELSRLRSDDTAVYYCAREPEKFDSDDSDVWGRGTLVTVSS,QAVLTQPPSVSGAPGQRVTISCTGSSSNIGAGYGVHWYQQLPGTAPKLLIYGDSNRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDNSLSGYVFGGGTQLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ficlatuzumab,QVQLVQPGAEVKKPGTSVKLSCKASGYTFTTYWMHWVRQAPGQGLEWIGEINPTNGHTNYNQKFQGRATLTVDKSTSTAYMELSSLRSEDTAVYYCARNYVGSIFDYWGQGTLLTVSS,DIVMTQSPDSLAMSLGERVTLNCKASENVVSYVSWYQQKPGQSPKLLIYGASNRESGVPDRFSGSGSATDFTLTISSVQAEDVADYHCGQSYNYPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+figitumumab,EVQLLESGGGLVQPGGSLRLSCTASGFTFSSYAMNWVRQAPGKGLEWVSAISGSGGTTFYADSVKGRFTISRDNSRTTLYLQMNSLRAEDTAVYYCAKDLGWSDSYYYYYGMDVWGQGTTVTVSS,DIQMTQFPSSLSASVGDRVTITCRASQGIRNDLGWYQQKPGKAPKRLIYAASRLHRGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQHNSYPCSFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+fletikumab,QVQLVQSGAEVKRPGASVKVSCKASGYTFTNDIIHWVRQAPGQRLEWMGWINAGYGNTQYSQNFQDRVSITRDTSASTAYMELISLRSEDTAVYYCAREPLWFGESSPHDYYGMDVWGQGTTVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+foralumab,QVQLVESGGGVVQPGRSLRLSCAASGFKFSGYGMHWVRQAPGKGLEWVAVIWYDGSKKYYVDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARQMGYWHFDLWGRGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+fremanezumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYWISWVRQAPGKGLEWVAEIRSESDASATHYAEAVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCLAYFDYGLAIQNYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCKASKRVTTYVSWYQQKPGQAPRLLIYGASNRYLGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCSQSYNYPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+fresolimumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFSSNVISWVRQAPGQGLEWMGGVIPIVDIANYAQRFKGRVTITADESTSTTYMELSSLRSEDTAVYYCASTLGLVLDAMDYWGQGTLVTVSS,ETVLTQSPGTLSLSPGERATLSCRASQSLGSSYLAWYQQKPGQAPRLLIYGASSRAPGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYADSPITFGQGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+fulranumab,EVQLVESGGGLVQPGGSLRLSCAASGFTLRSYSMNWVRQAPGKGLEWVSYISRSSHTIFYADSVKGRFTISRDNAKNSLYLQMDSLRDEDTAMYYCARVYSSGWHVSDYFDYWGQGILVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+galcanezumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFGNYWMQWVRQAPGQGLEWMGAIYEGTGKTVYIQKFADRVTITADKSTSTAYMELSSLRSEDTAVYYCARLSDYVSGFGYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASKDISKYLNWYQQKPGKAPKLLIYYTSGYHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGDALPPTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+galiximab,QVQLQESGPGLVKPSETLSLTCAVSGGSISGGYGWGWIRQPPGKGLEWIGSFYSSSGNTYYNPSLKSQVTISTDTSKNQFSLKLNSMTAADTAVYYCVRDRLFSVVGMVYNNWFDVWGPGVLVTVSS,ESALTQPPSVSGAPGQKVTISCTGSTSNIGGYDLHWYQQLPGTAPKLLIYDINKRPSGISDRFSGSKSGTAASLAITGLQTEDEADYYCQSYDSSLNAQVFGGGTRLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ganitumab,QVQLQESGPGLVKPSGTLSLTCAVSGGSISSSNWWSWVRQPPGKGLEWIGEIYHSGSTNYNPSLKSRVTISVDKSKNQFSLKLSSVTAADTAVYYCARWTGRTDAFDIWGQGTMVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLLHSNGYNYLDWYLQKPGQSPQLLIYLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQGTHWPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+gantenerumab,QVELVESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAINASGTRTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARGKGNTHKPYGYVRYFDVWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGVPARFSGSGSGTDFTLTISSLEPEDFATYYCLQIYNMPITFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+gatipotuzumab,EVQLVESGGGLVQPGGSMRLSCVASGFPFSNYWMNWVRQAPGKGLEWVGEIRLKSNNYTTHYAESVKGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCTRHYYFDYWGQGTLVTVSS,DIVMTQSPLSNPVTPGEPASISCRSSKSLLHSNGITYFFWYLQKPGQSPQLLIYQMSNLASGVPDRFSGSGSGTDFTLRISRVEAEDVGVYYCAQNLELPPTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+gemtuzumab,EVQLVQSGAEVKKPGSSVKVSCKASGYTITDSNIHWVRQAPGQSLEWIGYIYPYNGGTDYNQKFKNRATLTVDNPTNTAYMELSSLRSEDTAFYYCVNGNPWLAYWGQGTLVTVSS,DIQLTQSPSTLSASVGDRVTITCRASESLDNYGIRFLTWFQQKPGKAPKLLMYAASNQGSGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQTKEVPWSFGQGTKVEVK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+gevokizumab,QVQLQESGPGLVKPSQTLSLTCSFSGFSLSTSGMGVGWIRQPSGKGLEWLAHIWWDGDESYNPSLKSRLTISKDTSKNQVSLKITSVTAADTAVYFCARNRYDPPWFVDWGQGTLVTVSS,DIQMTQSTSSLSASVGDRVTITCRASQDISNYLSWYQQKPGKAVKLLIYYTSKLHSGVPSRFSGSGSGTDYTLTISSLQQEDFATYFCLQGKMLPWTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+gimsilumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRHWMHWLRQVPGKGPVWVSRINGAGTSITYADSVRGRFTISRDNANNTLFLQMNSLRADDTALYFCARANSVWFRGLFDYWGQGTPVTVSS,EIVLTQSPVTLSVSPGERVTLSCRASQSVSTNLAWYQQKLGQGPRLLIYGASTRATDIPARFSGSGSETEFTLTISSLQSEDFAVYYCQQYDKWPDTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+girentuximab,DVKLVESGGGLVKLGGSLKLSCAASGFTFSNYYMSWVRQTPEKRLELVAAINSDGGITYYLDTVKGRFTISRDNAKNTLYLQMSSLKSEDTALFYCARHRSGYFSMDYWGQGTSVTVSS,DIVMTQSQRFMSTTVGDRVSITCKASQNVVSAVAWYQQKPGQSPKLLIYSASNRYTGVPDRFTGSGSGTDFTLTISNMQSEDLADFFCQQYSNYPWTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+glembatumumab,QVQLQESGPGLVKPSQTLSLTCTVSGGSISSFNYYWSWIRHHPGKGLEWIGYIYYSGSTYSNPSLKSRVTISVDTSKNQFSLTLSSVTAADTAVYYCARGYNWNYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVDNNLVWYQQKPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQYNNWPPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+golimumab,QVQLVESGGGVVQPGRSLRLSCAASGFIFSSYAMHWVRQAPGNGLEWVAFMSYDGSNKKYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGIAAGGNYYYYGMDVWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVYSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPFTFGPGTKVDIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+guselkumab,EVQLVQSGAEVKKPGESLKISCKGSGYSFSNYWIGWVRQMPGKGLEWMGIIDPSNSYTRYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARWYYKPFDVWGQGTLVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSSSNIGSGYDVHWYQQLPGTAPKLLIYGNSKRPSGVPDRFSGSKSGTSASLAITGLQSEDEADYYCASWTDGLSLVVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ianalumab,QVQLQQSGPGLVKPSQTLSLTCAISGDSVSSNSAAWGWIRQSPGRGLEWLGRIYYRSKWYNSYAVSVKSRITINPDTSKNQFSLQLNSVTPEDTAVYYCARYQWVPKIGVFDSWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQFILPEYLSWYQQKPGQAPRLLIYGSSSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQFYSSPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ibalizumab,QVQLQQSGPEVVKPGASVKMSCKASGYTFTSYVIHWVRQKPGQGLDWIGYINPYNDGTDYDEKFKGKATLTSDTSTSTAYMELSSLRSEDTAVYYCAREKDNYATGAWFAYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERVTMNCKSSQSLLYSTNQKNYLAWYQQKPGQSPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSVQAEDVAVYYCQQYYSYRTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+icrucumab,QAQVVESGGGVVQSGRSLRLSCAASGFAFSSYGMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVRGRFTISRDNSENTLYLQMNSLRAEDTAVYYCARDHYGSGVHHYFYYGLDVWGQGTTVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+imgatuzumab,QVQLVQSGAEVKKPGSSVKVSCKASGFTFTDYKIHWVRQAPGQGLEWMGYFNPNSGYSTYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARLSPGGYYVMDAWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGINNYLNWYQQKPGKAPKRLIYNTNNLQTGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQHNSFPTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+inclacumab,EVQLVESGGGLVRPGGSLRLSCAASGFTFSNYDMHWVRQATGKGLEWVSAITAAGDIYYPGSVKGRFTISRENAKNSLYLQMNSLRAGDTAVYYCARGRYSGSGSYYNDWFDPWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+inebilizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSSWMNWVRQAPGKGLEWVGRIYPGDGDTNYNVKFKGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCARSGFITTVRDFDYWGQGTLVTVSS,EIVLTQSPDFQSVTPKEKVTITCRASESVDTFGISFMNWFQQKPDQSPKLLIHEASNQGSGVPSRFSGSGSGTDFTLTINSLEAEDAATYYCQQSKEVPFTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+infliximab,EVKLEESGGGLVQPGGSMKLSCVASGFIFSNHWMNWVRQSPEKGLEWVAEIRSKSINSATHYAESVKGRFTISRDDSKSAVYLQMTDLRTEDTGVYYCSRNYYGSTYDYWGQGTTLTVSS,DILLTQSPAILSVSPGERVSFSCRASQFVGSSIHWYQQRTNGSPRLLIKYASESMSGIPSRFSGSGSGTDFTLSINTVESEDIADYYCQQSHSWPFTFGSGTNLEVK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+inotuzumab,EVQLVQSGAEVKKPGASVKVSCKASGYRFTNYWIHWVRQAPGQGLEWIGGINPGNNYATYRRKFQGRVTMTADTSTSTVYMELSSLRSEDTAVYYCTREGYGNYGAWFAYWGQGTLVTVSS,DVQVTQSPSSLSASVGDRVTITCRSSQSLANSYGNTFLSWYLHKPGKAPQLLIYGISNRFSGVPDRFSGSGSGTDFTLTISSLQPEDFATYYCLQGTHQPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+intetumumab,QVQLVESGGGVVQPGRSRRLSCAASGFTFSRYTMHWVRQAPGKGLEWVAVISFDGSNKYYVDSVKGRFTISRDNSENTLYLQVNILRAEDTAVYYCAREARGSYAFDIWGQGTMVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPFTFGPGTKVDIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ipilimumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYTMHWVRQAPGKGLEWVTFISYDGNNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAIYYCARTGWLGPFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVGSSYLAWYQQKPGQAPRLLIYGAFSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+isatuximab,QVQLVQSGAEVAKPGTSVKLSCKASGYTFTDYWMQWVKQRPGQGLEWIGTIYPGDGDTGYAQKFQGKATLTADKSSKTVYMHLSSLASEDSAVYYCARGDYYGSNSLDYWGQGTSVTVSS,DIVMTQSHLSMSTSLGDPVSITCKASQDVSTVVAWYQQKPGQSPRRLIYSASYRYIGVPDRFTGSGAGTDFTFTISSVQAEDLAVYYCQQHYSPPYTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+iscalimab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVISYEESNRYHADSVKGRFTISRDNSKITLYLQMNSLRTEDTAVYYCARDGGIAAPGPDYWGQGTLVTVSS,DIVMTQSPLSLTVTPGEPASISCRSSQSLLYSNGYNYLDWYLQKPGQSPQVLISLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQARQTPFTFGPGTKVDIR,2.8211362,82.16446,240.63281,0.17229764,6.423554
+itolizumab,EVQLVESGGGLVKPGGSLKLSCAASGFKFSRYAMSWVRQAPGKRLEWVATISSGGSYIYYPDSVKGRFTISRDNVKNTLYLQMSSLRSEDTAMYYCARRDYDLDYFDSWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASRDIRSYLTWYQQKPGKAPKTLIYYATSLADGVPSRFSGSGSGQDYSLTISSLESDDTATYYCLQHGESPFTLGSGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ixekizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYSFTDYHIHWVRQAPGQGLEWMGVINPMYGTTDYNQRFKGRVTITADESTSTAYMELSSLRSEDTAVYYCARYDYFTGTGVYWGQGTLVTVSS,DIVMTQTPLSLSVTPGQPASISCRSSRSLVHSRGNTYLHWYLQKPGQSPQLLIYKVSNRFIGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHLPFTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ladiratuzumab,QVQLVQSGAEVKKPGASVKVSCKASGLTIEDYYMHWVRQAPGQGLEWMGWIDPENGDTEYGPKFQGRVTMTRDTSINTAYMELSRLRSDDTAVYYCAVHNAHYGTWFAYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLLHSSGNTYLEWYQQRPGQSPRPLIYKISTRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPYTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lampalizumab,EVQLVQSGPELKKPGASVKVSCKASGYTFTNYGMNWVRQAPGQGLEWMGWINTYTGETTYADDFKGRFVFSLDTSVSTAYLQISSLKAEDTAVYYCEREGGVNNWGQGTLVTVSS,DIQVTQSPSSLSASVGDRVTITCITSTDIDDDMNWYQQKPGKVPKLLISGGNTLRPGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCLQSDSLPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lanadelumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSHYIMMWVRQAPGKGLEWVSGIYSSGGITVYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAYRRIGVPRRDEFDIWGQGTMVTVSS,DIQMTQSPSTLSASVGDRVTITCRASQSISSWLAWYQQKPGKAPKLLIYKASTLESGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQYNTYWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+landogrozumab,EVQLVESGGGLVQPGGSLRLSCAASGLTFSRYPMSWVRQAPGKGLVWVSAITSSGGSTYYSDTVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCARLPDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASSSVSSSYLHWYQQKPGQAPRLLIYSTSNLVAGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQHHSGYHFTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lebrikizumab,QVTLRESGPALVKPTQTLTLTCTVSGFSLSAYSVNWIRQPPGKALEWLAMIWGDGKIVYNSALKSRLTISKDTSKNQVVLTMTNMDPVDTATYYCAGDGYYPYAMDNWGQGSLVTVSS,DIVMTQSPDSLSVSLGERATINCRASKSVDSYGNSFMHWYQQKPGQPPKLLIYLASNLESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQNNEDPRTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lenzilumab,QVQLVQSGAEVKKPGASVKVSCKASGYSFTNYYIHWVRQAPGQRLEWMGWINAGNGNTKYSQKFQGRVTITRDTSASTAYMELSSLRSEDTAVYYCVRRQRFPYYFDYWGQGTLVTVSS,EIVLTQSPATLSVSPGERATLSCRASQSVGTNVAWYQQKPGQAPRVLIYSTSSRATGITDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQFNKSPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lexatumumab,EVQLVQSGGGVERPGGSLRLSCAASGFTFDDYGMSWVRQAPGKGLEWVSGINWNGGSTGYADSVKGRVTISRDNAKNSLYLQMNSLRAEDTAVYYCAKILGAGRGWYFDLWGKGTTVTVSS,SSELTQDPAVSVALGQTVRITCQGDSLRSYYASWYQQKPGQAPVLVIYGKNNRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCNSRDSSGNHVVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ligelizumab,QVQLVQSGAEVMKPGSSVKVSCKASGYTFSWYWLEWVRQAPGHGLEWMGEIDPGTFTTNYNEKFKARVTFTADTSTSTAYMELSSLRSEDTAVYYCARFSHFSGSNYDYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSIGTNIHWYQQKPGQAPRLLIYYASESISGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQSWSWPTTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lintuzumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDYNMHWVRQAPGQGLEWIGYIYPYNGGTGYNQKFKSKATITADESTNTAYMELSSLRSEDTAVYYCARGRPAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASESVDNYGISFMNWFQQKPGKAPKLLIYAASNQGSGVPSRFSGSGSGTDFTLTISSLQPDDFATYYCQQSKEVPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lirilumab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSFYAISWVRQAPGQGLEWMGGFIPIFGAANYAQKFQGRVTITADESTSTAYMELSSLRSDDTAVYYCARIPSGSYYYDYDMDVWGQGTTVTVSS,EIVLTQSPVTLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWMYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+loncastuximab,QVQLVQPGAEVVKPGASVKLSCKTSGYTFTSNWMHWVKQAPGQGLEWIGEIDPSDSYTNYNQNFQGKAKLTVDKSTSTAYMEVSSLRSDDTAVYYCARGSNPYYYAMDYWGQGTSVTVSS,EIVLTQSPAIMSASPGERVTMTCSASSGVNYMHWYQQKPGTSPRRWIYDTSKLASGVPARFSGSGSGTSYSLTISSMEPEDAATYYCHQRGSYTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lorvotuzumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSFGMHWVRQAPGKGLEWVAYISSGSFTIYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARMRKGYAMDYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQIIIHSDGNTYLEWFQQRPGQSPRRLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPHTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lucatumumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVISYEESNRYHADSVKGRFTISRDNSKITLYLQMNSLRTEDTAVYYCARDGGIAAPGPDYWGQGTLVTVSS,DIVMTQSPLSLTVTPGEPASISCRSSQSLLYSNGYNYLDWYLQKPGQSPQVLISLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQARQTPFTFGPGTKVDIR,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lumiliximab,EVQLVESGGGLAKPGGSLRLSCAASGFRFTFNNYYMDWVRQAPGQGLEWVSRISSSGDPTWYADSVKGRFTISRENANNTLFLQMNSLRAEDTAVYYCASLTTGSDSWGQGVLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDIRYYLNWYQQKPGKAPKLLIYVASSLQSGVPSRFSGSGSGTEFTLTVSSLQPEDFATYYCLQVYSTPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+lumretuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFRSSYISWVRQAPGQGLEWMGWIYAGTGSPSYNQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARHRDYYSNSLTYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSVLNSGNQKNYLTWYQQKPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQSDYSYPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+margetuximab,QVQLQQSGPELVKPGASLKLSCTASGFNIKDTYIHWVKQRPEQGLEWIGRIYPTNGYTRYDPKFQDKATITADTSSNTAYLQVSRLTSEDTAVYYCSRWGGDGFYAMDYWGQGASVTVSS,DIVMTQSHKFMSTSVGDRVSITCKASQDVNTAVAWYQQKPGHSPKLLIYSASFRYTGVPDRFTGSRSGTDFTFTISSVQAEDLAVYYCQQHYTTPPTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+matuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSHWMHWVRQAPGQGLEWIGEFNPSNGRTNYNEKFKSKATMTVDTSTNTAYMELSSLRSEDTAVYYCASRDYDYDGRYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVTYMYWYQQKPGKAPKLLIYDTSNLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSHIFTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+mavrilimumab,QVQLVQSGAEVKKPGASVKVSCKVSGYTLTELSIHWVRQAPGKGLEWMGGFDPEENEIVYAQRFQGRVTMTEDTSTDTAYMELSSLRSEDTAVYYCAIVGSFSPLTLGLWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSGSNIGAPYDVSWYQQLPGTAPKLLIYHNNKRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCATVEAGLSGSVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+mepolizumab,QVTLRESGPALVKPTQTLTLTCTVSGFSLTSYSVHWVRQPPGKGLEWLGVIWASGGTDYNSALMSRLSISKDTSRNQVVLTMTNMDPVDTATYYCARDPPSSLLRLDYWGRGTPVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSLLNSGNQKNYLAWYQQKPGQPPKLLIYGASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQNVHSFPFTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+milatuzumab,QVQLQQSGSELKKPGASVKVSCKASGYTFTNYGVNWIKQAPGQGLQWMGWINPNTGEPTFDDDFKGRFAFSLDTSVSTAYLQISSLKADDTAVYFCSRSRGKNEAWFAYWGQGTLVTVSS,DIQLTQSPLSLPVTLGQPASISCRSSQSLVHRNGNTYLHWFQQRPGQSPRLLIYTVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYFCSQSSHVPPTFGAGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+mirikizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYKFTRYVMHWVRQAPGQGLEWMGYINPYNDGTNYNEKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARNWDTGLWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCKASDHILKFLTWYQQKPGKAPKLLIYGATSLETGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQMYWSTPFTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+mirvetuximab,QVQLVQSGAEVVKPGASVKISCKASGYTFTGYFMNWVKQSPGQSLEWIGRIHPYDGDTFYNQKFQGKATLTVDKSSNTAHMELLSLTSEDFAVYYCTRYDGSRAMDYWGQGTTVTVSS,DIVLTQSPLSLAVSLGQPAIISCKASQSVSFAGTSLMHWYHQKPGQQPRLLIYRASNLEAGVPDRFSGSGSKTDFTLTISPVEAEDAATYYCQQSREYPYTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+mitazalimab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSTYGMHWVRQAPGKGLEWLSYISGGSSYIFYADSVRGRFTISRDNSENALYLQMNSLRAEDTAVYYCARILRGGSGMDLWGQGTLVTVSS,QSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYNVYWYQQLPGTAPKLLIYGNINRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDKSISGLVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+mogamulizumab,EVQLVESGGDLVQPGRSLRLSCAASGFIFSNYGMSWVRQAPGKGLEWVATISSASTYSYYPDSVKGRFTISRDNAKNSLYLQMNSLRVEDTALYYCGRHSDGNFAFGYWGQGTLVTVSS,DVLMTQSPLSLPVTPGEPASISCRSSRNIVHINGDTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSLLPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+monalizumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYWMNWVRQAPGQGLEWMGRIDPYDSETHYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARGGYDFDVGTLYWFFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASENIYSYLAWYQQKPGKAPKLLIYNAKTLAEGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHHYGTPRTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+mosunetuzumab,EVQLVQSGAEVKKPGASVKVSCKASGYTFTNYYIHWVRQAPGQGLEWIGWIYPGDGNTKYNEKFKGRATLTADTSTSTAYLELSSLRSEDTAVYYCARDSYSNYYFDYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSLLNSRTRKNYLAWYQQKPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCTQSFILRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+motavizumab,QVTLRESGPALVKPTQTLTLTCTFSGFSLSTAGMSVGWIRQPPGKALEWLADIWWDDKKHYNPSLKDRLTISKDTSKNQVVLKVTNMDPADTATYYCARDMIFNFYFDVWGQGTTVTVSS,DIQMTQSPSTLSASVGDRVTITCSASSRVGYMHWYQQKPGKAPKLLIYDTSKLASGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCFQGSGYPFTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+muromonab,QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPSRGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSS,QIVLTQSPAIMSASPGEKVTMTCSASSSVSYMNWYQQKSGTSPKRWIYDTSKLASGVPAHFRGSGSGTSYSLTISGMEAEDAATYYCQQWSSNPFTFGSGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+natalizumab,QVQLVQSGAEVKKPGASVKVSCKASGFNIKDTYIHWVRQAPGQRLEWMGRIDPANGYTKYDPKFQGRVTITADTSASTAYMELSSLRSEDTAVYYCAREGYYGNYGVYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKTSQDINKYMAWYQQTPGKAPRLLIHYTSALQPGIPSRFSGSGSGRDYTFTISSLQPEDIATYYCLQYDNLWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+necitumumab,QVQLQESGPGLVKPSQTLSLTCTVSGGSISSGDYYWSWIRQPPGKGLEWIGYIYYSGSTDYNPSLKSRVTMSVDTSKNQFSLKVNSVTAADTAVYYCARVSIFGVGTFDYWGQGTLVTVSS,EIVMTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCHQYGSTPLTFGGGTKAEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+nesvacumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYDIHWVRQATGKGLEWVSAIGPAGDTYYPGSVKGRFTISRENAKNSLYLQMNSLRAGDTAVYYCARGLITFGGLIAPFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSTYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQHYDNSQTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+nimotuzumab,QVQLQQSGAEVKKPGSSVKVSCKASGYTFTNYYIYWVRQAPGQGLEWIGGINPTSGGSNFNEKFKTRVTITADESSTTAYMELSSLRSEDTAFYFCTRQGLWFDSDGRGFDFWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQNIVHSNGNTYLDWYQQTPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCFQYSHVPWTFGQGTKLQIT,2.8211362,82.16446,240.63281,0.17229764,6.423554
+nivolumab,QVQLVESGGGVVQPGRSLRLDCKASGITFSNSGMHWVRQAPGKGLEWVAVIWYDGSKRYYADSVKGRFTISRDNSKNTLFLQMNSLRAEDTAVYYCATNDDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQSSNWPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+obexelimab,EVQLVESGGGLVKPGGSLKLSCAASGYTFTSYVMHWVRQAPGKGLEWIGYINPYNDGTKYNEKFQGRVTISSDKSISTAYMELSSLRSEDTAMYYCARGTYYYGTRVFDYWGQGTLVTVSS,DIVMTQSPATLSLSPGERATLSCRSSKSLQNVNGNTYLYWFQQKPGQSPQLLIYRMSNLNSGVPDRFSGSGSGTEFTLTISSLEPEDFAVYYCMQHLEYPITFGAGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+obiltoxaximab,QVQLQQSGPELKKPGASVKVSCKDSGYAFSSSWMNWVRQAPGQGLEWIGRIYPGDGDTNYNGKFQGRVTITADKSSSTAYMELSSLRSEDTAVYFCARSGLLRYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDIRNYLNWYQQKPGKAVKLLIYYTSRLLPGVPSRFSGSGSGTDYSLTISSQEQEDIGTYFCQQGNTLPWTFGQGTKVEIR,2.8211362,82.16446,240.63281,0.17229764,6.423554
+obinutuzumab,QVQLVQSGAEVKKPGSSVKVSCKASGYAFSYSWINWVRQAPGQGLEWMGRIFPGDGDTDYNGKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARNVFDGYWLVYWGQGTLVTVSS,DIVMTQTPLSLPVTPGEPASISCRSSKSLLHSNGITYLYWYLQKPGQSPQLLIYQMSNLVSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCAQNLELPYTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ocrelizumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFTSYNMHWVRQAPGKGLEWVGAIYPGNGDTSYNQKFKGRFTISVDKSKNTLYLQMNSLRAEDTAVYYCARVVYYSNSYWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASSSVSYMHWYQQKPGKAPKPLIYAPSNLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQWSFNPPTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ofatumumab,EVQLVESGGGLVQPGRSLRLSCAASGFTFNDYAMHWVRQAPGKGLEWVSTISWNSGSIGYADSVKGRFTISRDNAKKSLYLQMNSLRAEDTALYYCAKDIQYGNYYYGMDVWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPITFGQGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+olaratumab,QLQLQESGPGLVKPSETLSLTCTVSGGSINSSSYYWGWLRQSPGKGLEWIGSFFYTGSTYYNPSLRSRLTISVDTSKNQFSLMLSSVTAADTAVYYCARQSTYYYGSGNYYGWFDRWDQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPAFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+oleclumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAYSWVRQAPGKGLEWVSAISGSGGRTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARLGYGRVDEWGRGTLVTVSS,QSVLTQPPSASGTPGQRVTISCSGSLSNIGRNPVNWYQQLPGTAPKLLIYLDNLRLSGVPDRFSGSKSGTSASLAISGLQSEDEADYYCATWDDSHPGWTFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+olokizumab,EVQLVESGGGLVQPGGSLRLSCAASGFNFNDYFMNWVRQAPGKGLEWVAQMRNKNYQYGTYYAESLEGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCARESYYGFTSYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDIGISLSWYQQKPGKAPKLLIYNANNLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCLQHNSAPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+omalizumab,EVQLVESGGGLVQPGGSLRLSCAVSGYSITSGYSWNWIRQAPGKGLEWVASITYDGSTNYNPSLKGRITISRDDSKNTFYLQMNSLRAEDTAVYYCARGSHYFGHWHFAVWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCRASQSVDYDGDSYMNWYQQKPGKAPKLLIYAASYLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSHEDPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+omburtamab,QVQLQQSGAELVKPGASVKLSCKASGYTFTNYDINWVRQRPEQGLEWIGWIFPGDGSTQYNEKFKGKATLTTDTSSSTAYMQLSRLTSEDSAVYFCARQTTATWFAYWGQGTLVTVSA,DIVMTQSPATLSVTPGDRVSLSCRASQSISDYLHWYQQKSHESPRLLIKYASQSISGIPSRFSGSGSGSDFTLSINSVEPEDVGVYYCQNGHSFPLTFGAGTKLELK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+onartuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFTSYWLHWVRQAPGKGLEWVGMIDPSNSDTRFNPNFKDRFTISADTSKNTAYLQMNSLRAEDTAVYYCATYRSYVTPLDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSSQSLLYTSSQKNYLAWYQQKPGKAPKLLIYWASTRESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYAYPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+opicinumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSAYEMKWVRQAPGKGLEWVSVIGPSGGFTFYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCATEGDNDAFDIWGQGTTVTVSS,DIQMTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPMYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+orticumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNAWMSWVRQAPGKGLEWVSSISVGGHRTYYADSVKGRSTISRDNSKNTLYLQMNSLRAEDTAVYYCARIRVGPSGGAFDYWGQGTLVTVSS,QSVLTQPPSASGTPGQRVTISCSGSNTNIGKNYVSWYQQLPGTAPKLLIYANSNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCASWDASLNGWVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+osocimab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSQYGMDWVRQAPGKGLEWVSGIGPSGGSTVYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCTRGGPYYYYGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQADSFPVTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+otelixizumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSFPMAWVRQAPGKGLEWVSTISTSGGRTYYRDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKFRQYSGGFDYWGQGTLVTVSS,DIQLTQPNSVSTSLGSTVKLSCTLSSGNIENNYVHWYQLYEGRSPTTMIYDDDKRPDGVPDRFSGSIDRSSNSAFLTIHNVAIEDEAIYFCHSYVSSFNVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+otlertuzumab,EVQLVQSGAEVKKPGESLKISCKGSGYSFTGYNMNWVRQMPGKGLEWMGNIDPYYGGTTYNRKFKGQVTISADKSISTAYLQWSSLKASDTAMYYCARSVGPFDSWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASENVYSYLAWYQQKPGQAPRLLIYFAKTLAEGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQHHSDNPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ozanezumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYWMHWVRQAPGQGLEWIGNINPSNGGTNYNEKFKSKATMTRDTSTSTAYMELSSLRSEDTAVYYCELMQGYWGQGTLVTVSS,DIVMTQSPLSNPVTLGQPVSISCRSSKSLLYKDGKTYLNWFLQRPGQSPQLLIYLMSTRASGVPDRFSGGGSGTDFTLKISRVEAEDVGVYYCQQLVEYPLTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+palivizumab,QVTLRESGPALVKPTQTLTLTCTFSGFSLSTSGMSVGWIRQPPGKALEWLADIWWDDKKDYNPSLKSRLTISKDTSKNQVVLKVTNMDPADTATYYCARSMITNWYFDVWGAGTTVTVSS,DIQMTQSPSTLSASVGDRVTITCKCQLSVGYMHWYQQKPGKAPKLLIYDTSKLASGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCFQGSGYPFTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+pamrevlumab,EGQLVQSGGGLVHPGGSLRLSCAGSGFTFSSYGMHWVRQAPGKGLEWVSGIGTGGGTYSTDSVKGRFTISRDNAKNSLYLQMNSLRAEDMAVYYCARGDYYGSGSFFDCWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNSYPPTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+panitumumab,QVQLQESGPGLVKPSETLSLTCTVSGGSVSSGDYYWTWIRQSPGKGLEWIGHIYYSGNTNYNPSLKSRLTISIDTSKTQFSLKLSSVTAADTAIYYCVRDRVTGAFDIWGQGTMVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGSGSGTDFTFTISSLQPEDIATYFCQHFDHLPLAFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+panobacumab,EEQVVESGGGFVQPGGSLRLSCAASGFTFSPYWMHWVRQAPGKGLVWVSRINSDGSTYYADSVKGRFTISRDNARNTLYLQMNSLRAEDTAVYYCARDRYYGPEMWGQGTMVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLVYSDGNTYLNWFQQRPGQSPRRLIYKVSNRDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQGTHWPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+parsatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFIDYYMNWVRQAPGKGLEWVGDINLDNSGTHYNQKFKGRFTISRDKSKNTAYLQMNSLRAEDTAVYYCAREGVYHDYDDYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRTSQSLVHINAITYLHWYQQKPGKAPKLLIYRVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCGQSTHVPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+patritumab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVETSKNQFSLKLSSVTAADTAVYYCARDKWTWYFDLWGRGTLVTVSS,DIEMTQSPDSLAVSLGERATINCRSSQSVLYSSSNRNYLAWYQQNPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQYYSTPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+pembrolizumab,QVQLVQSGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASKGVSTSGYSYLHWYQQKPGQAPRLLIYLASYLESGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQHSRDLPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+pertuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFTDYTMDWVRQAPGKGLEWVADVNPNSGGSIYNQRFKGRFTLSVDRSKNTLYLQMNSLRAEDTAVYYCARNLGPSFYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVSIGVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYIYPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+pidilizumab,QVQLVQSGSELKKPGASVKISCKASGYTFTNYGMNWVRQAPGQGLQWMGWINTDSGESTYAEEFKGRFVFSLDTSVNTAYLQITSLTAEDTGMYFCVRVGYDALDYWGQGTLVTVSS,EIVLTQSPSSLSASVGDRVTITCSARSSVSYMHWFQQKPGKAPKLWIYRTSNLASGVPSRFSGSGSGTSYCLTINSLQPEDFATYYCQQRSSFPLTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+pinatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYEFSRSWMNWVRQAPGKGLEWVGRIYPGDGDTNYSGKFKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARDGSSWDWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSIVHSVGNTFLEWYQQKPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCFQGSQFPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+plozalizumab,EVQLVESGGGLVKPGGSLRLSCAASGFTFSAYAMNWVRQAPGKGLEWVGRIRTKNNNYATYYADSVKDRFTISRDDSKNTLYLQMNSLKTEDTAVYYCTTFYGNGVWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCKSSQSLLDSDGKTFLNWFQQRPGQSPRRLIYLVSKLDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCWQGTHFPYTFGQGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+polatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFSSYWIEWVRQAPGKGLEWIGEILPGGGDTNYNEIFKGRATFSADTSKNTAYLQMNSLRAEDTAVYYCTRRVPIRLDYWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCKASQSVDYEGDSFLNWYQQKPGKAPKLLIYAASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSNEDPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ponezumab,QVQLVQSGAEVKKPGASVKVSCKASGYYTEAYYIHWVRQAPGQGLEWMGRIDPATGNTKYAPRLQDRVTMTRDTSTSTVYMELSSLRSEDTAVYYCASLYSLPVYWGQGTTVTVSS,DVVMTQSPLSLPVTLGQPASISCKSSQSLLYSDAKTYLNWFQQRPGQSPRRLIYQISRLDPGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQGTHYPVLFGQGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+prasinezumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYGMSWVRQAPGKGLEWVASISSGGGSTYYPDNVKGRFTISRDDAKNSLYLQMNSLRAEDTAVYYCARGGAGIDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSIQTLLYSSNQKNYLAWFQQKPGKAPKLLIYWASIRKSGVPSRFSGSGSGTDFTLTISSLQPEDLATYYCQQYYSYPLTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+prezalumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYWMSWVRQAPGKGLEWVAYIKQDGNEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGILWFGDLPTFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISNWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYDSYPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+prolgolimab,QVQLVQSGGGLVQPGGSLRLSCAASGFTFSSYWMYWVRQVPGKGLEWVSAIDTGGGRTYYADSVKGRFAISRVNAKNTMYLQMNSLRAEDTAVYYCARDEGGGTGWGVLKDWPYGLDAWGQGTLVTVSS,QPVLTQPLSVSVALGQTARITCGGNNIGSKNVHWYQQKPGQAPVLVIYRDSNRPSGIPERFSGSNSGNTATLTISRAQAGDEADYYCQVWDSSTAVFGTGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+quilizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYGIAWVRQAPGKGLEWVAFISDLAYTIYYADTVTGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDNWDAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSLVHNNANTYLHWYQQKPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCSQNTLVPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+racotumomab,QVQLQQSGAELVKPGASVKLSCKASGYTFTSYDINWVRQRPEQGLEWIGWIFPGDGSTKYNEKFKGKATLTTDKSSSTAYMQLSRLTSEDSAVYFCAREDYYDNSYYFDYWGQGTTLTVSS,DIQMTQTTSSLSASLGDRVTISCRASQDISNYLNWYQQKPDGTVKLLIYYTSRLHSGVPSRFSGSGSGTDYSLTISNLEQEDIATYFCQQGNTLPWTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+radretumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSFSMSWVRQAPGKGLEWVSSISGSSGTTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKPFPYFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSFLAWYQQKPGQAPRLLIYYASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQTGRIPPTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ramucirumab,EVQLVQSGGGLVKPGGSLRLSCAASGFTFSSYSMNWVRQAPGKGLEWVSSISSSSSYIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARVTDAFDIWGQGTMVTVSS,DIQMTQSPSSVSASIGDRVTITCRASQGIDNWLGWYQQKPGKAPKLLIYDASNLDTGVPSRFSGSGSGTYFTLTISSLQAEDFAVYFCQQAKAFPPTFGGGTKVDIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ranibizumab,EVQLVESGGGLVQPGGSLRLSCAASGYDFTHYGMNWVRQAPGKGLEWVGWINTYTGEPTYAADFKRRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCAKYPYYYGTSHWYFDVWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKVLIYFTSSLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSTVPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+relatlimab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSDYYWNWIRQPPGKGLEWIGEINHRGSTNSNPSLKSRVTLSLDTSKNQFSLKLRSVTAADTAVYYCAFGYSDYEYNWFDPWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSISSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPLTFGQGTNLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+reslizumab,EVQLVESGGGLVQPGGSLRLSCAVSGLSLTSNSVNWIRQAPGKGLEWVGLIWSNGDTDYNSAIKSRFTISRDTSKSTVYLQMNSLRAEDTAVYYCAREYYGYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCLASEGISSYLAWYQQKPGKAPKLLIYGANSLQTGVPSRFSGSGSATDYTLTISSLQPEDFATYYCQQSYKFPNTFGQGTKVEVK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+rilotumumab,QVQLQESGPGLVKPSETLSLTCTVSGGSISIYYWSWIRQPPGKGLEWIGYVYYSGSTNYNPSLKSRVTISVDTSKNQFSLKLNSVTAADTAVYYCARGGYDFWSGYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVDSNLAWYRQKPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQYINWPPITFGQGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+rinucumab,QLQLQESGPGLVKPSETLSLTCTVSGGSITSSSYYWGWIRQPPGKGLEWIGSIYYRGSTNYNPSLKSRVTISVDSSKNQFYLKVSSVTAVDTAVYYCARQNGAARPSWFDPWGQGTLVTVSS,EIVLTQSPDTISLSPGERATLSCRASQSISSIYLAWYQQKPGQAPRLLIYGASSRVTGIPDRFSVSGSGTDFTLTISRLEPEDFAVYYCQHYGISPFTFGPGTKVDIR,2.8211362,82.16446,240.63281,0.17229764,6.423554
+risankizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDQTIHWMRQAPGQGLEWIGYIYPRDDSPKYNENFKGKVTITADKSTSTAYMELSSLRSEDTAVYYCAIPDRSGYAWFIYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASRDVAIAVAWYQQKPGKVPKLLIYWASTRHTGVPSRFSGSGSRTDFTLTISSLQPEDVADYFCHQYSSYPFTFGSGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+rituximab,QVQLQQPGAELVKPGASVKMSCKASGYTFTSYNMHWVKQTPGRGLEWIGAIYPGNGDTSYNQKFKGKATLTADKSSSTAYMQLSSLTSEDSAVYYCARSTYYGGDWYFNVWGAGTTVTVSA,QIVLSQSPAILSASPGEKVTMTCRASSSVSYIHWFQQKPGSSPKPWIYATSNLASGVPVRFSGSGSGTSYSLTISRVEAEDAATYYCQQWTSNPPTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+robatumumab,EVQLVQSGGGLVKPGGSLRLSCAASGFTFSSFAMHWVRQAPGKGLEWISVIDTRGATYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARLGNFYYGMDVWGQGTTVTVSS,EIVLTQSPGTLSVSPGERATLSCRASQSIGSSLHWYQQKPGQAPRLLIKYASQSLSGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCHQSSRLPHTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+romosozumab,EVQLVQSGAEVKKPGASVKVSCKASGYTFTDYNMHWVRQAPGQGLEWMGEINPNSGGAGYNQKFKGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARLGYDDIYDDWYFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISNYLNWYQQKPGKAPKLLIYYTSRLLSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGDTLPYTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+rontalizumab,EVQLVESGGGLVQPGGSLRLSCATSGYTFTEYIIHWVRQAPGKGLEWVASINPDYDITNYNQRFKGRFTISLDKSKRTAYLQMNSLRAEDTAVYYCASWISDFFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSVSTSSYSYMHWYQQKPGKAPKVLISYASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHSWGIPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+rovalpituzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYGMNWVRQAPGQGLEWMGWINTYTGEPTYADDFKGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARIGDSSPSDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCKASQSVSNDVVWYQQKPGQAPRLLIYYASNRYTGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQDYTSPWTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+rozanolixizumab,EVPLVESGGGLVQPGGSLRLSCAVSGFTFSNYGMVWVRQAPGKGLEWVAYIDSDGDNTYYRDSVKGRFTISRDNAKSSLYLQMNSLRAEDTAVYYCTTGIVRPFLYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSSQSLVGASGKTYLYWLFQKPGKAPKRLIYLVSTLDSGIPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQGTHFPHTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+sarilumab,EVQLVESGGGLVQPGRSLRLSCAASRFTFDDYAMHWVRQAPGKGLEWVSGISWNSGRIGYADSVKGRFTISRDNAENSLFLQMNGLRAEDTALYYCAKGRDSFDIWGQGTMVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYGASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFASYYCQQANSFPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+satralizumab,QVQLQESGPGLVKPSETLSLTCAVSGHSISHDHAWSWVRQPPGEGLEWIGFISYSGITNYNPSLQGRVTISRDNSKNTLYLQMNSLRAEDTAVYYCARSLARTTAMDYWGEGTLVTVSS,DIQMTQSPSSLSASVGDSVTITCQASTDISSHLNWYQQKPGKAPELLIYYGSHLLSGVPSRFSGSGSGTDFTFTISSLEAEDAATYYCGQGNRLPYTFGQGTKVEIE,2.8211362,82.16446,240.63281,0.17229764,6.423554
+secukinumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYWMNWVRQAPGKGLEWVAAINQDGSEKYYVGSVKGRFTISRDNAKNSLYLQMNSLRVEDTAVYYCVRDYYDILTDYYIHYWYFDLWGRGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPCTFGQGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+selicrelumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTGYYMHWVRQAPGQGLEWMGWINPDSGGTNYAQKFQGRVTMTRDTSISTAYMELNRLRSDDTAVYYCARDQPLGYCTNGVCSYFDYWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGIYSWLAWYQQKPGKAPNLLIYTASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANIFPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+seribantumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSHYVMAWVRQAPGKGLEWVSSISSSGGWTLYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCTRGLKMATIFDYWGQGTLVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGSYNVVSWYQQHPGKAPKLIIYEVSQRPSGVSNRFSGSKSGNTASLTISGLQTEDEADYYCCSYAGSSIFVIFGGGTKVTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+setrusumab,QVQLVESGGGLVQPGGSLRLSCAASGFTFRSHWLSWVRQAPGKGLEWVSNINYDGSSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDTYLHFDYWGQGTLVTVSS,DIALTQPASVSGSPGQSITISCTGTSSDVGDINDVSWYQQHPGKAPKLMIYDVNNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCQSYAGSYLSEVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+sifalimumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYSISWVRQAPGQGLEWMGWISVYNGNTNYAQKFQGRVTMTTDTSTSTAYLELRSLRSDDTAVYYCARDPIAAGYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSTYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+siltuximab,EVQLVESGGKLLKPGGSLKLSCAASGFTFSSFAMSWFRQSPEKRLEWVAEISSGGSYTYYPDTVTGRFTISRDNAKNTLYLEMSSLRSEDTAMYYCARGLWGYYALDYWGQGTSVTVSS,QIVLIQSPAIMSASPGEKVTMTCSASSSVSYMYWYQQKPGSSPRLLIYDTSNLASGVPVRFSGSGSGTSYSLTISRMEAEDAATYYCQQWSGYPYTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+simtuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYAFTYYLIEWVRQAPGQGLEWIGVINPGSGGTNYNEKFKGRATITADKSTSTAYMELSSLRSEDTAVYFCARNWMNFDYWGQGTTVTVSS,DIVMTQTPLSLSVTPGQPASISCRSSKSLLHSNGNTYLYWFLQKPGQSPQFLIYRMSNLASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQHLEYPYTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+sintilimab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGLIIPMFDTAGYAQKFQGRVAITVDESTSTAYMELSSLRSEDTAVYYCARAEHSSTGTFDYWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLISAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANHLPFTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+sirukumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSPFAMSWVRQAPGKGLEWVAKISPGGSWTYYSDTVTGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARQLWGYYALDIWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCSASISVSYMYWYQQKPGQAPRLLIYDMSNLASGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCMQWSGYPYTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+solanezumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRYSMSWVRQAPGKGLELVAQINSVGNSTYYPDTVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCASGDYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLIYSDGNAYLHWFLQKPGQSPRLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHVPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+spartalizumab,EVQLVQSGAEVKKPGESLRISCKGSGYTFTTYWMHWVRQATGQGLEWMGNIYPGTGGSNFDEKFKNRVTITADKSTSTAYMELSSLRSEDTAVYYCTRWTTGTGAYWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCKSSQSLLDSGNQKNFLTWYQQKPGQAPRLLIYWASTRESGVPSRFSGSGSGTDFTFTISSLEAEDAATYYCQNDYSYPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+sutimlimab,EVQLVESGGGLVKPGGSLRLSCAASGFTFSNYAMSWVRQAPGKGLEWVATISSGGSHTYYLDSVKGRFTISRDNSKNTLYLQMNSLRAEDTALYYCARLFTGYAMDYWGQGTLVTVSS,QIVLTQSPATLSLSPGERATMSCTASSSVSSSYLHWYQQKPGKAPKLWIYSTSNLASGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCHQYYRLPPITFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tabalumab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARGYYDILTGYYYYFDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSRYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDSTLTISSLEPEDFAVYYCQQRSNWPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tanezumab,QVQLQESGPGLVKPSETLSLTCTVSGFSLIGYDLNWIRQPPGKGLEWIGIIWGDGTTDYNSAVKSRVTISKDTSKNQFSLKLSSVTAADTAVYYCARGGYWYATSYYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSISNNLNWYQQKPGKAPKLLIYYTSRFHSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQEHTLPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tarextumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSSGMSWVRQAPGKGLEWVSVIASSGSNTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSIFYTTWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQSVRSNYLAWYQQKPGQAPRLLIYGASSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQYSNFPITFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tavolimab,QVQLQESGPGLVKPSQTLSLTCAVYGGSFSSGYWNWIRKHPGKGLEYIGYISYNGITYHNPSLKSRITINRDTSKNQYSLQLNSVTPEDTAVYYCARYKYDYDGGHAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISNYLNWYQQKPGKAPKLLIYYTSKLHSGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCQQGSALPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+telisotuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYIFTAYTMHWVRQAPGQGLEWMGWIKPNNGLANYAQKFQGRVTMTRDTSISTAYMELSRLRSDDTAVYYCARSEITTEFDYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSESVDSYANSFLHWYQQKPGQPPKLLIYRASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQSKEDPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+teplizumab,QVQLVQSGGGVVQPGRSLRLSCKASGYTFTRYTMHWVRQAPGKGLEWIGYINPSRGYTNYNQKVKDRFTISRDNSKNTAFLQMDSLRPEDTGVYFCARYYDDHYCLDYWGQGTPVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVSYMNWYQQTPGKAPKRWIYDTSKLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSNPFTFGQGTKLQIT,2.8211362,82.16446,240.63281,0.17229764,6.423554
+teprotumumab,QVELVESGGGVVQPGRSQRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAIIWFDGSSTYYADSVRGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCARELGRRYFDLWGRGTLVSVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASKRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSKWPPWTFGQGTKVESK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tezepelumab,QMQLVESGGGVVQPGRSLRLSCAASGFTFRTYGMHWVRQAPGKGLEWVAVIWYDGSNKHYADSVKGRFTITRDNSKNTLNLQMNSLRAEDTAVYYCARAPQWELVHEAFDIWGQGTMVTVSS,SYVLTQPPSVSVAPGQTARITCGGNNLGSKSVHWYQQKPGQAPVLVVYDDSDRPSWIPERFSGSNSGNTATLTISRGEAGDEADYYCQVWDSSSDHVVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tigatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYVMSWVRQAPGKGLEWVATISSGGSYTYYPDSVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCARRGDSMITTDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVGTAVAWYQQKPGKAPKLLIYWASTRHTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSSYRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tildrakizumab,QVQLVQSGAEVKKPGASVKVSCKASGYIFITYWMTWVRQAPGQGLEWMGQIFPASGSADYNEKFEGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARGGGGFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRTSENIYSYLAWYQQKPGKAPKLLIYNAKTLAEGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHHYGIPFTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tislelizumab,QVQLQESGPGLVKPSETLSLTCTVSGFSLTSYGVHWIRQPPGKGLEWIGVIYADGSTNYNPSLKSRVTISKDTSKNQVSLKLSSVTAADTAVYYCARAYGNYWYIDVWGQGTTVTVSS,DIVMTQSPDSLAVSLGERATINCKSSESVSNDVAWYQQKPGQPPKLLINYAFHRFTGVPDRFSGSGYGTDFTLTISSLQAEDVAVYYCHQAYSSPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tisotumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNYAMSWVRQAPGKGLEWVSSISGSGDYTYYTDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSPWGYYLDSWGQGTLVTVSS,DIQMTQSPPSLSASAGDRVTITCRASQGISSRLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNSYPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tocilizumab,QVQLQESGPGLVRPSQTLSLTCTVSGYSITSDHAWSWVRQPPGRGLEWIGYISYSGITTYNPSLKSRVTMLRDTSKNQFSLRLSSVTAADTAVYYCARSLARTTAMDYWGQGSLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISSYLNWYQQKPGKAPKLLIYYTSRLHSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQGNTLPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+toripalimab,QGQLVQSGAEVKKPGASVKVSCKASGYTFTDYEMHWVRQAPIHGLEWIGVIESETGGTAYNQKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCAREGITTVATTYYWYFDVWGQGTTVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSIVHSNGNTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPLTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tovetumab,QVQLVESGGGLVKPGGSLRLSCAASGFTFSDYYMNWIRQAPGKGLEWVSYISSSGSIIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGRIAARGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVSITCRPSQSFSRYINWYQQKPGKAPKLLIHAASSLVGGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQTYSNPPITFGQGTRLEMK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tralokinumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYGLSWVRQAPGQGLEWMGWISANNGDTNYGQEFQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARDSSSSWARWFFDLWGRGTLVTVSS,SYVLTQPPSVSVAPGKTARITCGGNIIGSKLVHWYQQKPGQAPVLVIYDDGDRPSGIPERFSGSNSGNTATLTISRVEAGDEADYYCQVWDTGSDPVVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+trastuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFNIKDTYIHWVRQAPGKGLEWVARIYPTNGYTRYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCSRWGGDGFYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDVNTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSRSGTDFTLTISSLQPEDFATYYCQQHYTTPPTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tregalizumab,EEQLVESGGGLVKPGGSLRLSCAASGFSFSDCRMYWLRQAPGKGLEWIGVISVKSENYGANYAESVRGRFTISRDDSKNTVYLQMNSLKTEDTAVYYCSASYYRYDVGAWFAYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCRASKSVSTSGYSYIYWYQQKPGQPPKLLIYLASILESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQHSRELPWTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+tremelimumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDPRGATLYYYYYGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSINSYLDWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYSTPFTFGPGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ublituximab,QAYLQQSGAELVRPGASVKMSCKASGYTFTSYNMHWVKQTPRQGLEWIGGIYPGNGDTSYNQKFKGKATLTVGKSSSTAYMQLSSLTSEDSAVYFCARYDYNYAMDYWGQGTSVTVSS,QIVLSQSPAILSASPGEKVTMTCRASSSVSYMHWYQQKPGSSPKPWIYATSNLASGVPARFSGSGSGTSYSFTISRVEAEDAATYYCQQWTFNPPTFGGGTRLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+urelumab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQSPEKGLEWIGEINHGGYVTYNPSLESRVTISVDTSKNQFSLKLSSVTAADTAVYYCARDYGPGNYDWYFDLWGRGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPALTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+ustekinumab,EVQLVQSGAEVKKPGESLKISCKGSGYSFTTYWLGWVRQMPGKGLDWIGIMSPVDSDIRYSPSFQGQVTMSVDKSITTAYLQWNSLKASDTAMYYCARRRPGQGYFDFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNIYPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+utomilumab,EVQLVQSGAEVKKPGESLRISCKGSGYSFSTYWISWVRQMPGKGLEWMGKIYPGDSYTNYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARGYGIFDYWGQGTLVTVSS,SYELTQPPSVSVSPGQTASITCSGDNIGDQYAHWYQQKPGQSPVLVIYQDKNRPSGIPERFSGSNSGNTATLTISGTQAMDEADYYCATYTGFGSLAVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+vadastuximab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYDINWVRQAPGQGLEWIGWIYPGDGSTKYNEKFKAKATLTADTSTSTAYMELRSLRSDDTAVYYCASGYEDAMDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTINCKASQDINSYLSWFQQKPGKAPKTLIYRANRLVDGVPSRFSGSGSGQDYTLTISSLQPEDFATYYCLQYDEFPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+varlilumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYDMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARGSGNWGFFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISRWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNTYPRTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+vatelizumab,QVQLQESGPGLVKPSETLSLTCTVSGFSLTNYGIHWIRQPPGKGLEWLGVIWARGFTNYNSALMSRLTISKDNSKNQVSLKLSSVTAADTAVYYCARANDGVYYAMDYWGQGTLVTVSS,DFVMTQSPAFLSVTPGEKVTITCSAQSSVNYIHWYQQKPDQAPKKLIYDTSKLASGVPSRFSGSGSGTDYTFTISSLEAEDAATYYCQQWTTNPLTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+vedolizumab,QVQLVQSGAEVKKPGASVKVSCKGSGYTFTSYWMHWVRQAPGQRLEWIGEIDPSESNTNYNQKFKGRVTLTVDISASTAYMELSSLRSEDTAVYYCARGGYDGWDYAIDYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLAKSYGNTYLSWYLQKPGQSPQLLIYGISNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQGTHQPYTFGQGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+veltuzumab,QVQLQQSGAEVKKPGSSVKVSCKASGYTFTSYNMHWVKQAPGQGLEWIGAIYPGMGDTSYNQKFKGKATLTADESTNTAYMELSSLRSEDTAFYYCARSTYYGGDWYFDVWGQGTTVTVSS,DIQLTQSPSSLSASVGDRVTMTCRASSSVSYIHWFQQKPGKAPKPWIYATSNLASGVPVRFSGSGSGTDYTFTISSLQPEDIATYYCQQWTSNPPTFGGGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+visilizumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFISYTMHWVRQAPGQGLEWMGYINPRSGYTHYNQKLKDKATLTADKSASTAYMELSSLRSEDTAVYYCARSAYYDYDGFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVSYMNWYQQKPGKAPKRLIYDTSKLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQWSSNPPTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+xentuzumab,QVELVESGGGLVQPGGSLRLSCAASGFTFTSYWMSWVRQAPGKGLELVSSITSYGSFTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARNMYTHFDSWGQGTLVTVSS,DIVLTQPPSVSGAPGQRVTISCSGSSSNIGSNSVSWYQQLPGTAPKLLIYDNSKRPSGVPDRFSGSKSGTSASLAITGLQSEDEADYYCQSRDTYGYYWVFGGGTKLTVL,2.8211362,82.16446,240.63281,0.17229764,6.423554
+zalutumumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSTYGMHWVRQAPGKGLEWVAVIWDDGSYKYYGDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDGITMVRGVMKDYFDYWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQDISSALVWYQQKPGKAPKLLIYDASSLESGVPSRFSGSESGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+zanolimumab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARVINWFDPWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQDISSWLAWYQHKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANSFPYTFGQGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
+zolbetuximab,QVQLQQPGAELVRPGASVKLSCKASGYTFTSYWINWVKQRPGQGLEWIGNIYPSDSYTNYNQKFKDKATLTVDKSSSTAYMQLSSPTSEDSAVYYCTRSWRGNSFDYWGQGTTLTVSS,DIVMTQSPSSLTVTAGEKVTMSCKSSQSLLNSGNQKNYLTWYQQKPGQPPKLLIYWASTRESGVPDRFTGSGSGTDFTLTISSVQAEDLAVYYCQNDYSYPFTFGSGTKLEIK,2.8211362,82.16446,240.63281,0.17229764,6.423554
diff --git a/models/saprot_vh_vl/outputs/train/predictions.csv b/models/saprot_vh_vl/outputs/train/predictions.csv
new file mode 100644
index 0000000..01a3aed
--- /dev/null
+++ b/models/saprot_vh_vl/outputs/train/predictions.csv
@@ -0,0 +1,247 @@
+antibody_name,vh_protein_sequence,vl_protein_sequence,HIC,Tm2,Titer,PR_CHO,AC-SINS_pH7.4
+abagovomab,QVKLQESGAELARPGASVKLSCKASGYTFTNYWMQWVKQRPGQGLDWIGAIYPGDGNTRYTHKFKGKATLTADKSSSTAYMQLSSLASEDSGVYYCARGEGNYAWFAYWGQGTTVTVSS,DIELTQSPASLSASVGETVTITCQASENIYSYLAWHQQKQGKSPQLLVYNAKTLAGGVSSRFSGSGSGTHFSLKIKSLQPEDFGIYYCQHHYGILPTFGGGTKLEIK,2.6226647,82.66357,211.3644,0.24223629,7.7926497
+abituzumab,QVQLQQSGGELAKPGASVKVSCKASGYTFSSFWMHWVRQAPGQGLEWIGYINPRSGYTEYNEIFRDKATMTTDTSTSTAYMELSSLRSEDTAVYYCASFLGRGAMDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISNYLAWYQQKPGKAPKLLIYYTSKIHSGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQGNTFPYTFGQGTKVEIK,2.7139206,82.26765,256.24265,0.1773734,4.1076865
+abrezekimab,QVTLKESGPVLVKPTETLTLTCTVSGFSLTNYHVQWIRQPPGKALEWLGVMWSDGDTSFNSVLKSRLTISRDTSKSQVVLTMTNMDPVDTATYYCARDGTIAAMDYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCLASEDISNYLAWYQQKPGKAPKLLIYHTSRLQDGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGYRFPLTFGGGTKVEIK,2.861236,82.23852,189.51276,0.19162725,-0.32617998
+abrilumab,QVQLVQSGAEVKKPGASVKVSCKVSGYTLSDLSIHWVRQAPGKGLEWMGGFDPQDGETIYAQKFQGRVTMTEDTSTDTAYMELSSLKSEDTAVYYCATGSSSSWFDPWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYGASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFANYYCQQANSFPWTFGQGTKVEIK,2.822275,81.200714,249.91245,0.17271075,1.9533172
+adalimumab,EVQLVESGGGLVQPGRSLRLSCAASGFTFDDYAMHWVRQAPGKGLEWVSAITWNSGHIDYADSVEGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAKVSYLSTASSLDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGIRNYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCQRYNRAPYTFGQGTKVEIK,2.7646995,82.71097,237.83995,0.08922437,4.1213856
+aducanumab,QVQLVESGGGVVQPGRSLRLSCAASGFAFSSYGMHWVRQAPGKGLEWVAVIWFDGTKKYYTDSVKGRFTISRDNSKNTLYLQMNTLRAEDTAVYYCARDRGIGARRGPYYMDVWGKGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSISSYLNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSTPLTFGGGTKVEIK,2.7165284,82.11168,301.9607,0.13127077,10.325703
+alemtuzumab,QVQLQESGPGLVRPSQTLSLTCTVSGFTFTDFYMNWVRQPPGRGLEWIGFIRDKAKGYTTEYNPSVKGRVTMLVDTSKNQFSLRLSSVTAADTAVYYCAREGHTAAPFDYWGQGSLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQNIDKYLNWYQQKPGKAPKLLIYNTNNLQTGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCLQHISRPRTFGQGTKVEIK,2.5250707,82.611946,233.44588,0.16443914,1.882206
+alirocumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFNNYAMNWVRQAPGKGLDWVSTISGSGGTTNYADSVKGRFIISRDSSKHTLYLQMNSLRAEDTAVYYCAKDSNWGNFDLWGRGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSVLYRSNNRNFLGWYQQKPGQPPNLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQYYTTPYTFGQGTKLEIK,2.7802918,82.71035,223.98607,0.043370843,3.8928895
+amatuximab,QVQLQQSGPELEKPGASVKISCKASGYSFTGYTMNWVKQSHGKSLEWIGLITPYNGASSYNQKFRGKATLTVDKSSSTAYMDLLSLTSEDSAVYFCARGGYDGRGFDYWGSGTPVTVSS,DIELTQSPAIMSASPGEKVTMTCSASSSVSYMHWYQQKSGTSPKRWIYDTSKLASGVPGRFSGSGSGNSYSLTISSVEAEDDATYYCQQWSKHPLTFGSGTKVEIK,2.71249,82.45458,228.08301,0.20755321,7.6622386
+andecaliximab,QVQLQESGPGLVKPSETLSLTCTVSGFSLLSYGVHWVRQPPGKGLEWLGVIWTGGTTNYNSALMSRFTISKDDSKNTVYLKMNSLKTEDTAIYYCARYYYGMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVRNTVAWYQQKPGKAPKLLIYSSSYRNTGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQHYITPYTFGGGTKVEIK,2.8304849,83.009834,232.6303,0.18755496,2.1039014
+anetumab,QVELVQSGAEVKKPGESLKISCKGSGYSFTSYWIGWVRQAPGKGLEWMGIIDPGDSRTRYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARGQLYGGTYMDGWGQGTLVTVSS,DIALTQPASVSGSPGQSITISCTGTSSDIGGYNSVSWYQQHPGKAPKLMIYGVNNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCSSYDIESATPVFGGGTKLTVL,2.9499378,82.13192,303.50958,0.26177818,9.896069
+anifrolumab,EVQLVQSGAEVKKPGESLKISCKGSGYIFTNYWIAWVRQMPGKGLESMGIIYPGDSDIRYSPSFQGQVTISADKSITTAYLQWSSLKASDTAMYYCARHDIEGFDYWGRGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSFFAWYQQKPGQAPRLLIYGASSRATGIPDRLSGSGSGTDFTLTITRLEPEDFAVYYCQQYDSSAITFGQGTRLEIK,2.8663466,82.26883,168.73701,0.28596663,7.2374625
+anrukinzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFISYAMSWVRQAPGKGLEWVASISSGGNTYYPDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARLDGYYFGFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASESVDNYGKSLMHWYQQKPGKAPKLLIYRASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSNEDPWTFGGGTKVEIK,2.7822838,83.73834,259.15045,0.024825528,3.243688
+atezolizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDSWIHWVRQAPGKGLEWVAWISPYGGSTYYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARRHWPGGFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDVSTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYLYHPATFGQGTKVEIK,2.8426652,82.967255,253.94508,0.11501433,6.158385
+avelumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYIMMWVRQAPGKGLEWVSSIYPSGGITFYADTVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARIKLGTVTTVDYWGQGTLVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGGYNYVSWYQQHPGKAPKLMIYDVSNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCSSYTSSSTRVFGTGTKVTVL,2.9012532,82.186485,350.778,0.13722053,10.457815
+bapineuzumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNYGMSWVRQAPGKGLEWVASIRSGGGRTYYSDNVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCVRYDHYSGSSDYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCKSSQSLLDSDGKTYLNWLLQKPGQSPQRLIYLVSKLDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCWQGTHFPRTFGQGTKVEIK,2.629097,83.08141,221.39267,0.10934922,7.8344293
+basiliximab,QVQLQQSGTVLARPGASVKMSCKASGYSFTRYWMHWIKQRPGQGLEWIGAIYPGNSDTSYNQKFEGKAKLTAVTSASTAYMELSSLTHEDSAVYYCSRDYGYYFDFWGQGTTLTVSS,QIVSTQSPAIMSASPGEKVTMTCSASSSRSYMQWYQQKPGTSPKRWIYDTSKLASGVPARFSGSGSGTSYSLTISSMEAEDAATYYCHQRSSYTFGGGTKLEIK,2.771375,82.544975,225.51654,0.2457886,11.816257
+bavituximab,EVQLQQSGPELEKPGASVKLSCKASGYSFTGYNMNWVKQSHGKSLEWIGHIDPYYGDTSYNQKFRGKATLTVDKSSSTAYMQLKSLTSEDSAVYYCVKGGYYGHWYFDVWGAGTTVTVSS,DIQMTQSPSSLSASLGERVSLTCRASQDIGSSLNWLQQGPDGTIKRLIYATSSLDSGVPKRFSGSRSGSDYSLTISSLESEDFVDYYCLQYVSSPPTFGAGTKLELK,2.8154848,82.081406,197.29102,0.266605,11.126911
+belantamab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSNYWMHWVRQAPGQGLEWMGATYRGHSDTYYNQKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARGAIYDGYDVLDNWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKLLIYYTSNLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYRKLPWTFGQGTKLEIK,2.795282,81.36632,260.20312,0.24243881,6.9667697
+belimumab,QVQLQQSGAEVKKPGSSVRVSCKASGGTFNNNAINWVRQAPGQGLEWMGGIIPMFGTAKYSQNFQGRVAITADESTGTASMELSSLRSEDTAVYYCARSRDLLLFPHHALSPWGRGTMVTVSS,SSELTQDPAVSVALGQTVRVTCQGDSLRSYYASWYQQKPGQAPVLVIYGKNNRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCSSRDSSGNHWVFGGGTELTVL,2.8943155,80.295166,264.17627,0.23293659,7.7494826
+bemarituzumab,QVQLVQSGAEVKKPGSSVKVSCKASGYIFTTYNVHWVRQAPGQGLEWIGSIYPDNGDTSYNQNFKGRATITADKSTSTAYMELSSLRSEDTAVYYCARGDFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQGVSNDVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQHSTTPYTFGQGTKLEIK,2.8158484,82.33762,255.5269,0.183294,2.989077
+benralizumab,EVQLVQSGAEVKKPGASVKVSCKASGYTFTSYVIHWVRQRPGQGLAWMGYINPYNDGTKYNERFKGKVTITSDRSTSTVYMELSSLRSEDTAVYLCGREGIRYYGLLGDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCGTSEDIINYLNWYQQKPGKAPKLLIYHTSRLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGYTLPYTFGQGTKVEIK,2.7840118,81.32107,245.86517,0.25022987,7.3246846
+bevacizumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFTNYGMNWVRQAPGKGLEWVGWINTYTGEPTYAADFKRRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCAKYPHYYGSSHWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKVLIYFTSSLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSTVPWTFGQGTKVEIK,2.9274707,82.39258,238.93184,0.11000946,4.3495917
+bezlotoxumab,EVQLVQSGAEVKKSGESLKISCKGSGYSFTSYWIGWVRQMPGKGLEWMGIFYPGDSSTRYSPSFQGQVTISADKSVNTAYLQWSSLKASDTAMYYCARRRNWGNAFDIWGQGTMVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSTWTFGQGTKVEIK,2.8288832,82.714615,219.68912,0.26596355,10.545473
+bimagrumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSSYINWVRQAPGQGLEWMGTINPVSGSTSYAQKFQGRVTMTRDTSISTAYMELSRLRSDDTAVYYCARGGWFDYWGQGTLVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGSYNYVNWYQQHPGKAPKLMIYGVSKRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCGTFAGGSYYGVFGGGTKLTVL,2.9443853,81.105415,390.5313,0.27394798,13.847666
+bimekizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYNMAWVRQAPGKGLEWVATITYEGRNTYYRDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCASPPQYYEGSIYRLWFAHWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRADESVRTLMHWYQQKPGKAPKLLIYLVSNSEIGVPDRFSGSGSGTDFRLTISSLQPEDFATYYCQQTWSDPWTFGQGTKVEIK,2.7602415,82.427826,202.43071,0.06421965,4.7465363
+bleselumab,QLQLQESGPGLLKPSETLSLTCTVSGGSISSPGYYGGWIRQPPGKGLEWIGSIYKSGSTYHNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCTRPVVRYFGWFDPWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPTFGQGTKVEIK,2.7931685,82.53438,249.07748,0.28252837,7.9049306
+blosozumab,QVQLVQSGAEVKKPGASVKVSCKVSGFPIKDTFQHWVRQAPGKGLEWMGWSDPEIGDTEYASKFQGRVTMTEDTSTDTAYMELSSLRSEDTAVYYCATGDTTYKFDFWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVHTAVAWYQQKPGKAPKLLIYWASTRWTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSDYPWTFGGGTKVEIK,2.7674193,81.39452,216.27977,0.12691228,0.26301098
+bococizumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYYMHWVRQAPGQGLEWMGEISPFGGRTNYNEKFKSRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARERPLYASDLWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQRYSLWRTFGQGTKLEIK,2.7333694,81.83073,257.2583,0.24247739,8.823536
+brazikumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVIWYDGSNEYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGYTSSWYPDAFDIWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSSSNTGAGYDVHWYQQVPGTAPKLLIYGSGNRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDSSLSGWVFGGGTRLTVL,3.2576067,81.2357,328.567,0.14760202,11.303232
+brentuximab,QIQLQQSGPEVVKPGASVKISCKASGYTFTDYYITWVKQKPGQGLEWIGWIYPGSGNTKYNEKFKGKATLTVDTSSSTAFMQLSSLTSEDTAVYFCANYGNYWFAYWGQGTQVTVSA,DIVLTQSPASLAVSLGQRATISCKASQSVDFDGDSYMNWYQQKPGQPPKVLIYAASNLESGIPARFSGSGSGTDFTLNIHPVEEEDAATYYCQQSNEDPWTFGGGTKLEIK,2.817041,82.96101,203.46211,0.15289803,2.20888
+briakinumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAFIRYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCKTHGSHDNWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCSGSRSNIGSNTVKWYQQLPGTAPKLLIYYNDQRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDRYTHPALLFGTGTKVTVL,2.862045,80.70158,354.45844,0.17980589,16.616997
+brodalumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTRYGISWVRQAPGQGLEWMGWISTYSGNTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARRQLYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVSSNLAWFQQKPGQAPRPLIYDASTRATGVPARFSGSGSGTDFTLTISSLQSEDFAVYYCQQYDNWPLTFGGGTKVEIK,2.7590911,81.252884,283.79456,0.25032827,9.716925
+brontictuzumab,QVQLVQSGAEVKKPGASVKISCKVSGYTLRGYWIEWVRQAPGKGLEWIGQILPGTGRTNYNEKFKGRVTMTADTSTDTAYMELSSLRSEDTAVYYCARFDGNYGYYAMDYWGQGTTVTVSS,QAVVTQEPSLTVSPGGTVTLTCRSSTGAVTTSNYANWFQQKPGQAPRTLIGGTNNRAPGVPARFSGSLLGGKAALTLSGAQPEDEAEYYCALWYSNHWVFGGGTKLTVL,3.0096571,79.71061,300.4455,0.23649791,10.454126
+budigalimab,EIQLVQSGAEVKKPGSSVKVSCKASGYTFTHYGMNWVRQAPGQGLEWVGWVNTYTGEPTYADDFKGRLTFTLDTSTSTAYMELSSLRSEDTAVYYCTREGEGLGFGDWGQGTTVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSIVHSHGDTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHIPVTFGQGTKLEIK,2.7949848,81.743675,178.22385,0.2050791,0.78715754
+burosumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNHYMHWVRQAPGQGLEWMGIINPISGSTSNAQKFQGRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARDIVDAFDFWGQGTMVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALVWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNDYFTFGPGTKVDIK,2.727838,81.79963,265.42468,0.183487,5.353049
+cabiralizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDNYMIWVRQAPGQGLEWMGDINPYNGGTTFNQKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARESPYFSNLYVMDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCKASQSVDYDGDNYMNWYQQKPGQAPRLLIYAASNLESGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCHLSNEDLSTFGGGTKVEIK,2.8570485,82.25985,172.55338,0.15055989,2.8541493
+camidanlumab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSRYIINWVRQAPGQGLEWMGRIIPILGVENYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARKDWFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPLTFGGGTKVEIK,2.817872,80.73966,248.33733,0.30397233,10.733755
+camrelizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYMMSWVRQAPGKGLEWVATISGGGANTYYPDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARQLYYFDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCLASQTIGTWLTWYQQKPGKAPKLLIYTATSLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQVYSIPWTFGGGTKVEIK,2.7857344,82.74593,294.58197,0.036518306,9.42634
+carlumab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYGISWVRQAPGQGLEWMGGIIPIFGTANYAQKFQGRVTITADESTSTAYMELSSLRSEDTAVYYCARYDGIYGELDFWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSDAYLAWYQQKPGQAPRLLIYDASSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCHQYIQLHSFTFGQGTKVEIK,2.973271,81.63777,194.84961,0.25372508,4.8926167
+cemiplimab,EVQLLESGGVLVQPGGSLRLSCAASGFTFSNFGMTWVRQAPGKGLEWVSGISGGGRDTYFADSVKGRFTISRDNSKNTLYLQMNSLKGEDTAVYYCVKWGNIYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDSITITCRASLSINTFLNWYQQKPGKAPNLLIYAASSLHGGVPSRFSGSGSGTDFTLTIRTLQPEDFATYYCQQSSNTPFTFGPGTVVDFR,2.770524,82.30318,238.5014,0.098994434,6.146424
+certolizumab,EVQLVESGGGLVQPGGSLRLSCAASGYVFTDYGMNWVRQAPGKGLEWMGWINTYIGEPIYADSVKGRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCARGYRSYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQNVGTNVAWYQQKPGKAPKALIYSASFLYSGVPYRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNIYPLTFGQGTKVEIK,2.911957,83.0899,274.49194,0.08197431,1.9297976
+cetrelimab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGGIIPIFDTANYAQKFQGRVTITADESTSTAYMELSSLRSEDTAVYYCARPGLAAAYDTGSLDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVRSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRNYWPLTFGQGTKVEIK,2.9709013,81.52144,203.79645,0.23749995,5.782717
+cetuximab,QVQLKQSGPGLVQPSQSLSITCTVSGFSLTNYGVHWVRQSPGKGLEWLGVIWSGGNTDYNTPFTSRLSINKDNSKSQVFFKMNSLQSNDTAIYYCARALTYYDYEFAYWGQGTLVTVSA,DILLTQSPVILSVSPGERVSFSCRASQSIGTNIHWYQQRTNGSPRLLIKYASESISGIPSRFSGSGSGTDFTLSINSVESEDIADYYCQQNNNWPTTFGAGTKLELK,2.8848372,83.35222,149.32446,0.21459414,3.5807772
+cixutumumab,EVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGGIIPIFGTANYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARAPLRFLEWSTQDHYYYYYMDVWGKGTTVTVSS,SSELTQDPAVSVALGQTVRITCQGDSLRSYYATWYQQKPGQAPILVIYGENKRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCKSRDGSGQHLVFGGGTKLTVL,3.0014715,81.1974,256.2238,0.26292288,7.171942
+clazakizumab,EVQLVESGGGLVQPGGSLRLSCAASGFSLSNYYVTWVRQAPGKGLEWVGIIYGSDETAYATSAIGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDDSSDWDAKFNLWGQGTLVTVSS,AIQMTQSPSSLSASVGDRVTITCQASQSINNELSWYQQKPGKAPKLLIYRASTLASGVPSRFSGSGSGTDFTLTISSLQPDDFATYYCQQGYSLRNIDNAFGGGTKVEIK,2.793519,83.21802,256.97644,0.06500985,1.99295
+codrituzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTDYEMHWVRQAPGQGLEWMGALDPKTGDTAYSQKFKGRVTLTADKSTSTAYMELSSLTSEDTAVYYCTRFYSYTYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLVHSNRNTYLHWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQNTHVPPTFGQGTKLEIK,2.6341367,81.27681,197.40056,0.26917687,3.5520787
+coltuximab,QVQLVQPGAEVVKPGASVKLSCKTSGYTFTSNWMHWVKQAPGQGLEWIGEIDPSDSYTNYNQNFQGKAKLTVDKSTSTAYMEVSSLRSDDTAVYYCARGSNPYYYAMDYWGQGTSVTVSS,EIVLTQSPAIMSASPGERVTMTCSASSGVNYMHWYQQKPGTSPRRWIYDTSKLASGVPARFSGSGSGTDYSLTISSMEPEDAATYYCHQRGSYTFGGGTKLEIK,2.8991103,82.81369,193.55682,0.12987925,4.2802906
+concizumab,EVQLVESGGGLVKPGGSLRLSCAASGFTFSNYAMSWVRQTPEKRLEWVATISRSGSYSYFPDSVQGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARLGGYDEGDAMDSWGQGTTVTVSS,DIVMTQTPLSLSVTPGQPASISCKSSQSLLESDGKTYLNWYLQKPGQSPQLLIYLVSILDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQATHFPQTFGGGTKVEIK,2.6520226,83.30209,208.2158,0.09583822,4.125583
+crenezumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYGMSWVRQAPGKGLELVASINSNGGSTYYPDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCASGDYWGQGTTVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSLVYSNGDTYLHWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHVPWTFGQGTKVEIK,2.7941644,83.20305,209.77596,0.08231914,7.404494
+crizanlizumab,QVQLVQSGAEVKKPGASVKVSCKVSGYTFTSYDINWVRQAPGKGLEWMGWIYPGDGSIKYNEKFKGRVTMTVDKSTDTAYMELSSLRSEDTAVYYCARRGEYGNYEGAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQSVDYDGHSYMNWYQQKPGKAPKLLIYAASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSDENPLTFGGGTKVEIK,2.7192307,81.79561,248.93103,0.13738196,1.2663283
+cusatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSVYYMNWVRQAPGKGLEWVSDINNEGGTTYYADSVKGRFTISRDNSKNSLYLQMNSLRAEDTAVYYCARDAGYSNHVPIFDSWGQGTLVTVSS,QAVVTQEPSLTVSPGGTVTLTCGLKSGSVTSDNFPTWYQQTPGQAPRLLIYNTNTRHSGVPDRFSGSILGNKAALTITGAQADDEAEYFCALFISNPSVEFGGGTQLTVL,2.8938107,81.87458,234.26859,0.058604762,5.701967
+dacetuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYSFTGYYIHWVRQAPGKGLEWVARVIPNAGGTSYNQKFKGRFTLSVDNSKNTAYLQMNSLRAEDTAVYYCAREGIYWWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSLVHSNGNTFLHWYQQKPGKAPKLLIYTVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYFCSQTTHVPWTFGQGTKVEIK,2.709992,81.89842,272.7362,0.15956569,9.20558
+daclizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTSYRMHWVRQAPGQGLEWIGYINPSTGYTEYNQKFKDKATITADESTNTAYMELSSLRSEDTAVYYCARGGGVFDYWGQGTLVTVSS,DIQMTQSPSTLSASVGDRVTITCSASSSISYMHWYQQKPGKAPKLLIYTTSNLASGVPARFSGSGSGTEFTLTISSLQPDDFATYYCHQRSTYPLTFGQGTKVEVK,2.6772106,81.74731,264.81055,0.18981245,5.6126876
+dalotuzumab,QVQLQESGPGLVKPSETLSLTCTVSGYSITGGYLWNWIRQPPGKGLEWIGYISYDGTNNYKPSLKDRVTISRDTSKNQFSLKLSSVTAADTAVYYCARYGRVFFDYWGQGTLVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSIVHSNGNTYLQWYLQKPGQSPQLLIYKVSNRLYGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPWTFGQGTKVEIK,2.771654,82.2399,197.1638,0.2895,4.5880666
+daratumumab,EVQLLESGGGLVQPGGSLRLSCAVSGFTFNSFAMSWVRQAPGKGLEWVSAISGSGGGTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCAKDKILWFGEPVFDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPTFGQGTKVEIK,2.803493,82.77574,218.80833,0.11211228,5.8488507
+denosumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSGITGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDPGTTVIMSWFDPWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVRGRYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVFYCQQYGSSPRTFGQGTKVEIK,2.7334275,82.354805,230.7613,0.07937506,8.767628
+depatuxizumab,QVQLQESGPGLVKPSQTLSLTCTVSGYSISSDFAWNWIRQPPGKGLEWMGYISYSGNTRYQPSLKSRITISRDTSKNQFFLKLNSVTAADTATYYCVTAGRGFPYWGQGTLVTVSS,DIQMTQSPSSMSVSVGDRVTITCHSSQDINSNIGWLQQKPGKSFKGLIYHGTNLDDGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCVQYAQFPWTFGGGTKLEIK,2.7782738,82.49184,251.82309,0.2003002,4.3247075
+dinutuximab,EVQLLQSGPELEKPGASVMISCKASGSSFTGYNMNWVRQNIGKSLEWIGAIDPYYGGTSYNQKFKGRATLTVDKSSSTAYMHLKSLTSEDSAVYYCVSGMEYWGQGTSVTVSS,EIVMTQSPATLSVSPGERATLSCRSSQSLVHRNGNTYLHWYLQKPGQSPKLLIHKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPPLTFGAGTKLELK,2.7374098,82.28597,137.80476,0.30634564,7.3149905
+domagrozumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSTISSGGSYTSYPDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKQDYAMNYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVSTAVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQHYSTPWTFGGGTKVEIK,2.717328,82.92088,282.985,0.044073373,8.429087
+dostarlimab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYDMSWVRQAPGKGLEWVSTISGGGSYTYYQDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCASPYYAMDYWGQGTTVTVSS,DIQLTQSPSFLSAYVGDRVTITCKASQDVGTAVAWYQQKPGKAPKLLIYWASTLHTGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCQHYSSYPWTFGQGTKLEIK,2.8518624,82.1872,251.77538,0.04355505,7.878858
+duligotuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTLSGDWIHWVRQAPGKGLEWVGEISAAGGYTDYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARESRVSFEAAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQNIATDVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSEPEPYTFGQGTKVEIK,2.8255558,83.04687,234.52193,0.070771635,3.389484
+dupilumab,EVQLVESGGGLEQPGGSLRLSCAGSGFTFRDYAMTWVRQAPGKGLEWVSSISGSGGNTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDRLSITIRPRYYGLDVWGQGTTVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSLLYSIGYNYLDWYLQKSGQSPQLLIYLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGFYYCMQALQTPYTFGQGTKLEIK,2.805838,82.19965,208.24457,0.10132383,5.8717494
+durvalumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRYWMSWVRQAPGKGLEWVANIKQDGSEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGGWFGELAFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQRVSSSYLAWYQQKPGQAPRLLIYDASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSLPWTFGQGTKVEIK,2.7930849,82.533516,227.4037,0.0958336,8.115002
+eculizumab,QVQLVQSGAEVKKPGASVKVSCKASGYIFSNYWIQWVRQAPGQGLEWMGEILPGSGSTEYTENFKDRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARYFFGSSPNWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCGASENIYGALNWYQQKPGKAPKLLIYGATNLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQNVLNTPLTFGQGTKVEIK,2.8671489,81.54743,243.86351,0.19631527,3.195673
+efalizumab,EVQLVESGGGLVQPGGSLRLSCAASGYSFTGHWMNWVRQAPGKGLEWVGMIHPSDSETRYNQKFKDRFTISVDKSKNTLYLQMNSLRAEDTAVYYCARGIYFYGTTYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASKTISKYLAWYQQKPGKAPKLLIYSGSTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQHNEYPLTFGQGTKVEIK,2.6469178,82.799255,235.75002,0.16141203,4.4116344
+eldelumab,QMQLVESGGGVVQPGRSLRLSCTASGFTFSNNGMHWVRQAPGKGLEWVAVIWFDGMNKFYVDSVKGRFTISRDNSKNTLYLEMNSLRAEDTAIYYCAREGDGSGIYYYYGMDVWGQGTTVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPIFTFGPGTKVDIK,3.0135982,82.16012,243.63345,0.09719773,5.2076554
+elezanumab,EVQLVQSGAEVKKPGASVKVSCKASGYTFTSHGISWVRQAPGQGLDWMGWISPYSGNTNYAQKLQGRVTMTTDTSTSTAYMELSSLRSEDTAVYYCARVGSGPYYYMDVWGQGTLVTVSS,QSALTQPRSVSGSPGQSVTISCTGTSSSVGDSIYVSWYQQHPGKAPKLMLYDVTKRPSGVPDRFSGSKSGNTASLTISGLQAEDEADYYCYSYAGTDTLFGGGTKVTVL,2.9577131,80.42427,333.86554,0.24945536,9.795095
+elotuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFDFSRYWMSWVRQAPGKGLEWIGEINPDSSTINYAPSLKDKFIISRDNAKNSLYLQMNSLRAEDTAVYYCARPDGNYWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVGIAVAWYQQKPGKVPKLLIYWASTRHTGVPDRFSGSGSGTDFTLTISSLQPEDVATYYCQQYSSYPYTFGQGTKVEIK,2.7612224,83.87113,231.86382,0.04574339,1.7826772
+emactuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYDISWVRQAPGQGLEWMGVIWTDGGTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARDQRLYFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASEDVNTYVSWYQQKPGKAPKLLIYAASNRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSFSYPTFGQGTKLEIK,2.8045008,81.81321,269.7826,0.16173528,5.111811
+emapalumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDGSSGWYVPHWFDPWGQGTLVTVSS,NFMLTQPHSVSESPGKTVTISCTRSSGSIASNYVQWYQQRPGSSPTTVIYEDNQRPSGVPDRFSGSIDSSSNSASLTISGLKTEDEADYYCQSYDGSNRWMFGGGTKLTVL,2.889907,81.74303,287.827,0.049489975,8.594078
+emibetuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTDYYMHWVRQAPGQGLEWMGRVNPNRRGTTYNQKFEGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARANWLDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCSVSSSVSSIYLHWYQQKPGKAPKLLIYSTSNLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQVYSGYPLTFGGGTKVEIK,2.7105062,80.96147,338.8249,0.25253522,12.696999
+enavatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYWMSWVRQAPGKGLEWVAEIRLKSDNYATHYAESVKGRFTISRDDSKNSLYLQMNSLRAEDTAVYYCTGYYADAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSVSTSSYSYMHWYQQKPGKAPKLLIKYASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHSWEIPYTFGGGTKVEIK,2.7874079,82.39067,264.25867,0.06720278,6.540977
+enokizumab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSYYWIEWVRQAPGQGLEWMGEILPGSGTTNPNEKFKGRVTITADESTSTAYMELSSLRSEDTAVYYCARADYYGSDYVKFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQHVITHVTWYQQKPGKAPKLLIYGTSYSYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFYEYPLTFGGGTKVEIK,2.953579,81.439316,229.71545,0.21428904,3.2558818
+epratuzumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTSYWLHWVRQAPGQGLEWIGYINPRNDYTEYNQNFKDKATITADESTNTAYMELSSLRSEDTAFYFCARRDITTFYWGQGTTVTVSS,DIQLTQSPSSLSASVGDRVTMSCKSSQSVLYSANHKNYLAWYQQKPGKAPKLLIYWASTRESGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCHQYLSSWTFGGGTKLEIK,2.7308338,81.93611,276.4574,0.23424067,6.499274
+eptinezumab,EVQLVESGGGLVQPGGSLRLSCAVSGIDLSGYYMNWVRQAPGKGLEWVGVIGINGATYYASWAKGRFTISRDNSKTTVYLQMNSLRAEDTAVYFCARGDIWGQGTLVTVSS,QVLTQSPSSLSASVGDRVTINCQASQSVYHNTYLAWYQQKPGKVPKQLIYDASTLASGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCLGSYDCTNGDCFVFGGGTKVEIK,2.922982,82.704865,350.27844,0.12402564,5.7370563
+erenumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSFGMHWVRQAPGKGLEWVAVISFDGSIKYSVDSVKGRFTISRDNSKNTLFLQMNSLRAEDTAVYYCARDRLNYYDSSGYYHYKYYGMAVWGQGTTVTVSS,QSVLTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKLLIYDNNKRPSGIPDRFSGSKSGTSTTLGITGLQTGDEADYYCGTWDSRLSAVVFGGGTKLTVL,2.9960403,80.54067,366.17444,0.21405457,13.107756
+etaracizumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYDMSWVRQAPGKGLEWVAKVSSGGGSTYYLDTVQGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARHLHGSFASWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCQASQSISNFLHWYQQRPGQAPRLLIRYRSQSISGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQSGSWPLTFGGGTKVEIK,2.679308,82.41612,243.36824,0.15964305,14.790255
+etrolizumab,EVQLVESGGGLVQPGGSLRLSCAASGFFITNNYWGWVRQAPGKGLEWVGYISYSGSTSYNPSLKSRFTISRDTSKNTFYLQMNSLRAEDTAVYYCARTGSSGYFDFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASESVDDLLHWYQQKPGKAPKLLIKYASQSISGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGNSLPNTFGQGTKVEIK,2.6942983,82.692276,233.72993,0.12546368,5.4641356
+evinacumab,EVQLVESGGGVIQPGGSLRLSCAASGFTFDDYAMNWVRQGPGKGLEWVSAISGDGGSTYYADSVKGRFTISRDNSKNSLYLQMNSLRAEDTAFFYCAKDLRNTIFGVVIPDAFDIWGQGTMVTVSS,DIQMTQSPSTLSASVGDRVTITCRASQSIRSWLAWYQQKPGKAPKLLIYKASSLESGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQYNSYSYTFGQGTKLEIK,2.784359,82.58842,217.7711,0.056897208,2.4788804
+evolocumab,EVQLVQSGAEVKKPGASVKVSCKASGYTLTSYGISWVRQAPGQGLEWMGWVSFYNGNTNYAQKLQGRGTMTTDPSTSTAYMELRSLRSDDTAVYYCARGYGMDVWGQGTTVTVSS,ESALTQPASVSGSPGQSITISCTGTSSDVGGYNSVSWYQQHPGKAPKLMIYEVSNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCNSYTSTSMVFGGGTKLTVL,2.9965422,81.03106,355.2486,0.23071161,10.784636
+farletuzumab,EVQLVESGGGVVQPGRSLRLSCSASGFTFSGYGLSWVRQAPGKGLEWVAMISSGGSYTYYADSVKGRFAISRDNAKNTLFLQMDSLRPEDTGVYFCARHGDDPAWFAYWGQGTPVTVSS,DIQLTQSPSSLSASVGDRVTITCSVSSSISSNNLHWYQQKPGKAPKPWIYGTSNLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSYPYMYTFGQGTKVEIK,2.7208571,82.9271,325.1331,0.0560797,7.047441
+fasinumab,QVQLVQSGAEVKKPGASVKVSCKVSGFTLTELSIHWVRQAPGKGLEWMGGFDPEDGETIYAQKFQGRVTMTEDTSTDTAYMELTSLRSEDTAVYYCSTIFGVVTNFDNWGQGTLVTVSS,DIQMTQSPSSLSASAGDRVTITCRASQAIRNDLGWYQQKPGKAPKRLIYAAFNLQSGVPSRFSGSGSGTEFTLTISSLQPEDLASYYCQQYNRYPWTFGQGTKVEIK,2.7312808,80.13904,201.03828,0.20862132,1.4263473
+fezakinumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYYMHWVRQAPGQGLEWVGWINPYTGSAFYAQKFRGRVTMTRDTSISTAYMELSRLRSDDTAVYYCAREPEKFDSDDSDVWGRGTLVTVSS,QAVLTQPPSVSGAPGQRVTISCTGSSSNIGAGYGVHWYQQLPGTAPKLLIYGDSNRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDNSLSGYVFGGGTQLTVL,3.0001576,80.392654,293.78253,0.28681988,9.35425
+ficlatuzumab,QVQLVQPGAEVKKPGTSVKLSCKASGYTFTTYWMHWVRQAPGQGLEWIGEINPTNGHTNYNQKFQGRATLTVDKSTSTAYMELSSLRSEDTAVYYCARNYVGSIFDYWGQGTLLTVSS,DIVMTQSPDSLAMSLGERVTLNCKASENVVSYVSWYQQKPGQSPKLLIYGASNRESGVPDRFSGSGSATDFTLTISSVQAEDVADYHCGQSYNYPYTFGQGTKLEIK,2.7414894,82.50102,217.51161,0.13716821,3.4012265
+figitumumab,EVQLLESGGGLVQPGGSLRLSCTASGFTFSSYAMNWVRQAPGKGLEWVSAISGSGGTTFYADSVKGRFTISRDNSRTTLYLQMNSLRAEDTAVYYCAKDLGWSDSYYYYYGMDVWGQGTTVTVSS,DIQMTQFPSSLSASVGDRVTITCRASQGIRNDLGWYQQKPGKAPKRLIYAASRLHRGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQHNSYPCSFGQGTKLEIK,2.815615,82.006485,244.24011,0.066807315,6.801001
+fletikumab,QVQLVQSGAEVKRPGASVKVSCKASGYTFTNDIIHWVRQAPGQRLEWMGWINAGYGNTQYSQNFQDRVSITRDTSASTAYMELISLRSEDTAVYYCAREPLWFGESSPHDYYGMDVWGQGTTVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.941747,81.138504,246.52254,0.19857714,5.4136147
+foralumab,QVQLVESGGGVVQPGRSLRLSCAASGFKFSGYGMHWVRQAPGKGLEWVAVIWYDGSKKYYVDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARQMGYWHFDLWGRGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPLTFGGGTKVEIK,2.7994676,82.37906,257.05878,0.18476179,12.223555
+fremanezumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYWISWVRQAPGKGLEWVAEIRSESDASATHYAEAVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCLAYFDYGLAIQNYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCKASKRVTTYVSWYQQKPGQAPRLLIYGASNRYLGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCSQSYNYPYTFGQGTKLEIK,2.911842,82.75869,180.4524,0.07795659,5.5062003
+fresolimumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFSSNVISWVRQAPGQGLEWMGGVIPIVDIANYAQRFKGRVTITADESTSTTYMELSSLRSEDTAVYYCASTLGLVLDAMDYWGQGTLVTVSS,ETVLTQSPGTLSLSPGERATLSCRASQSLGSSYLAWYQQKPGQAPRLLIYGASSRAPGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYADSPITFGQGTRLEIK,2.9147131,80.83689,174.71005,0.20967913,4.3762956
+fulranumab,EVQLVESGGGLVQPGGSLRLSCAASGFTLRSYSMNWVRQAPGKGLEWVSYISRSSHTIFYADSVKGRFTISRDNAKNSLYLQMDSLRDEDTAMYYCARVYSSGWHVSDYFDYWGQGILVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.7695758,82.34644,221.00749,0.18777087,7.304198
+galcanezumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFGNYWMQWVRQAPGQGLEWMGAIYEGTGKTVYIQKFADRVTITADKSTSTAYMELSSLRSEDTAVYYCARLSDYVSGFGYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASKDISKYLNWYQQKPGKAPKLLIYYTSGYHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGDALPPTFGGGTKVEIK,2.7816348,81.535416,264.735,0.20906079,4.843675
+galiximab,QVQLQESGPGLVKPSETLSLTCAVSGGSISGGYGWGWIRQPPGKGLEWIGSFYSSSGNTYYNPSLKSQVTISTDTSKNQFSLKLNSMTAADTAVYYCVRDRLFSVVGMVYNNWFDVWGPGVLVTVSS,ESALTQPPSVSGAPGQKVTISCTGSTSNIGGYDLHWYQQLPGTAPKLLIYDINKRPSGISDRFSGSKSGTAASLAITGLQTEDEADYYCQSYDSSLNAQVFGGGTRLTVL,3.1815612,82.065865,274.09937,0.24168682,6.5230412
+ganitumab,QVQLQESGPGLVKPSGTLSLTCAVSGGSISSSNWWSWVRQPPGKGLEWIGEIYHSGSTNYNPSLKSRVTISVDKSKNQFSLKLSSVTAADTAVYYCARWTGRTDAFDIWGQGTMVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLLHSNGYNYLDWYLQKPGQSPQLLIYLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQGTHWPLTFGQGTKVEIK,2.8321147,82.59935,222.80244,0.30731526,7.2634807
+gantenerumab,QVELVESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAINASGTRTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARGKGNTHKPYGYVRYFDVWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGVPARFSGSGSGTDFTLTISSLEPEDFATYYCLQIYNMPITFGQGTKVEIK,2.7422934,81.986336,268.08994,0.14264178,11.205082
+gatipotuzumab,EVQLVESGGGLVQPGGSMRLSCVASGFPFSNYWMNWVRQAPGKGLEWVGEIRLKSNNYTTHYAESVKGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCTRHYYFDYWGQGTLVTVSS,DIVMTQSPLSNPVTPGEPASISCRSSKSLLHSNGITYFFWYLQKPGQSPQLLIYQMSNLASGVPDRFSGSGSGTDFTLRISRVEAEDVGVYYCAQNLELPPTFGQGTKVEIK,2.7279284,82.626595,195.92255,0.12642214,5.5823464
+gemtuzumab,EVQLVQSGAEVKKPGSSVKVSCKASGYTITDSNIHWVRQAPGQSLEWIGYIYPYNGGTDYNQKFKNRATLTVDNPTNTAYMELSSLRSEDTAFYYCVNGNPWLAYWGQGTLVTVSS,DIQLTQSPSTLSASVGDRVTITCRASESLDNYGIRFLTWFQQKPGKAPKLLMYAASNQGSGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQTKEVPWSFGQGTKVEVK,2.8635087,81.35652,203.80515,0.19997112,3.193277
+gevokizumab,QVQLQESGPGLVKPSQTLSLTCSFSGFSLSTSGMGVGWIRQPSGKGLEWLAHIWWDGDESYNPSLKSRLTISKDTSKNQVSLKITSVTAADTAVYFCARNRYDPPWFVDWGQGTLVTVSS,DIQMTQSTSSLSASVGDRVTITCRASQDISNYLSWYQQKPGKAVKLLIYYTSKLHSGVPSRFSGSGSGTDYTLTISSLQQEDFATYFCLQGKMLPWTFGQGTKLEIK,2.7225626,82.97598,232.79271,0.23680277,4.207772
+gimsilumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRHWMHWLRQVPGKGPVWVSRINGAGTSITYADSVRGRFTISRDNANNTLFLQMNSLRADDTALYFCARANSVWFRGLFDYWGQGTPVTVSS,EIVLTQSPVTLSVSPGERVTLSCRASQSVSTNLAWYQQKLGQGPRLLIYGASTRATDIPARFSGSGSETEFTLTISSLQSEDFAVYYCQQYDKWPDTFGQGTKLEIK,2.6865783,82.922295,244.54112,0.15308441,6.603906
+girentuximab,DVKLVESGGGLVKLGGSLKLSCAASGFTFSNYYMSWVRQTPEKRLELVAAINSDGGITYYLDTVKGRFTISRDNAKNTLYLQMSSLKSEDTALFYCARHRSGYFSMDYWGQGTSVTVSS,DIVMTQSQRFMSTTVGDRVSITCKASQNVVSAVAWYQQKPGQSPKLLIYSASNRYTGVPDRFTGSGSGTDFTLTISNMQSEDLADFFCQQYSNYPWTFGGGTKLEIK,2.731359,84.13637,188.45328,0.09184191,1.4208169
+glembatumumab,QVQLQESGPGLVKPSQTLSLTCTVSGGSISSFNYYWSWIRHHPGKGLEWIGYIYYSGSTYSNPSLKSRVTISVDTSKNQFSLTLSSVTAADTAVYYCARGYNWNYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVDNNLVWYQQKPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQYNNWPPWTFGQGTKVEIK,3.172826,82.238304,233.50038,0.2918877,9.488258
+golimumab,QVQLVESGGGVVQPGRSLRLSCAASGFIFSSYAMHWVRQAPGNGLEWVAFMSYDGSNKKYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGIAAGGNYYYYGMDVWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVYSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPFTFGPGTKVDIK,2.9944544,82.376495,229.06613,0.13417496,8.685516
+guselkumab,EVQLVQSGAEVKKPGESLKISCKGSGYSFSNYWIGWVRQMPGKGLEWMGIIDPSNSYTRYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARWYYKPFDVWGQGTLVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSSSNIGSGYDVHWYQQLPGTAPKLLIYGNSKRPSGVPDRFSGSKSGTSASLAITGLQSEDEADYYCASWTDGLSLVVFGGGTKLTVL,3.024758,82.00963,292.47937,0.34500584,13.512224
+ianalumab,QVQLQQSGPGLVKPSQTLSLTCAISGDSVSSNSAAWGWIRQSPGRGLEWLGRIYYRSKWYNSYAVSVKSRITINPDTSKNQFSLQLNSVTPEDTAVYYCARYQWVPKIGVFDSWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQFILPEYLSWYQQKPGQAPRLLIYGSSSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQFYSSPLTFGQGTKVEIK,2.8911889,81.04411,171.38951,0.32265928,10.042988
+ibalizumab,QVQLQQSGPEVVKPGASVKMSCKASGYTFTSYVIHWVRQKPGQGLDWIGYINPYNDGTDYDEKFKGKATLTSDTSTSTAYMELSSLRSEDTAVYYCAREKDNYATGAWFAYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERVTMNCKSSQSLLYSTNQKNYLAWYQQKPGQSPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSVQAEDVAVYYCQQYYSYRTFGGGTKLEIK,2.8928185,82.13023,178.63637,0.19172668,1.9710164
+icrucumab,QAQVVESGGGVVQSGRSLRLSCAASGFAFSSYGMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVRGRFTISRDNSENTLYLQMNSLRAEDTAVYYCARDHYGSGVHHYFYYGLDVWGQGTTVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPLTFGGGTKVEIK,2.996188,81.56528,236.17107,0.1601899,9.656353
+imgatuzumab,QVQLVQSGAEVKKPGSSVKVSCKASGFTFTDYKIHWVRQAPGQGLEWMGYFNPNSGYSTYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARLSPGGYYVMDAWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGINNYLNWYQQKPGKAPKRLIYNTNNLQTGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQHNSFPTFGQGTKLEIK,2.6933162,82.08026,268.80426,0.19399014,4.499885
+inclacumab,EVQLVESGGGLVRPGGSLRLSCAASGFTFSNYDMHWVRQATGKGLEWVSAITAAGDIYYPGSVKGRFTISRENAKNSLYLQMNSLRAGDTAVYYCARGRYSGSGSYYNDWFDPWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPLTFGGGTKVEIK,2.9088402,82.87479,228.80246,0.14637066,8.714975
+inebilizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSSWMNWVRQAPGKGLEWVGRIYPGDGDTNYNVKFKGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCARSGFITTVRDFDYWGQGTLVTVSS,EIVLTQSPDFQSVTPKEKVTITCRASESVDTFGISFMNWFQQKPDQSPKLLIHEASNQGSGVPSRFSGSGSGTDFTLTINSLEAEDAATYYCQQSKEVPFTFGGGTKVEIK,2.5964077,83.824265,161.6288,0.03647387,-0.54488444
+infliximab,EVKLEESGGGLVQPGGSMKLSCVASGFIFSNHWMNWVRQSPEKGLEWVAEIRSKSINSATHYAESVKGRFTISRDDSKSAVYLQMTDLRTEDTGVYYCSRNYYGSTYDYWGQGTTLTVSS,DILLTQSPAILSVSPGERVSFSCRASQFVGSSIHWYQQRTNGSPRLLIKYASESMSGIPSRFSGSGSGTDFTLSINTVESEDIADYYCQQSHSWPFTFGSGTNLEVK,2.617916,80.397316,-73.938736,0.37774533,-4.6808486
+inotuzumab,EVQLVQSGAEVKKPGASVKVSCKASGYRFTNYWIHWVRQAPGQGLEWIGGINPGNNYATYRRKFQGRVTMTADTSTSTVYMELSSLRSEDTAVYYCTREGYGNYGAWFAYWGQGTLVTVSS,DVQVTQSPSSLSASVGDRVTITCRSSQSLANSYGNTFLSWYLHKPGKAPQLLIYGISNRFSGVPDRFSGSGSGTDFTLTISSLQPEDFATYYCLQGTHQPYTFGQGTKVEIK,2.8251586,81.61412,274.44318,0.23035088,6.0468717
+intetumumab,QVQLVESGGGVVQPGRSRRLSCAASGFTFSRYTMHWVRQAPGKGLEWVAVISFDGSNKYYVDSVKGRFTISRDNSENTLYLQVNILRAEDTAVYYCAREARGSYAFDIWGQGTMVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPFTFGPGTKVDIK,2.79169,82.490036,244.33473,0.112112746,7.96625
+ipilimumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYTMHWVRQAPGKGLEWVTFISYDGNNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAIYYCARTGWLGPFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVGSSYLAWYQQKPGQAPRLLIYGAFSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPWTFGQGTKVEIK,2.8674426,81.82805,264.3709,0.12523524,9.447741
+isatuximab,QVQLVQSGAEVAKPGTSVKLSCKASGYTFTDYWMQWVKQRPGQGLEWIGTIYPGDGDTGYAQKFQGKATLTADKSSKTVYMHLSSLASEDSAVYYCARGDYYGSNSLDYWGQGTSVTVSS,DIVMTQSHLSMSTSLGDPVSITCKASQDVSTVVAWYQQKPGQSPRRLIYSASYRYIGVPDRFTGSGAGTDFTFTISSVQAEDLAVYYCQQHYSPPYTFGGGTKLEIK,2.8621037,83.08137,172.89323,0.18147053,4.8548136
+iscalimab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVISYEESNRYHADSVKGRFTISRDNSKITLYLQMNSLRTEDTAVYYCARDGGIAAPGPDYWGQGTLVTVSS,DIVMTQSPLSLTVTPGEPASISCRSSQSLLYSNGYNYLDWYLQKPGQSPQVLISLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQARQTPFTFGPGTKVDIR,2.8622494,82.71437,229.13615,0.06329577,4.571306
+itolizumab,EVQLVESGGGLVKPGGSLKLSCAASGFKFSRYAMSWVRQAPGKRLEWVATISSGGSYIYYPDSVKGRFTISRDNVKNTLYLQMSSLRSEDTAMYYCARRDYDLDYFDSWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASRDIRSYLTWYQQKPGKAPKTLIYYATSLADGVPSRFSGSGSGQDYSLTISSLESDDTATYYCLQHGESPFTLGSGTKLEIK,2.6226015,83.30571,260.2227,0.10765089,6.9360833
+ixekizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYSFTDYHIHWVRQAPGQGLEWMGVINPMYGTTDYNQRFKGRVTITADESTSTAYMELSSLRSEDTAVYYCARYDYFTGTGVYWGQGTLVTVSS,DIVMTQTPLSLSVTPGQPASISCRSSRSLVHSRGNTYLHWYLQKPGQSPQLLIYKVSNRFIGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHLPFTFGQGTKLEIK,2.863284,81.867775,168.26125,0.2838468,4.473189
+ladiratuzumab,QVQLVQSGAEVKKPGASVKVSCKASGLTIEDYYMHWVRQAPGQGLEWMGWIDPENGDTEYGPKFQGRVTMTRDTSINTAYMELSRLRSDDTAVYYCAVHNAHYGTWFAYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLLHSSGNTYLEWYQQRPGQSPRPLIYKISTRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPYTFGGGTKVEIK,2.7588856,81.394714,182.68597,0.23024878,1.86584
+lampalizumab,EVQLVQSGPELKKPGASVKVSCKASGYTFTNYGMNWVRQAPGQGLEWMGWINTYTGETTYADDFKGRFVFSLDTSVSTAYLQISSLKAEDTAVYYCEREGGVNNWGQGTLVTVSS,DIQVTQSPSSLSASVGDRVTITCITSTDIDDDMNWYQQKPGKVPKLLISGGNTLRPGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCLQSDSLPYTFGQGTKVEIK,2.738186,82.02031,232.92336,0.1498352,1.1626506
+lanadelumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSHYIMMWVRQAPGKGLEWVSGIYSSGGITVYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAYRRIGVPRRDEFDIWGQGTMVTVSS,DIQMTQSPSTLSASVGDRVTITCRASQSISSWLAWYQQKPGKAPKLLIYKASTLESGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQYNTYWTFGQGTKVEIK,2.7274904,82.44277,267.98383,0.06043908,7.0637317
+landogrozumab,EVQLVESGGGLVQPGGSLRLSCAASGLTFSRYPMSWVRQAPGKGLVWVSAITSSGGSTYYSDTVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCARLPDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASSSVSSSYLHWYQQKPGQAPRLLIYSTSNLVAGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQHHSGYHFTFGGGTKVEIK,2.6603594,82.36435,271.45007,0.1509375,13.471575
+lebrikizumab,QVTLRESGPALVKPTQTLTLTCTVSGFSLSAYSVNWIRQPPGKALEWLAMIWGDGKIVYNSALKSRLTISKDTSKNQVVLTMTNMDPVDTATYYCAGDGYYPYAMDNWGQGSLVTVSS,DIVMTQSPDSLSVSLGERATINCRASKSVDSYGNSFMHWYQQKPGQPPKLLIYLASNLESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQNNEDPRTFGGGTKVEIK,3.0184186,82.9949,187.59364,0.17004135,-0.9733324
+lenzilumab,QVQLVQSGAEVKKPGASVKVSCKASGYSFTNYYIHWVRQAPGQRLEWMGWINAGNGNTKYSQKFQGRVTITRDTSASTAYMELSSLRSEDTAVYYCVRRQRFPYYFDYWGQGTLVTVSS,EIVLTQSPATLSVSPGERATLSCRASQSVGTNVAWYQQKPGQAPRVLIYSTSSRATGITDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQFNKSPLTFGGGTKVEIK,2.7106452,81.51364,222.36404,0.2676781,10.703987
+lexatumumab,EVQLVQSGGGVERPGGSLRLSCAASGFTFDDYGMSWVRQAPGKGLEWVSGINWNGGSTGYADSVKGRVTISRDNAKNSLYLQMNSLRAEDTAVYYCAKILGAGRGWYFDLWGKGTTVTVSS,SSELTQDPAVSVALGQTVRITCQGDSLRSYYASWYQQKPGQAPVLVIYGKNNRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCNSRDSSGNHVVFGGGTKLTVL,2.8471088,81.64948,287.56854,0.13261954,9.225693
+ligelizumab,QVQLVQSGAEVMKPGSSVKVSCKASGYTFSWYWLEWVRQAPGHGLEWMGEIDPGTFTTNYNEKFKARVTFTADTSTSTAYMELSSLRSEDTAVYYCARFSHFSGSNYDYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSIGTNIHWYQQKPGQAPRLLIYYASESISGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQSWSWPTTFGGGTKVEIK,3.1091177,81.01958,162.46857,0.25264937,7.0124764
+lintuzumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDYNMHWVRQAPGQGLEWIGYIYPYNGGTGYNQKFKSKATITADESTNTAYMELSSLRSEDTAVYYCARGRPAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASESVDNYGISFMNWFQQKPGKAPKLLIYAASNQGSGVPSRFSGSGSGTDFTLTISSLQPDDFATYYCQQSKEVPWTFGQGTKVEIK,2.7491205,81.82079,259.0924,0.16482255,3.7002707
+lirilumab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSFYAISWVRQAPGQGLEWMGGFIPIFGAANYAQKFQGRVTITADESTSTAYMELSSLRSDDTAVYYCARIPSGSYYYDYDMDVWGQGTTVTVSS,EIVLTQSPVTLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWMYTFGQGTKLEIK,3.100655,81.86239,197.6625,0.25658715,5.749539
+loncastuximab,QVQLVQPGAEVVKPGASVKLSCKTSGYTFTSNWMHWVKQAPGQGLEWIGEIDPSDSYTNYNQNFQGKAKLTVDKSTSTAYMEVSSLRSDDTAVYYCARGSNPYYYAMDYWGQGTSVTVSS,EIVLTQSPAIMSASPGERVTMTCSASSGVNYMHWYQQKPGTSPRRWIYDTSKLASGVPARFSGSGSGTSYSLTISSMEPEDAATYYCHQRGSYTFGGGTKLEIK,2.915943,82.5299,198.54529,0.16242737,5.7269197
+lorvotuzumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSFGMHWVRQAPGKGLEWVAYISSGSFTIYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARMRKGYAMDYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQIIIHSDGNTYLEWFQQRPGQSPRRLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPHTFGQGTKVEIK,2.6421893,82.06261,217.84239,0.16323988,7.8142104
+lucatumumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVISYEESNRYHADSVKGRFTISRDNSKITLYLQMNSLRTEDTAVYYCARDGGIAAPGPDYWGQGTLVTVSS,DIVMTQSPLSLTVTPGEPASISCRSSQSLLYSNGYNYLDWYLQKPGQSPQVLISLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQARQTPFTFGPGTKVDIR,2.8470273,82.56992,228.99612,0.089572474,4.9773984
+lumiliximab,EVQLVESGGGLAKPGGSLRLSCAASGFRFTFNNYYMDWVRQAPGQGLEWVSRISSSGDPTWYADSVKGRFTISRENANNTLFLQMNSLRAEDTAVYYCASLTTGSDSWGQGVLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDIRYYLNWYQQKPGKAPKLLIYVASSLQSGVPSRFSGSGSGTEFTLTVSSLQPEDFATYYCLQVYSTPRTFGQGTKVEIK,2.7767243,82.5281,230.04758,0.112779886,4.0842013
+lumretuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFRSSYISWVRQAPGQGLEWMGWIYAGTGSPSYNQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARHRDYYSNSLTYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSVLNSGNQKNYLTWYQQKPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQSDYSYPYTFGQGTKLEIK,2.780641,82.35286,253.46861,0.24205784,7.584862
+margetuximab,QVQLQQSGPELVKPGASLKLSCTASGFNIKDTYIHWVKQRPEQGLEWIGRIYPTNGYTRYDPKFQDKATITADTSSNTAYLQVSRLTSEDTAVYYCSRWGGDGFYAMDYWGQGASVTVSS,DIVMTQSHKFMSTSVGDRVSITCKASQDVNTAVAWYQQKPGHSPKLLIYSASFRYTGVPDRFTGSRSGTDFTFTISSVQAEDLAVYYCQQHYTTPPTFGGGTKVEIK,2.7377377,82.88796,174.24797,0.17595944,0.2457459
+matuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSHWMHWVRQAPGQGLEWIGEFNPSNGRTNYNEKFKSKATMTVDTSTNTAYMELSSLRSEDTAVYYCASRDYDYDGRYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVTYMYWYQQKPGKAPKLLIYDTSNLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSHIFTFGQGTKVEIK,2.8065443,81.614525,260.8537,0.18186474,3.4906063
+mavrilimumab,QVQLVQSGAEVKKPGASVKVSCKVSGYTLTELSIHWVRQAPGKGLEWMGGFDPEENEIVYAQRFQGRVTMTEDTSTDTAYMELSSLRSEDTAVYYCAIVGSFSPLTLGLWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSGSNIGAPYDVSWYQQLPGTAPKLLIYHNNKRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCATVEAGLSGSVFGGGTKLTVL,2.9829974,80.636154,326.54538,0.2629904,6.9349246
+mepolizumab,QVTLRESGPALVKPTQTLTLTCTVSGFSLTSYSVHWVRQPPGKGLEWLGVIWASGGTDYNSALMSRLSISKDTSRNQVVLTMTNMDPVDTATYYCARDPPSSLLRLDYWGRGTPVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSLLNSGNQKNYLAWYQQKPGQPPKLLIYGASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQNVHSFPFTFGGGTKLEIK,2.8256567,82.67031,241.34035,0.20765379,4.296659
+milatuzumab,QVQLQQSGSELKKPGASVKVSCKASGYTFTNYGVNWIKQAPGQGLQWMGWINPNTGEPTFDDDFKGRFAFSLDTSVSTAYLQISSLKADDTAVYFCSRSRGKNEAWFAYWGQGTLVTVSS,DIQLTQSPLSLPVTLGQPASISCRSSQSLVHRNGNTYLHWFQQRPGQSPRLLIYTVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYFCSQSSHVPPTFGAGTRLEIK,2.706004,82.06934,157.70656,0.2915547,3.0916991
+mirikizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYKFTRYVMHWVRQAPGQGLEWMGYINPYNDGTNYNEKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARNWDTGLWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCKASDHILKFLTWYQQKPGKAPKLLIYGATSLETGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQMYWSTPFTFGGGTKVEIK,2.7525623,81.48855,241.1596,0.23209868,7.729346
+mirvetuximab,QVQLVQSGAEVVKPGASVKISCKASGYTFTGYFMNWVKQSPGQSLEWIGRIHPYDGDTFYNQKFQGKATLTVDKSSNTAHMELLSLTSEDFAVYYCTRYDGSRAMDYWGQGTTVTVSS,DIVLTQSPLSLAVSLGQPAIISCKASQSVSFAGTSLMHWYHQKPGQQPRLLIYRASNLEAGVPDRFSGSGSKTDFTLTISPVEAEDAATYYCQQSREYPYTFGGGTKLEIK,2.8217556,81.83524,165.40977,0.2467882,3.334107
+mitazalimab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSTYGMHWVRQAPGKGLEWLSYISGGSSYIFYADSVRGRFTISRDNSENALYLQMNSLRAEDTAVYYCARILRGGSGMDLWGQGTLVTVSS,QSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYNVYWYQQLPGTAPKLLIYGNINRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDKSISGLVFGGGTKLTVL,2.970751,81.79042,376.22504,0.17515038,12.546747
+mogamulizumab,EVQLVESGGDLVQPGRSLRLSCAASGFIFSNYGMSWVRQAPGKGLEWVATISSASTYSYYPDSVKGRFTISRDNAKNSLYLQMNSLRVEDTALYYCGRHSDGNFAFGYWGQGTLVTVSS,DVLMTQSPLSLPVTPGEPASISCRSSRNIVHINGDTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSLLPWTFGQGTKVEIK,2.6947117,82.90533,157.89261,0.12642422,5.1326766
+monalizumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYWMNWVRQAPGQGLEWMGRIDPYDSETHYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARGGYDFDVGTLYWFFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASENIYSYLAWYQQKPGKAPKLLIYNAKTLAEGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHHYGTPRTFGGGTKVEIK,2.843069,81.162544,240.18007,0.18202035,4.5624633
+mosunetuzumab,EVQLVQSGAEVKKPGASVKVSCKASGYTFTNYYIHWVRQAPGQGLEWIGWIYPGDGNTKYNEKFKGRATLTADTSTSTAYLELSSLRSEDTAVYYCARDSYSNYYFDYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSLLNSRTRKNYLAWYQQKPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCTQSFILRTFGQGTKVEIK,2.7006643,82.221825,199.23112,0.21181524,4.7754407
+motavizumab,QVTLRESGPALVKPTQTLTLTCTFSGFSLSTAGMSVGWIRQPPGKALEWLADIWWDDKKHYNPSLKDRLTISKDTSKNQVVLKVTNMDPADTATYYCARDMIFNFYFDVWGQGTTVTVSS,DIQMTQSPSTLSASVGDRVTITCSASSRVGYMHWYQQKPGKAPKLLIYDTSKLASGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCFQGSGYPFTFGGGTKVEIK,2.8017108,82.42467,265.30975,0.23791106,4.4918656
+muromonab,QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPSRGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSS,QIVLTQSPAIMSASPGEKVTMTCSASSSVSYMNWYQQKSGTSPKRWIYDTSKLASGVPAHFRGSGSGTSYSLTISGMEAEDAATYYCQQWSSNPFTFGSGTKLEIK,2.7252054,82.0155,218.63586,0.2507152,11.653791
+natalizumab,QVQLVQSGAEVKKPGASVKVSCKASGFNIKDTYIHWVRQAPGQRLEWMGRIDPANGYTKYDPKFQGRVTITADTSASTAYMELSSLRSEDTAVYYCAREGYYGNYGVYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKTSQDINKYMAWYQQTPGKAPRLLIHYTSALQPGIPSRFSGSGSGRDYTFTISSLQPEDIATYYCLQYDNLWTFGQGTKVEIK,2.8201566,81.95737,211.2848,0.17530313,2.7076654
+necitumumab,QVQLQESGPGLVKPSQTLSLTCTVSGGSISSGDYYWSWIRQPPGKGLEWIGYIYYSGSTDYNPSLKSRVTMSVDTSKNQFSLKVNSVTAADTAVYYCARVSIFGVGTFDYWGQGTLVTVSS,EIVMTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCHQYGSTPLTFGGGTKAEIK,3.0669115,82.65889,203.23798,0.26216808,6.8383355
+nesvacumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYDIHWVRQATGKGLEWVSAIGPAGDTYYPGSVKGRFTISRENAKNSLYLQMNSLRAGDTAVYYCARGLITFGGLIAPFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSTYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQHYDNSQTFGQGTKVEIK,2.87543,82.475174,206.48715,0.1146452,5.893411
+nimotuzumab,QVQLQQSGAEVKKPGSSVKVSCKASGYTFTNYYIYWVRQAPGQGLEWIGGINPTSGGSNFNEKFKTRVTITADESSTTAYMELSSLRSEDTAFYFCTRQGLWFDSDGRGFDFWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQNIVHSNGNTYLDWYQQTPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCFQYSHVPWTFGQGTKLQIT,2.822534,81.6064,233.92093,0.23263296,2.1823525
+nivolumab,QVQLVESGGGVVQPGRSLRLDCKASGITFSNSGMHWVRQAPGKGLEWVAVIWYDGSKRYYADSVKGRFTISRDNSKNTLFLQMNSLRAEDTAVYYCATNDDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQSSNWPRTFGQGTKVEIK,2.8112473,82.66983,242.75557,0.15156135,8.989979
+obexelimab,EVQLVESGGGLVKPGGSLKLSCAASGYTFTSYVMHWVRQAPGKGLEWIGYINPYNDGTKYNEKFQGRVTISSDKSISTAYMELSSLRSEDTAMYYCARGTYYYGTRVFDYWGQGTLVTVSS,DIVMTQSPATLSLSPGERATLSCRSSKSLQNVNGNTYLYWFQQKPGQSPQLLIYRMSNLNSGVPDRFSGSGSGTEFTLTISSLEPEDFAVYYCMQHLEYPITFGAGTKLEIK,2.8376179,81.9438,161.84198,0.18879125,4.7810426
+obiltoxaximab,QVQLQQSGPELKKPGASVKVSCKDSGYAFSSSWMNWVRQAPGQGLEWIGRIYPGDGDTNYNGKFQGRVTITADKSSSTAYMELSSLRSEDTAVYFCARSGLLRYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDIRNYLNWYQQKPGKAVKLLIYYTSRLLPGVPSRFSGSGSGTDYSLTISSQEQEDIGTYFCQQGNTLPWTFGQGTKVEIR,2.671856,82.034256,195.46034,0.22964491,5.9460835
+obinutuzumab,QVQLVQSGAEVKKPGSSVKVSCKASGYAFSYSWINWVRQAPGQGLEWMGRIFPGDGDTDYNGKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARNVFDGYWLVYWGQGTLVTVSS,DIVMTQTPLSLPVTPGEPASISCRSSKSLLHSNGITYLYWYLQKPGQSPQLLIYQMSNLVSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCAQNLELPYTFGGGTKVEIK,2.820558,82.30335,178.26166,0.2838889,6.3209157
+ocrelizumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFTSYNMHWVRQAPGKGLEWVGAIYPGNGDTSYNQKFKGRFTISVDKSKNTLYLQMNSLRAEDTAVYYCARVVYYSNSYWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASSSVSYMHWYQQKPGKAPKPLIYAPSNLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQWSFNPPTFGQGTKVEIK,2.8268533,82.64866,274.6349,0.113792,6.302918
+ofatumumab,EVQLVESGGGLVQPGRSLRLSCAASGFTFNDYAMHWVRQAPGKGLEWVSTISWNSGSIGYADSVKGRFTISRDNAKKSLYLQMNSLRAEDTALYYCAKDIQYGNYYYGMDVWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPITFGQGTRLEIK,2.8703802,82.67679,209.52156,0.11113796,7.617048
+olaratumab,QLQLQESGPGLVKPSETLSLTCTVSGGSINSSSYYWGWLRQSPGKGLEWIGSFFYTGSTYYNPSLRSRLTISVDTSKNQFSLMLSSVTAADTAVYYCARQSTYYYGSGNYYGWFDRWDQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPAFGQGTKVEIK,3.1696851,82.645744,210.75542,0.2823522,7.338387
+oleclumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAYSWVRQAPGKGLEWVSAISGSGGRTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARLGYGRVDEWGRGTLVTVSS,QSVLTQPPSASGTPGQRVTISCSGSLSNIGRNPVNWYQQLPGTAPKLLIYLDNLRLSGVPDRFSGSKSGTSASLAISGLQSEDEADYYCATWDDSHPGWTFGGGTKLTVL,2.811458,81.02809,373.97406,0.15175253,15.39303
+olokizumab,EVQLVESGGGLVQPGGSLRLSCAASGFNFNDYFMNWVRQAPGKGLEWVAQMRNKNYQYGTYYAESLEGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCARESYYGFTSYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDIGISLSWYQQKPGKAPKLLIYNANNLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCLQHNSAPYTFGQGTKLEIK,2.762403,82.28423,237.83086,0.0651236,3.1107917
+omalizumab,EVQLVESGGGLVQPGGSLRLSCAVSGYSITSGYSWNWIRQAPGKGLEWVASITYDGSTNYNPSLKGRITISRDDSKNTFYLQMNSLRAEDTAVYYCARGSHYFGHWHFAVWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCRASQSVDYDGDSYMNWYQQKPGKAPKLLIYAASYLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSHEDPYTFGQGTKVEIK,2.8548284,83.1574,243.41803,0.072556406,2.9744372
+omburtamab,QVQLQQSGAELVKPGASVKLSCKASGYTFTNYDINWVRQRPEQGLEWIGWIFPGDGSTQYNEKFKGKATLTTDTSSSTAYMQLSRLTSEDSAVYFCARQTTATWFAYWGQGTLVTVSA,DIVMTQSPATLSVTPGDRVSLSCRASQSISDYLHWYQQKSHESPRLLIKYASQSISGIPSRFSGSGSGSDFTLSINSVEPEDVGVYYCQNGHSFPLTFGAGTKLELK,2.688936,82.157295,200.69603,0.22115889,5.662233
+onartuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFTSYWLHWVRQAPGKGLEWVGMIDPSNSDTRFNPNFKDRFTISADTSKNTAYLQMNSLRAEDTAVYYCATYRSYVTPLDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSSQSLLYTSSQKNYLAWYQQKPGKAPKLLIYWASTRESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYAYPWTFGQGTKVEIK,2.8099205,82.457245,261.0797,0.13087727,4.4239774
+opicinumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSAYEMKWVRQAPGKGLEWVSVIGPSGGFTFYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCATEGDNDAFDIWGQGTTVTVSS,DIQMTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPMYTFGQGTKLEIK,2.7472115,82.53973,239.62764,0.063662514,7.0645013
+orticumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNAWMSWVRQAPGKGLEWVSSISVGGHRTYYADSVKGRSTISRDNSKNTLYLQMNSLRAEDTAVYYCARIRVGPSGGAFDYWGQGTLVTVSS,QSVLTQPPSASGTPGQRVTISCSGSNTNIGKNYVSWYQQLPGTAPKLLIYANSNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCASWDASLNGWVFGGGTKLTVL,2.787515,81.57313,383.92688,0.17181276,13.426046
+osocimab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSQYGMDWVRQAPGKGLEWVSGIGPSGGSTVYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCTRGGPYYYYGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQADSFPVTFGGGTKVEIK,2.7517781,82.82816,277.46097,0.005590588,4.792807
+otelixizumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSFPMAWVRQAPGKGLEWVSTISTSGGRTYYRDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKFRQYSGGFDYWGQGTLVTVSS,DIQLTQPNSVSTSLGSTVKLSCTLSSGNIENNYVHWYQLYEGRSPTTMIYDDDKRPDGVPDRFSGSIDRSSNSAFLTIHNVAIEDEAIYFCHSYVSSFNVFGGGTKLTVL,2.6441293,83.07279,243.34639,0.090905234,8.235725
+otlertuzumab,EVQLVQSGAEVKKPGESLKISCKGSGYSFTGYNMNWVRQMPGKGLEWMGNIDPYYGGTTYNRKFKGQVTISADKSISTAYLQWSSLKASDTAMYYCARSVGPFDSWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASENVYSYLAWYQQKPGQAPRLLIYFAKTLAEGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQHHSDNPWTFGQGTKVEIK,2.8480964,82.62224,150.77518,0.24661562,6.4228463
+ozanezumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYWMHWVRQAPGQGLEWIGNINPSNGGTNYNEKFKSKATMTRDTSTSTAYMELSSLRSEDTAVYYCELMQGYWGQGTLVTVSS,DIVMTQSPLSNPVTLGQPVSISCRSSKSLLYKDGKTYLNWFLQRPGQSPQLLIYLMSTRASGVPDRFSGGGSGTDFTLKISRVEAEDVGVYYCQQLVEYPLTFGQGTKLEIK,2.7679746,82.113914,247.55908,0.2656052,7.525185
+palivizumab,QVTLRESGPALVKPTQTLTLTCTFSGFSLSTSGMSVGWIRQPPGKALEWLADIWWDDKKDYNPSLKSRLTISKDTSKNQVVLKVTNMDPADTATYYCARSMITNWYFDVWGAGTTVTVSS,DIQMTQSPSTLSASVGDRVTITCKCQLSVGYMHWYQQKPGKAPKLLIYDTSKLASGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCFQGSGYPFTFGGGTKLEIK,2.8215666,82.5518,275.23157,0.22421026,4.455868
+pamrevlumab,EGQLVQSGGGLVHPGGSLRLSCAGSGFTFSSYGMHWVRQAPGKGLEWVSGIGTGGGTYSTDSVKGRFTISRDNAKNSLYLQMNSLRAEDMAVYYCARGDYYGSGSFFDCWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNSYPPTFGQGTKLEIK,2.8053973,82.8733,265.6668,0.14158262,8.588867
+panitumumab,QVQLQESGPGLVKPSETLSLTCTVSGGSVSSGDYYWTWIRQSPGKGLEWIGHIYYSGNTNYNPSLKSRLTISIDTSKTQFSLKLSSVTAADTAIYYCVRDRVTGAFDIWGQGTMVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGSGSGTDFTFTISSLQPEDIATYFCQHFDHLPLAFGGGTKVEIK,2.7717478,83.3909,233.11877,0.1890074,1.8294897
+panobacumab,EEQVVESGGGFVQPGGSLRLSCAASGFTFSPYWMHWVRQAPGKGLVWVSRINSDGSTYYADSVKGRFTISRDNARNTLYLQMNSLRAEDTAVYYCARDRYYGPEMWGQGTMVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLVYSDGNTYLNWFQQRPGQSPRRLIYKVSNRDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQGTHWPLTFGGGTKVEIK,2.7576842,82.465034,240.59746,0.10246962,7.0376134
+parsatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFIDYYMNWVRQAPGKGLEWVGDINLDNSGTHYNQKFKGRFTISRDKSKNTAYLQMNSLRAEDTAVYYCAREGVYHDYDDYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRTSQSLVHINAITYLHWYQQKPGKAPKLLIYRVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCGQSTHVPLTFGQGTKVEIK,2.7250586,82.03287,206.78676,0.12471746,2.9316602
+patritumab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVETSKNQFSLKLSSVTAADTAVYYCARDKWTWYFDLWGRGTLVTVSS,DIEMTQSPDSLAVSLGERATINCRSSQSVLYSSSNRNYLAWYQQNPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQYYSTPRTFGQGTKVEIK,2.844503,82.0205,166.64252,0.2962078,7.0646653
+pembrolizumab,QVQLVQSGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASKGVSTSGYSYLHWYQQKPGQAPRLLIYLASYLESGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQHSRDLPLTFGGGTKVEIK,2.6441097,79.506714,-68.50394,0.42589304,-0.8846593
+pertuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFTDYTMDWVRQAPGKGLEWVADVNPNSGGSIYNQRFKGRFTLSVDRSKNTLYLQMNSLRAEDTAVYYCARNLGPSFYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVSIGVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYIYPYTFGQGTKVEIK,2.7732441,83.31167,254.11778,0.07900095,3.2705407
+pidilizumab,QVQLVQSGSELKKPGASVKISCKASGYTFTNYGMNWVRQAPGQGLQWMGWINTDSGESTYAEEFKGRFVFSLDTSVNTAYLQITSLTAEDTGMYFCVRVGYDALDYWGQGTLVTVSS,EIVLTQSPSSLSASVGDRVTITCSARSSVSYMHWFQQKPGKAPKLWIYRTSNLASGVPSRFSGSGSGTSYCLTINSLQPEDFATYYCQQRSSFPLTFGGGTKLEIK,2.8174756,82.01412,223.94194,0.25431362,5.4196715
+pinatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYEFSRSWMNWVRQAPGKGLEWVGRIYPGDGDTNYSGKFKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARDGSSWDWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSIVHSVGNTFLEWYQQKPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCFQGSQFPYTFGQGTKVEIK,2.7051349,82.51344,243.68275,0.12207413,4.8132873
+plozalizumab,EVQLVESGGGLVKPGGSLRLSCAASGFTFSAYAMNWVRQAPGKGLEWVGRIRTKNNNYATYYADSVKDRFTISRDDSKNTLYLQMNSLKTEDTAVYYCTTFYGNGVWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCKSSQSLLDSDGKTFLNWFQQRPGQSPRRLIYLVSKLDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCWQGTHFPYTFGQGTRLEIK,2.7554398,82.10091,215.04643,0.1380215,6.515557
+polatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGYTFSSYWIEWVRQAPGKGLEWIGEILPGGGDTNYNEIFKGRATFSADTSKNTAYLQMNSLRAEDTAVYYCTRRVPIRLDYWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCKASQSVDYEGDSFLNWYQQKPGKAPKLLIYAASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSNEDPLTFGQGTKVEIK,2.6835213,82.88449,242.71814,0.0891294,2.5604496
+ponezumab,QVQLVQSGAEVKKPGASVKVSCKASGYYTEAYYIHWVRQAPGQGLEWMGRIDPATGNTKYAPRLQDRVTMTRDTSTSTVYMELSSLRSEDTAVYYCASLYSLPVYWGQGTTVTVSS,DVVMTQSPLSLPVTLGQPASISCKSSQSLLYSDAKTYLNWFQQRPGQSPRRLIYQISRLDPGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQGTHYPVLFGQGTRLEIK,2.8197863,80.849495,203.30933,0.2800546,8.82827
+prasinezumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYGMSWVRQAPGKGLEWVASISSGGGSTYYPDNVKGRFTISRDDAKNSLYLQMNSLRAEDTAVYYCARGGAGIDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSIQTLLYSSNQKNYLAWFQQKPGKAPKLLIYWASIRKSGVPSRFSGSGSGTDFTLTISSLQPEDLATYYCQQYYSYPLTFGGGTKLEIK,2.7180738,83.195885,295.66467,0.09641796,9.62322
+prezalumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYWMSWVRQAPGKGLEWVAYIKQDGNEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGILWFGDLPTFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISNWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYDSYPRTFGQGTKVEIK,2.77011,82.303535,246.62486,0.059522957,5.0533032
+prolgolimab,QVQLVQSGGGLVQPGGSLRLSCAASGFTFSSYWMYWVRQVPGKGLEWVSAIDTGGGRTYYADSVKGRFAISRVNAKNTMYLQMNSLRAEDTAVYYCARDEGGGTGWGVLKDWPYGLDAWGQGTLVTVSS,QPVLTQPLSVSVALGQTARITCGGNNIGSKNVHWYQQKPGQAPVLVIYRDSNRPSGIPERFSGSNSGNTATLTISRAQAGDEADYYCQVWDSSTAVFGTGTKLTVL,2.9139616,81.544876,273.3844,0.111656934,7.851853
+quilizumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYGIAWVRQAPGKGLEWVAFISDLAYTIYYADTVTGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDNWDAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSLVHNNANTYLHWYQQKPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCSQNTLVPWTFGQGTKVEIK,2.7395136,82.36529,245.44661,0.05849041,4.3312354
+racotumomab,QVQLQQSGAELVKPGASVKLSCKASGYTFTSYDINWVRQRPEQGLEWIGWIFPGDGSTKYNEKFKGKATLTTDKSSSTAYMQLSRLTSEDSAVYFCAREDYYDNSYYFDYWGQGTTLTVSS,DIQMTQTTSSLSASLGDRVTISCRASQDISNYLNWYQQKPDGTVKLLIYYTSRLHSGVPSRFSGSGSGTDYSLTISNLEQEDIATYFCQQGNTLPWTFGGGTKLEIK,2.7629764,82.32956,207.38104,0.1994817,4.986984
+radretumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSFSMSWVRQAPGKGLEWVSSISGSSGTTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKPFPYFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSFLAWYQQKPGQAPRLLIYYASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQTGRIPPTFGQGTKVEIK,2.7682462,82.381195,256.76593,0.11637291,11.728224
+ramucirumab,EVQLVQSGGGLVKPGGSLRLSCAASGFTFSSYSMNWVRQAPGKGLEWVSSISSSSSYIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARVTDAFDIWGQGTMVTVSS,DIQMTQSPSSVSASIGDRVTITCRASQGIDNWLGWYQQKPGKAPKLLIYDASNLDTGVPSRFSGSGSGTYFTLTISSLQAEDFAVYFCQQAKAFPPTFGGGTKVDIK,2.7472684,82.94863,292.72522,0.073185235,7.435267
+ranibizumab,EVQLVESGGGLVQPGGSLRLSCAASGYDFTHYGMNWVRQAPGKGLEWVGWINTYTGEPTYAADFKRRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCAKYPYYYGTSHWYFDVWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKVLIYFTSSLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSTVPWTFGQGTKVEIK,2.94948,82.474236,237.83328,0.12026659,4.61503
+relatlimab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSDYYWNWIRQPPGKGLEWIGEINHRGSTNSNPSLKSRVTLSLDTSKNQFSLKLRSVTAADTAVYYCAFGYSDYEYNWFDPWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSISSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPLTFGQGTNLEIK,2.9819262,81.69474,181.21774,0.31753922,7.5763783
+reslizumab,EVQLVESGGGLVQPGGSLRLSCAVSGLSLTSNSVNWIRQAPGKGLEWVGLIWSNGDTDYNSAIKSRFTISRDTSKSTVYLQMNSLRAEDTAVYYCAREYYGYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCLASEGISSYLAWYQQKPGKAPKLLIYGANSLQTGVPSRFSGSGSATDYTLTISSLQPEDFATYYCQQSYKFPNTFGQGTKVEVK,2.8506434,82.47383,261.2832,0.13671473,5.4443326
+rilotumumab,QVQLQESGPGLVKPSETLSLTCTVSGGSISIYYWSWIRQPPGKGLEWIGYVYYSGSTNYNPSLKSRVTISVDTSKNQFSLKLNSVTAADTAVYYCARGGYDFWSGYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVDSNLAWYRQKPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQYINWPPITFGQGTRLEIK,3.1559548,82.45036,215.02194,0.30964652,7.6863894
+rinucumab,QLQLQESGPGLVKPSETLSLTCTVSGGSITSSSYYWGWIRQPPGKGLEWIGSIYYRGSTNYNPSLKSRVTISVDSSKNQFYLKVSSVTAVDTAVYYCARQNGAARPSWFDPWGQGTLVTVSS,EIVLTQSPDTISLSPGERATLSCRASQSISSIYLAWYQQKPGQAPRLLIYGASSRVTGIPDRFSVSGSGTDFTLTISRLEPEDFAVYYCQHYGISPFTFGPGTKVDIR,2.9181464,82.17202,179.89493,0.26993108,9.129965
+risankizumab,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDQTIHWMRQAPGQGLEWIGYIYPRDDSPKYNENFKGKVTITADKSTSTAYMELSSLRSEDTAVYYCAIPDRSGYAWFIYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASRDVAIAVAWYQQKPGKVPKLLIYWASTRHTGVPSRFSGSGSRTDFTLTISSLQPEDVADYFCHQYSSYPFTFGSGTKLEIK,2.8060265,82.48474,211.68414,0.22856617,3.6078544
+rituximab,QVQLQQPGAELVKPGASVKMSCKASGYTFTSYNMHWVKQTPGRGLEWIGAIYPGNGDTSYNQKFKGKATLTADKSSSTAYMQLSSLTSEDSAVYYCARSTYYGGDWYFNVWGAGTTVTVSA,QIVLSQSPAILSASPGEKVTMTCRASSSVSYIHWFQQKPGSSPKPWIYATSNLASGVPVRFSGSGSGTSYSLTISRVEAEDAATYYCQQWTSNPPTFGGGTKLEIK,2.9148147,81.96754,238.07951,0.26820678,9.31879
+robatumumab,EVQLVQSGGGLVKPGGSLRLSCAASGFTFSSFAMHWVRQAPGKGLEWISVIDTRGATYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARLGNFYYGMDVWGQGTTVTVSS,EIVLTQSPGTLSVSPGERATLSCRASQSIGSSLHWYQQKPGQAPRLLIKYASQSLSGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCHQSSRLPHTFGQGTKVEIK,2.6893702,82.017685,235.30034,0.12423557,11.675722
+romosozumab,EVQLVQSGAEVKKPGASVKVSCKASGYTFTDYNMHWVRQAPGQGLEWMGEINPNSGGAGYNQKFKGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARLGYDDIYDDWYFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISNYLNWYQQKPGKAPKLLIYYTSRLLSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGDTLPYTFGGGTKVEIK,2.8604987,81.164825,246.10999,0.16151722,2.141745
+rontalizumab,EVQLVESGGGLVQPGGSLRLSCATSGYTFTEYIIHWVRQAPGKGLEWVASINPDYDITNYNQRFKGRFTISLDKSKRTAYLQMNSLRAEDTAVYYCASWISDFFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSVSTSSYSYMHWYQQKPGKAPKVLISYASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHSWGIPRTFGQGTKVEIK,2.7641006,82.33292,224.83748,0.12954888,5.8747354
+rovalpituzumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYGMNWVRQAPGQGLEWMGWINTYTGEPTYADDFKGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARIGDSSPSDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCKASQSVSNDVVWYQQKPGQAPRLLIYYASNRYTGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQDYTSPWTFGQGTKLEIK,2.9516008,81.92599,217.5195,0.19506146,3.3213468
+rozanolixizumab,EVPLVESGGGLVQPGGSLRLSCAVSGFTFSNYGMVWVRQAPGKGLEWVAYIDSDGDNTYYRDSVKGRFTISRDNAKSSLYLQMNSLRAEDTAVYYCTTGIVRPFLYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSSQSLVGASGKTYLYWLFQKPGKAPKRLIYLVSTLDSGIPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQGTHFPHTFGQGTKLEIK,2.6703267,82.64523,251.82964,0.08612774,6.3871293
+sarilumab,EVQLVESGGGLVQPGRSLRLSCAASRFTFDDYAMHWVRQAPGKGLEWVSGISWNSGRIGYADSVKGRFTISRDNAENSLFLQMNGLRAEDTALYYCAKGRDSFDIWGQGTMVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYGASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFASYYCQQANSFPYTFGQGTKLEIK,2.6281447,82.602425,266.3631,0.083897114,7.017157
+satralizumab,QVQLQESGPGLVKPSETLSLTCAVSGHSISHDHAWSWVRQPPGEGLEWIGFISYSGITNYNPSLQGRVTISRDNSKNTLYLQMNSLRAEDTAVYYCARSLARTTAMDYWGEGTLVTVSS,DIQMTQSPSSLSASVGDSVTITCQASTDISSHLNWYQQKPGKAPELLIYYGSHLLSGVPSRFSGSGSGTDFTFTISSLEAEDAATYYCGQGNRLPYTFGQGTKVEIE,2.720224,82.2898,200.07333,0.106571645,3.644158
+secukinumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYWMNWVRQAPGKGLEWVAAINQDGSEKYYVGSVKGRFTISRDNAKNSLYLQMNSLRVEDTAVYYCVRDYYDILTDYYIHYWYFDLWGRGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPCTFGQGTRLEIK,2.9763446,81.818375,209.73732,0.16002747,6.665233
+selicrelumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTGYYMHWVRQAPGQGLEWMGWINPDSGGTNYAQKFQGRVTMTRDTSISTAYMELNRLRSDDTAVYYCARDQPLGYCTNGVCSYFDYWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGIYSWLAWYQQKPGKAPNLLIYTASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANIFPLTFGGGTKVEIK,2.8725605,80.91453,224.8217,0.20199353,4.4003625
+seribantumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSHYVMAWVRQAPGKGLEWVSSISSSGGWTLYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCTRGLKMATIFDYWGQGTLVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGSYNVVSWYQQHPGKAPKLIIYEVSQRPSGVSNRFSGSKSGNTASLTISGLQTEDEADYYCCSYAGSSIFVIFGGGTKVTVL,2.8678532,81.770615,348.15118,0.12650561,11.420786
+setrusumab,QVQLVESGGGLVQPGGSLRLSCAASGFTFRSHWLSWVRQAPGKGLEWVSNINYDGSSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDTYLHFDYWGQGTLVTVSS,DIALTQPASVSGSPGQSITISCTGTSSDVGDINDVSWYQQHPGKAPKLMIYDVNNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCQSYAGSYLSEVFGGGTKLTVL,2.8736231,82.48599,332.20215,0.076782584,9.1661
+sifalimumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYSISWVRQAPGQGLEWMGWISVYNGNTNYAQKFQGRVTMTTDTSTSTAYLELRSLRSDDTAVYYCARDPIAAGYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSTYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPRTFGQGTKVEIK,2.8486102,81.348465,239.21347,0.250156,9.989567
+siltuximab,EVQLVESGGKLLKPGGSLKLSCAASGFTFSSFAMSWFRQSPEKRLEWVAEISSGGSYTYYPDTVTGRFTISRDNAKNTLYLEMSSLRSEDTAMYYCARGLWGYYALDYWGQGTSVTVSS,QIVLIQSPAIMSASPGEKVTMTCSASSSVSYMYWYQQKPGSSPRLLIYDTSNLASGVPVRFSGSGSGTSYSLTISRMEAEDAATYYCQQWSGYPYTFGGGTKLEIK,2.9124584,82.8078,214.65753,0.17644545,7.3404946
+simtuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYAFTYYLIEWVRQAPGQGLEWIGVINPGSGGTNYNEKFKGRATITADKSTSTAYMELSSLRSEDTAVYFCARNWMNFDYWGQGTTVTVSS,DIVMTQTPLSLSVTPGQPASISCRSSKSLLHSNGNTYLYWFLQKPGQSPQFLIYRMSNLASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQHLEYPYTFGGGTKVEIK,2.7483165,81.849754,210.30644,0.2473526,5.1590695
+sintilimab,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGLIIPMFDTAGYAQKFQGRVAITVDESTSTAYMELSSLRSEDTAVYYCARAEHSSTGTFDYWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLISAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANHLPFTFGGGTKVEIK,2.888326,81.35488,257.9921,0.22884144,3.6447997
+sirukumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSPFAMSWVRQAPGKGLEWVAKISPGGSWTYYSDTVTGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARQLWGYYALDIWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCSASISVSYMYWYQQKPGQAPRLLIYDMSNLASGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCMQWSGYPYTFGGGTKVEIK,2.9035969,82.491005,239.24492,0.08460195,11.140427
+solanezumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRYSMSWVRQAPGKGLELVAQINSVGNSTYYPDTVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCASGDYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLIYSDGNAYLHWFLQKPGQSPRLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHVPWTFGQGTKVEIK,2.7242243,82.886185,224.30467,0.10713796,6.5871053
+spartalizumab,EVQLVQSGAEVKKPGESLRISCKGSGYTFTTYWMHWVRQATGQGLEWMGNIYPGTGGSNFDEKFKNRVTITADKSTSTAYMELSSLRSEDTAVYYCTRWTTGTGAYWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCKSSQSLLDSGNQKNFLTWYQQKPGQAPRLLIYWASTRESGVPSRFSGSGSGTDFTFTISSLEAEDAATYYCQNDYSYPYTFGQGTKVEIK,2.785476,81.93649,236.03343,0.1897954,5.4353967
+sutimlimab,EVQLVESGGGLVKPGGSLRLSCAASGFTFSNYAMSWVRQAPGKGLEWVATISSGGSHTYYLDSVKGRFTISRDNSKNTLYLQMNSLRAEDTALYYCARLFTGYAMDYWGQGTLVTVSS,QIVLTQSPATLSLSPGERATMSCTASSSVSSSYLHWYQQKPGKAPKLWIYSTSNLASGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCHQYYRLPPITFGQGTKLEIK,2.7651355,81.94966,284.57092,0.10796864,10.660582
+tabalumab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARGYYDILTGYYYYFDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSRYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDSTLTISSLEPEDFAVYYCQQRSNWPRTFGQGTKVEIK,3.0198298,82.24733,187.85634,0.31893325,6.9151607
+tanezumab,QVQLQESGPGLVKPSETLSLTCTVSGFSLIGYDLNWIRQPPGKGLEWIGIIWGDGTTDYNSAVKSRVTISKDTSKNQFSLKLSSVTAADTAVYYCARGGYWYATSYYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSISNNLNWYQQKPGKAPKLLIYYTSRFHSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQEHTLPYTFGQGTKLEIK,2.8779292,82.482056,214.91927,0.2251917,3.1149602
+tarextumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSSGMSWVRQAPGKGLEWVSVIASSGSNTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSIFYTTWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQSVRSNYLAWYQQKPGQAPRLLIYGASSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQYSNFPITFGQGTKVEIK,2.7927272,82.45222,281.13654,0.11407164,11.776169
+tavolimab,QVQLQESGPGLVKPSQTLSLTCAVYGGSFSSGYWNWIRKHPGKGLEYIGYISYNGITYHNPSLKSRITINRDTSKNQYSLQLNSVTPEDTAVYYCARYKYDYDGGHAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISNYLNWYQQKPGKAPKLLIYYTSKLHSGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCQQGSALPWTFGQGTKVEIK,2.9155045,81.77033,227.30699,0.26426345,4.6464553
+telisotuzumab,QVQLVQSGAEVKKPGASVKVSCKASGYIFTAYTMHWVRQAPGQGLEWMGWIKPNNGLANYAQKFQGRVTMTRDTSISTAYMELSRLRSDDTAVYYCARSEITTEFDYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSESVDSYANSFLHWYQQKPGQPPKLLIYRASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQSKEDPLTFGGGTKVEIK,2.6854343,82.13585,195.6648,0.1596476,2.3023396
+teplizumab,QVQLVQSGGGVVQPGRSLRLSCKASGYTFTRYTMHWVRQAPGKGLEWIGYINPSRGYTNYNQKVKDRFTISRDNSKNTAFLQMDSLRPEDTGVYFCARYYDDHYCLDYWGQGTPVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVSYMNWYQQTPGKAPKRWIYDTSKLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSNPFTFGQGTKLQIT,2.6578324,81.88198,268.67957,0.17010212,10.0450115
+teprotumumab,QVELVESGGGVVQPGRSQRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAIIWFDGSSTYYADSVRGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCARELGRRYFDLWGRGTLVSVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASKRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSKWPPWTFGQGTKVESK,2.7678294,82.471016,250.89977,0.14295742,11.135031
+tezepelumab,QMQLVESGGGVVQPGRSLRLSCAASGFTFRTYGMHWVRQAPGKGLEWVAVIWYDGSNKHYADSVKGRFTITRDNSKNTLNLQMNSLRAEDTAVYYCARAPQWELVHEAFDIWGQGTMVTVSS,SYVLTQPPSVSVAPGQTARITCGGNNLGSKSVHWYQQKPGQAPVLVVYDDSDRPSWIPERFSGSNSGNTATLTISRGEAGDEADYYCQVWDSSSDHVVFGGGTKLTVL,2.8465466,81.06438,300.1679,0.11828688,7.8911834
+tigatuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYVMSWVRQAPGKGLEWVATISSGGSYTYYPDSVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCARRGDSMITTDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVGTAVAWYQQKPGKAPKLLIYWASTRHTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSSYRTFGQGTKVEIK,2.7732255,83.203354,284.13995,0.047853574,7.9195876
+tildrakizumab,QVQLVQSGAEVKKPGASVKVSCKASGYIFITYWMTWVRQAPGQGLEWMGQIFPASGSADYNEKFEGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARGGGGFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRTSENIYSYLAWYQQKPGKAPKLLIYNAKTLAEGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHHYGIPFTFGQGTKVEIK,2.7872782,81.89906,232.58122,0.20269403,3.2913241
+tislelizumab,QVQLQESGPGLVKPSETLSLTCTVSGFSLTSYGVHWIRQPPGKGLEWIGVIYADGSTNYNPSLKSRVTISKDTSKNQVSLKLSSVTAADTAVYYCARAYGNYWYIDVWGQGTTVTVSS,DIVMTQSPDSLAVSLGERATINCKSSESVSNDVAWYQQKPGQPPKLLINYAFHRFTGVPDRFSGSGYGTDFTLTISSLQAEDVAVYYCHQAYSSPYTFGQGTKLEIK,2.9225314,83.42366,212.88202,0.21795681,3.407103
+tisotumab,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNYAMSWVRQAPGKGLEWVSSISGSGDYTYYTDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSPWGYYLDSWGQGTLVTVSS,DIQMTQSPPSLSASAGDRVTITCRASQGISSRLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNSYPYTFGQGTKLEIK,2.736268,82.80449,266.12576,0.060606405,8.281864
+tocilizumab,QVQLQESGPGLVRPSQTLSLTCTVSGYSITSDHAWSWVRQPPGRGLEWIGYISYSGITTYNPSLKSRVTMLRDTSKNQFSLRLSSVTAADTAVYYCARSLARTTAMDYWGQGSLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISSYLNWYQQKPGKAPKLLIYYTSRLHSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQGNTLPYTFGQGTKVEIK,2.6699018,82.05319,244.26506,0.25402492,7.9189935
+toripalimab,QGQLVQSGAEVKKPGASVKVSCKASGYTFTDYEMHWVRQAPIHGLEWIGVIESETGGTAYNQKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCAREGITTVATTYYWYFDVWGQGTTVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSIVHSNGNTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPLTFGQGTKLEIK,2.8265693,81.47708,167.08968,0.25595132,1.3111744
+tovetumab,QVQLVESGGGLVKPGGSLRLSCAASGFTFSDYYMNWIRQAPGKGLEWVSYISSSGSIIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGRIAARGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVSITCRPSQSFSRYINWYQQKPGKAPKLLIHAASSLVGGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQTYSNPPITFGQGTRLEMK,2.6942844,82.28551,279.14987,0.05393547,7.7694993
+tralokinumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYGLSWVRQAPGQGLEWMGWISANNGDTNYGQEFQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARDSSSSWARWFFDLWGRGTLVTVSS,SYVLTQPPSVSVAPGKTARITCGGNIIGSKLVHWYQQKPGQAPVLVIYDDGDRPSGIPERFSGSNSGNTATLTISRVEAGDEADYYCQVWDTGSDPVVFGGGTKLTVL,2.9272952,81.41515,274.97742,0.21828112,7.507506
+trastuzumab,EVQLVESGGGLVQPGGSLRLSCAASGFNIKDTYIHWVRQAPGKGLEWVARIYPTNGYTRYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCSRWGGDGFYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDVNTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSRSGTDFTLTISSLQPEDFATYYCQQHYTTPPTFGQGTKVEIK,2.8002017,82.480354,256.09433,0.08320162,2.8990932
+tregalizumab,EEQLVESGGGLVKPGGSLRLSCAASGFSFSDCRMYWLRQAPGKGLEWIGVISVKSENYGANYAESVRGRFTISRDDSKNTVYLQMNSLKTEDTAVYYCSASYYRYDVGAWFAYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCRASKSVSTSGYSYIYWYQQKPGQPPKLLIYLASILESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQHSRELPWTFGQGTKVEIK,2.8367398,82.63303,175.00665,0.12231211,4.046249
+tremelimumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDPRGATLYYYYYGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSINSYLDWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYSTPFTFGPGTKVEIK,2.8775897,81.6793,291.02454,0.07295713,8.455494
+ublituximab,QAYLQQSGAELVRPGASVKMSCKASGYTFTSYNMHWVKQTPRQGLEWIGGIYPGNGDTSYNQKFKGKATLTVGKSSSTAYMQLSSLTSEDSAVYFCARYDYNYAMDYWGQGTSVTVSS,QIVLSQSPAILSASPGEKVTMTCRASSSVSYMHWYQQKPGSSPKPWIYATSNLASGVPARFSGSGSGTSYSFTISRVEAEDAATYYCQQWTFNPPTFGGGTRLEIK,2.9073827,81.66661,210.13179,0.26744533,10.475912
+urelumab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQSPEKGLEWIGEINHGGYVTYNPSLESRVTISVDTSKNQFSLKLSSVTAADTAVYYCARDYGPGNYDWYFDLWGRGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPALTFGGGTKVEIK,3.05496,81.74912,192.4002,0.28517,7.6077657
+ustekinumab,EVQLVQSGAEVKKPGESLKISCKGSGYSFTTYWLGWVRQMPGKGLDWIGIMSPVDSDIRYSPSFQGQVTMSVDKSITTAYLQWNSLKASDTAMYYCARRRPGQGYFDFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNIYPYTFGQGTKLEIK,2.7310781,82.72851,220.15227,0.24702862,5.6915708
+utomilumab,EVQLVQSGAEVKKPGESLRISCKGSGYSFSTYWISWVRQMPGKGLEWMGKIYPGDSYTNYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARGYGIFDYWGQGTLVTVSS,SYELTQPPSVSVSPGQTASITCSGDNIGDQYAHWYQQKPGQSPVLVIYQDKNRPSGIPERFSGSNSGNTATLTISGTQAMDEADYYCATYTGFGSLAVFGGGTKLTVL,2.9887989,81.85462,288.08032,0.24900216,9.79616
+vadastuximab,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYDINWVRQAPGQGLEWIGWIYPGDGSTKYNEKFKAKATLTADTSTSTAYMELRSLRSDDTAVYYCASGYEDAMDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTINCKASQDINSYLSWFQQKPGKAPKTLIYRANRLVDGVPSRFSGSGSGQDYTLTISSLQPEDFATYYCLQYDEFPLTFGGGTKVEIK,2.7414804,82.6946,238.99998,0.16767749,1.7379637
+varlilumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYDMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARGSGNWGFFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISRWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNTYPRTFGQGTKVEIK,2.7481947,82.265526,256.2436,0.10193391,7.288139
+vatelizumab,QVQLQESGPGLVKPSETLSLTCTVSGFSLTNYGIHWIRQPPGKGLEWLGVIWARGFTNYNSALMSRLTISKDNSKNQVSLKLSSVTAADTAVYYCARANDGVYYAMDYWGQGTLVTVSS,DFVMTQSPAFLSVTPGEKVTITCSAQSSVNYIHWYQQKPDQAPKKLIYDTSKLASGVPSRFSGSGSGTDYTFTISSLEAEDAATYYCQQWTTNPLTFGQGTKVEIK,2.8052373,83.763794,227.39798,0.13032337,3.2457495
+vedolizumab,QVQLVQSGAEVKKPGASVKVSCKGSGYTFTSYWMHWVRQAPGQRLEWIGEIDPSESNTNYNQKFKGRVTLTVDISASTAYMELSSLRSEDTAVYYCARGGYDGWDYAIDYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLAKSYGNTYLSWYLQKPGQSPQLLIYGISNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQGTHQPYTFGQGTKVEIK,2.8200202,81.69323,197.71368,0.22860382,3.6987562
+veltuzumab,QVQLQQSGAEVKKPGSSVKVSCKASGYTFTSYNMHWVKQAPGQGLEWIGAIYPGMGDTSYNQKFKGKATLTADESTNTAYMELSSLRSEDTAFYYCARSTYYGGDWYFDVWGQGTTVTVSS,DIQLTQSPSSLSASVGDRVTMTCRASSSVSYIHWFQQKPGKAPKPWIYATSNLASGVPVRFSGSGSGTDYTFTISSLQPEDIATYYCQQWTSNPPTFGGGTKLEIK,2.8791971,81.912,268.20627,0.20446637,5.8271127
+visilizumab,QVQLVQSGAEVKKPGASVKVSCKASGYTFISYTMHWVRQAPGQGLEWMGYINPRSGYTHYNQKLKDKATLTADKSASTAYMELSSLRSEDTAVYYCARSAYYDYDGFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVSYMNWYQQKPGKAPKRLIYDTSKLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQWSSNPPTFGGGTKVEIK,2.793611,81.66522,279.943,0.23540747,8.595249
+xentuzumab,QVELVESGGGLVQPGGSLRLSCAASGFTFTSYWMSWVRQAPGKGLELVSSITSYGSFTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARNMYTHFDSWGQGTLVTVSS,DIVLTQPPSVSGAPGQRVTISCSGSSSNIGSNSVSWYQQLPGTAPKLLIYDNSKRPSGVPDRFSGSKSGTSASLAITGLQSEDEADYYCQSRDTYGYYWVFGGGTKLTVL,3.0824277,81.32672,332.4135,0.12253915,12.256296
+zalutumumab,QVQLVESGGGVVQPGRSLRLSCAASGFTFSTYGMHWVRQAPGKGLEWVAVIWDDGSYKYYGDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDGITMVRGVMKDYFDYWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQDISSALVWYQQKPGKAPKLLIYDASSLESGVPSRFSGSESGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.784802,82.28748,262.57794,0.08658855,6.2212586
+zanolimumab,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARVINWFDPWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQDISSWLAWYQHKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANSFPYTFGQGTKLEIK,2.82828,82.6387,249.74211,0.27218518,6.232471
+zolbetuximab,QVQLQQPGAELVRPGASVKLSCKASGYTFTSYWINWVKQRPGQGLEWIGNIYPSDSYTNYNQKFKDKATLTVDKSSSTAYMQLSSPTSEDSAVYYCTRSWRGNSFDYWGQGTTLTVSS,DIVMTQSPSSLTVTAGEKVTMSCKSSQSLLNSGNQKNYLTWYQQKPGQPPKLLIYWASTRESGVPDRFTGSGSGTDFTLTISSVQAEDLAVYYCQNDYSYPFTFGSGTKLEIK,2.7408457,82.64567,242.21046,0.20611049,8.760937
diff --git a/models/saprot_vh_vl/pixi.lock b/models/saprot_vh_vl/pixi.lock
new file mode 100644
index 0000000..ee43c2a
--- /dev/null
+++ b/models/saprot_vh_vl/pixi.lock
@@ -0,0 +1,4032 @@
+version: 6
+environments:
+  default:
+    channels:
+    - url: https://conda.anaconda.org/conda-forge/
+    - url: https://conda.anaconda.org/bioconda/
+    - url: https://conda.anaconda.org/pytorch/
+    indexes:
+    - https://pypi.org/simple
+    packages:
+      linux-64:
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/aria2-1.37.0-hbc8128a_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/biopython-1.86-py311h49ec1c0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_8.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.5-hb9d3cd8_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.11.12-hbd8a1cb_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.3.1-pyh707e725_0.conda
+      - conda: https://conda.anaconda.org/bioconda/linux-64/foldseek-10.941cd33-h5021889_1.tar.bz2
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gawk-5.3.1-hcd3d067_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.2-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.45-default_hbd61a6d_104.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.25.1-h3f43e3d_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-2_h4a7cf45_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-2_h0358290_openblas.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.7.3-hecca717_0.conda
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+  - phonemizer ; extra == 'audio'
+  - kenlm ; extra == 'audio'
+  - torchaudio ; extra == 'speech'
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+  - pyctcdecode>=0.4.0 ; extra == 'speech'
+  - phonemizer ; extra == 'speech'
+  - kenlm ; extra == 'speech'
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+  - pyctcdecode>=0.4.0 ; extra == 'torch-speech'
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+  - pyctcdecode>=0.4.0 ; extra == 'tf-speech'
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+  - kenlm ; extra == 'tf-speech'
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+  - phonemizer ; extra == 'flax-speech'
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diff --git a/models/saprot_vh_vl/pixi.toml b/models/saprot_vh_vl/pixi.toml
new file mode 100644
index 0000000..47ab60a
--- /dev/null
+++ b/models/saprot_vh_vl/pixi.toml
@@ -0,0 +1,35 @@
+[workspace]
+name = "saprot_vh_vl"
+version = "0.1.0"
+description = "Saprot_VH_VL baseline - protein language model predictions on VH and VL sequences and Structures"
+channels = ["conda-forge", "bioconda", "pytorch"]
+platforms = ["linux-64", "osx-64", "osx-arm64"]
+
+[dependencies]
+python = "3.11.*"
+numpy = ">=1.24"
+pandas = ">=2.0"
+scikit-learn = ">=1.3"
+typer = ">=0.9"
+foldseek = "*"  
+biopython = ">=1.81"
+
+
+[pypi-dependencies]
+abdev-core = { path = "../../libs/abdev_core", editable = true }
+saprot_vh_vl = { path = ".", editable = true }
+transformers = ">=4.30"
+torch = ">=2.0"
+
+[environments]
+default = []
+dev = ["dev"]
+
+[feature.dev.dependencies]
+pytest = ">=7.0"
+ruff = ">=0.1"
+
+[feature.dev.tasks]
+lint = "ruff check src && ruff format --check src"
+test = "pytest tests -v"
+
diff --git a/models/saprot_vh_vl/pyproject.toml b/models/saprot_vh_vl/pyproject.toml
new file mode 100644
index 0000000..55e2842
--- /dev/null
+++ b/models/saprot_vh_vl/pyproject.toml
@@ -0,0 +1,20 @@
+[build-system]
+requires = ["setuptools>=64", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "saprot_vh_vl"
+version = "0.1.0"
+description = "Saprot_VH_VL baseline - protein language model predictions on VH and VL sequences and structures"
+requires-python = ">=3.11"
+dependencies = [
+    "abdev-core",
+    "pandas>=2.0",
+]
+
+[tool.setuptools.packages.find]
+where = ["src"]
+
+[tool.setuptools.package-dir]
+"" = "src"
+
diff --git a/models/saprot_vh_vl/runs/my_run/embeddings.npy b/models/saprot_vh_vl/runs/my_run/embeddings.npy
new file mode 100644
index 0000000..52ab416
Binary files /dev/null and b/models/saprot_vh_vl/runs/my_run/embeddings.npy differ
diff --git a/models/saprot_vh_vl/runs/my_run/models.pkl b/models/saprot_vh_vl/runs/my_run/models.pkl
new file mode 100644
index 0000000..7762a65
Binary files /dev/null and b/models/saprot_vh_vl/runs/my_run/models.pkl differ
diff --git a/models/saprot_vh_vl/src/saprot_vh_vl/__init__.py b/models/saprot_vh_vl/src/saprot_vh_vl/__init__.py
new file mode 100644
index 0000000..4799edb
--- /dev/null
+++ b/models/saprot_vh_vl/src/saprot_vh_vl/__init__.py
@@ -0,0 +1,4 @@
+"""Saprot_VH_VL baseline - protein language model predictions."""
+
+__version__ = "0.1.0"
+
diff --git a/models/saprot_vh_vl/src/saprot_vh_vl/__main__.py b/models/saprot_vh_vl/src/saprot_vh_vl/__main__.py
new file mode 100644
index 0000000..b546135
--- /dev/null
+++ b/models/saprot_vh_vl/src/saprot_vh_vl/__main__.py
@@ -0,0 +1,7 @@
+"""Entry point for model CLI."""
+
+from .run import app
+
+if __name__ == "__main__":
+    app()
+
diff --git a/models/saprot_vh_vl/src/saprot_vh_vl/model.py b/models/saprot_vh_vl/src/saprot_vh_vl/model.py
new file mode 100644
index 0000000..7005d31
--- /dev/null
+++ b/models/saprot_vh_vl/src/saprot_vh_vl/model.py
@@ -0,0 +1,338 @@
+"""Saprot_VH_VL model implementation."""
+
+from pathlib import Path
+import pandas as pd
+from abdev_core import BaseModel, PROPERTY_LIST
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModel
+import pickle
+from sklearn.linear_model import Ridge
+import subprocess
+import os
+import time
+from Bio.PDB import PDBParser, PDBIO, Select # Needed to split Heavy and Light chains from PDBs
+import tempfile
+
+class Saprot_VH_VL_Model(BaseModel):
+    """Saprot_VH_VL: baseline using protein language model features.
+    
+    This model trains separate Ridge regression models for each property
+    using embeddings from the SaProt protein language model.
+    
+    This baseline embeds heavy (VH) and light (VL) chains separately using their sequences and structures for input.
+    """
+    
+    MODEL_NAME = "westlake-repl/SaProt_35M_AF2"
+    ALPHA = 1.0
+    
+    # Use MOE structures for both (they contain both chains)
+    structure_dir_train = Path("../../data/structures/MOE_structures/GDPa1")  
+    structure_dir_heldout = Path("../../data/structures/MOE_structures/heldout_test") 
+    
+    def __init__(self) -> None:
+        """Initialize model (lazy load transformers on first use)."""
+        self.tokenizer = None
+        self.model = None
+        self.device = None
+
+    def _initialize_model(self) -> None:
+        """Lazy initialize the transformer model and tokenizer."""
+        if self.model is not None:
+            return
+
+        #Adding PyTorch Optimization for Apple Silicon
+        device = ""
+        if torch.cuda.is_available():
+            device = "cuda"
+        elif torch.backends.mps.is_available():
+            device = "mps"
+        else:
+            device = "cpu"
+        
+        self.device = device
+        self.tokenizer = AutoTokenizer.from_pretrained(self.MODEL_NAME)
+        self.model = AutoModel.from_pretrained(self.MODEL_NAME).to(self.device)
+        self.model.eval()
+
+    # NEW: Chain splitting helper
+    class _ChainSelect(Select):
+        """Select specific chain from PDB structure."""
+        def __init__(self, chain_id):
+            self.chain_id = chain_id
+        
+        def accept_chain(self, chain):
+            return chain.id == self.chain_id
+
+    # NEW: Extract chain to temporary file
+    def _extract_chain_to_temp(self, pdb_path, chain_id):
+        """
+        Extract specific chain from PDB to temporary file.
+        
+        Args:
+            pdb_path: Path to complexed PDB file
+            chain_id: Chain ID to extract ('B' for VH in MOE, 'A' for VL)
+        
+        Returns:
+            temp_pdb_path: Path to temporary PDB file with single chain
+        """
+        try:
+            parser = PDBParser(QUIET=True)
+            structure = parser.get_structure('antibody', pdb_path)
+            
+            # Create temporary file
+            temp_fd, temp_path = tempfile.mkstemp(suffix='.pdb')
+            os.close(temp_fd)
+            
+            # Write chain to temporary file
+            io = PDBIO()
+            io.set_structure(structure)
+            io.save(temp_path, self._ChainSelect(chain_id))
+            
+            return temp_path
+        except Exception as e:
+            print(f"Warning: Chain extraction failed for {pdb_path}, chain {chain_id}: {e}")
+            return None
+
+    def get_structure_aware_seq(self, sequence: str, pdb_path) -> str:
+        """
+        Encode PDB structure using Foldseek and create structure-aware sequences.
+        Falls back to sequence only mode in case of missing/erroneous structures.
+        """
+        try:
+            tmp_save_path = f"foldseek_tmp_{os.getpid()}_{time.time()}.tsv"
+            
+            cmd = [
+                "foldseek", "structureto3didescriptor",
+                "-v", "0",
+                "--threads", "1",
+                "--chain-name-mode", "1",
+                str(pdb_path),
+                tmp_save_path
+            ]
+            
+            result = subprocess.run(cmd, capture_output=True, text=True, check=False)
+            
+            if result.returncode != 0:
+                raise RuntimeError(f"Foldseek failed: {result.stderr}")
+            
+            if not os.path.exists(tmp_save_path):
+                raise FileNotFoundError(f"Foldseek output not found: {tmp_save_path}")
+            
+            with open(tmp_save_path, 'r') as f:
+                line = f.readline().strip()
+                if not line:
+                    raise ValueError("Foldseek output is empty")
+            
+            parts = line.split('\t')
+            if len(parts) < 3:
+                raise ValueError(f"Unexpected output format: {len(parts)} columns")
+            
+            aa_seq = parts[1]
+            struc_seq = parts[2]
+            
+            # Clean up
+            try:
+                os.remove(tmp_save_path)
+                if os.path.exists(tmp_save_path + ".dbtype"):
+                    os.remove(tmp_save_path + ".dbtype")
+            except:
+                pass
+        
+            # Interleave amino acids with structure tokens (lowercase)
+            # SaProt format: Aa Bb Cc where A,B,C = amino acids, a,b,c = structure tokens
+            structure_aware_seq = ''.join(
+                f"{aa}{st.lower()}" for aa, st in zip(aa_seq, struc_seq)
+            )
+        
+            # Validate length
+            if len(aa_seq) != len(sequence):
+                print(f"Warning: Length mismatch - PDB has {len(aa_seq)} residues, "
+                      f"sequence has {len(sequence)}. Using sequence-only mode.")
+                return sequence
+        
+            return structure_aware_seq
+        
+        except Exception as e:
+            print(f"Warning: Structure encoding failed for {pdb_path}: {e}")
+            print("Falling back to sequence-only mode")
+            return sequence  # Fallback: use sequence without structure
+
+       
+    def extract_saprot_embedding(self, sequence: str, pdb_path=None) -> np.ndarray:
+        """
+        Extract SaProt embedding from sequence (with optional structure).
+    
+        Args:
+            sequence: Amino acid sequence
+            pdb_path: Optional path to PDB file for structure-aware encoding
+    
+        Returns:
+            embedding: Numpy array of shape (480,)
+        """
+        # Get structure-aware sequence if PDB provided
+        if pdb_path.exists():
+            input_seq = self.get_structure_aware_seq(sequence, pdb_path)
+        else:
+            input_seq = sequence  # Sequence-only mode
+    
+        # Tokenize
+        inputs = self.tokenizer(
+            #input_seq,
+            str(input_seq),
+            return_tensors="pt",
+            padding=False,  # Single sequence, no padding needed
+            truncation=True,
+            max_length=1024
+        )
+    
+        input_ids = inputs["input_ids"].to(self.device)
+        attention_mask = inputs["attention_mask"].to(self.device)
+    
+        # Extract embeddings with output_hidden_states
+        with torch.no_grad():
+            outputs = self.model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                output_hidden_states=True
+            )
+        
+            hidden_states = outputs.hidden_states[-1]
+        mask_expanded = attention_mask.unsqueeze(-1).float()
+        sum_embeddings = torch.sum(hidden_states * mask_expanded, dim=1)
+        sum_mask = torch.sum(mask_expanded, dim=1)
+        mean_pooled = sum_embeddings / sum_mask
+        
+        embedding = mean_pooled.detach().cpu().numpy().squeeze(0)
+        
+        return embedding
+
+    def _generate_embeddings(self, antibody_names: list[str], 
+                           vh_sequences: list[str], 
+                           vl_sequences: list[str],
+                       structure_dir: Path = None) -> np.ndarray:
+        """Generate concatenated VH+VL embeddings for all antibodies."""
+        self._initialize_model()
+        
+        embeddings_list = []
+        
+        for antibody_name, vh_seq, vl_seq in zip(antibody_names, vh_sequences, vl_sequences):
+            # Use same PDB file for both (contains both chains)
+            complexed_pdb = structure_dir / f"{antibody_name}.pdb"
+            
+            # Extract chains to temporary files if PDB exists
+            if complexed_pdb.exists():
+                # MOE uses chain B for VH, chain A for VL
+                temp_vh_pdb = self._extract_chain_to_temp(complexed_pdb, 'B')
+                temp_vl_pdb = self._extract_chain_to_temp(complexed_pdb, 'A')
+                
+                try:
+                    vh_pdb_path = Path(temp_vh_pdb) if temp_vh_pdb else complexed_pdb
+                    vh_embed = self.extract_saprot_embedding(vh_seq, vh_pdb_path)
+                    
+                    vl_pdb_path = Path(temp_vl_pdb) if temp_vl_pdb else complexed_pdb
+                    vl_embed = self.extract_saprot_embedding(vl_seq, vl_pdb_path)
+                    
+                finally:
+                    # Clean up temporary files
+                    if temp_vh_pdb and os.path.exists(temp_vh_pdb):
+                        os.unlink(temp_vh_pdb)
+                    if temp_vl_pdb and os.path.exists(temp_vl_pdb):
+                        os.unlink(temp_vl_pdb)
+            else:
+                # Fallback: sequence-only mode
+                print(f"Warning: PDB not found for {antibody_name}, using sequence-only mode")
+                vh_embed = self.extract_saprot_embedding(vh_seq, Path("nonexistent.pdb"))
+                vl_embed = self.extract_saprot_embedding(vl_seq, Path("nonexistent.pdb"))
+            
+            combined_embedding = np.concatenate([vh_embed, vl_embed])
+            embeddings_list.append(combined_embedding)
+        
+        embeddings = np.stack(embeddings_list)
+        
+        return embeddings
+
+    def train(self, df: pd.DataFrame, run_dir: Path, *, seed: int = 42) -> None:
+        """Train Ridge regression models on SaProt embeddings for each property."""
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        
+        run_dir.mkdir(parents=True, exist_ok=True)
+        
+        embeddings = self._generate_embeddings(
+            df["antibody_name"].tolist(),
+            df["vh_protein_sequence"].tolist(),
+            df["vl_protein_sequence"].tolist(),
+            structure_dir=self.structure_dir_train 
+        )
+        
+        models = {}
+        for property_name in PROPERTY_LIST:
+            if property_name not in df.columns:
+                continue
+            
+            not_na_mask = df[property_name].notna()
+            df_property = df[not_na_mask]
+            
+            if len(df_property) == 0:
+                print(f" Skipping {property_name}: no training data")
+                continue
+            
+            X = embeddings[not_na_mask]
+            y = df_property[property_name].values
+            
+            model = Ridge(alpha=self.ALPHA, random_state=seed)
+            model.fit(X, y)
+            models[property_name] = model
+        
+        models_path = run_dir / "models.pkl"
+        with open(models_path, "wb") as f:
+            pickle.dump(models, f)
+
+        embeddings_path = run_dir / "embeddings.npy"
+        np.save(embeddings_path, embeddings)
+
+
+
+    def predict(self, df: pd.DataFrame, run_dir: Path) -> pd.DataFrame:
+        """Generate predictions for all samples using trained models.
+        
+        Args:
+            df: Input dataframe with VH/VL sequences
+            run_dir: Directory containing trained models
+            
+        Returns:
+            DataFrame with predictions for each property
+        """
+        # Load trained models
+
+        self._initialize_model()
+        
+        models_path = run_dir / "models.pkl"
+        if not models_path.exists():
+            raise FileNotFoundError(f"Models not found: {models_path}")
+
+        with open(models_path, "rb") as f:
+            models = pickle.load(f)
+
+        #check if its heldout data or not
+        if len(df) == 80: #Hardcoded solution, not ideal, best would be to modify abdev-core functionality
+            structure_dir_val=self.structure_dir_heldout
+        else:
+            structure_dir_val=self.structure_dir_train
+
+        # Generate embeddings for input data
+        embeddings = self._generate_embeddings(
+            df["antibody_name"].tolist(),
+            df["vh_protein_sequence"].tolist(),
+            df["vl_protein_sequence"].tolist(),
+            structure_dir=structure_dir_val
+        )
+        
+        df_output = df[["antibody_name", "vh_protein_sequence", "vl_protein_sequence"]].copy()
+
+        for property_name, model in models.items():
+            predictions = model.predict(embeddings)
+            df_output[property_name] = predictions
+        
+        return df_output
diff --git a/models/saprot_vh_vl/src/saprot_vh_vl/run.py b/models/saprot_vh_vl/src/saprot_vh_vl/run.py
new file mode 100644
index 0000000..0326f2a
--- /dev/null
+++ b/models/saprot_vh_vl/src/saprot_vh_vl/run.py
@@ -0,0 +1,12 @@
+"""CLI interface for Saprot_VH baseline."""
+
+from abdev_core import create_cli_app
+from .model import Saprot_VH_VL_Model
+
+
+app = create_cli_app(Saprot_VH_VL_Model, "Saprot_VH_VL")
+
+
+if __name__ == "__main__":
+    app()
+
diff --git a/notebooks/.ipynb_checkpoints/MOE_visualization-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/MOE_visualization-checkpoint.ipynb
new file mode 100644
index 0000000..d029f23
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/MOE_visualization-checkpoint.ipynb
@@ -0,0 +1,9291 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2nz_qmKhDBn8"
+   },
+   "source": [
+    "# Antibody Developability Challenge - Data Visualization\n",
+    "\n",
+    "Gingko DataPoints - Fall 2025\n",
+    "\n",
+    "**Authors**: Shyam Chandra, Valentin Badea\n",
+    "\n",
+    "Contact: valentin.badea@hms.harvard.edu, shyam.chandra@hms.harvard.edu"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g2Yt7V2PDSgS"
+   },
+   "source": [
+    "# Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "z6IW_K1W4Q1l"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set style\n",
+    "sns.set_style(\"whitegrid\")\n",
+    "plt.rcParams['figure.dpi'] = 100\n",
+    "plt.rcParams['savefig.dpi'] = 300\n",
+    "plt.rcParams['figure.figsize'] = (12, 8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "COZ613EJDYEu"
+   },
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 2546,
+     "status": "ok",
+     "timestamp": 1761927784582,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "PrKXMjYEAnAZ",
+    "outputId": "a34ff08b-7232-45bf-8db1-85e711e6185e"
+   },
+   "outputs": [],
+   "source": [
+    "# Load the MOE features and targets\n",
+    "path_to_data = \"../data/features/processed_features/GDPa1/MOE_properties.csv\"\n",
+    "path_to_target = \"../data/GDPa1_v1.2_20250814.csv\"\n",
+    "\n",
+    "df_master = pd.read_csv(path_to_data, index_col=0)\n",
+    "df_target = pd.read_csv(path_to_target, index_col=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total MOE features: 46\n"
+     ]
+    }
+   ],
+   "source": [
+    "out_features = ['antibody_name', 'mseq']\n",
+    "moe_features = [col for col in df_master.columns if col not in out_features]\n",
+    "print(f\"Total MOE features: {len(moe_features)}\")\n",
+    "df_master_filtered = df_master[moe_features]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target_features = ['HIC', 'Titer', \"PR_CHO\", 'AC-SINS_pH7.4', 'Tm2']\n",
+    "df_target_properties = df_target[target_features]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Vtj14B4TF6d7"
+   },
+   "source": [
+    "# Dimensionality reduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GsC7excRDb4g"
+   },
+   "source": [
+    "## PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "id": "rZIJzs20BmZy"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.decomposition import PCA"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "id": "aZe0scI_BuDX"
+   },
+   "outputs": [],
+   "source": [
+    "scaler = StandardScaler()\n",
+    "df_master_filtered_scaled = scaler.fit_transform(df_master_filtered)\n",
+    "pca = PCA(n_components=2)\n",
+    "df_master_filtered_pca = pca.fit_transform(df_master_filtered_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "antibody_id\n",
+       "GDPa1-001    2.590\n",
+       "GDPa1-002    2.545\n",
+       "GDPa1-003    2.705\n",
+       "GDPa1-004    2.565\n",
+       "GDPa1-005    2.495\n",
+       "             ...  \n",
+       "GDPa1-242    2.495\n",
+       "GDPa1-243    4.500\n",
+       "GDPa1-244    2.590\n",
+       "GDPa1-245    2.660\n",
+       "GDPa1-246    2.530\n",
+       "Name: HIC, Length: 246, dtype: float64"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_target_properties[target_features[0]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 2431,
+     "status": "ok",
+     "timestamp": 1761795914363,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "hmmnqsSLByCM",
+    "outputId": "c2a57c0b-527d-4221-f9e8-90b5dd0ab11b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_dim_red(df_master_filtered_pca, df_properties, ax, property = 'HIC'):\n",
+    "  df_master_filtered_pca_df = pd.DataFrame(df_master_filtered_pca, columns=['PC1', 'PC2'])\n",
+    "  df_master_filtered_pca_df[\"property\"] = df_properties[property].values\n",
+    "  sns.scatterplot(data=df_master_filtered_pca_df, x='PC1', y='PC2', hue=df_master_filtered_pca_df[\"property\"], ax=ax)\n",
+    "  return ax\n",
+    "\n",
+    "fig, ax = plt.subplots(3,2, figsize=(20,20))\n",
+    "fig.suptitle('PCA of MOE features')\n",
+    "for i, property in enumerate(target_features):\n",
+    "  ax[i//2, i%2] = plot_dim_red(df_master_filtered_pca, df_target_properties, ax[i//2, i%2], property)\n",
+    "  ax[i//2, i%2].set_title(property)\n",
+    "  ax[i//2, i%2].set_xlabel('PC1')\n",
+    "  ax[i//2, i%2].set_ylabel('PC2')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "WyQGytbNF4Fw"
+   },
+   "source": [
+    "## UMAP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "id": "6xw9Fx-tF3cP"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\badea\\micromamba\\envs\\molml_env\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from umap import UMAP\n",
+    "\n",
+    "df_master_filtered_scaled = scaler.fit_transform(df_master_filtered)\n",
+    "umap = UMAP(n_components=2)\n",
+    "df_master_filtered_umap = umap.fit_transform(df_master_filtered_scaled)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 2216,
+     "status": "ok",
+     "timestamp": 1761795885878,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "Zchh-BnMGEIs",
+    "outputId": "5deedf9d-89a9-4e46-ce93-6cb09b648b7a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x2000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(3,2, figsize=(20,20))\n",
+    "fig.suptitle('UMAP of MOE features')\n",
+    "for i, property in enumerate(target_features):\n",
+    "  ax[i//2, i%2] = plot_dim_red(df_master_filtered_umap, df_target_properties, ax[i//2, i%2], property)\n",
+    "  ax[i//2, i%2].set_title(property)\n",
+    "  ax[i//2, i%2].set_xlabel('PC1')\n",
+    "  ax[i//2, i%2].set_ylabel('PC2')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jvHnnxH7GnzO"
+   },
+   "source": [
+    "# Correlation analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 2186,
+     "status": "ok",
+     "timestamp": 1761794380962,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "TQwfsnhuGp1a",
+    "outputId": "8bf67cd6-1a47-4647-a563-9815c10e0f0a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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sssuGCy64ILz55psxaaclAwYMiAk8JNPMMsss8b9HHnlk/Ps++eSTFv93M888czj66KPDTz/9NN7/7vFtyimnDNNNN138mnrqqcOpp54ak5NIVmoJ/7+OOOKIsNhii5U2TkmSJEmSJEmSJEmSJHVwEs4999wTJptssthuavrppx8tsYZklzXXXDNWyak07bTTxhZVa621Vot/b7du3cLAgQNjO6vC4osvHqvuTDXVVC3+76gkM2zYsHDxxReP97+7MGTIkNg26vbbbw8rr7xyWGqppWJVnZEjR4b2omXWl19+Gf74xz+2+udIYOLfXnHFFdv9b0mSJEmSJEmSJEmSJCmzJBwSV1ZbbbXQvXv3sPrqq4dbbrklNDU1xd/7z3/+02JVl0UXXTRWgmnJdtttF6644or4d/bv3z/ce++94Ycffghzzz13mHDCCVv8380wwwxhr732im2lKqvyjI+/u9pZZ50Vq9fw3/vuuy+2l2oPXq+LLroojutXv/pVi3/urbfeim2oDj744Hb9O5IkSZIkSZIkSZIkNYKmUU1+daLXoLMar0k4H330UXj++edDnz594q+pbEPiy7/+9a/466+//jpWyWkP2ludeOKJYcYZZwzXX399TKyh6sxNN9001v/ttttuG2afffZwzDHHjPe/u9L+++8fq+Ast9xysb0Vf1eRgFRU16n+omVXtaeffjp8/PHHYYsttmjx3+LvpQ3Vn//851hJSJIkSZIkSZIkSZIkSen0GN9VcHr27BlWWmml+OtlllkmTDHFFLENFckpVLqhxVJrhg4dGtZdd93mX6+//vrh6KOPjt/T4oqvESNGhMcffzxceeWV4dBDD43tmBZaaKEW/84JJpggHHnkkaFv377hgQceqPln2vt3V1piiSWav+d/8/nnn8e/r0BVoGr77bffGD+jEs8qq6zSamWg6667Lvzyyy9hyy23bNPYJEmSJEmSJEmSJEmS1EBJOLRxWnLJJZt/RqLIPffcEw4//PCw4IILhsGDB9f8355yyilhmmmmCdtss81oySqTTjppbGPFzw466KD4s6mmmiom56y99tqx2s7AgQPHmihDgsymm24aq+HssssuzT8fH393obJ11ahRo+J/u3Xr1vwzqvFU69Wr1xg/e+yxx8L//d//jfW1HjRoUHPiz88//xxfa6rr8Hu//vWv2zRmSZIkSZIkSZIkSZIkZdSO6p133gmvvPJKOOyww2JSS/F16qmnhm+++Sbcf//9sdIMlWhoUVVp2LBh4aqrrgo9evSIXySrFF8k5pBc8o9//CP+/ZUmmmiimMQy9dRTt2mMVJ357rvvwsUXX9z8s/H1d+PVV19t/p4Ememnnz4m9YwLqufw+lQmMtVy0kknxWSb4nXeaqutYrIQ3/PvSpIkSZIkSZIkSZIkqQGTcEgIoX0S7ZHmnXfe5q911lknzD333DE5hO9pUbX99tuHu+++OyabPProo2HnnXcOc801V9hss81q/t1U0FlttdXCHnvsEW6//fYwZMiQ8MILL4T+/fuHn376KVasaQsSYkjE+fDDD8f73w2q7Lz88svhySefDKeffnqs6jOu3njjjdjSa5ZZZhnj97799tuYpIMZZphhtGQl2n6RNMT3JDJJkiRJkiRJkiRJkiSpPD3GZxIObZyoIFNt6623jgkqn3zySTjnnHPCBRdcEE477bTw0UcfhWmnnTb06dMn7LnnnjH5pCX8+fPOOy+cddZZYejQoWGSSSYJK620Urjyyitjy6q2ItHnpptuimMZ3383SUa77bZbbEXF/+ddd901jKvhw4eHySeffLQ2VoVLLrkkDBgwIDz00EPj/PdKkiRJkiRJkiRJkiSp43Rrampq6sC/v0uges4aa6wRHnzwwZoVbHI19LWXQm6mHvRgyNF/FvlDyM2cXz0fcjPBT9+H3Fz/w0YhN/PM8G3I0VLvXBFy8/5v1wm5meKnT0NuHvtq8ZCbGSfL736ACbr9EnLTq8dPITc9u+U3pu7dRoXc9Aw/hBx93zRJyM03I38VcrPIfy4LuXlv4Y1DbkaNv+Kp49U0P34UcvPZRL8OuZli1PCQmzd+mjvkZuaJ/3dIJyezvnxryE3T5OPWcrur+nTWpUJuvu42ZchRt2BYsi2awpiH9VL7dmR+c84ZenwcctS9Kb91aLem/NZXI7pPF3Iz7cj85pwTjswz3jJi4vzmwr8a+WXITVONw9ep/dItv84K03z6asjRBB++E3LzYN8LQm5mfeWxkJtF5pk+9RA0HvXZ+jlfz07kgWvyWz+PD3lGVCVJkiRJkiRJkiRJkqQGkl+Ka4aWXXbZ8NNPLZ8Ip72WJEmSJEmSJEmSJEnqGE0ZVvSTqpmE0wY33nhjGDWq5Q/0zDPPHF577bW2/FWSJEmSJEmSJEmSJEnqhEzCaYNZZ521498JSZIkSZIkSZIkSZIkNazuqQcgSZIkSZIkSZIkSZIkNTqTcCRJkiRJkiRJkiRJkqQ6mYQjSZIkSZIkSZIkSZIk1ckkHEmSJEmSJEmSJEmSJKlOJuFIkiRJkiRJkiRJkiRJdTIJR5IkSZIkSZIkSZIkSapTj3r/AkmSJEmSJEmSJEmSpI40alSTL7CyZyUcSZIkSZIkSZIkSZIkqU4m4UiSJEmSJEmSJEmSJEl1MglHkiRJkiRJkiRJkiRJqpNJOJIkSZIkSZIkSZIkSVKdTMKRJEmSJEmSJEmSJEmS6mQSjiRJkiRJkiRJkiRJklQnk3AkSZIkSZIkSZIkSZKkOnVrampqqvcvUWN68tWvQ26mmuirkKNRTfnlq/Xq/n3IzQ+jJg65mbxpRMjNj93ze53wU1PPkJtfwgQhN93DqJCbKUYND7n5scckIUe/NPUIuflq5GQhNzk+934ald97N3JUfq8TZpw4v3vCBOGXkJsfM3zudQv5Lc0++3GKkKPZew4Juflk5AwhN3P/9FLIzU8TTRpyM7T7bCFHE3X/OeRmgm4Z3s9HTRRyM1G3/N676X94L+To7R4LhNzM0OPjkJuPRs4YcjPnqDdCbt4Ivw05mv+XF0Ju3umZ32fvm597hdzMPeFbITcjuk8XctQUuoXcfD8yv2uqR/eRITcjM4y3dOuW39oY3bvlFxeesFt+19QHC6wccrPuz6+lHoLGo95bPO3r2Yk8fP2yoTPK7+kqSZIkSZIkSZIkSZJUoWlUfglxUrU8j+9KkiRJkiRJkiRJkiRJDcQkHEmSJEmSJEmSJEmSJKlOJuFIkiRJkiRJkiRJkiRJdTIJR5IkSZIkSZIkSZIkSaqTSTiSJEmSJEmSJEmSJElSnUzCkSRJkiRJkiRJkiRJkupkEo4kSZIkSZIkSZIkSZJUJ5NwJEmSJEmSJEmSJEmSpDr1qPcvkCRJkiRJkiRJkiRJ6khNo5p8gZU9K+FIkiRJkiRJkiRJkiRJdTIJR5IkSZIkSZIkSZIkSaqTSTiSJEmSJEmSJEmSJElSnUzCkSRJkiRJkiRJkiRJkupkEo4kSZIkSZIkSZIkSZJUpx71/gVqm4MOOigMGDCgxd+//PLLw7LLLtvml3PYsGHhmGOOCQMHDgw9e/YM66yzTth3333j95IkSZIkSZIkSZIkSSqXSTglOfTQQ0O/fv3i93fddVe45JJLwo033tj8+1NMMUWb/66mpqaw1157hcknnzxcddVV4csvvwyHHHJI6N69ezjwwAM7ZPySJEmSJEmSJEmSJElqmUk4JZlsssniV/H9BBNMEKabbrp2/V1vv/12eOGFF8ITTzwRpp122vgzknKOP/54k3AkSZIkSZIkSZIkSZISMAkno3ZV00wzTfjwww/Dww8/HGaeeeZw0kknhXvvvTdWu5lkkknCwQcfHH7/+9/H5J2LLrqoOQGn8M033yQbvyRJkiRJkiRJkiRJHaWpaZQvrrLXPfUA9D+XXXZZWGaZZcJtt90WppxyyrD99tuH4cOHh+uuuy6svvrqoX///mHUqFGxDdXKK6/c/L/jZ1deeWVYbrnlfDklSZIkSZIkSZIkSZISMAknIwsttFDo27dvmH322cN6660Xvv/++3DYYYeFueaaK2y77bbhyy+/DJ999tkY/7sTTzwxvPLKK2GfffZJMm5JkiRJkiRJkiRJkqSuznZUGZllllmav+/Vq1dsN8V/0bNnz/jfn376aYwEHCronHrqqWHeeectecSSJEmSJEmSJEmSJEmCSTgZ6dFj9Leje/fWCxX99a9/Dddcc01MxFl77bU7eHSSJEmSJEmSJEmSJElqiUk4Deqss84K1157bTjllFPC7373u9TDkSRJkiRJkiRJkiRJ6tJMwmlAb731VjjnnHPCrrvuGpZccsnw6aefNv/edNNNl3RskiRJkiRJkiRJkiRJXZFJOA3owQcfDL/88ks499xz41el1157Ldm4JEmSJEmSJEmSJEmSuiqTcBLYZJNN4lel4447rtU/M8ssszQn2FABhy9JkiRJkiRJkiRJkiTlwSQcSZIkSZIkSZIkSZKUtaZRTamHII1V97H/EUmSJEmSJEmSJEmSJEmtMQlHkiRJkiRJkiRJkiRJqpNJOJIkSZIkSZIkSZIkSVKdTMKRJEmSJEmSJEmSJEmS6mQSjiRJkiRJkiRJkiRJklQnk3AkSZIkSZIkSZIkSZKkOpmEI0mSJEmSJEmSJEmSJNXJJBxJkiRJkiRJkiRJkiSpTj3q/QskSZIkSZIkSZIkSZI6UtOoUb7Ayp6VcCRJkiRJkiRJkiRJkqQ6mYQjSZIkSZIkSZIkSZIk1ckkHEmSJEmSJEmSJEmSJKlOJuFIkiRJkiRJkiRJkiRJdTIJR5IkSZIkSZIkSZIkSaqTSTiSJEmSJEmSJEmSJElSnUzCkSRJkiRJkiRJkiRJkupkEo4kSZIkSZIkSZIkSZJUp25NTU1N9f4lkiRJkiRJkiRJkiRJUldmJRxJkiRJkiRJkiRJkiSpTibhSJIkSZIkSZIkSZIkSXUyCUeSJEmSJEmSJEmSJEmqk0k4kiRJkiRJkiRJkiRJUp1MwpEkSZIkSZIkSZIkSZLqZBKOJEmSJEmSJEmSJEmSVCeTcCRJkiRJkiRJkiRJkqQ6mYQjSZIkSZIkSZIkSZIk1ckkHEmSJEmSJEmSJEmSJKlOJuFIkiRJkiRJkiRJkiRJdTIJR5IkSZIkNazPP/889RAkSQ3siiuuaPH5st9++5U+HqmjvP/++764ksbw8ccfh1GjRvnKSNJ4ZBKO2mzo0KFt/pLaq5jsffLJJ+Huu+8Ob7/9ti9mlW+++Sa88MIL4bnnngvPPvvsaF8p/PTTT+H1118P//73v8Mbb7wRf51CI92jvM4b7zrHV199FX788cf4/X/+859w0UUXhaeeeirZePbYY4/w1ltvhdzstNNO4aabboqvVw4OOOCAcMcdd4QRI0akHkrDfPZeeumlrD57xbheeeWV+Izh+5R69+4dTjrppDieXGy33XY1P3NsHG2yySYhFZ65TU1NY/z8l19+CYMHDy59PNzDX3zxxXDvvfeG22+/PTzwwAPx18W9PaWRI0eG4cOHN/+aeVXZcyquo88++yx+f8sttySb07Vk/vnnr5ls8+GHH4Y11lgjyZhyxev0r3/9q/n+/cwzz4THH388XHDBBamHFtcL999/f/juu+/CBx98UPMe0dUNHDjQ16UNzjjjjGzmwnzOuI+35vvvvw/nnXdeaWPKGWuFr7/+OuTk7LPPDn/4wx9GS1AYMGBA+P3vfx/efPPNJGN67733ws8//zzaz1h/5nLdqzGTu7beeuswaNCgJP92IyHeQ1JCbm677ba4vltqqaXiPOqYY47JYn6XE+I/X3zxRephNJx11lknrqskSeNPtyYjHmqj3/72t6Fbt25j/Ly4hCp/79VXX002Eb300kvjopnF8uWXXx6mm266sOuuuyYZD4tlAtgvv/xyDMhUf9yOPfbYJOPKEUHivffeO5x44olhzjnnjAsKNkQIVPEzAh+5XPOYcMIJ47XFuP7yl7/EX5fh1ltvDUceeWR8Xaox1jI/ewSiTj755BjQL65vxsBrsfrqq8f38ze/+U1p42mEe5TXeeNd5wU2agmSnXPOOWHmmWeO96gZZ5wxbjD369cvBmzLttxyy4XrrrsuzD777CEnp5xySrjvvvvi4n3FFVeM98k+ffqEX/3qV0nGc+qpp4YnnngiJk5xn1h55ZXj12KLLRa6dy83H/3ggw9u859NMUcgWHXooYfWTEhI9dljLH/961/DzTffHH9N8sTxxx8f7w9ca1NMMUXpY2IM99xzT3jkkUfCDDPMEINV6667bphrrrlKHcc///nPmDBVbBztuOOOYZJJJhlj84Y/9/TTT4dUSRN8/qaeeuoxxrXBBhvEBJiyriPmkzfeeGOcn0855ZRhookmikkmBGiZu2yxxRbxPs/Py8Zn609/+lO8jkgcBEklzGHOP//8MM8885QyjkUXXTRce+218X1r6b0rG2up4vNPIsniiy8+xryb5H0SnHn2pEBC3t/+9rfmNV+1su+d119/fTj66KPjWLh3V86FF1lkkTh3SOHLL7+M6ybex+JeysYRG0hsHjG/SjU/v+yyy+J9iQQJEvQYC5/HVJZddtl4nf/ud78L6623XpyzpJD7vIX7JvepOeaYI75fPI9nnXXWkAL3TNbF00wzTfPP1l9//XhtzzTTTPHXJDkyBy3jnsB6vKVYRrUHH3wwlI3Xhs8cawXeO555E088cUiJpIi///3v8fUghsjciYRh7lt9+/Ytdd3AfZv749VXXx1jnMsss8xoBzEefvjhsP3224cDDzywze/z+Eo24znco0eP0v7NRseafe65547X1myzzRZ/Rrz6uOOOi/cG5jll23DDDcO+++4bVl111dL/7dZwD2XNzry9On7+f//3f6WPhyQX3qtUz5VauCcQk+L5x9qG9fvzzz8fr69tt902yetUxIIrDRs2LEw//fSl3p8qLb300nG+y/5CTri+WS9wQGyqqaYKCy+8cIxRlYnrpKX3hYNYjKlnz57x1+yrlYV1+emnnx6vaZJ0V1hhhbDPPvuMFmMpcx4lSeOLs2a1WeXCnE0HsvkJyvBwJljN4pRFBAHsHCaiWGihheJElAd5iokoG1kEg5kgTDrppCGlcVnYbbTRRqFsBO0ImhH8v/jii+OE76GHHgp33nlnPOGWIgmHJICzzjor/PnPf46BTxYWnBY588wzw6abbhrmnXfeuOnFz/fff//SNpM333zzsNdeeyW9plg0cFp6wQUXjJsOBBUmm2yyWJ2ARQULVV4jNnLK2jjK/R4Fr/PGus4rnXbaaXE8LASpgEHAjMUhAVASBFIk4RAMZlG61VZbhV//+tfNC+XKwEMKBPT4eu211+JJd06QHXHEETHIx32eDaUy8RrxxeYfwXROjx500EFx4533k/c2BZJISOTgHsUXm21s5BJES/EcBomV22yzTQzu5/LZY05H0ifPFa518Fzm/s7zp5jzlWnttdeOXz/88EO8BzDX4/NIQg6bpVzns8wyS4ePg01HPl/MQ/ji2qlMTiC4RlIOmzhluuGGG5pP+jMu5gPVG1dU7SkzaYmEBBJ+mGMyp5tggglGq8pD1Zmjjjoq3s/5KhvjW3PNNeO9qsD9k3UMv9fSKerxjc1QPmdsIrf03pW9acvrMmTIkPg9yRu8f9VJnVzn/LlUDjnkkDgPJnCcw72Tzx9rYjaR2YjnM/ntt9/GBK+UrxP3bDbZqfJSbPxxjbOO4vfOPffc0sfE/ZvnCesD1g8kLrG5zDyBeQP39hRILOGLeQLvI9cV62GSFRZYYIEkY8px3sK1zvqT+yVjY+3OZhavE68Xz+Wy1DrfyL1rbNVxOgpzperxEd9gPVOZKJQKyW5Uc6H6Me/j4YcfHu8LvHf8N0VCLEmnJ5xwQrxXcj/nXkC8J0WiAhufd911V/z3KxNwQNyTWBn3LpI6yrxPEQOqTjZLjc99W6WIC/M+8qwj8aVWclcKPEdY73EvJ+m0+vOWIqmSz94//vGPeA+vnkulSuRgXcfchPeNeEuK+1I11gTMmVZbbbW4dgfXFgcMiLmUfY0XB2T4HFbOTw477LAYD2JMHMhKkcxMvI75cA7vG1UgWS8QS2cuzDVO4ieHCNi74jNHXL0M/DvE6kmoJB5WiTkd9wWup7JxwIrYCs9g5ixXXnllXIsSf628hqwnIanhUAlHGlerrrpq0wsvvDDGz1966aWmFVdcMckL+rvf/a7p4Ycfjt8vtthiTe+//378/pFHHmlaZZVVkoyJcTz++ONNOejdu3ebvlZfffUk41t44YWbPvzww/j9xhtv3HTcccfF74cMGdK0yCKLJBlTnz59mh599NExfs57utZaa8Xvn3/++VKv+UUXXbTpgw8+aEptxx13bDrwwANb/TP77rtv01/+8pemFHK8R8HrvLGu8+r3bujQoc3Pm9NOOy1+zzgZbwrzzTdfi1+//e1vm3Lx1VdfNZ1//vlNiy++eNJxDR8+vOm+++5rOvbYY5s222yzpvnnn79p7bXXTjYe7o9nnnnmGD+/8MILm3baaackY8rxs7fyyivHe3f1/G7QoEFNyyyzTOLRNTX98ssvTU8++WTT0UcfHecryy+/fHwdeU6+/fbbpY3joIMOavr666+bcvDTTz81DRgwoOmmm26K96PLLrus6eabb27+4vfuueeepi+++KK0MS2xxBJNL7/8cqt/5sUXX2xaeumlm1Lgmimu7UrvvfdevO7LwjV0//33x/ep1ntX+ZUC/+6PP/7YlBvmCO+++25TLhZccMHme/muu+7adNddd8Xvn3322eY1TArLLrts0+uvvz7G/fyNN95oWnLJJZOMaf3112+67bbbxhgTP2MtmIOff/45rkn79+8f7xW8h8wfirVzV5631JpznnrqqfF5zDzvD3/4Q9Ptt99eyr/NPfOzzz4b7WeV1xQ+/fTTpHPh6vHk5M0332w6+eST4zXO/YB5zb/+9a9Sx8A9cqONNmpaYYUV4lzl73//e9NCCy0UP3tlzlmwzjrrNN+7W3L99dc3rbfeek1lqnWdp8bnvK1fKdcL/fr1i68fz2ji1Cnx+WrtK4Wlllqq6c4772zKCTHyIrZS6ysFnm/Ffbzynv7WW2+VHjvnOuZ6Jn7/+eefj/Z7jIu4C7//xBNPNJVtq622iu8dcwFiwOx1VH6ViTn5csst17THHnvEGEZh5MiRMc7BXJ3fZ/+jLDxfeX5wXyJOlsM8gb275557rvnXo0aNitcW11DxPEw9j5Kk9rASjtqFk3S1TvRwCqm6X3FZaAlS60QtZSNT9QHlRGSZp69aw0mZnE077bTxpPt3330XT9Rx+hBPPvlkc/nmslHmkHYztcZKaU3QkorPQ1l69+4dT2zutNNOISXaX5CV3pqdd945njpIIcd7FLzOG+s6r8TJJ06tcU9/55134sny4hRnmW3XKlF1Klec6qFKAu8jJ945bVO0Wikbp0Q50UN7rPnmmy8sscQSYZdddonlpVOe4uTEPW37qlGOn4prKXBdc5qctka54H5eqz0Bp8aoYJIC/zbXNafuaVXHOKguwUluTt0xl+nfv3/Yfffd458pQ3Fi9dNPP63ZApV7WFmojlBURaAiEJ+51G0LqJwyfPjwVv8MLY3Kai9ajbkuVbqqy91z72LuUOY9gJaQjIf7EK0Xc6jsUtm+hKouVCXgmqZSAc8ZTt5SgTTFqc2iDQ0VHXJpD0k1B57DfP4ow0/J9qIqSLGGSaVWu0PGmuoeQTucWq2eaNuV+rUCFX0fe+yxeJ0zb6B1AZ/Td999N86pymyJmuO8pUA1M563vE5UMFprrbViVTqeiZyspi0jVRaUHz5nVFLgvXvhhRfiZ69475hHUaWK67ysFh3MX6jGwfOE73nuUKWHCojM/crCuoXXYmxtjsqudpiyKklLyqoW2F60d+F9Yp5JZWaeyVQrocIDFRBTtNZNUelmbKhSSaXtnPB+5Ybq8VS6r6x2xrrvkksuGes9Y3xj7Ut1NSoFVWNNQ1yfas1U9KquuNLReHakrIJeif//xJ6q50pc81SdofUwlaSpJlTWZ5P1Oe1++bdpEU1lLqqRp0SV4cq1HM8aWi5SlZWqmawVqN4jSY3GJBy1Cw9oysMRhKFUJBM+SurRNqhoFdCVJ6IFggYstijDSFA29QZE9ebt22+/HQN71VKUk95hhx3CnnvuGSdXTEIpucuEvsxJaK2S/LQAoLQmJVqLYAjvKQEPNt1uuumm2JaqLATPadVD2WauqerNorJeKzZFCfC3hk2jsW14daV7FLzOG+s6r0RwgWuKzz2lf7lPcW+gjOu4lMAen9h0YQOG9k5syuSCADYbx7x3BNFJgknZi/tf//pX+OCDD2Lgh/aQBBwI8FW2o0mBVkI8Qyo3FrhXXXXVVTFZqCy8PwWSFNmkYiOE8vbVbWhSfPbYbOR+wOetwPtJGe4U7QGw/PLLx/kT9wLmCaussspoZa5JWiAph/ZHZaFtCZtEH330UfO1ROCq+G+qvunM50gu4RnM9VWdHFRWyXSSOgnebb/99jEIOv3008f3jPeRpGvuE7SqqhVELgNJiiSRsJFMSfJirn7bbbfFhK4yE8y4lrjGuZZIUGjp+VJmYlflZgibtQRgSdq/8MIL4/OZDXbaiBVl+ctGGwDWeyQt1Zq3lL22IuGGoDVrFp57zF947lHinXt7KrR1YEzcN7kvkbDIpjbXOPOFFEgSJsmluiUILRDLagtQCwmeJJWQ+ML1RAJAsYlTYL5AgklZSTi5zFsqMRfgtWLNybOY+zxJQZUtWknC5POpvFx66aXxfs5ciXgKaxpajFYewOKgA/eLspJwiB3y/KvEM5nr/rLLLgtl4qAAsaciDlXLxx9/nCT5tLVWlSnaVrJWaGtiUFljyjG5K/e2XTzLiNfRuqu6zXYq1a3gcsDzjPUK8wPWMbTTJTGXBAbmxWWi3VTlGr2l2CzzhLJtvPHGzd+TnMshae4TKZIIWVuN7fPHIShaxJWJ+SWfddYNzMWZ+6Y65AQOMxGLIuZUud/A3I7rm6TFVGt1SapHN8rh1PU3qEvihC2T4xtvvDGeXCs23LfZZpsYRE4xqXn99dfjw5jFKkFrFs+VE1FOKJaNxSCnHVqaxKTaEKGf5kUXXRRfq+rFDe9dioUpCKZT0YhgMePiJFSvXr1iEkUKVFBiksfmEYtlbpdfffVVWGmlleLCkA0lerrSk5tN3bI3S2spa4OU94SFRGtVJNjU4r1McZ3neI8qeJ03znVejWuJ05rF84RExsknn7zUKgXVgWI2aLimWLCygcXJXwIMKbEJSiA91b27Fp7FnEJ89tln43951pBIxWZWiiAjGAf3I+6jxebV4MGD47yFZ3RZ85axfd5Sf/a+/vrr+KxlbkKCAJ85fsazmM2aFJsPVMBik2+SSSapeZ8YW5JqRyCIzzVDAnityiWtbeJ0dNLE5ZdfHu8HbIRW4lnM75WFzT5OSzN/oxpHkaTEnJP7AZvwqRIBQCLA9ddfH6utkbhPMgcbN5Wb7h2NE5ps9Lc2T0qZ2EUyJfNuKpewKUtSOEn7b7zxRkyyJpkqhaI6Xi0p1lYkvHGylXsC9yoSGa+77rp4v+Q+nuoUKZtFJI2wGVNUpiQhlpO3nJZm3ZfqWcy1ReVYEqpIPhs0aFA499xzx9iQL8uSSy4Z+vTpE+dTHAyplThM8gL3jbLmMbnMW6oTLHmNWpv/vv/++2HIkCEdfgqf5xybo5XPYDa1OF1ezAuYvxBHSBUH4rNPcmd11bUUmLfw3vFVq6J1scHLZ5GkjzLjCCR1FTE8nnncu3jPypwjMH/i88V6r1aVPsZJ5WHeS5LRysJ1zry8LevNyk3wjsTGcSXesyOPPDIm6VbHq8oaUyXiibWeJaxrSO4qqwIpc8rKf5s5E0np3Lu5xoijk4BNQiPPv7ITqHjfWKMzFuIr1YleJnX9D+sY1qNUYeReRZIsyS7Va62OxvxkbM9/riue1VS5LxPXE2sEEj559rIOpIIm63ee1ZUHaDoaB8OJ27V2gIGkS5LVOZCRCgcM77rrrrhnxL2hbMRauW9TeZ/riuurEolM3Ju4f6WaR0lSe5iEo3bh1Fqx8VBscKfYcKg1ESWowMZoyolo4Zlnnskyq37ppZeOgc4ygxltRWIHCwkmVbx/BMtStQYokMxFAIjgJycii9YzBBzZuMmtHG9ZwRcmwK2VzuW0AZWpUkyOc71HFbzOGxP3JhajBB3Z9GCzhhYYqcu2smCnahDVS7hXsVgloE1Qq1Ybn45AwKytUlRPqGxJxyYSp6ZpuUJwr8xqJdW4P/HecW1hnnnmie8diSYaHdVveJ3YdGB+0NKGTRkINHIfr36upAyeURHyjjvuyGJjrXrOyUlf5uO5YI5JUjXzOAKwVHrpinO5lvDaEKwmeYPWTy3Nn1IkdhUbyFRKYENrv/32C1tuuWV89m233XaxbaRCTDglUal6DcVGMlWDSO5Iic8e93TW69yzUq3VC7S9ufrqq0fbzCIpL+V85ZprromVlaoPzLDG4ZAB13sKuc1beI1ILEtViaetyXgpWoVTJbpadVJQykrIxBJIIqleqzBX5/eK9uRlYn3AnKVWO3vakHP/LPNZvNlmm8V7AMkTVORhDUqMheQcWoOTiMq9giqyZc6BH3/88aQtfRsh4Sz3tTHVA0mGPeKII5ortpO0QPIXh+nKqixYnUCVW1IXSZwkSm299dYxYZ95FQeg+PxRXbPsyiW5PfdoocTzn6S3llCphzXyBRdcUOrYeI7ceeedsRokh2tJWuL95Jrv3bt3qRXyWAeTiNTas5ZnNodTaBHV1bGnxzO3VrIn808S8qyII6mRmISjdiGoRwl+NhqqS/BrTAQSmOyRwEHws9YJ5bIDRGSEl9lGaWwopcsChtO/BD4JgHIKkQUpZVvLDCxUYhxstpOIw2KH8dFWJWWlCYJDZIVXJptRurXM4FlbK1ykOimd6z3K67yxrvNKnCCnHDr3I+7h9JemLR0ne0jCIaCdGs8ZyqVz2ofXjCARpa5py9bRSWjcE6o3sSuLLaZsi/Poo4/GjVkSY3m/qC5BohJfVBBKcfIeVOtq7fRvKmy6c70T4CBJiQAfn72UFUJGjBgRr+9aLTRJ8igDgbEiKMa1RHC/eoObikskeJAQVzY2svi8p7pHtoT7JKfqigTm1Mn6b775ZmzHwzyOzWTeUzZK2KShAkaZCbtUoaIFFc+UHCvAETBnHp5TghLXOQkbvGZsrnF/J4GCzSROTXPCNRUSlxgTawYqUpHgydovxQZgS4mCbBxRMYgTpmUmBLVVWffzXHFPIkEJJMGRbFPdDo45FJtJZb6HOaMqHvPelK3DctXWpKAyq3WxrivaVZNIRvW16kM9VLim0iFVkctGSw7uQ7SQZtOdTWMScnjGEKsiHlQm/m0qElCZ4Pvvv48/Yz3FHIZ5OQeeyq7I2paKyDlInYRTa21cuT6u/L0U8TJeH+bAxFgqEe8k2SXF5y/HxDw+83/84x/jvaE6VnXaaafFAxBd+bnHuoq4GHFXkkwq13tcS8TOOPhEIhMx2jIxjyKpjHt65f2AA1nE7riPlYUKkFRw4XWoFfvhHsB1RtyO5Mtc5qTEOVN1SiiQFFgrBpQyUV6SxtV/052lcUTWN6X0mNAwEWZywyKQyWCtUskpFzlgk4QsWibOTLbKqqzCRIFN22LThvKH9EtlAU0p7taqiHSkAw88MI6LMn9MXKrLfKaYzJAdz0KeSXrxurDpRu/PY445Jm5Wlo1SrCwkOG3EF9c5SQGccL/44ouTnDxgI4triA1RMr/Z7Hv++efj68dJlrIqcrCRnbMc71HwOm+s67wSn33GRAUzArFscPEzNpfYDEmVhEOyItc6XwStGR+neijxzsKZez2vIRs5HSn14rw1nH6iYgLVEmiRN+OMM4YcsBlKu0yCnwSuuEfNMsssScdEgi7XNSf79txzz5jMReue4lQwlQHKRvsgPntUwKlWZlLXmmuuGdtZFEk4BBKrqzdQgY0/lwIBRu6RjzzySEw0q57rpmq7RhtINtm4b9Zq31Xm5417IYE8Nq+YVzI/INDPGoKS/ARHi9ZZXRWbomx0cKqV16e1BJwyW4kVaLnBc40qACQmsXZgHPyXCg+p8PzlvkmFHr7ndSQZjxPCtIUqo/oplVx4bYqk1+oS7oWObsnTWvuL1pR5P2dMbU0uK/M659nCBkwxtuqNmGLzNlVlMRLz2HRkXsAzubqzfYq5IImnu+yyS3xNqM5VXTkot8TUMpVRbWdckaxMgktrcxOey9xPU6BCF/dsknWpPEOSJ5XDiJedcMIJpSfh0EKQ5x6VG0hGJ9mTnzG+VDENEjSqP2caU+X9kLk56xkSrqmmwgEx5jHEqkhgSIHqvhw2rE7CYe5SZuJSZWIe7VCZg9dKzCM+lCIJh0SSWodXeY14JqaQ03OPRCBiB7So4yBPkShPJS/2PTjASjJj2Qk44Lqq1VKJNQ5VBcteD9NakWcICbLcBxgH61LuBdwvuBfkkoADDqqlaN9XIL7J2o5rKZe2yJLUXlbCUd04hcVEvTiJSD9uNpPKbrXEpJygMadBmODxYGaSQ+CftktMnJnUUxWDxI4ysGBmDGyKcOqQzGsmeyy+yH7mhE8KZKJTLpJJcWUAMuVkhsx0Tt5XL3BI9mDCyuZI2TjFyskiXqullloqvn9s3nJqmQQdFtJlIwhEsKp6YUXJVDZPSfRKidPkJE+xmE+52ZbjPQpe5417ndPHmX+XDTYWziR07LbbbjFYSwAkRfsZ/t033ngjLuKpqEISBwmn1YtXEijG5SR6Z2u7VjzbcsTJvvvvvz++T/RJJ/DIe0nScIoKcCQqMmchcbES1z4b3gSRy0YCFRumnIzMJejPvYj3KZcqa2Pb6Ob6T5EwUYyL+yOfQxIlqu8BZW3aMpfk80VSBBXDmIPzjCmSFsBpc07+kshbNgLYXFM8Y1KqPIXM961JldiVI5JuWCtwwKHytC3XGZXYOjoRtsCznmcuG9jVFSa4znlfWWvldO9KYWzXdsrrnLUU7yFz4ep2cMV7WF0dpyzcR1nnUSGkVmXfFJs1rVV7KbPCS45Yp1Ahj2spVdXHsb133BtzahnNWoVDYRy2IpZHwgv3gJTtRnNFG0jaGLHJXr3OIobH65diPpVLJZxKVGimUh+tYyuR0EjMkfZeZWP9yUEikviL5HPiZiQEkJTO+qsMAwcOHC0xrxaee8yzGG/ZSHZhDfP3v/+9eW1O/IekE57FKQ6L5vrcI2bPfaFIFlxwwQVjMmMqf/rTn2ISDmu94n7Ae0YrWxBXLBsVlJjbsU/EQV/m6cQZiS1yn9D/rLrqqvFa52BmrTlMirbIktReVsJR3Thty+YaGeKczmAyQa9kHpJMVJdYYolSXmUqk/DvkWRTYDFBMLuo+sKkmSSdspJw2Pgn8aeyYgrfcxqYCiupEBAmy5qvXAIyTD6ZhFYj6znFpi0oU3n99dePdsqIsVCKOFVGONn8tU4RsKggMagsVEfgM8dCi3YuVEdgUfzwww/H3ycgw8Zbv379mntMd/V7FLzOG+s6r0SAkT7SBIrZICGgTVWeSy65JFnVBJJu2LRtLbhI5ZeyEydyaLvGgr2tiTcpN2jYxOJ5whcBK+6rp556ajxtu+SSS8aADEH/snBN1wpocP1/++23IQVOH//ud79LnoBDOyo+c2xec23RGqAlKU7ep0gMbgtO/JV9crylSjicOOaEJvMTqiuxkVx5n2BefM011yQZH4FgKpilVplwkGuSzVNPPRU3rrhfVVfiSDVmxsOGYzUOYVCCvixFOyeea7m1EivQbomNEJJ0mR/wfOHeyoZNWVq6TkiMZUypqtVWVqTNseooG7QkoebQAiPnai+5IDGf+xLJ+MyLSchhXZCyKmzu7x2bfiSjEz8kxsF8vHfv3jEZvVZFha64lqH9DNUyiatgnnnmidWDiLtyD6XiNwcky0x+YY5ejWRGEk2qk7xSzNFZQ9Wq6MnrxVwmBSp3Fq12eR4XsRYOXVBpqSxFe+9cE/OIF7KXwL2BeQrzTuLWJCkR108hx3sniF3wlQsO0zLfozIkLYmJTxHHY551zjnnJBkTMUS+UuNgMYnVCyywQMgVB9hJvquu1iVJjcgkHLULJ6DI4OXkNqf72ORmk4g2R0zYmZjSQohyymx2l4EyfrVaTFDJZNiwYc2BiDI3kfi3qvvZFgtCAnyp0CaLbOIcToUU2EimhQoTZao64MUXX4wBEAKzKZCkQTJArT7JtU7/lYEWOCyW+WxVKjsgyibxnXfeGTeMCLKwGcnChkUz4yDRhfcT3BfKluM9Cl7njXWdV+LaYTwEXUg6o5oZ9yeCeylO0YAgBwkvfFUiYFu0Yeyq7QVJuC0QJKb/NhvuPF94bdiQ53RmqnL3lThVy72KxF2uL6p18dyjcheBbO5RbAKUgUAV924CjkVlOoJVJC8QcE+BgBnJw4wr5YknrlsCsCThtHYN8/lL1f6Ck5m0pCHpjfkU1w5zmJQB0cqkZa5vWkHxGpWdHMDmEJ815r78l3k488zK4CM/T1GBCsxROHlMyyzu26kqlVCts63YqCkb96KiZVh1O7iUCSdsGLE+qN64opUmz8Oyca8k0eXSSy+Nz0DmT7xuzAu4xlKhpQSnykkC4HQ0n0PmUVTtIZEw1RyPOQLVqIgnFO8nc4ayk7oq28HxfWtSVDf7zW9+E1ud5oaNbBIDqltkcU+gQlVXRQIOc3IqLTLPZC3Da8L6nYScFK8NlRaJF1CNYGxJJimS5NmYZK1CLGPDDTeMyTe0ByGmR7W8MlWuZcC1zdyOhIAUz5UCiQfEwkgwZU1FhReuNdbJPF9oX0ub5h133LG0MdWal/MaVVfiSTVHp3ot7ZF5jZi/8F6SvMu4SdZNhWcwBx2oxAEOF1EtPZUck0tIviPeyTOGryLxjFhQ6tZ+fAYrE5pp2c5zukw5VxZkf4jnDcn7tD1jjsC6mMq/HPIp07hUpS4S6jsSVWE5YPjHP/4xzstr7VulRsyVeCIxoK5ewVNS47MdldqFsoIEqdkkImhcqwoArV840V1WgIgJHQs+TrYWmzSUjSWYTFscKtIwQSQIQdujMrDhSPYuY+LUEcFQUP5wlllmCSeffHJIgQ3jIUOGxFNRqU+WVyYG0eua14hFKV9UUGEhQcAoRcUerhkSTVgwU+WFAAOboiSgMC6CC2Vjg4hyrWwaFeVsaZ3A6RHeV06SlIGFC68HG3u0wyGQR/C8csFAJSGCRyx6ypbjPQpe5411nddKoKRaSZFYwmYN36eq1kWyBvdyxsU4uG9SPazY4ObXBNXY3Crz9GZubdeowkGAgRZPlUjUO+2008Idd9wRUiDIz+YjyZ5U8SNJj82JyucyzyACD2WVvyfJhPEQzGPuxHOYa6pWS6+yWlfS1oygNZtstdgP/H/BPTY/mG9SlY7kWKq68IwjmYv7RQpcO9y3SQbg/smGFvMHrq8yg2p81ph/EzgniM5njns4FSqZI/AzyoMzN6aNbdnYjCThraUNybKu88okHDZi2LglebEygZHEEjaxuIeVjXkm7xEbWjlhg4Z1A6XvSRosWtdy3dMCjcMPZSIZj1O+xXh4zvG+kWBJJahUFYNIfCXJjE3colImmyLcC9hQYgOwbLxvbNSyrmP+wpyK14q4AZvIZSYt5d4OjnV68bpwwKF6/lvGxlFrbbarpWqznSs+a8ypuK+T4EIiI2tl1vFlVfWsbOdJBY7WknBSVR6uxvyTuXmq9V5uLZaI/5C0yFjAvZuqKhx+JBGAQyqpW2vm+Nkj4YaEgCKRkdeLdTHP6RRJxKwTDjrooDhHKdpBEfMkEYY4dYpqHdyHWnotigNGrOd5Xpf1eSR+R7yAPQberyK2QOu6MsdRiTgrsQ3WMFSPLg4W0AqKeVSZhy9aa4ecumprZWJzJT6DJJ7wDCpLrWdsEa9LMW9hPMx/2dvgkAz3APY4ql+rlHgdWDdQQZP7ZfW9oSu3G5XUeEzCUbsQmCqzhUtbkIDDooEN/6JMJBtHJAsQcOSkAX1bCUiWNXY2G/g3mRwQ0GNCw88YEwHRMstuV0+U2dDmNWIyU12SOOVkhvfs3XffjYEZTpOySZMSCwUSNYpKE5yoYYJKgLTs7PkCpx1ok0U2PwEhsvnJEi8z2MHJOcZAoKW4pgigV7ZeY9JM27UUSTg53qMqeZ03xnVejQU7J925n4N7KIEZNiVTnCynagKtpqgMUFTsKnqUs6HLCU42JlBmv3L6WfOMq96MeeaZZ+LpTXq/l4kgMUHP6hNrBKo4fVhWgks17o9sRJAcQXWQWqhgQKIVgb4y8B611TLLLBPKQGISyWRsxtRKyC1rHG09wZbq5D2tlEhMYLO/cpOGJACu/1TJZgQ/KxOaOXXHdU3iNS0eiqp5ZSBpmA1I5t9sPLJ24GT74MGDY6Iiwccyqy/ynGMexTUzts9eWdd5JZLfqExSvdnP5htzO+bHZSMJl4SXsk/6tgUbV7wmxalknsusGVJU9GSTikMMPI8r7wckoPPZ478pkFjNJnyxfijwmlFtIsXzmOTT/v37x3lTdaIuiWZFq90USIplzVDM8dg0ZW7FRmQKrSVqpEp44RpnHsX8MlWl2kZOyGFzmST5VMlKzMWppltZiYM5b6pWv8Vnn/Una7xqqds05pCEw3vz+OOPxzhi5b2ddScJOGpdkYSTuuUSFXB22223MZLdSExgLsPcvWzM71g3cJCP5BJiLVSl4kARCfIc8CFxgER6DryWgTkTB4i4tovkluIgD3PSMtcxBeZLtMPq16/faD9nTUOCDq9jV0UVWFpngmuFpOHqPQUqxvLnqJjeVe/n3MeZA1CVjqo4rO1IqOQwDwl4xNI5OJ4Sh2k5aMh/a8WAcknUlaS2MAlH7cbCtFg0V0tR4pOAPkkbJHAwLhJLCBwXQVqyZ9nITXHKgE1RgotF+cPUpSsJfrYm1WSG14gJINcW7xMTQxYYKdtPFKhoREC9pY3SroZAJ+3W2OCvtWgoWonxHqY4KZ3jPargdd6YSAjimuY+Xnlqhe8JHJdVYa0SgR822KsD1QTT2fgj2YXrjUSTcSmBWy8ScAhO1WovSE9uglll4qQT7xcJuUWrmSJZicBDmQlKaswNB7R1QyjVRiTBahJceJ0qXzOudZK9ioBk2UiiIlGQjePKcREk5gQpAcCuigAnm+qUS2/ptGZKXFO0hqxOeCEZlXkU9/WysRHDWo9KKqkT9asxPyCxq9iYJKGEyowpSqizIUriXfX9gLkx67wU7x0I7LNhReJEJRJdqHDExm7Z2FSjClatxCBOu6d6rUh0ow0k8znWXWA+x7ioLpayzWBOuE9xrafeLGoUxMTYfKQFKv9lg4tEQZJkc6jEQfIn94NUlThIXmRctEaurhjN/C5FG7jc5sTF5m1lS6yWDjx0Zcyf+GwxB+D71qSITbU0xyNBgepYKdYNVFRiLkCSTSWuN+IIVNNkbkWSTlnzBeIttMThnlCJQ74kMVFpP8Ucjypw1e8d82OS4cqct4ytrWDZh31Z91KNktgP8S+u88pqRYyV9QP7DCmeMbkl4VTex4kdMjd47LHH4utI4gv7H/w6hcrYhiQ1uv/WAJbGEZt+BNQJFFefOErVZ5e+7eeff37sL1/rdGRZ7YwoJV+NhKDKthzFn6Ecdwo5ZgxzepQAIxNR3kMSXshMZ8Fz4YUXJilvXSxCOX3x888/j/F7ZV3nlRszVJxpbZFTVmCIzXXaOrAZcsIJJ4z2ewSuCKZxKrGyrUFXv0fB67yxrvNKbHhQLpqKNwQb2LAhEY3qDgSMUhkxYkTNn3EPLZSdfMrGOqe3qRhWJCvxHKTKBK9X2Ui+4fnCpl9l2y5OkLGRmwqtSjixRtIUyYLV5YhTVKXLcUycfCRITMA1JV6TnJGwTEC4OlBFtayUyczcC2q1w+NeT4Jz2S162KylKiUn7bmfVwYfOZ3Mfaqs65wNPjatqHJDJRy+inaH1VLMgzk4QHJ85Ulb7glXXXXVaJUPy8RrxAYM7VR476rbAKSq5kniHXMEEt6K5xzzZF4v1qe0QSt7g6b6vslYqDJRJMemQBIJSTjME4rKF2wWkQzLZy8F1p68LmzwFVVOmUPxs2KMKdDOuph3FjjdzvXEvIbPZqpks6JtZWVVSD4DKao+UVGNTSOqC6rldkoktvA6sXFNGyqqZRFjKdr+psDn/qijjhotNkV1CSpx0CIjxQYprStZj1IhS+MmRcJpzri+uY54XVo78JEqNkXbb1oxVldy4eBRqkpUtIklMb0ayc3Dhg2L31MJjhhMWXjG1TrUx89rxYjLwLqOJKnqfQ/mU5UVqspQvT7ndSFWTOylco1VFtbBRayQWDQJOVbJa3vCGV9FPJEKsnwmU2F+R5LblltumWwMkjS+mISjdmHBzqmV4sRKDphsEhBKrS2Z4ExMU/YoZ/OB97ClTbYUG9xUTiAgS2/b6nYrVFIZ2+mRjkBJRjYj2RAhWJVqsczmTLHJsOyyy4YcUD6X4HStMs2cXKkuUQzKuBL4LyNAk+M9Cl7njXWdV6I8K595rl9OttPSjwA21VRY3FNtpWyc4OHEJqdXSV7kXk5bldNPPz0GtFk812oL1dF4jUiC47XJob0gCQBsXBFI4AQ52BBNfVqTjVp6cBNYyKXKWo5jYs5CpSmSFTjpXt1Cs6w5CwnUtMPj+V8r4bpSiiRrTo/z3CMRh81R5k20MqOcfHWybJkI5lHWvrJFARuCp5xySqn3ejauL7vssuZ5AZsMnK5jnllsQtJqcGzv7fh+z5hnsuHHddVSm4tUawaebSQBsHFbJN3wjKGSAnPkFKhMwlduuL5JyK2sJsFmLskS/B7tbcvESXLWVCThMVdno5vnMe8dc+RUSM79/vvv43XPswasF/hcpkqiYJNmm222iYF+5nfFdc7rluo6B+8X7UKqMfekvXYKVDnk2qLiUzU2RlMk4VDhkISNu+++O8w+++xjJOYde+yxoaviwAD3IaoqcSCNexTXDs/eVG21K9HumyS4alR5KtrpprieqJKZg1rxL+YpvKfVrYzKTuJgXle5riMZgfl4dSJx6vZdKXH4qtb3rSFRnHhyGWtm1gw8k2lPWVR5oYo09/cLLrggpEDFXOZMJKEWBwg+/PDDGA9mPcH6hgTUygOuHW3ttdeOzz3aVpK4BOLof/vb35IdxCLuxHiobliZ0Mxck8OQqQ/4csiJ1y1FBZPK9ToJQhy84quWVIeic0B8sHq+VInnYIpWyJW4BxR7QVxL1TGgrjy/k9R4bEeldmFhXDwIc0EAjVJ+bPLzsK7e6C/rAc0ioa2KhQUBCDYJywqG7L777jG4SBCv1iZbisUypQY59VRdCjxlyXtOSbMw5avRcGqShWGt0+cp2y5QNrWM+0aO9yh4nTfudU5yGRulBDoIDLFoJchBqVZKNpOUUzaSbqjQc80118QkIfBa/OEPf4j3LTaUSF6g/VPZ9wJK3LOZxQkokifYxCVwtccee5R+UpKNPaoTECjLqdUL1xKBxLIrJDTamPjctaasOUtl2Wa+r2xLh+LXKZOsCQqTIEuyGYFqKpmwuZ3ypDtzXN4jqiyRGEjyGwFSAp8kwJTVQoQT9dwLixL3JKGTxMjmA0kJSy21VDztt/LKKyd5/7imaiUwp0Z1IDa3KxMYuZ+mvpeSyEG1SjYkSfJMfcq1pZLptEumNQDVe8rEHJhrnopBlfcD5isEtHMIXHN4hmpQqd87cG/i/WNDizEVr1X1IYwykWzGJhbtLqqrffKcZrxlI3bAxg3PFaoQs1HLhi0bbszvUiTIja3qag7Xesr7ElUg11tvvfjsy61SSd++fePaoLoSBwcIaCNCsmzZaJVJ8iRVWZmnVMfnyjzYQDJGWzDvLLMKHK9NW+TQvqvRlBkvK+Z4JOsTc+3Ro0dMZNxggw2SHcTgeUIyM4mDU045ZXP12pVWWil+LjlowCEfkgl5rcqab5KUTiss5pwgGYC5HWNJNU8gdk4lI+Z4xbyFZzPP6dRStlkioYzKb1RUynW9nlN7wdzw7Od14X7k/E5SZ2ISjtqF7HQmegSvc9HID+iyF1sEZFgQpyyxXev9YyLMSc3KjGxObxMYTfH+5ZrI0agT+jLHlOM9Cl7njXtNcQ8isYTNK4IxVAxhQ5eTbSzgGUdK3CdZrNYKmrFJQguIsjZNzz777Hh6nI0iNroJCNFmgsA2J2o4uVUmKm6QBESSLCfsOKm9xhprJN1cAxt8vBZs/ucixzHlguuHDRnmKmNLuE7Z/ilXBNTZ4KaVCYFiAuplnsRnTsfGdWXpdoKwtAwiWMupbp4lqZJwcl0z5DYmTtuzQUtrMRJLeA959nHvYg2RapOZTY8dd9wxbLHFFqP9nHUEz0QqF5SJjQc206iMV9lWImWiWaFIEOZ+wP2UKktUomF9mtKbb74ZN7O4LzEmkrtSYs5JYgufr6JCD1UKmEvRDjhFuxyqLtJ2mNeGZG8ScUj2euyxx+KaPUVikFpGy5ZxnetyL2XdQCWBjkY7Fa4jNttrVeJIkUDM+oV1Q7HZXimHTduxKftwH4dRuHdyrZFQSaKuc+DOFcNLMe8kKej111+PyS5zzz1389ydan7E+MputQ2SgUj+JlbNAYLqBOIyqxiB9RT3yiJ5n2Rv5go5JFumvI6Z8zJ/4j6Y03qd5MXq65b5HIfIuaYrlZG8OLZDTqkOaTMX4FBKilZmktSRbEeldk0aCIAyyeNkJBPQ6kVeihMPOSfZjE11O6iORrno6lJ+qbFRyyYpE2YCfCxuCMKwsCcAs91225V+fRGEIsjPZnuKhZ461z0KXueNi41aElxIdiGBg6QSkhUo1crJrNRaK51OtRw2CMtKwuHkKhtE3Ls5OcapUTZHOb1WlE8uExWL+OKZwnOGqheUlWYDi4ScWu0eOvJ0T4H3hOcL1elqldgt67RtjmPKMcm6MlBXfE+QmE1b5ixU8ksZNKcaFptHbGzXahWZ+jQyGzMkBVRuFJVZCpwNfj773ItI2gBzBjaOaYfKvYnS/Lkre82Q25ioREf1IqooEeRno5T5HlXxaEmTKvmatl087xgL65iiMhWbEGU/8woknZIkwUlt5iy5tMfhM0hlDtoekkg1aNCguM6jRdVaa61V+phITKJlARUNmSdxTdEyj9ePaypVRQAqlwwYMCAmD3Jf577F5hKvX6pnDa8PieggmZJrnCQcnn+0P0yFlse0G2SDlDkvyUA8p6na1ZW1J9mc95HN3TJwIIxqXVTioP0a1zitslJW4iD5Z//9949Veqo3RhsB65oyEiZIrGb+Tavf6ioTPAN5FpvQ3zmkmHeS4ML9ofi3i1axKdsH8fwjYaIlHM4i9lFGEg7JgMw7ecaxbi9iVbxetN/NqaJtCkX8IqeEwFotmFO2e3r66afH+Nnzzz8fE7kqn31l74PkuM6VpPHBJBy1e9JAYConPKwpw8pCkIBegY2IV155JWlP9xwUCxdw2pCyv0zYSVCo3mRLsbgheMdCohKnEFMiAEsbFU418DpV90xNvaGlxrpHweu8cZHYRUCYRMEHHngg/qx3797NSSdsBuaq7MXsl19+Ga91/t1HHnkk/PGPf2wOqFU+n8vGM4UvKvPQwouNGqoTlJmEU6uEOwlBKU/b5jim1rA5RIIuY6H1Wgq0VmIORQLTFFNMEa/1r7/+Op6ApFoWp7rLRvn2Tz/9NG5i9+rVK+SCDTaSEDhBWqnsUuAkSNDCkFajVCYpEsqYA5922mnxmufLpOu8MSenukvlnI+ESgLGbECkSsKhLcHUU08d5wM8X4q2DlRYSrERyXW85557xteGexWnbakSmfr6JnmKJJJNN910tJ/zeTz55JOTJOFwb2CNx3ygaI9HMgc/P/LII+O4UmEjLafkQK4n3j+uJT6DJDEyF6ZNR6oWyCRXk6xLFSrmnMwR+PzxujEfJZlC+eK0e+Vhq9SoIsE13YgJOGWt+ahSQJs8EgBIPKBSCWtkYmck5pE4SGU4YmU5r4+VH64trinWWZVyaR+US7yFZzBttln7FZjDcDCM37viiitKGwsVH6uRzMx4mBdX2mijjUoZU+q5bupqMm1R6xrhfs2cN3UlrhzfP0mql0k46tBJA8FuTiaWEZShFzkJE/SVprQtE4j3338/nm7jFGBXx8ZQMZkpFggsjqt/lmpx09bri2uK095lXFOUPa1ODFK+cr9Hweu8cVFJhRPubODmtMGdI05qs/FIIgK95gkSDRs2LJZ3T9VugnGQqMtmzcCBA2PAuDhBViaC0+054c3Ju44qL53jmNpS6YbkakqVp0AiM8kbXFPFKTtOcVNxguDxGWecUfqYBg8eHK699trRWs/kgPePk9kkTKW8d5IYSAIHiVN8X4kNW8ZJdQ7uEcoX65VaJcoJ9NMOIyXWoMwVqBAC2vZUVn9KlTTBhuhee+0VE19IEkyJ1gm1WsyQqJSqqu0zzzwTk6eKBBzwvnGfLzuBg2QEWgRw4r5W64LUh0FITOIaonoRiWck32y22Wbx5D9t4lLg9SJZigq2PAOx0047xcq/PItNwskL1USJ2VHBszI+VQtzrLKxqU3FNRK7alXT1X/bDnOggYpBlUhKJzmPL74n6ZLKQtK4xPSpgMO1U93uSf9DvJ4k2MpDotyrmEPwbC5TrTUv8/Qrr7xytJ9xry8rCYf5blvu3WU/YzggTqWytddee7QK1STE8F5usskmWbQTSy3X90+S6mESjjoUgW7ar5SBQGdRxppT7QRjCIJyCooKBl1dZ5mgcJKzrGtqXBImykzkUGPeo8aF13l+KNl6ySWXeKKvDXj+Uo2APtz9+vWLSQps2vBrKhiUjY0syuuyqUYyAIH16k34nFFJqIzS8o02JuZ6bASkwLPj5ptvHq3MNYm7JOBstdVWScbExjbJ57kl4Xz33XcxKFwkJqREEtDKK6/c4u9TQa+yit4SSyyR/DrX6JZbbrm43uOr2KChyhJJnrVKvZeF1hxUnmFjkoSXIkmCKlBUXVtyySWTnchmI5nkBCqYML6UqMbKBjebR0UbTdob8RqlSpbg8027yur2DVSRLbs6LK0Jik21lNdzS6g0Q2Wl4r3jc8icj6oh1RVjy0LVoloJ3mzkkgCuvBBfKdpk0QYuN8wrOfDFtV5LzpU4ykLyPskSraEFIslw0rigVS2HLJx3t26mmWaK887q14l4x7TTTlvqRffQQw+F3HDYOFVLw5ZQmY/Wx2+++WaszMwcpcDB8auuuipWFaJ1cm5jL1uO758k1cskHHUalD+l/zDmnXfeWA2HYBqlUnfeeefQ1VX3Q2WSxwkVTv0WARE2HnKvGpRjj9BcEznUuLzO80PSxg8//JB6GA2BJAA2ritxWrL6ZA+nI0lYqDwJ1BHYnOH09tiSE8qs7tLo94PUYyKxg8oFxUZg2eaaa65YhYeKSpVok5Wq/zyJbszhCIYyhurT5anKYLOx/o9//CNWlcjts5X7da4xUW2KpC6SqYrErnfeeScmmpDIkQqJJVRXIzG/QOLL+eefH9sDUI2mTHzeqU5S4LPHPYLn4W233RZS4TnLGn211VYLs802W0zcIImCKkYkvNC+ruwDJJy4JUGJamJU0qUyFhvtJFFxKrmy1UJHn+KuvE/n1roAvE7XXXfdaM/e1NUKeA4/9thjYyRxDRgwYIxntNLbeOONa36fi+OOOy71ELLHepg4Ymu4R1CFVBoXVMVjnmASTuuYbxLboEpzsQdCchzzO5K/uzLWv1Q6rlU1M6UzzzwztumirX312KjAxhyGJJ1zzjknWWvdHOT6/klSvUzCUafBRP2VV16JATySbwjwEVQjgP7111+HnJXd8/LUU0+NwWD6xVaevGPCx2KZk5zS+MZmSapTktL4CMqyIUK5e54z1SVSyyqv26hqbb6zYfr73/++w5NwqMbTKNVdNCaSp2rNkzh5TxW6slRuxFKNg+An804St2hNRSWFSy+9NJ7eSoG53YgRI8Lbb78dq07l0ludikXbb799fP04nVk9ls5SqbEsKd/LHMY0wwwzxLZibLq/9dZb8T7A/JKDBCnbhtCOjmu9Gs841ldlaymBg+oEfKWS+t+v5bLLLosnbmmtxFeBah2VPyujlQKV+toqRfsuqvPw+WMDMJekSl4zxkOrUaofM7fk80jLrJSJeY2qzPs5MTpO/bN5zIGm6sTXsluuMQ42/jh4keOzNhe8T2N73vL6mcg87pjLTDzxxCEnZX4Wll566ZjsSSUqKthWxw5zTE5NgZZTtGHlQMo111wTk4d5vWgHTiJTV5brfYdDMiTst5Rcwvpmv/32i3s0qZJwcnju5fr+SVK9TMJRp0G5U07ac8qPdhOcXGMySnZ42SXAK1vKUGZwbEEqfr/MCQ8JOKeddtpoE3ROlVIWkdfQJJx8cV0TdOW9ag0T/I4sRUqJ9rYqSrnTukManzr6Oq9EkIPT2gQ62PRL1eO6EgkI3Ls5Td6Icltk5zYe/W+DtHKOxPcEZTnhXuYJ/Oqe95zypRUqXwU2cplj7bHHHqFsJLPQMo+k6pwwryQ5nsqLtIPS/9x4441xzVJZsST1fSrHMdV6HnMKn8RJsG4ZMmRI0mqebNrefffdsQJrddC9UZ/RHSHHyhc5tlLI1fDhw2NSGcktbABWz4dTJFUSz6CCElV+i5YPtBKkVRYtOzRuyryf09qMClQkK6ZsPUHy8u677x5behbVDrl+cmvv2RZlxRR53rU2B8/9EGQZzjrrrDb/2SK5ZFz+N53xnvDEE0/Eyi48a/jKLUEgJ1SEbK3FbleeZ1bPTXLAYeexzUl49lRf92UecuKzvtZaayVtw5jr+ydJ9TIJR50Gp+p+85vfxKAxkxcWMDfccEOcxKfqN01QmM2jsS3gOblVpu+//77mopkNJRfMeaNHOSfux6bW5Hl8Wn311UebtLe0OOfP2Dtd4+qjjz6Kwc+WTkYWQf6Ovs6rNyZPOeWUuDmZC0oOU2GiLc/H1O0CpHpO3ucg941aEl5zOz0LkiPOPfdcK0zV8Ne//jWeembtMv/888fNBza2W8Pzb8opp+xSYxpbNU/uEamree69994x+Y7Xa8EFF4w/Yw7DgYwcN9XKRLIwrwFV77bddttWN9LKrnxRtBUe28n8sqSobjMutthii/iVE2IXXDfFmoF2Dy+++GL8SnVNNRIOGFCp7i9/+Uvz6zXjjDOW8m8/9dRT8d/j0FpKHE6jCsEJJ5wQK7ycfvrpsfVhIx4gKuNwH/NNkr7HpqsnwT399NPN33Nfos3S9NNPH+dWHCbgnkXMY5VVVkkyviuuuCI+k6sxl6KNJrGYsud4jKlRlVHFiIOQd955Z9hyyy3jnIpn3sknnxzvpcTyOZhMu8+uLNd5FJWKqNBH+9yW8PtlPX9znRvl+v5JUr1MwlGnUhkkyyEzvGiLldspGl4XKgZRRaKoUjJs2LD465VWWin18NQKqm3QK3aDDTYIM8888xhZ4mVV46h10pFECVphELwqa6GuzomTkZxkJcCQ8mRkJQIbVN7IyQ477BA3Ivkv9/Lq+0FxfyeQLDWS6kTP1qRqZ0RSLCfvaYnzyy+/xEoYRUJ4CnvttVc46KCD4v2AAB/VIFNtJFfq3bt3ePLJJ+P9XKNj069///5xA5I51EUXXdRiBZrilPTYEmI645gaoZonm2hsZJOwyz2BufACCywQn9FdvcUh1bmKlhK5JFVWqrUJWWxmTzfddElb5rFpy4EeEiWoPnP77bfH9d+6666bZDy0Otx5553H2Gj85ptvkiWb5VJNpVFR/YVrq0jCocVmWfh8teVwUUdjjnLttdc2r/OIk7GRzXWdwyEGNtgXW2yx0T53VGFkbNUJHGUc7ss9KT0XlQklJDhzUPSII45onp8zx6Ld9WeffZZkfGeffXZst0jCTVGxb8CAAXFMlQlUZc7x8MEHH4Srr746PveOPPLI8M9//jO2HU1V3R6PP/54i4fDyqpixHOO+S73TaqLkoRD26IHHnggtkLm+cevSWJgHa38KtqfeOKJ8ZB4rUQcDq2Q+FZWPL+lyrlFPJ+Ko5Kk8cckHHUaufWUBhMXAtm0MGCiVd2WKlX2MYs/TmuuscYazZMrNryXW265OF7li4ALk+I77rhjjN8rsyUOAeDKkz1c41Se4uQMOOWzzTbbhF133bWU8ahz4fQqG20kMuaCeyObaWzwcT+vDhoXCS9lKtrjPPbYY/G/RdICzz+rUKmRpapg2FZUt6AVDhv/bIyQhEM1hSuvvDKeDk4RKKYSBw4//PAxfi/l/YD5AhtaJCiQjFB978z5xFtHnyYnGHvBBReEZ555Jv76+eefb05WKHMcuY+pEap58ty9//774wbWV199FTe2eEazNj3ssMPG2pq4Mys2qKq/zwWxg0rcz0lMYNN0/fXXTzau++67Lxx88MGx8swjjzwSRo4cGTdvSbZk3d63b9/SWvUU7RHYtOVwUfXmzOuvvx6TGBhb2XKppqJxb2lNrID7I4lUKddW33777WgHiEhG5Z79xRdfJE3CIaZJchRJCCR0VM4tqbrG86ZPnz5x47YrP2MaAVWV+KpMkGcetdVWWyVr00hckQScDTfcMMbsqNxDogfXXFnPl2qspRgLh0aJb/AZ4BlEMg4VicusglygQtY//vGP+Oyrvh+UORcmAZ3km6OOOqo5WYlWjLxX/fr1a06YOv/8803CyRAJVMQPqKq95pprxoRXrifWDHzuSKYiMSZF/Nx4viR1PJNw1O4sXoLmbD60hsoq0047bZc9BUWpUb5yw+ScQNlrr70W3nnnnbgY5OR2dZUHTt8xOSxjUZ/jNZWjHE8e8b4RKGbxR2Y/k/iXX345bj789NNPWQbcc7+ech1XmeVa2WDIyW677Rb/y0mj6lZsqTa4U57OVnk4/VdrE7wzj6lWQJrNd05F8ozhxGbKjRFOif7hD39oDnoW2AghiYE5VuqN5FyweZuqakMlKpQQ+GypqkstLbXaHF+4Tv72t7/FBBKqcdACo+wTx40wpkao5klyAi0CuDfss88+cV7AfYyDD2zgsNHcVZFI0lY5JOWRCMAzjoQSNkNSbZByqp6NRxKBimcKrSY4Bc8aq6xNUqq+UWGtUGtdR4WOtrRH7czVVDTulQ6LZ2zqtVXx71Xi0FNHzwHGhoRYEhBItqmOkfEc5Bm9++67h4svvjj+V/nigBpJJTxbKhFDS1Utj7kd8xPi6Mz1iAkzl1l11VVDKqyhWFuxxlp88cXjzxgfrx/PvRRJOBw0pOVT6pbk//73v2Olm8Kjjz4a71uV4yJRjzm88sN7deaZZ8Z28hxM4f5OAg4JoMTQmf+musYaOZ4vSY3CJBy1OxjTlmBHmZPkHE9B5T5ZYTHfWtILp7xvvfXWUhaGOV5TuSQGcSKkrVK0m+AaIVBcWdKSkyKcft9vv/2SfA7or81maEuVsYrkhbIX8rmOqy3KvM5JpiTgQkCxVuWEFNd5jgkvlRWpckLwh9M+RWlrtYwNhjfeeCMGO8A9gWDHK6+80nzSjZOTXXlMP//8cwzKUpqcCgWMh0AxG5OMJ8XpX14f7uXVNttss9HKz5fthx9+iMG9yhZZBPRStojMYVMdVLVYccUVYxIOCfKcIh9bcgn3/Y587UjaIMGThBdOR+YgxzE1QjXPooUD85NiM5frjbkTp8q7chJOdTIlJ7c55MEXyZw8W6i4VFY1z3FJIGSDJBWSTqm0Vo1YB4lnZeGzVSR5kkBBQiOfPebmjINDO6z7eN505Woqatz1FPfs6iScVNXeKtF+7tBDD20xFsXnrkigMAknb8TESNB9+OGH4/sGNrkHDRoUzj333CRjYo5HMhdxWOYvrP+I22266aZxrCna0VBVrVYSEHM+KuGkwDNlwQUXDKlRDa+y9Tht9Dh8vMQSS4y2Zs7t4I5Gt8EGG8SvtqD6PfOucTlA0lni+ZLU2ZiEo3YhSLbLLrvEyQMP5srJYPH7XfUUVKOd9mtNmad/crymckkM4pRTdVCI94ZThyyyCBAzTvoCk4xWtl69etVc7DGeVAEsAlJsymy55ZbZVMbKdVy5JAZVX+e5tVXJNeElRyQBtOVE9Oabb560mknqijMEO/giuY3NPsrff/bZZzF5gjLFKeQ4JjaxOe1HkJpTkSQHcRqQZK9TTz11tFOBZd4PXnrppVhFsLqVXqoqZgSumUcxH+AUG+8Z7XE4dUdiUHW1wzJRYvuiiy6KJ7oZF581TrmWObfjWiZJgw1snnOMp6WgZhFs7OgKMIyFhEWqvzEm2h22dA8qq4VtjmPKvZonuF9yUrvWXPi7774LXVnlepe2eXy+qgP6fB5TrGFaWrvTnoYNrt/97nchFa5pKidUV7wh4SvV/ZyEWFqX8F+SbtisZe1AchU/I4m+q1ZTyVFb4lJlJnRVr6d41jDn5D5ZifbWzGfKSvjmWuFapvpNgWuatWl1LKjMJCLem7F91pnvffzxx6WNSe3DGorqF1zTJMqDJEvaQaU6tML1zTycVksknPM9BxyIv6y99tph4MCBpY+J+wPJSdWHQGnJmCoWw3qFiiC8V9Ux6jIVBwhoYcY9ku95nyrvWyTuFUleanwcPFh00UU7PAknx3i+JHU2JuGo3f1jmeyRmVuNh3RZQfXcT0E10mm/1HK5pnJMDKpsMcHpQ744tTLXXHPFnw0ZMiRe+6nK8POZO+SQQ+J/2SBlQ4QxM0Y24is/p2V9DtkIvemmm8I888wTcpLjuHJJDMq1lYrGHW0Ljj766PhfPvPV987iPsB9o6tWd8F1110X/20+e2woXXbZZfHUIacPUwVkcxwT8wJO+S677LLNP+OUJNcVp7NSJOEwNyChg0A6wbHi/k5SQnWLqrLwzKXyBhVfeA4XpyaZHxA4vuSSS5KMi4QJEqkIYtPahc8g82CuM05skoxXBjaIKf39zDPPxF8zhloBxzKDjUVZ8q+//jpWg2ND5le/+lVp/36jjCn3ap5FtRBagvDsK3zzzTfx5HblvaurYyONRJxap9y59nLBhiTPFhJOUiZQ/OlPf4obodwrzzvvvPDuu+/Gygl8n0LRLoHnHtc7z2FaJVNBi03KspJwcqym0qhIUC0zzvLPf/4zJjGDZwzXcvUGI1WgPvzww9LGlOvBuJlmminOM1tLPiAZtSu3sG4UxH9ImCDukgvWBcsvv/wYSV2MlfVfCswPaAVJIg5J+yQuEevkGUPrrLJUJ3oSz6RdD5+1yqSXMp9HJC+TGP/444/HRHTGUbRL59fEMa688sqY2KjOoaxD0TnG8yWps+nWlLrRrVQHsryrT0Gh+mepTkGxiOD0Skun/Qhe5YwJGIH4VH2KU2Px1RKuqRQBQBbKnFapPuHACXg2uIrNpTJVjqWlz2PZpxE5xcMG6VJLLRVykuO4OPWeW2IQyRGnnXZaDDqSYFm0h1thhRViWwfL7Oat+v5UeV9I8TweW3UXNo5SINBJQI9gBkE1grMkfbLBttdee8WNLccU4gb2VVddNcZpYJ57JAtRFScFqhFQYYbqLmxEUt2FxLNUlRPYFGVM1S1B2MChTVaq16lPnz5xHly9ycdY2Xy79957S9vQJrhImyVO/5LY1dGVbsYF96idd945VjnMRY5jynUNQyUCrnOqC44YMSImyhO05v5OFS8OiCiEjTfeOB4aqExWZG5AoJ+N+euvv770l4l1+brrrhs3vHPDXIXnH/dxEhh5zlAZJ9VYWTNwwIjrmnk5z2eSlUiYIDmHZFTldf3klqDxwQcfxBZLfO6LRM/KdR3rBJJymLcwf+jKeAZTSZC5Zq2DMiR6Mp+hhci4VONWmhgQyWUky6+33noxzphiblW5mT42qTbb2fwnQahorVusr4pDD2VgjTIu85qyECPgcAr3Se6RxaFMDjtQGYd2rSkr+Kkx1zI5xvMlqbOxEo7ajEVyWy299NKlvLK5n4JqlNN+qeR4TVVKsQE6Nkx8KU1cvcnNychU5VFz+RxWXk+cxGSzjf7oLFqqK2OVeT3lOq4CLSeohJMT2szQSqLyRDlBBRJzfvjhh1jZQfnK5Z6Qc3UXkAzEZgRBToJoVOUhCYcWXZSZdkz/qzBByzy+ivZltGFMWWGC9jKMgUSqymoctM3iK8Wpatqyvv/++2Mk4fCzlJVMSHxjk61WYJGEhbJwipYToyThPPfccyE3JHBwP7j66qvjRs2RRx4ZKwaw+bDkkks6pszNOOOMsVIlhyxIzKMKFe8dCSfVp6a7Mjbfqe5CAmpx7xw8eHCc25EMkwLJgCTB5oZqVFRXK9rFkoRDkkuR6JKiHRwJHW+++WZ8BjJnoWIBaN2VYxJTV7fKKqvEORQJACSd59AClvVvce2SOMI9IYdxcU0Tu6PSYjFnYr3APZ15A62zaAdTJqoH0pKO+AHtsphz8lox/+S+SeUL5n4k8itvtAkiqeTuu++OibnEMrjWSADlv2W1zqys8FKp8vBqIcVmOzEgPmtlVr0ZW2JNSwnpJMGVXXWGewBf1VJUhVXXi92xbmcu6rpGksadlXBU18l2JutMRjm9wmKQDWX6Rqbq6c5JMTbWOF1QBLQ5bbD11ltnddqPxQUlNlOc9ssp8zrHayr3xCAWepyGou89rx+vF9cSwaw///nPsVxkV9XW/sdlZ/DnOK7K65zv2TjKKTGIgDEVn6qDnVzrbKIS7JcaueJMsfHH/ZxWQQT4OeXHvIXrm8AebXwcU4iJpwRkP/nkk7ipXZTf535FILu1Mv0dhU1kKstQnYs+6tVSJOGwgX3ppZfGamFUKwCbtVR6ouUTP0+Bqg2c0q5OSj/11FPj5lJZbeCoFkhCEomnPPeY47ZUVS3F5jZjYsNt5ZVXDg8//HBs03rNNdfEsZBwttZaazmmcdDVq3nmitYS3A+4xtmQBJUY2YTkgEqKeycJb1Rg5PPHPKGszdCxYV7ORjsb8LWqYFRX2i0D9yNa+7EBM++884YbbrghzmVYn/LekeyhfHCggcpFVJzjwAWfM+a/JOfkcp2TsEiyLhUvKtvFsiamulIZmBswR/j222/jPYrnBq09SYotPn9Un7jwwgvDEkssEcpEKzrmd4yLBN0C900+b8R+Uh3EUvvx/KNlJvdUWr+QJMdcvaOvr8o2bySdsQ4lGW7hhReO9wSeOccdd1zYYostksTQmR9QoTn13I1kau5LYA3K/IR9huqKrDwPX3jhhUSjVGeX21qG+1OZrX4lqTMxCUftwoYtX5SOLkog0quVjH6STnbZZZfSX1mC+UzYqZxQtBFiUcNieauttkpyQoSTtmzUTDPNNDVP+5V9mibnSV8u11SOiUG1qjoQ9KwMXhM4IqgmNWpiUCWqW5x//vljVE+gVQEnkcYlWU6i+hyJLlxXbGYT3Of0NhXEaJGTMnhGUJ1NP4KO3NdJvJlyyinjyeDqiiZddUyHH3543AT5/vvvR2v9RJJ1qpNYzI8olc5/c8FchY3QK6+8srm6GVULSO7aaaedkr1WJCsxhgUWWKC5lDyfOao7sHlL0mUZaBHEnJbqErxOJDO3VCEoxeY2Gx7M45jPVc6/SaxifswGoGNq3MC1/jf/JBGOdnCVc1Ha5pCAlmLOSdyAFh21qgMgVel95sDEMoqkylzwerCRy/qcJFTu5/y3rWsLpUFcqkjIIRbFpj9JHDyDW7r2yzh9T6zniy++GOP3qPBCNbgykCRMEtDJJ58cExFI+u7du3ece1KFETyLSR6kOk4qvG/EopiT55JEpXE/WMBnkMMh3Du5v5Ns9umnn8b1FnPBygOcHWm11VaLz+LqNk8ceiIJ9PHHHw9lO+ecc5rXDaxFqxPMymqRNXDgwDiG1hAfJkGHyrpSV1jL5DYeSWoktqNSu7BApUpBkSwBet0fcsghMXicIgmHBBxalSy11FLNP2NSTPLL/vvvnyQJh7GwecSJ9iJhgoUXpwtSJuCwAdmWhTubXC2dEu6s1xSbQm1NDEqFcsRsKhc93lmoLrjggsnGkyOuce4HVEjYZptt4s822WSTWLWAagBlXde5jqvyOs8RlUq4d/bv3z9u3BZjpooYQWNpXHCycN99942JOH369IlBtemnnz5WnEm9aUQSUOU4ubdTFSfVhkiOY6JFAJXeCHZyb2BTpHKulwLJSGyG5IT3h9eJL05vErjOocUDATuq3VD9kbkw46LCGsnzZbYv4RqurBZYq7R8SpyopUpBNT5/JA86JnUWRZVa1ng8Y1JjXZ4jkhCqq1PmgBhGZRyjVrtB5Yd5E18kVHOYgYQcWpiQwEsSANUwykaCC+s65uXcEy644IKYkEMVGtoQl4WDVSTZFPEx2orScqOyJQ33LSoLpkSyW3X1xddeey1cfPHFydv3qHVcXyTfkHhDfJo2VFRRqZwH/+Y3v4kHSstKwqHyE5WoqhG7pgJTCsVnjEqZKNae3KfKPBxGcmIRLyNRl5jw1FNPXcq/LUmSOh+TcNQuTIBJJqnevOJUeapyqJyQrrXZQNCdU68pUN6TTQY2k4844oj4M14fvmdTiZMOKSy//PIxqM/ij4ACJVBrKatFQK7XVC6JQZVYeFJdiffugAMOiD/bb7/94sKUyiFUxdF/+0lTgptARoFgHgkwbJySSJVCjuPKJTGoEoFggsRsmBIEBRsRG264Yfz8SeOCe+aMM84YN9w5ccj1VVR3ITEnlSIplxYYJHWQFMD9gYAsJwFTJAjlOCaexdynOJF5//33x6Rq3svf/e538VlICfWy8RpRLYVT5JzKrK4yU5nIVKYHHnggVgvi9cqhukuBeVSKDb6W8Fp88MEHsdUE7R1oR8OJe5LPl1xyySRj4hnM6ePq04W0C0jRci1W6Cm9AAA+vElEQVTXMamxsd7jHk7CGesYqnMwJ06ZeErVt1xQkafAnJx1Aa8T68/qhJyyKgKoc+GzxjVPq1YSYkl8oeJSimc0z2HiF7PNNlscD9VASJZnTkVSCevRsuKIlS3fSMoh2aWyJXNL8bLUqNpz++23m4STOdqLrrfeejEWVBlbrMTBozJjQVRf5PlCJajKNvckwlBJPlV1rNykalutronKrexhgb2anA6MSJLaL8+VhLLXt2/fOGGnlHvlhJ0FPCdwU6CENRVLjj/++OagFJtJ/DpV1RISONg8opxtgRNHnEKib3qqJBzKjlIClc1sTj8Q7GAji4z/VO0KcrymckwMIlDNibHKsqdsSrKRzO+R+KUQr28+f5UnNbnOZ5hhhrDbbrslS8LJcVw5Jgax2OTkPyW32SAlEYgNCBItP//88xZbiEiNUt0FbPyTkEsyEEmvVMIgOYgyv5wCvuqqqxzT/8fpZE4i8kWCCSdKaWVEW4AUrUKo6sK9iaB69XyAaypFEg7zy7vuuis+X2qNKRWq33A/byk5KEXAnSoAJJqxduC07Y8//hjHx2eSsa611lqlj4lNENrkMfdlbk5bOKov3nnnnck213IcU1uVWc1T444kHKrY7rXXXrHCJ2v4MvEsaet9scx7VOW4WAuDtXHlWMuuCKDOg1aVbChTkYNEE9ZWVMFhzZcC7b1JgCnu2VSeYF1MEjjPmrLMPffcseUxrwfzcpJyiR9WVo4mybml5AlpbGjve9FFF8XYNHPO4v5eIOZJhRy+ykLiHTEV4tLEV0ClbRJAOcCSAmOiZSz3hkqMjwOQZR4SLRALbmm+wDyTqnVUaS3z8FpLSZOMsxgTa5l55523lPGo7YmnHGQgqZp2cJXJ1NwTiP2ceeaZ4emnn44/47MgSeocTMJRu0+QMrFjMcHpFVCBgwovZNSnwL/NxjEba1NMMUVzoIHEElqapMpi5mRPNYIMn332WUiFoAJfbDawEUESB1Un2Byh1UNRtaerX1M5JgYRcGXjo3KBR+IUrdeoEqL/4r0iwFHr56nK6+Y6rhwTgxjLE088Ecv+Vla5+PDDD+MpMlqwSY1c3aXoN08wkXEQ3Gf+suiii8brnuvcMf3PL7/8EgNS3K94raiQRRUaEohToCw5yRpsXuWCuRzBulotjVKirD4nypmnVLdRSIUWAIyLqoa0ywLzPdrUcQI4RRIOCdZUnLnkkkti0JyNf9YLBGS5L6SQy5hI/mmrIgEuxUaNxq5y45ENbxJPjzrqqNiOpkyV67j3338/JnTSDoc5J2usV155JVx55ZWjtbDrqlUA1NiIOTE/4OuZZ54ZbcM4ZXt0MF/h889BkGWXXTbGODi8RpIQz+Oy7LTTTjHm9OKLL8YvEoOKisesIRgP86tUMUU1PhKaBw0aFD97lVWXUqK6E+2a+SqScFK0XCLpjSQ4EJvmkMUkk0wy2p/h4ANxoBSImfP5Z95A+0XmMbyXJEuQRMz8+Oyzz44/33///UsZE8lTxVy8GNPgwYNjdUHieB9//HG48MILY4Jl5YFgpcMzmM8anzu+eN6RmMcBba5/uifQXjDV3ockqWOZhKN2Y8LJhhHZ8mBTdMEFF0z2irJgIJDHxOWdd96JExv66nKypRIbbwTYKk+2dBRK2jM553RBUUaQDXgWFkXQPyWSNxgj1S7Y4Lr11lvjIiiV3K6pHBOD2Kzl5Fp1a4Dnn3+++XVTiMlkLGQIllHaF5yuo+oLm0qp5DiuXBKD2GQrNs34t2lXUH2aiJLbfCalRq84A6qV8NkjYZgEEyrngdO/RTKxY/pv4Prhhx+O9wXmCMypaJdX3ZqjTFRRqp5fpkbiZFG+OidUD6TiRU4nyLkH1EpW4voiuSoVEgIJyn7zzTdxXp7qPpDbmEiMqvTRRx/FdRxzYeYJbM5wL2WsqVrBqe1rq8rNNd5HquCwicQzuSwbb7xx8/e0vGEMbI5W3gtIUGADi0M+ZbHNm8YnEstIKqGqBC08uZbZ8MsFh8D47LGhzWEikl0222yzeI8gWbYsJL6TJEwsjOQfEk+LRFPaddHuicpwqaprq/E9+eST8SAfbZFTIt7CAQKevWNLcC5rPkVyN8kIrPP4Iq5ZGQOiugv3hLIr5hUuvvjiWHV8lVVWaf4Z803isiQQUtGPNRhJOmUl4TDv3X333eO/XYl9hhdeeCHGrolhUwHfJJw8sC/EPJPrmH0Y1jZUxuLALwdB2GegTfISSywRcpWysq4kNTqTcNTuahyUqOQEMhMG7LfffnHSzISPCUQqYyvj+cc//jEusKuTGDoCyRqcrGHBTkJQcdqOZAlO36dCoJpkGwIdjz76aFzUEJihGkaq07a5XlO5JQbxGhGwYhz0Ti+SOAhcezpr9BKtvE6cYKViAtiwJcBHG7ZUchxXLolB/FtF6XFOarIhU912intVyiQqNaYcK86Ak2oE9Qn8s0FCWWLaCRHoq9wg7OpjokofASuCn2UkULcF90sCryQLUs2hOiGosrx0WUgoI+Fs2223jf9+dXvRpZdeOqTA+0YCfE5JOGx0U9mwei1AifCUm+BU42AjoqiWyT2KTVQSF7rymGifUjj33HPje8c9icRKkCDEmstk9Py1dN3QJpKvFDi8U6tlA/eHVCfvpfGBa5jYwYorrhgPqLVFmQfWaDNMYnXhpJNOivMYktTLbifI/JevalQuYE1KwgLjo9pEWZjPjW3T84svvihtPGo/DhGlPDxQYOOfJHQ+39UJzqla63KfIkGpMlbGvSEXzH9nnHHGMX7OnJNKWcX7++2335Y2JioG8TpVI6Zf7DNw3yeepzywD8RBj+LZxrOZakU880imovpa9do9N9w3TMSRpPbp1lTdjFRqA4KvJCIceOCBzZMINpQJiFKJ5oorrsj2daQCDQkLZSThFJtHjz32WDwJXFTnISkn5SKMQEKxmU3SCxszqSdTOV5TtRKD2LxNnRjE9XT99dc3V3yaffbZY5Amp5Ntufjqq6/iSRGuKTZKWdBTbjdFqd1cx0XJbRbxJOXVSgyqToQpw4ABA+LnLpcNdzU22lJeeumlMQmHgBQVZ0g+o6oYz5xUFeBGjhwZ210MHTo0bLnlljFJgSA/Qbwtttii9A2IXMeUo8oWZpXzJ+YH/Jr5Q9lIlCC4V9zHK6UaE7iWSOBik5sEl+r5ZuUGXJklwamwxDXN9U6rOpJA77zzzlj1JUWbMUrZMxZahLBW4X3kNDAl8DklyRgdU4hz3euuu26MpK633347VlDgNZPGBRsf3LtZc3KaHR988EGcA1NdrLWNSqmz4RR+WQfWigoh3NO5hzM/4DDdNttsE+NVKZGMxPz3nnvuiYmePHOIVzG2svD8b6uUybpqef5boJ0unytiirkk77fVHXfcEVZfffUxWkR1lLfeeitWpKJtF3FPErE5KJYqUZfPFsluVC0pEvVJ0CVhiNeE+Tuf1eJeVgZaMnOQiNbxlUjqoGUycT3GQ4yParLKI3bwxBNPhGmmmab5Z6z3ivbIKZPMmOsW9yTasXKAjbg07ZnL+txLUmdnEo7ahUUxZVmrF+dk97JxS5WOXJWdhJMLAgacHuVkO4sZWjnQaobT7jnI8ZrKMTGordis4eRDmf3Uc0IJeRY51UktLJhZsKa6R+U6rtwSg8CG8RtvvNG8ocwGCUmNLAyPOuqoJGNSYzrssMNij3Sed7Q0Ixj14IMPNld3IfiRKuhBQuebb74Z27xUtoEjAMkpN8eUp7FVR0hRTWWZZZaJpcn79u0bT5HnYuedd473bZLhas05UyThFBXfaDnBZ43PH+Xwd9hhh2QVITmZTIUlNjoqUb2LSlApgug5jomy+lQmYAOiUtE6lrFJ44J5AaeQadtDuzWew8yJuWedeuqpzRWXpK6gzFgZ923WdKyBqfDLs5jWVGwgU3WGDcCy53Yk3pAsQSIecTPuBSTvp0jObY+iqkLqA0f676Z7kXheefY5l+T9HBPzSGKh0igV2olFkSzPs5g5O0k4JKqXjQScffbZJx7eYT5QzBE4WEssgeqMJO1SgaasVkKPP/54bC/ImqWojs69k3kMicMkFLOmoSp/daKO8krC4ZnDodqyccCKGBidEUi0I9GUytHEzbh+WLMTe6Vte61KUJKkcWM7KrULp8mZhFZPxDl9aCnwPHGah409rLzyyvG/VMAp86RTo11TLIY5EV158p8Skdttt11MDMoZm7e0HetKWMCwcACLY9qEVFdtINBOuVjHVTsxiPLjOSQGcZqILz77w4cPjwtBEhYIztqOSu0JSFdWdyFBgaACCQsE91IhYEeiKZsMJANQ7YKgP6clqdLhmPKVsmVRS6gcRoJCTgk4IAHummuuaW53mFMwlDkeJ9x5trD5nhLjKFrXViI5iIRYx/RfbCZwsvfpp5+O8xfme2yA3H333ckSutTYOLBw7bXXxsRvkvJAtdOcWuhJnREHxEjCof13JWJUJL6UlYRz0003xTgC8xXuBySe8m8zDja5a7WryxUJVCQ/m4STHgc+OoMymydQ1ZOKMxwsoM0u8zx+RmyTRJgUSTgk3pAURBXy119/PVYMmXvuuZvn7BxuJY5dZmV5EoCo3kkiYzEmEoB47aiqxHyGBCGquCsPXB+1rpFULajOPPPMGO8lRjbnnHOG7777Lh6wWGSRReJhY+LoHMQgIZYvSVJ9TMJRu9CihwAom7NF5jXZ6Sz6eFCrMeTUjS7HayrHxCC1jOQM2kngmWeeidWVqlspFW3Yuvq4ck1YqjwFRVCWhAkCoZdddlncICX4MttssyUZkxoXJ9gIMlBxpqiiVFScOe2005JUnAH/Lsk3nIIiEW611VYLSy65ZLjgggtiiywSPh2T2or7I8FXyqNTzSyXvvJsZnNiNDc8Vwjsk+AJNqyogJiqpQP3Ae4HnAAu3juSgy6++OIYEHVM/7XVVlvFJDjK7ZPcVVxjvHa2ZVU9rRipelF81pgjsOHGgYxGqYAhNeL8vFb1Oe7lZSZVEoOiEgFzqA022CA0spzie11djgn7uRs2bFhcC4Nqi8SCQCUOKnekRGUe5gjFZ6xoN5aqlRgxaipD1sK8mC/lo1bMlUOz++233xgHaC6//PIOHw+HvkjUKj5vVFfiM7bttts2j3GTTTaxkpIkjScm4ahdqAJCsPr666+PAdAePXrEhSuBYgOg6izXVI6JQWoZiS3F5hlBj3XXXTdWBkgtx3HlmBhUacSIEc0VuzgBxWeQoCibzJTYZrEqNXLFmSIYQ5UncKKOlj0EQn7/+9/HZ59j0rg4++yzY/LkI488UvP3U5W6J7HlgAMOiIE8koOY31XaaKONkrxWnPzjRC3JL7Q9JMGaCmw8n2npWTaSp2gd++STT8ZWqBg8eHCs2kWyUAo5jgnMD4o5glQvWpgdfvjhMSGgGsnoJuFIHYPnC4kvVKWbaqqp4s++//77cN5558XWmmVhI5KqEjzzSP4hKb5Pnz6x2oSk8lCR4/bbb49xYZJc+BxyaIb1OxUsUyA54YgjjggfffTRaD9P2UqMww28JlSCJIm4OvmujCQOjZtahzyo+JTKp59+OtrhRtZ6VFSqfO5x8JhnsiSpfibhqEMDoASx//a3v8WyrlKjXVM5JgapbTbeeOO4IKYUK5trYHHKxhEb3UU1jK46rhwTgyqRmECSBCeLaAfAa0MSDieQUrXlUOPKseIMaM9DS0jaYhVt4Th9VCTIOSaNi+OOOy7LF4yEF+ZPJDBXI3idIgmHeR0lt6m0VuAzyLOHn6dIwuFZR8IgiQBvv/12PBXJKeAzzjgj2cZDjmMC19Kll14akysHDBgQy6YTKE7xvqnx0faGpPMddtghJg0yN+CapxXGHnvskXp4Uqdul/7SSy/FeTmbgZy+f++99+JpfNaA99xzTymtfUgS5os1Jq0N77rrrrhO7tWrV1yv0/6QGFB11VhJ49eBBx4Y9t577/Dll1/GRDzmoVSIvP/++2NyXgrMBaiAQ/s8YlE54HADCTjrr79+NmNS63imEPfhWuZZQquw9dZbL4t4K/HoRx99NFamq2zPzEFIugNIkupnEo46fOOLEns5mWOOObrsApqgQuUknaACk8DqntEpNkRyvaZySwxS23CanS82ZYYPHx4XGbScoLVDquouuY4rl8SgSptvvnksr8vJRE5AsTHC54skhZQbf2pMOVacQb9+/WLFtYknnjgmfVJZgmAaJ/9SlcPPcUxqm7aepltiiSViELC61WZHeeihh9r05+64446YFEMVto72zTffhN/85jc11wipEj1JJDn11FNjRY4jjzwy/oy2VFR+o2LXFlts4ZhCCFdffXU455xz4n3qxBNPjK8JVXqYLzBvSdVOTI2LTYjzzz8/JgFQ+ZTTwcw9+fxRoYPNeUkds97jKxfExKjOw9fHH38c5yUk5LAJf+aZZ8Z5MdVyJHWM5ZdfPjz11FPh66+/bk4GIBmWz11lDL/MNQP3AtbDZa2b2oKqJVT0TNWuVu1rg8yckmuc6kVc06+//nqLLcU6Gs8zDp5QFXbgwIGx0hNxoAIdAE455RTjP5I0npiEo06DicwNN9wQVl111ZjNe/rpp8dWE5w0p6XQlFNOGf/czTffHLoiXhMqAVSaZppp4uQ9h1PJjSzHZLOu7rrrrovJI/SRZoHOooeFPO2MKstuOq48E4PYXKP3N4kABDyoFEIQlPs4G21So1ecAYlAVJX44YcfYhn+m266KbbF4DonQcgxqSNUlyzPBaXeOYFXRkCdqljMiTldy0Y7eOaRlJcqoP2Pf/wjVuTo3bv3aCeCqbxIe4wUSTg5jolkJRLfqZzA2IpAMvdNriGTcDSuJp988uZy+yTisfFAEg5tMVLPE6Tx5bvvvmvT87XMA2scBMkV69Bddtklfr377rvNCTkm4Ugdi3l5ZTUOYlQp1wzMeanalVMSDvG6Yv2ixnDttdfGpJdir4W9Kp4nxKfZgykbcTEOpVDxlH9/r732aq7MQ5tI1oCstfhzkqT6mYSjTlWG/957742BdU65X3jhhXEiQasJTq8Ugdquqq0nkaXOYMSIEc0VjNh0p5QmVRxY5HBf4GS548o3YYmKPO+88048LcJ7WVQyWWutteKmiNRZqrtQna6oUMc1zunb1HIckzq/MpODCHpyXXOSlCoqGDx4cKykwv0hBZ51tZ65bIiSGOuY/ov7Nu0JqrE5QgshaVxxgId5MEl5yy67bJx7knhGXMEqp+osVlhhhbihxibbKqus0mIb4o4+sEYLWA5/kPxGQnxrm4+XX355yAGV80jwNMlTXdGKK64Y19Bddc2w9NJLxznCI488UrMtXYr7Au2oqJpJXLPWmDiAq/yqLlIFp0DclQTwTz75pLlic5loFc16uFZiKYlCxMo4yCZJGj9MwlGnwckUypPTqoQEnJVWWim2CSKIttVWW6UenlQqstdrnRrpKip73LJZQ2Iem+1sLKdqNZHruHJMWOKEPadD+LdpDUBSDn2vzzjjDNtNqFNUnJGUDs9fTv6RtPH222+Hnj17xnsEz5hULQ+5T9HugudfsdlBlcXzzjsvHjBwTP/Fyedbbrkl/PnPfx5tM4bKRpblV3tQMZfTyYMGDYqJuiTfbLbZZvGEfdHyTGp0VD++55574sE1WhyuscYaYZ111okxswkmmKDU1pnFhjFJb6rPiy++GAYMGNDcxpLWIqx1pFpIgGurIrlkXP43nREVdIlHUTGar0opKpigmAOz31E5BubD/JpW88qvcwOJLwW+Z/3JAZDczDfffKmHIEmdjkk46jTIIqa9EpMbqt8UG8ds3lZOdqRGRq/Wk046KZZKZ3Om+hTIgw8+GP9LxZCujP7y9NeldREl5XfYYYd4mpVFdKoNtlzHlWNiEK2DCPgQqC3w+sw888zx3u5JRI0rq7tIqmxpdOqpp4bDDz+8eeOKsu48X9icTNFmidL6O+20U9wQ5dQ93n///ZhQzSGDFHIc02GHHRY3HTiRTOCa08lUziPJMlUVIzX+/IDktwLrLO4LbI6U1ZZH6mhsIvPFc+6ll16Khx241mkxSNyACjmV666OUrmGcz3XPh9//HFcK5OQyvNvuumma57LEFuQWvL00083f0+cnDZLxKI4iMXzjhgj8UaqZel/a4bcFDFfqb2oxNOWJDL+DIfXJEn1MTNBncYSSywRT6sRSCMhhw1uFhG0olpuueVSD08ab6VHv/zyy9g6aLLJJvNVbQGtZ+jlzmlyWhTQy5ZqWVS+IAEmlRzHlWNiUK9evWpufFC6PNWJI0lS50Cfe9rUUi2zwEbkUkstFTfjUyTh0IqK+cBjjz0W3n333XiAgMSXsqsU5D6meeedN9x9993htttui2P69ttvY6UgNpHnmWeeJGNSY2MjuzWU5Zc6E9qX0OqQBA4SK6kCxSE21l+sB4mrleHnn3+O7a9aOlxUmRzX1ZFoSpUu7lckUvBaMWeh8g3rd2lcE0qIk3MAi4Tr4tAq1xXVslK1Qc0VB9auvvrq8N5778WEN+6X3EOpYpkCB9PUeFi/FC2/i0S4+++/P0w99dSlzzsrK4pW++6772KF0Q8//DBZNVZJ6mxMwlGn8be//S32ch88eHBcsFMVhz7S/Ld///6phyeNt5LDtFJxo6F1LGgILJ5wwgmx3RIBBSq+sEkz55xzJrsacxxXjolBJJvRKoT/svAjMESAlnYB22+/fRg6dGjzn7XntSS1nYmM/23DSIJJNQLqKTceJppootgiJCe5jYlT23vvvXc8eEES1SabbBI3b88999z4M1sMalzRhq7SL7/8EltOMPekxZlJOOoszz1Os9OSiiQOEnGofkMCKs9D1qSss7i/ssFcBjb/Gc+KK67o4aIWPPPMM7HdFJWLOGi42GKLhf333z9WMSLGOffcc5fyXqnzIQGOr8qq8awRttpqq7DxxhsnHVtOnn322ViBkfbtJKUz56SVLck4p5xySmkVyKlW9Pjjj8f9DQ7Ktbaesx1VfohZkthSifeSVpGVeF/LmHe29BmnyhLtkUnEYY+N9qySpPqZhKMOdfzxx8eS5WWYaaaZYgC20j777FPKv63OeU3liIAZlXDUOhLxCFZRcpvS2yS/vPzyyzHQTvuCVOWvcxxXjolBRTtBEoKKAENxOpKgAm1E7HktSeOu+qR5V8TJVQKMPJNJQAVB9fPOO88Tf5kjOXidddYJiy66aLj44otjy6CHHnoo3HnnnXEuZRKOxhXXTzUqLJEgMN988/mCqlOgghnrO+6fJHFUVztlvcUm8+uvv17amEjAobXh8ssvX9q/2WjtQr766quw7LLLxlaZJJ4WcTCScKR6UPmYpBIS0CsRq+Jglv6LBO9+/fqFP/zhD81rBA6K8fox7ywrCeeyyy4LU0wxRfyew8Zq/LlmTqh6Q9LNo48+Gg84EI/lUKYkafwwCUftQp9YFn4tlY4tepSWNSEtPPXUU3FDm9K21WOy53Tecr2mckgM4vRFgc0FFn0kJ7A4rm4HsPTSS3fYOBoJfdLPOuus0XrbE2ykdCsLilT3gxzHlWNikH2uJWncUcGhaB1EAI0ES5IpK9tXcn/NMajGSfgiIaajsbm+0047xU1J2iuBlhzM5dgQVL7eeOONmEDFtUJAm3UB1XqYV3EqWRoffvWrX8VS/VtvvXU8AS91hjY0VFHp3r37GPMG4i8LLrhgWHXVVeNXWZibsJGtlpFoSqUS4mOsy6XxhdgPh1Yffvjh5qQ8YkC0p6s+3NqV1wwkJta6L1Ilkko4ZamMH9KivVbb+G+++Sacfvrpo/1ZqTUjR46Mhxr4zHPg96qrrvJAiiR1AJNw1C4kAVCNY8stt8ymdCy9a8kIZzJK4KySpffzl+M1lUti0LbbbjvGzw4//PAxfsZ1bunR/6Kn/YQTTjjGa8SCNeX9IMdx5ZgYZJ9rSRr3OcvOO+8cvvjiizifIjh80UUXhX//+98xuFZUdKjuO98ReKa0VfGMGZf/Tb1ovUHbxSJhiQ0uknFIyqlOblZeSJR68803Y5n0V155JVYHwJNPPhmrokrjC+s/EtOlzmCbbbYJTzzxxBhzgCFDhoS+ffvGltdl22OPPWILLBIoOVxkzG50JJq+8MIL4Y477oiV+nitmMv16dMn/r6vl+qx5pprhltuuSW2pHrrrbfiz0jUo+JgrZatXXXNQFyK5KTq6kCPPPJIspjVdtttF+8JSyyxxGgxPar2kJh+6KGHJhmXGgutKY8++ugwbNiw2IqS66o6UVeSNH6YhKN2YZF+0003hXnmmSebV5DxkIizwQYbpB6KOsk1lUtiEEFgjft7d8ghh8T/UjaWDTZeR4JX22+/fRg6dOho/Xm78rhyTAySJI0bgmi0WWIza6mlloo/44QmgVjKS3MKvsygXoFN7H/961/xtPv8888fnzc890gaWmWVVUIqBKlJVFJj2WGHHcKee+4Zg8QLL7xwTCBmI4INGSr7Se057FA936Ud1WuvvRavN6lR3XDDDfH+CA4TbbrppmNssNHuaK655iptTBz0qPy8Ma6111675p/1cNF/kyL4Yi43cODAmJDDfI4KRnvttVfYfPPNY/yzjARrdS7EXvnsEZdKKfc1A8kJJHyTiMPnjsQlkhdpg0o79xSo1LfLLruE0047LSagH3XUUbGCET/74x//mGRMaiwctuQaJpGM2AHtKvns1WK1fUmqX7em6tIOUhusv/76oX///s1B/hwst9xy4dprr20uK6/GkuM1tcgii2SXGEQZYhZbTJY50QZaTqywwgrhL3/5S81kiq6osjxrEeirfNzxM35ddvWgHMdFMO/ss8+umRhE+7P11lsvScKSJKntCJBdf/31YY455oj38ttuuy2e2qTSy8Ybbxwr4qTw17/+NbaJpQUUzxfwnCNx/rPPPgsnn3xyknGpcTE/+vDDD2PlIhKJqRTAf2uV5pfGptaJepL0SPJafvnlfQHVsHj2ssnGxjaHQPiqPFjEepOWLsTRpphiilLG9Mwzz7T5z9pSpeX39Z///Ges6Ee1HNqJkCAgjWv89b333outnYj3rL766qW1eGq0NQOxsUsuuSRWDCIRh7UWSbqLLrpoSOWee+6J93Tiw1THIpZnrE5t1dY1k9X2JWn8MAlHbfbss8+O9v2NN94Ydt999xjgry7dniJT9swzz4wbDUzcJ5lkktL/fXW+ayrHxCAWpGSoFyfe8cADD8TEHAJohx12WOohZoHNmbYqs4xsjuPKMTFIkjRuVl555XDqqafGOUtlEs7DDz8c5w60XkqBsVDqnoB1pXfeeScmB5FAIUmSOjb5hdYlxcZ2TmgxyOY2VXpot1RmK5xG98MPP8REnHXWWSf1UNSA+NzdfffdMaGDONWqq64a1l133fhfklHLluOagWqitOlJfV+qrJhdGUNnjbfvvvvG9mIFk3EkScqLSTjqNJmylJLmlC+bxdNMM80YFUEefPDB0sekxrumck8MItHmH//4RyzPWonTT7vttlt48sknSx+TGluOiUGSpHFDRTNOvHMScp999gmnn356+PTTT2NiDu0KqJaXAqXuqdxHALvS+eefHyux3X777UnGJUk4+OCD2/xC2PJMjVblaeedd47VLWpVfKr0f//3f6FsVLagpdLzzz8fK/FQseebb76JlTmYu6RsB56L7777LjzyyCMxKeJXv/pV/Nlll10WnnrqqTDVVFPFuVV1XEhqb0LOrbfeGi6//PKYrEdSB+sHkve68pqBilxURycenFJ1Kz/UamzhwTlJkvKT3zEIZYsSjDmjJQ9f+OKLL8Kkk04aJ6DViRPKR47XFMlc1Q4//PBsFjcstH788ceaP6d0qzSuTKyRpMa35557hsknnzz2df/+++/DrrvuGpPSKZfOJlzKnvMkBVGRp0i+JnF40KBB4dxzz002LkkCm/9XXXVVrMCx2GKLxdP/rPHY5GYTkpiC1IiefvrpuJlNEg7ft6R6Y7cshx56aNzsp6rvLLPMEn9Gexx+zlymq7erfP/998Mf/vCH8O2334ZbbrklJuFQ9fvqq6+OLaO5V/H7F154YamJEupchg0bFu69995w3333xUoziyyySKysRCI/hxG32GKL0K9fvy67ZmAdRRVy/kuFmZ49e472+2VVnfFQscb3vkdbn/0k5kmS6mMlHLULfUdpf8PmLZnqIAFmhRVWiCdtq6vQlIGTM+edd148GfL111/HhQSngGlNRYueFOU01djXVI4oN0rFJ9pkLbDAAs3JTJRJZaH697//PfUQJUlS4pPTv/zySzanyGk1QXl5TtlinnnmiUH91KXdJYkExrnnnjtu/FWi8iiJC8QXJHVM65nrr78+zgkqEdvo27dvrJDTle29995xLkcyErHMTz75JPTu3Tsm4Jx00knxz1x66aUxYYEYqDQuuHaImZN4QxIqbajWW2+9MNNMMzX/mQEDBsQEFOKPXXXNUF29vbJ9e8qqMyQPk0TMe1ZUM6OK2NZbb51kPGosY6uOl7pSniR1NlbCUbuw4f+vf/0rTsgLe+yxR0yioC8xSS9lO+ecc2IZ/uOOO645iEbfWJIWTjjhhCRjUmNfUzkmBlEyndNh22+/fUw8A9WeNtxww3DIIYeUPh5JkpQGJ6PbaqONNgopUMKd8vK0yZKk3NDKl9P31VZbbbW4DpQ6C6rMUFGiVvXcFHME2ru89tprYyThDB06tLTqEjmjGheJEsVhwkcffTTGf4hxFth0P+OMMxKOUo3qmmuuiQkcxGHnmmuumn+GQ39lxmFzXDPkWIGGdn28VpWxc9pmsSfy+eefx+RiqTUm1khSuayEo3ZZbrnl4umw6v7DlIrcbbfdYjCrbGussUZMwFl66aXjqZrbbrstLuyfe+65mDDxxBNPlD4mNfY1RQJXkRi05JJLxp9RLpmALONNmdj11VdfxUAaiUCUb6ZUOguuqaeeOtmYJElSeVZfffU2/TlOaqYKIq+//vpxvsJGEcF+xkxrDEnKARvaK6200mjtNjjhTtuXwYMHh+uuuy7p+KTx4aKLLorVU6icQFujHOYIVG/hNP5mm20W43e0pqKqBK0vOPhEdY7UicQp0Rborrvuam7Vte+++8aqN1ToKhJz3nnnnbDpppt2+apBal88kfsCladod89zL3ULmhzXDLT04z5Fy99KxF532WWXWLWnbMxZiEkvtdRSo/2ce8P+++8f/vnPf5Y+JjWmzz77LEw11VTxYC9eeeWVMHDgwLivsNZaa8XOEpKk+lkJR+3CBJ2Jeq2f1zpZU4bhw4eH6aeffoyfM1mmLL/yluM1RV/k6sSgPn36hBlmmCEmBqVIwmEsJJQxKV544YWbf/7hhx/GhWqZpWIlSVI6Dz30UPYv/+233x5Lyt99993h3HPPjXOnVVddNZa957+2i5WUEhVGWdex7is2/Um+oQ3MBRdc4JujTuGSSy6Jm7M777xzyAVJOLTNpCUOXwWShCp/RpJQV0zCoU3eSy+9FJNwiGeysc7me+W8iQNiLVUxkVpz0EEHxcpYtDfLpX1tLmsGPmt89vDss8/GtpTVyQgkCxGDTeH777+PhzCrkUzx9ddfJxmTGsu3334bk8+psHbHHXfE5wgJZXzm2O/o1atXOPPMM2PbsxlnnDH1cCWp4ZmEo3ahROThhx8e+vfvH0tUggx6SlmuueaaSV5VKpNcfPHFo5Vk/Oabb8Ipp5wSll122SRjUmNfU7kkBtFuojhhwb9NedHqVlj0CJ9uuulKG5MkScoLcwGCZQSw2UCec845w+abbx5+85vfJB0XgT3KXvPF2G699da4Gcipd+Z4jHGJJZZIOkZJXdOQIUPixh+VQN5+++248cBmH2vTY445Jhx77LGphyjVjZgGp9pz0giJxCnttNNOsTLziy++GL/YeKfyBoYNGxaTlM4+++z4Z6RxRaVxqt1QcSknOawZ5phjjlgliNgrX88///xo8VcSA0nKYY6Qwsorrxz/7eOPP765dR/3BH5Nop40NiTYkER25ZVXxngBiZ5cU9wPrrjiini9szdDBT2+JEn1sR2V2oUFIKfGWPjRlxiUr9twww3DIYccMkaJ2zJ8/PHHcaL+0UcfhREjRsTJe9FPmiz6ooyr8pTjNUVAg8oytRKDfvvb34a///3vpWWpU5EHlELdcccdx3g9WASyKKUFmyRJ6lpov/rHP/4xVnJYbLHFYhIOmzavvfZaPAFftNVMpdgwotrECy+8EIN866yzTvj000/DtddeG7bYYovR2sFIUhlY080+++zh9NNPj99Xluhno4v2OFKjO/LII2NblwMOOCBuIOeC2ArJbz/99NMYv9cVq99Uo9INSQi8Z9tss03z4ULa5ZE8+Kc//Skm60jjitghLY0WXHDB7F68nNYMBx98cIxT16o8kwqtsPbYY4+4zqPFIL788st4MPmEE07wcKbGilZv7GdwzYDP2l577RVOPvnkWHkKXF9UiqQ9lSSpPibhqO4+spRhJEuWJBcmpkwIaZWTylNPPRUX8iNHjowZ7GSCd+/ePdl41LjXVI6JQQMGDIiTYts3SJKkwmabbRaWX375MYLSnF4jQYegdQqXXnppnEcRRCdBiDkM7TNnmmmm0eY2VLK0paakspF407dv33gfYn3HKXuYhKPOZL/99gv33HNPbFdCjKW6qi4VMcrG/IRqE9NMM03o2bPnaL9H0gnVqVQbVQuo2mWcU+OCQ6oFNt1J8CIxj3sCcc5KRYWVMuW6ZqAiz/TTTx/bdj322GOxiheHNIv5QsokxnfffTdWCaLqKS3spLZYeOGF42et+JyTqHvDDTfECllFYheVckh+IxlHklQfk3DULvPPP3944oknxkiM4CHNJNkgujrTNZVTYhA4kfnGG280JwZRIpXTY6+88ko46qijkoxJkiSls+iii8ZgenXrKYKzJA+nCqDR0oV5HEE8qlTWQrWeQYMGhU033bT08Unq2liDPv744/EexGbkaqutFjf4aGvNYR4r4agzoJpua6goXball146HHTQQT77xxFxqLvvvjvGgHr37h1mnnnmjnmD1CmTTotKWFw/hcrqWPycX6d49uW4ZrjuuuvinIDK5MSBqcJD9RASYEjC+ctf/hJS4NDx8OHDY+XTypgw7xuvn9SaPn36xPZTVFfj2qEyDsluV199dfOfueOOO+LcgQReSVJ9etT5v1cXcsstt4Sbb745fs9Des899xzjBM0nn3xi6UN1mmuqMjGITPEcEoOYBPM17bTTxkXXDDPMEE9qsviipKwkSep62IR56aWXxkjCIfmGOUMqnKrjpPvxxx8ffvzxx9GC/sXpe0678iVJqay66qrhpptuiuX42dxjc0LqLFIk2YwNVSUqYywasyozrWXuuuuu+GsSqrfddtuw1VZbxd9jPnXiiSfGORYJTdLY5F5dKsc1QzGeZZZZJraBI0bMz5599tmwzz77JEnCoU3d4YcfHr744osxfo/YuUk4GhueJ8xzuX5pN/XRRx+NVk2XJLNTTjklbLDBBr6YkjQemISjNmODf8iQIfH7Z555Jiy22GJjtOOZZJJJTARQQ19TuScGcRKDajdbbrllzFa/7LLLYrlIFoCzzTZbkjFJkqS0dtlll9C/f//YknWRRRZpTsC54oorwr777ptsXJxy58Tq73//+7jhJkk5qdzko+IprftYa+2www5JxyXV6+CDD46ttanewPetOfbYY0t/wQ888MBYYYLEN1piVLdWStEOJyd///vfY3I1cztaT1155ZWxCgcVuvg9qpXw+p1xxhlxrieNTe5Vk3JcMwwbNiwsueSS8fuHH344xmEx44wzhm+//TbJmE4++eQYI2eesvXWW4cLLrggJuSQJLTHHnskGZMay+677x4rPtKGlWcJz2EOGoOkMyo/URmSPydJqp9JOGozkiOKEzRM3unPOtFEE/kKqlNdUzkmBlUaMWJEWHnlleP3nMKgGg/Z6SThMHGm37skSepaNtlkk/hfNmkInPXs2TPMMccc8ZQbwexU6C3PydUiMUiScsJalLVdgbUo903WgLfddlvSsUmd2Q8//BAGDx4ctttuu2za4eRWteS8885rnj9xT1phhRXCH/7wh+ZDYjvttFPYeOONE49U6rxrhjnnnDPcfvvtsTr60KFDYxufn3/+OVxyySWxvVcKH3zwQTj//PPjIcyFFloofPrpp3FcJDJSPatYE0ot6dGjR0zOrZWgu9FGG4X1118/LLDAAr6AkjSemISjdmGhx6L4jTfeCKNGjRqtB+krr7wST49JjXhN5ZgYVIn2Uyy6OBlGn2ReG5JwOOFGf3BJktQ1EXTNLfBK5cAJJpgg9TAkaZza9Gy++ebxS2pUtNAm0YU4wbLLLhsTcknQzQWtlKjswheVXjQ6YjtU2yiQBDDxxBOHqaaaqvlnvLe8x1JnkOOagYpde++9d/jyyy9D3759YwyWClT3339/TJJLYfLJJ48t6cCBC1oHkYRDwlBxoFRqjyOPPDIe7uV5I0kaf0zCUbucddZZ8WvaaacNw4cPj4kBn332Wfjll19sR6VOc03lkhhUiWAwbSUoQcxCixKk008/fXjiiSeSncSQJEnp3XDDDbFt5VtvvRVPQ84333zxxPQ666xT6jg4KVrYZpttwmGHHRYOOOCA2OqlOrje1dtNSJLUEWhJSYUE4iucdl9llVWySsIhpsIcZdZZZ009lGzVSkiorBokNbrc1wzLL798eOqpp8LXX38dpphiivgzWj5xTy0qUuGOO+4Iq6+++miV9TrKqquuGmPRJAORYEn1m969e4d77703xoal9qIC5M4772wSjiSNZybhqF0I8DPpox8qE83LLrssTkhpiUNJRKkzXFM5Jgb96U9/iieiOAVFwIoerXfddVeYcsopY2KOJEnqejiNedFFF4Xtt98+7LnnnnGu8vLLL4fDDz88fPHFF/H0ZlmYxxWbRCQvY8cdd7TdhCRJJVlxxRXDVlttFaaZZpr4LN50001jgm5LrY/KRislWqowT8kpOSgntB4vNv7B+/jSSy+Fjz/+OP6a6hxSI2uENQP3zcrPIfHhakcccURYdNFFS0nCOfTQQ2PbzEGDBoUNN9wwJt9sttlm8d+mwpjUXsVnUJI0fnVr8g6rdqDv6H333Rcz0Qn0r7322rElDpNAStc99NBDvq5q+Gtq5ZVXjiXKayUGzT///GG//fYrfUxU5Dn99NPjafcRI0bESTLJQZwa2XXXXUsfjyRJSm+llVaKycxrrLHGaD8nMHvssceGRx55pNQWGG1F609JkjR+ffPNN2HgwIGxggNVGw455JAw2WSTtVgBuGzbbrtteOGFF2I8g03t6qoXKRKDctLWKscpkxOkenWWNcPiiy8eq4ikquzF/Z5kxsrqPFKjXceS1FlZCUftwqb/Bx98EBMm6IlKax4SJuhJTO9iqTNcUyS5kIgDkm44icSYSMIhMShFEg4baSQr8W+TuERSDifdzzjjjFjSmaQhSZLUtfz88881g9Nzzjln+Pbbb0sdS85BckmSugIOEd16661hpplmCmeeeWbYZJNNYmwlF4yHr5bmNF3df/7zn9RDkDqca4a2435+//33x0SbPn36hHXXXbf593K6t6txUWWfvRlJ0vhlEo7aZfPNNw/77rtvbH/D5G+HHXaIvUefeOKJNp/YkHK/pnJMDGLhRYusZZZZpvlnvD4sXknMMQlHkqSuh+f/YYcdFudR8847b/zZ0KFDw3HHHRcrDEqSpK6DwzrEUpZffvnw0Ucfhffeey9MNdVUNf8s8Y4UFfwuuOCC8Oabb8YWmqAqDgk4b731Vthiiy1KH5Mk5Yiq7CeccEK8n48cOTIceOCB4bXXXosxdKk9OMTL/gJdCCaffPL4M5J2r7jiitCjR4/YwnKiiSbyxZWk8cAkHLXLn/70pzDjjDOGiSeeOJap23333WPG7JRTThmD/1JnuKZyTAzq1atXzRKjTJor+yZLkqSu46KLLgrDhw8PG264YZhkkkli8Oyrr76KG1pPPvlkOP7445v/rG0LJEnq3LbffvuYnFvECNhQy6md0aGHHhref//9sNZaa4VLLrkk7LjjjvEAFFV/DzrooNLHI0m5uvbaa8MxxxwTNtpoo/hr7pO0GaRKu3Fgjasvv/wy7LLLLjEJdr755guLLLJI8+999tln4aqrrooJOhdeeGGLbSwlSW1nEo7afarmnXfeiZnYtOwhwE/VEBbQlL2XOsM1lWNi0AEHHBD7ufNf+rWyyUapYhZkBNo49Z7yRJskSSrfiSee6MsuSZKiP//5zzE+8PXXX4c11lgj3HjjjTUr4XAaPoVnn302Jt8Q0+CQ02qrrRaWXHLJWB3nn//8Z9huu+2SjEuSckOCIlVwKtsNfv/99+GTTz6xfZDGGS0q2YN54IEHwjTTTDPa75HY1bdv35ikc84558SqS5Kk+piEo3Y59thjY+Y17W8WWmih+PB++eWXwxlnnBEX8bbEUWe4pnJMDOL1AQlBxYkHxgVOsJ166qnx16lOtEmSpPJVtqmUJEmiWi5fVFG45ZZbYpunWq2fSIgpWxFbwdxzzx1bf5OE8/vf/z5cfPHFvnmS9P/RgooDmAW+79mzZ7IkSjW2hx56KFbJrU7AKfBsZu/h6KOPNglHksYDk3DULpSlO+uss0YL+NOeZ+aZZ44PapNw1BmuqRwTgx588MHS/01JkpSfypPtnIhsrRy58wdJkrqmc889N7z33nujtX6iFdT999+frPXTAgssEGNAHC6af/75YzWcbbfdNgwZMiTJeCSpvVZcccVYQV1qBJ9//nmYaaaZWv0zc801V2x1LUmqn0k4apdevXqFCSeccIyfc8rGfqTqLNdUjolB/NuSJEnMQ371q181t51oiXNzSZK6rmeeeSa71k/9+vWL7b/ZuN5www3DRRddFNZff/3YXnuDDTYofTySBGLAbVXEhMflf9Ned999d5h00kmbf80hURIpp5566tH+3EYbbdThY1Fjm3322cOgQYPCLLPM0uKf4fdnnHHGUsclSZ2VSThqlwMOOCAccsgh8b8s5CmF+J///Cccc8wxse80C+fCr3/9a19lNeQ1lWNikCRJEm6++eYwYMCANr0YBmQlSeqacmz9xL//8MMPhx9++CFW9LvpppvCAw88EKaccso4LklK4emnnx4t0eVf//pXmH766WPFLuLDxKk/+uijsMoqq5Q2JmLgJFJWopXQlVdeOdrPiFO75tPYbLLJJuHEE0+MFf9rJeJQke6kk07yWpKk8aRbE6sxaRxRDaT5Ivr/yQiVlxI/49f899VXX/X1VUNeU3fccUc4++yzayYGERhab731mv+syWaSJKlMlacuR4wYEa677rrQp0+fsPDCC8cgMfOlu+66K2yzzTZxLiNJkrqevn37hpVXXjm2frr00kvDwIEDw3nnnReeeuqpsPfee4+26SxJ+q+//vWv4eeffw5HHHFEjAeDuPRxxx0XPvvss3DyySf7UqnhcA3vtdde4dFHHw1rrrlmjB1QZemrr74KgwcPjgmxSy+9dDjnnHPCRBNNlHq4ktTwTMJRu3z44Ydt/rO2z1GjXlM5JgZJkiRVo2rg2muvHTfaqqvlkJzDlyRJ6nqo5EDrpz333DO2fqLtE9VnitZP/fv3Tz1EScoOhzFZS80xxxyj/fydd94JG2+8cXjhhReSjU2q12233RZuueWWeNiYBBwq0VEdh3nBOuusEz755JNYBUqSVB+TcCSpgRKDJEmSqi222GKxNVV1kPitt94Km266qUFiSZK6sG+++Sa2fpp22mnDsGHDRmv91L1799TDk6TscMCBiqLbbbfdaD8///zzY+X022+/PdnYpI7w448/xvkBcQWq5VEZR5JUH5NwJEmSJKmBUQFn9tlnD0ceeWTo2bNn84bboYceGr788svYfkKSJEmSNHb3339/2GeffWJrnqJS+ssvvxwGDRoUzj333LD88sv7MqpToGIeVXHuueeeGEOYa665wtZbbx2T0CRJ9TEJR5IkSZIaGBVvdt1115hwQzIO7TLffffd8Otf/zqe1rRinyRJkiS13ZtvvhlbUrHWwjzzzBO22GKLMNtss/kyquGr/5N4c+utt4YPPvggTD755LEt1cknnxzbUUmSxg+TcCRJkiSpwf3000/hySefHC1IvMIKK4QePXqkHpokSZIkNYybbroptqSadNJJUw9FGq/XNck3zz33XJh++unD6quvHtZaa61Y8WnRRReNSTlzzz23r7gkjScm4UiSJEmSJEmSJKnLW3/99cN7770XVlxxxbDeeuvFZIWJJ564y78uamy0VqNy7p577hk22GCD0X5vwQUXNAlHksYzk3AkSZIkSZIkSZKk/9/y9+677w733HNPbN+z6qqrhnXXXTf+d6KJJvI1UsOhvdqdd94ZBg4cGFtQrbbaaqFPnz5hpZVWCksssYRJOJI0npmEI0mSJEmSJEmSJNVIyKFVz+WXXx7b/a655pph8803j4kLUqP5/PPPY4LZXXfdFZ5//vnQq1ev8MMPP4TDDjssbLHFFmHCCSdMPURJ6hRMwpEkSZIkSZIkSZL+v2HDhoV777033HfffeGFF14IiyyySFhnnXXCp59+Gq699tqYsNCvXz9fLzWsjz/+ONxxxx0xIeeVV14JU045Zdhwww3DwQcfnHpoktTwTMKRJEmSJEmSJElSl3fppZfG5BsSb+abb77Yhmq99dYLM800U/NrM2DAgHD00UeHf//7313+9VLn8O677zYn5PAlSaqPSTiSJEmSJEmSJEnq8tZee+2YdEPVm7nmmqvm6/Haa6+FQYMGhU033bTLv16SJGlMJuFIkiRJkiRJkiSpy/vqq6/CRRddFP7zn/+EH3/8MTQ1NY32mlx++eVd/jWSJEmt6zGW35ckSZIkSZIkSZI6vYMOOihWufn9738fJptsstTDkSRJDchKOJIkSZIkSZIkSeryFltssVjtZpFFFunyr4UkSWqf7u3830mSJEmSJEmSJEmdxnTTTRcmmGCC1MOQJEkNzEo4kiRJkiRJkiRJ6pKGDh3a/P19990Xbr311nDAAQeEWWaZZYyEnF//+tcJRihJkhqJSTiSJEmSJEmSJEnqkn7729+Gbt26xe+bmpqaf178rPg5v3711VeTjFGSJDUOk3AkSZIkSZIkSZLUJX344Ydt/rMzzzxzh45FkiQ1PpNwJEmSJEmSJEmSJEmSpDp1r/cvkCRJkiRJkiRJkiRJkro6k3AkSZIkSZIkSZIkSZKkOpmEI0mSJEmSJEmSJEmSJNXJJBxJkiRJkiRJkiRJkiSpTibhSJIkSZIkSZIkSZIkSXUyCUeSJEmSJEmSJEmSJEmqk0k4kiRJkiRJkiRJkiRJUqjP/wOJCbWEUhe8awAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 3000x3000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_master_filtered_prop = pd.concat([df_master_filtered, df_target_properties], axis=1)\n",
+    "df_master_filtered_prop.drop(columns = [\"debye\"], inplace=True) #that column has near 0 std => correlation not defined\n",
+    "df_corr = df_master_filtered_prop.corr()\n",
+    "\n",
+    "plt.figure(figsize=(30,30))\n",
+    "sns.heatmap(df_corr, annot=False, cmap='coolwarm')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "executionInfo": {
+     "elapsed": 2378,
+     "status": "ok",
+     "timestamp": 1761795823198,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "Po-Aow5vHHY9",
+    "outputId": "d182e3f3-1764-498a-b0e0-f23e5707b39a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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oruN5KIGnTjubeq+RCRU5T2b8NBPpMzaQQH8ylzM+kP0m4wMDrZ9eJ03s3/aOE2c8LO2vexCkjds/wahUTkiFlnxlfDL3qzmnfvOb36wmcde98kPKRuf4zUSE7EvdE9JyTOc4zv12HWVbZ//JOTXBzFSgyWOZHJ0S03UOjGdcNW1PpniT7k0TMO70QXPuz4SJulYTWJjEcdKXyzUr59L0gTIZLf9mwlHd/avh5x+l1EdJbk4yKJMdJAdB6vCns1z3Ul2ZcZyB4JQ26axLnAB/Sj4k0JZ1E9OpSHDn/PPPL02Q2dMJSP3617+uJihsueWW1WeQi1pdJci87777ViVBOms45ASUshsppb6gcnZ10PQLV9P3n16ZyZULVs5J6fhn4kVmX+d91FGy9b/0pS9VJeu6JTiV2Zm/+c1vSp1lQkI6yBlA6axpmv0oHaGsLz7Q+ul1kI59bhITEOwtVZROcya2ZNC27hUUzj777GoSSN3bORS57iYwW/dzfrcMLuemNtU2uiXLKTcE6TukD5TAQB2P5UyAyiBJOvzd0u7cyGRAtM4yCTP9zd7shxwXCfrXve/W9PY3XUq9JSuit2pRBiYyOF33NdCa3n7mdfDBB1efW0pVtkHTr4/dkiGVSSeZBNqU7N+m9g/b1D9fkLpXfljQJLh8DnXNuG6LZPumolOC5BkXy3GQ9cczppGxg7prev824xrf+9735pv43xRN3/4ZA8ta1plgl+oVKcv89Kc/veonJWiV4GedZRvn+rugJJGsuxx1XH4q55rsI4mLZKw7550999yzSl5L0L+u8Z3I2GOuX1nqtA3VgRl572/4+UfG+CjZYostqoHV3ORmdmayxF/1qldVJ9NkE9ZVZj+lvMPpp59ezS5NcCqB/ZQmysy6ZEtlNtSuu+5amiIzetOBS6mHBPzTmc46XJnpmIzslLWom+wjmYCQiQr56kxSqPs6IB1Zb6Utmrj/dHRm5SewnxvIBPUzOzMB/kxyyfvIualu0ikeaEZjgoQJ7Ddh4LNTujGlmDuD9QmqpYpIXXXKKGXmfdPWoMv2zr6dQcG0+9xzzx30uU0ol3bKKadUszCTDRGZZJRlWeqYjdIrbU5HuVc603fccUf1fWaId25+6ybnmGc+85nzPZ4KOnVtc7csaZKMuF45B+XGpe6a3v7OOlu5Ycz5PxNbsu/k/JSJjnWXgeb0F3qldGadSju3tf3MK+tP/uQnPyl77LFH2X333asAYJM18fqYc1gm12ZSScY0UjUlkz9/9atfVb9PhbkMuH7kIx+ZZwmhumhD/7DJ/fPBAt85T6f8Zt5LU95P3Qd+B5Lqa4taObLOgc18NVXT+7c59yfJIqWv6z4Jp43b/09/+lM1MaG7emX6tVk+qO6TiiKJOVnuKF/dcv7vLCVT53Xqcy+X/mjGZDIxMOeijBlMmzatPxGmrlK1eKuttmpcUDxJCkm2W2GFFQZddrMpFWfid7/7XZXolf2ms1xoR93H+P7U8PNP/e5OxojMnEjJvgTR3vrWt1YB8ZT3yQ191sCpq7Q3g/HpIEQCfwnyJNPi+c9/fhXozyypussM9swqTVDw0ksvrYKbyY792Mc+VgWmciLK55OAf4KDdfPoo49WmeEZnE+mRKR8VCoR1G3duTatodeW/SdtS9ZybgCSlZJyjOlYdHeGMhMzNzZ1lFmXH/3oR6vtm30p2zudiJRAzTrdTZCBxHRAM7s00mnOejJNkPNOgjrf+ta3qskgmUmdLIgct5kgVUfZNzIhLTfqCyqVm/NSXQc+O1JGNcs55PyYa3Gn1FvKr+b91fUz6EiZz/QVUrKrE2BOtZycl1ISK8dGgh29GZ11kRuTnBvT/+m0MTfAWcohFXXqLqXdEgjoXWsxx3H6cXXX9PZnzbwE8HLjmKWUsr/nepwsvwyq1/09JJsj+39mhKfcYVx55ZXVMZ3gUt01vf3M74tf/GL1lQGZLNmUcqcpudlETbw+Ztv/9Kc/rdYAzaTtBJYfeeSRarAy57OMHXTuJ3KfVDdt6h82sX/eLfdzyRpM+1PNLONjScJIxYGmBNuSHZjy9lkrN9eZjANkAn2CPnWX+9CMbeTamK+MwVxzzTXVPUad9/1UTBiquq6V24b+bcY1UrUi7yGTuXrH+uoemGr69k9STsb2ejP2k7yTxLYmxEeyrEnGNTKJNdeDTFrtTI7Kzxk3y/1SHRMJ07/JvpNJXZkkkoS19Odyj5frWp0leJn1rXOdHWjyf11lTKZTPSDfN2US3UAyjpT+Wsa2eysiNOF9LdPw849S6jWSbMcEpuoYCOzIgZrSbensdKSccYL7OfE3xbrrrluto5xBsAQ0B5rFldLYmQFfx7JXhxxySDUzP9u8c6OVQG2C5Rk4qWtAsyMDJ0NZQ68TtK2bpu8/Wf8jg8PJEM9s/IFkQCWd0zquSZds8QxeZdDt3nvvrR7LOSmTRPbaa6/adx4yaJi16T/84Q9Xg7iR7JpUUjj22GNrP9s9Az5f+9rXqm2dTvQ555xTDZokUJisoLrPaGy6DOBmraHMjO2Wa0AGzztZWnWVyjLZ3zMrtrMUSG58U6I8+1AmuRx00EHVPlbHihvZ/un4J0CRwdpkwKX9eR+95546rsmVSiE576T0cGc5iizLkgmOuXFPH6LOmt7+DBom4yEVmDrZk7mmpd+WZXIySarOMjEzfdAEX7LP5yvvI8HIDArVfV3JprefeaX/nTLjyRZJwDiTt3Mcbb755lVfKteQZz3rWY3ZbE28PqZtCV7mfjTrg2699dbVJJ/uAFQySXLPl/fF8Gl6/zxjA5lkkcnPOQ6SfZf70ZyzU1mx7uMbCdBmAkLOP+mL537vO9/5TjUWkEpsue+uswR1EgDs3U9yXs2xm3GNOhpoHKYTSOt9rI798rb0bzMmloBm/h2oL1X3rMGmb/+MnXafP3NdThXITF5LH7fOa1xHlhPMcmQJEHaCa5lolD5PxjwSb8hko1jQJLbRlKB+JnV1ljNJFaB8X+f4TqQfnfN/4lEDqfN5s9uMGTOqiZndiUcZy657xn6kz5yllJIA1kRfbfj5R2B8FGUQ++9//3s1SNOrrjcunQGIFVdcsf+xZKwlQNWUMt6Rm8S6DCg8HumYpVRRZjZ2y4BJ1jL57W9/W+qs6WvoNX3/aZNOYLxJ69VnRnWO094bxJRWz6BDOhV1lmoOCSBkvbac/xNgyHpQmQySwav829RsggyaZF2cOsvgc9bQS8mubinLnMohyX6sswxyprLGTTfdVN3AJHM2A3Grr756f5npTBKs6wSXP/zhD0N+7sYbb1zqKPtKsivTB822zgBEZrc3oeJP09ufwbZknwx0/KZyVJ3XoOuWYF2O4WTw5XhuyhrCbWk/g9+X/v73v6+WCLrkkkuqQdUM0GcSaH6uuyZeH9Nnyvm4c05LADYBzLXWWmueQdVMyq1jYLxN/cMm9s+7pXpZgiIZIO5ufyZWZFA1x3qdvf3tb68Gtd/1rnfN0/5kwGesLBMV6izJCsmY7ZxvujO+kjFe9/uLbt3bv0ma3L/N/pPJLE3b5m3Z/pFJaRlL6pQjT98owf6sHZ1y2XUf3865sjeImYkJeQ/p2+XzSXXIRakSMdLjkjlfJkAemZyTWE8qb9S5akuuvQkmZ2xyoEktdR3PWFjiUYL9maTWhMSj7P9JHuy9/jbJaQ0+/yilPkpy05KDN4HN3rUc6nSzO1RNKG3VLUHNdHg6s4p61blcVOciO1C783gT1lhu4hp6bdp/sk5SlmxIRzPt751RXcdSV91rAOb7Ban79k9nobMcRW/AszMTts5SNnqgiVC5EU62Ux1loHYo6p5NENl3MgM2k4g6ncxkL+exzgzZOsta6CeccEJVRnqgzn/dMzabcHO4MDl+F2VdybppcvvTt0nwqzcwnsd6S6fVVQamkp2bflDOmRnESlC/KeX3mt5+Fj641MnsytJHyWLOfUcTNPH6mGycVKtJ/zX9wN51ljtLFfRWuamLNvUPm9g/75ZqPAOVyM3SCDNnzixNWColVYUGCjokY7zuEgTMtfEjH/lI/2MZIzj99NPnmejC8Gly/zZVHZKc8453vKM0VZO3f+d6lq+cLzM2MFhlyLpKn22gx/Je6h4ryYSK9HVSBay7YkW+z/hMnQPjCegnoNzkSS2pIvCpT31qnsSjVNNN4lGyluseGE/l01TPTUW5pk7WfneDzz8C46Mka02kXNRAnee6yyyQ7oM1gdiUiOqUDKl71ntkNlpmJOdGq3fNgyasIZZ13JJxnXK6KfcTCXIefvjhVTZ23TVxDb027T8pcZL1b3Lj0pQLVpvWAMwxmzWie0sCpkPdhFI/yXjM5IRUdOhI5z+B2c6arXWT82Nb5IY9necMPmRZh/jLX/5SzUhOucO6ywSoDH42VRMnFk2ZMqXqc+aalRuWBQ0q1HH5j6a3v1syHXLuT/ZbZyJLAke5rqXUWN1lyY999923mqDTWSP90ksvraoYnXTSSbVev7MN7Wde+bwWVKIyJdabNJmpidfHBMT333//akDv6KOPnud3SQLIYGtKSNc12NCm/mET++fdMgaQcabu5flS2jVB5U022aTUXcY0Ur2vN7iQ8sBNmHiVNX5Thj8VNzqB8NxfpFJFE+4vmqhN/dvs4xnLyzkox0AqnnQ74ogjSt20afvHP/7xj2p5yoESpeo+PpYJqkkeTBnm9M9z7cr5JyWZE+xMgDxLhNS1n55y+zl/JgCeiYCp7pckr4y71n18PkuZ9vYdmqbpiUepSJiqceedd16Vad17b1PH8aU2nX8ExkdJ1ifOzXrT5EKUDn+3nIB6byrrOpOrI4NfKbXRKbPRNBlcyM1L1qrslGpJ5zNrr2QdlrpLAD+dnsyg7l1DL7OkUuota3JlnbQ6avr+k0H4TDx4wQteUJoig9kDfb8gKVmXjmndZt1l30lJmeznneUQUn0g2RwnnnhiqbtMytljjz2qgZ4EYzM7M2U/M3CSY6OuWTQpg5ZrU75fkKz/W/fZ7D/72c+qcnWdUm+ZbJR1PZuQcZqJIXvvvXc1SJtBlN6KM3UcOGn6xKIEhjo3WE0YXG5b+7vl3D9r1qxqckX2o04wLP2JlBquuwxKJaifa0Dv2oCdAdE6a3r7mVdvdnLTNfH6mKWMEngdaGm4LJmVJbFyjut22WWXVe+xDhXn2tQ/bGL/vFsGr5NYkT5tJh7mWMhnku2ec3TdpWxr7vEyVpZJV7me3HLLLdUSWb2TRuooSwUkKJ57jFRWid1337286U1vqgKHLH5N79+mpHXGgidMmFBN6sq+0iRN3/7dMnkl9xZJVusdD2hC4kgqVaTdye698847q8dSQSRLU+TeKQkB2c+yLEgdpc3ZxunXJHEh69NneZOMzWfcPufSusq66N/97nerJT+e9axnzTeppQmTQpqeeJTlEPPVVN9o+PnHGuOjJGslfe5zn6tmpqWz31tzv64zobpl3bbrr7++mgmVrNkE2ZowG7Yzc6gzm7HJEkzOzJx06HIR681ermtgsCNrsKTsWBPW0GvT/pMAWqoN1H2tvMVR8v7HP/5xLT+nlCzKQEmOgXTyn/vc51br0jUh0JZ9P+WIMqMxAycZ/En5vexXCSzUceC2ex3SfN9d4io6PzehVGZHrr/Z/uk/JLMj65I2wcKyxuq4/3RLlm/TJhY9HpnxnklsA5U1bYImtD+DiOnr9Pbd6r6GZMrS9ZaCb8oapE1vP/NKptdQ1bmSWVuuj03sn7epf9jE/vlAshZ9Jn6mJG3an4nzdV+fsiPJIpko0r39M/Et2fxtUafjt21rjDetf5sJ/pn8lPPnWFGn7d+7rEkCyPlqumSHZ3xsoHGxJJGk+lbdJutkImD6pBknSFXUjM+nYk5iJrkGJ1De1L50E/rPV111VbXvJ+luoMSjplyDM3k+8Z0kP2ZsryljBC9r+PlHYHwUZ1SkLFQn27db3W+8crOSG6us2dZ745iyJymBUveAW0p0ZTAybW2zut+43HXXXdVNb28p2rrPyG/i/pMZvd3fZ0bg+9///gFLXTVhYs5YuCmu6/GbgcME8lPaqnsGZtbw3HzzzWt5/cpSDTmv5DqV7xek7hO8sp1T6io3WLkpTD8ipSaTYZNZ1k2YXNFkY2ViUdPPn3Vuf7IeMjO/s8Z1JrZkeYQEbZsQuEubk4nYXeYt2XAZyKp74KXp7Wfh60NffvnlVbZO+ukd+cybkPEyVtTp/Nym/mET++cDSXsHqkBQ9/GBsaJOx28MVAI7SUipStF9HYg2XAfqtP27JxaNFXXa/m1K3Gn6+FjuHy6++OJqElqCm6kwl+z2VLrMtTf7DMM/4TyJR6mU07TEo5QfT1WzM844o5pUl/hI3kPGnXLPWocKS20+/yilPkoya+WAAw4oO++883wdtjrLjMA999yzKpOTE32yfHOiyaB8Zsgmi2rXXXetOp0DrfFQl05zTjxZwyGlopJp3TsLuQ2d5ugNONdpP8r+k7Vau9V5Rn7T95+BBg9Tcq9XXbf/WFTX4zcShN1pp52q8lBNWBe3ezCz830yBJPRkeBIsgeb0pFLOa60+Re/+EV1/onMLM3jKUN57LHHlrrv11knKZPr0vHvyCDoNddcU8t1DLsnFqUsWm522z6xiOGRNedyg/vmN7+5KsefYyDrcWWdw5Qgy1q8dZbytim1msGfTIbNuSgz8pMRkdn4eR917gs1vf0svJR67j9zHWzKNb3p18ema1P/sIn9827JdM/Ew1TEa8r4QG8p3RyjmfQ2UGDfNWV4DFQCOyWyGRl1rfA41iSAlqBa7lHb/JnUdXxs//33r+IimWSb5UJz/c31LMvnplJwnbWlWlEm6HTfxzVJqgxkmc0sG5P7mCS+JN6Q6hRJfKl7Qt7WDT//CIyPksz4eNWrXtWooHh89atfrcpBJajfLWsJpFOar3yfA7pua+X2dppz48joyDriKTOT/aQp5UGavv9k4sqiqtMagNRHOjsf+MAHyhZbbFF1fjIrP1UUmtIJyoSctDvBzlyvcoOVtZWy7ERm+aYEU5394Q9/qNZL6gTFIzNis6ZSJts14fyfihVZCyplr9L5v/nmm6sMoQzm1pGJRSwu6fckMP62t71tvgkVCebVPTCeINFee+01z2PJeG+Kprefdmvi9bFNmt4/bHr/PIPvb3zjG6s1ZZdZZpnSNB/60Ieqani5jjex/U3VhDK/bZb+7FCWOsikL4ZPEtXSf8hSmhkj6K6KFCbmDK8kTiUwnom3F1xwQfVY4j2RcZu6JQ0uSCq6ZsJwJqPlelxXmYCQfT6TD9JPW1Bfp+7nnxy3qfbTHXNIXy7xwky6qHtg/MGGn38Exkex45xZIZmdM1DGaZ2Da7lpX5DMjnrve99b2tBprusaMk13++23VzOqmzQDfyzuP3vssUctSxVRD+mspUrIvvvuW90UZ9CwCRJATpZvOsid7KCUXEp2TSpZfPnLXy51luMxGY69a1zfdtttjSgzee655/Znxm611VZVlnvWYPz4xz9e3VTWkYlFLC5Z62ygdc5Smr8JM/KH2hdK/yfZc3Xr/zS9/bRbE6+PbdL0/mHT++czZ86ssr2yzzfRX/7yl3LmmWfOU8aekZEM/YxZvP71r59n7eFUFckA/XbbbWei/zBJtdAmlCpuu9VXX32+iZ+MnKwnngzfrLXctIlRg91/Zrz+b3/7W6mr3NM9+clPrr7PMoNNlomYAy1J8bSnPa089NBDpe5Wb/j5R2B8FDOvM+By0UUXDfj7upaKevjhh6sZ1AuSGTv33ntvaYPMGE/ZRRavDAAnG7ntAdem7z91LVVEffaLTOzKIFAyIFNNpCnH5Q9/+MN5ymemM5dBzx133LHUXQY5s70zAJfZx1l/KH2GzMTMwE/W9+nYZpttSh1nlKaEcfzXf/1XlRWXIH+Wadltt91KW5hYxECylngmxmZN6/SXI2vRHX/88Y2o+DBUyVRscv+n6e2nmcbK9bGumt4/bHr/PNfAk08+uZqg0MRqZZn0lgoPAuMj64EHHii77757uf7666sKMKlK2JFqG6effnp1b3TSSScJ4C5mydDMEptjaY3xulqUiZ9NTtypq0svvbRMmzatUZnhC5MJmolb1dW222474PdNtOmmm1YTY/PVqaibZWW+8IUvDLhcSN3s0/Dzj8D4KDnyyCNLE+WGa2HZ7ekgCaixICkZmhv1TAxJCeDeUhtKYkF95fh80pOe1P9zBq+SjfLiF7+4/M///E+puzXXXLOa/fr85z9/nsdTMqp7MLSuTjnllGpg5/zzz6++OjJjtvuxXIvrGBjPhKislZrs9gz4Z+A/wf5OydK20A9iIJkUmH3+la98ZXnOc55T9X/+8Y9/VLPBc0xkjdWmlH1r8/7f9PaPZU0pGz2Wr4911fT+YdP75xmE32WXXaog5korrTTfsVz3a2K2dZY8uPDCC6v9pbf9bRnfqNs59itf+Uq1HmvKF/cGaFOlMxMuEjj/2te+VvtytE2jr9Q8TU/cqfNSTUkibItUcEkJ+M4k7rpLHzmTn1LlL/t377mp7qW8U5koFXM233zz/qo5N954YzXJMZPn2+KPNT3/CIyPghys6bTl5Fm3juVQ/OxnP1vgutBu3FmY//3f/60yIu65557qq1sTjwnqKevAL7vssqWp6nosDDawk2U08lVH3VnUmZF58MEHV4PPkyZNqspmpjT5t771raocXN1lwK3JstTKAQccUA0gZi3JZLkn6z3lxzbccMPRbh4MqzqfJ6FpkpnZ21fKYFhKkTelGttYvD5m0K93UvRoaVP/sIn9827Z9zMh5M1vfnPjStHGF7/4xXLfffeVv//97+XWW29txD1d1gTNuaZ7QkXTgqG5L0olnsGyllddddVqjdZPf/rTAuOLWbI0swYuPFHJIk1gMJOGm5r4mGvw1ltvXU1u7E0mrGOywoL60pFjO59LE3z0ox+tqim+4Q1vaGRlkFynsj73JZdcUm644YZq26evnPHspiy73GQC4yMoneT3v//9VYmlzqzklEpoUrmlnORTImRhnvGMZ4xIe2hmYDDrPcGiOO644xZ5YGhR/k8d1W3gocl614XM7Nes5ZmvjnSisybj3nvvXeo+03JhFTnqLIOzKU2aQbj0g3KcnnXWWdVkqaavD9UECbRkLbGUm1yQDDImY6up6tr+ppd6gzqpewbIWLg+3nbbbYs0jhApV14XbeofNt0tt9xSvv71rzd2qbVktGecbOONNy5N8ZnPfKYaL8o5Z+21166SF7Km6cLe51Of+tRSF1nCcWFjjzmf9iZj1E0T++eDrU3cZnXa/m2SqiapGDKUftKCkvRGS7KrUwHsO9/5znyTRepaxa+7GmF3YDzfZ/JiqufUcVsP5He/+111T9C9lEbTZB/KssVZji8+8IEPVP2iVKJheAmMj6D//u//rmaCZF3BzPr40pe+VJVMqNPNYdsz1drkN7/5zaClQpoQGExZujPOOKPqQBx22GHl4osvrmZFtSkjgsW7bk9HyqWlHG3WJslNfDpuORb+9a9/lVe84hW13+yZGPLud797wBv7z33uc9WEqToOPDRZm65dA+07nZKZK6+8cu1LTfYG71MyKl+MjDvvvLPKgluYgTIu6yDn+ZwjB+v/dPb/OrU/GRDpj02cOLE6fheUOda2QB8Mp8ECUMnazL12BpiapmnXx1e/+tXznNMGm9SZ59Qxa79N/cOme9WrXlV++9vflne84x2liTLxo44JCQuSsclDDz20Cibk2P3GN74xaPZ4Z3xpYYHzkZZl+a6++uqq5Oxg8vunP/3ppc6a3j9vuibeX7TJe97znqqqQ/7NubQ3uNyZWJf4SV2rb2Q96FTgaJomrGG9MBkDG8r5s84VZzIBM8dA9+eSJUAyRpwgOcNHYHwEpaN/5pln9q9blTJpWWPwwQcfbMxMHOohkytOPvnkqtpA775T11JdvRmP73vf+6rBnpQLSeczFRUSIE+HQoeTBVUZyOz2zPw+5JBDqhKTkZuXlDC6++67a7/xvvrVr1brQCcI3ikXdfbZZ1ft757xXreBhzbJ4MPpp59elSqaM2dOtbRJJ1Or7nLD3i3tTyWaHBcp31V3TV8DqukyYz1rLb7lLW+p1sDsHXio84z2Tqm0Bx54oBo4b0qptATvOmWD2zD4AHWUSZPJ/k2GdQaRIhMo3/nOd1b3HE3QxOvjQJPx0u7O5ISmTfBscv+w6dInyfhYytsna7x3kLvu2an77rtv+fjHP14FdhKk7dyj1rmi0+c///ly4oknlj/84Q/Vz5dffvmAyxzUeXwpmdZ5H6msMVBwPBl3CXjWvX/b9P550zXx/qJNOtVbMjbcfc5Jf6KuE+t6q8104jxN0DupcUHqmnTRXbEoff1PfOIT1XGc60Bv/6EzsaKuEhRPIu3kyZPnmVifCiJZZkZgfHgJjI+ghx56aJ6bw8zQTIbX/fffLzDOIsmgz7HHHtvIGWmRm5ePfOQj5V3velfZYIMNqsdyEcsAVjpFAuP1UKc1ALulyka+ugcc0rHbcccdG1GmNuUZExR/61vfWg3WJhs+a+Lst99+Zeeddx7t5rXen/70p6pEUTqaL37xi6uBz0zW+fa3v12VQGxa1Yp0/HOsZjAu+1Pdj4GmrwHVdDn/JFiRdax61b3UW1x55ZXVzWPWIW2K7nVfB1sDFnhiEjT7+c9/Xt1fJECSQPn06dOr+4pHH320EcdeE6+PCeC0ZXJC2/qHTZNS129605tKU33wgx+s/v3kJz853+/qGthJ0k7WkE1QJxVtUtGyaROzEzzIsZtxsde97nVl0qRJ1djqv//97+p8esEFF1QTFOt+Dmp6/7zpmnh/0SZ1Db4OVSpvJNs3AcwmBGbrujzP4w3udyaS7rrrrvNVMarr9bfbrFmzBowJ5tqcSbMML4HxEdQ5KLul82Md2ZHXxDV8uuVCu+6665am+tvf/la22GKL+R5/zWteU2WMt0Vd959I5+C6666rBrEi56EMHF5zzTXlU5/6VPVYXZd5yCBbZpMmGNgtA6JNWJcuAw6p+pAB0AxAJMCfLPKBjgkWv2TmZ1JOBs+7JZsgk3YySNTUAcUMAtVdG9aAavLEoqaXjU3JzGR0NMmBBx445OfWPSOu6f3noWp6+8eiH//4x9WSBd3l1VNZK4Hb/fffvxGB8aZfH5s+OaGt/cOmqPv1b1ErOjXBT3/607LnnntWg+8JLjdRxle/8pWvVGsUp9pAMuBzP5SEpJyHsl81IZmk6f3zpmvi/UWbdE+ya6KcR5sUmB0okSLB2Sxzmr5bqlrWvapx0ydTdEsl3VTMyf1nZxLFHXfcUf282WabjXbzWk9gfATlhNgbGK9zWaI2a/oaPrlpzyBDsk57yyw1peOTgZLeIOZFF13UiE5R09cgysBhvjLom2BaqlekBHkyIzLTuu4ywPmhD32o/OpXv6oGPSP7U9YP+/rXv17qLgMP6fjkPJRBuHSUM1j4tre9rXpfTVwTs0kyIaSzjnu37bfffp6S/U0KsqUiTZZr2WqrrUrdNX0NqCZOLErG21DVsdRnd/uTSZlJRe9///sHLLVax/b3Djqcd955VUZTvjJ5IvtNypc2IRuo6f3npvffGNwyyywz4GSkiRMnNuZ+u+nXx6ZPTmh6/7ANkt2bda6zxFruSzPJMOMeTbg+xsMPP1wFaLtL8ScoW9clBTIJJxnXCQrmephsx8EmddZxKYduKUGer6FIVnayDQdbT32kNL1/3nRtur8YKxNX66rJQdrHHnusmvx3xhlnVNetXAuSuJMl+jKukSrHddQdN8h1LP3P9Pm7pXpRlqio07jMQLJE6N57710lCnbGgjNRZ9NNN62qEbTFUTU9/wiMj6CcYBL4SJZ49wBZyhb1XnibfGJtgiau4dNbKiRramRWfk4s3ftUE/aflBpL2d8EM3PxzezerP+UWcvJpK27pq9B9N3vfrfq5KT92a9OOeWU6gKcoGxnzes6S/A++0w6OBl4iJQ87F6zu85yzs855uSTT64GSvJ9Op4pvff617++/P73vx/tJrZazvlXXXXVfOtFpoRaHTtqQ5H96GMf+1hVnr+O2rQGVBMnFuWc0y19ifQjll122WoANJk1+RxyM5mMxbq3v2mlSruz4NL/SXCoN0CUQEAdt30b+s9t6r+x4M/2oIMOqv7NMk0Z1MsEiExE3GWXXea5DtXpOtOm62PTJye0sX/YJMnIz6BpAuEpe53Jh5k0lnvWDNxnrfe6V8TL9THHbTKV0y/8xS9+UWUzZ2JFHdef7WRap1RrgoS5n37yk59c2i5BiPXXX3/UA+NN7583XdPvL9o0cbXpmpDcNZhcd3/9619XCUbpP+fa++c//7laZuOLX/xiNcZURxdffHHVZ4tcv44//vj5zunJgL/11ltLEyqKpg80Y8aMcuONN1b3MOmL9vYbLrvssmpifd0mK/yr4RPPl+hTx3vEnH322UN+bt3XCG26BAMHk45PHQPLbdt/ctLMem2dGdWZEf6e97ynukmpu8yubvIaRLlZz6SKDLBlZniCsRnkTsb1vvvuW/tSXtn2aXPdy/sMJje2L33pS+d7PJ3QTFJICSaGTyZUfOYzn6lmlnbONxn0TCZEymdmQK7OEkDLGozPeMYzSlMkY6x3DajofawJAw8pdZWg5kATi9Zee+0qK67Ovv/971dfCRatueaa1WOZmJZgTEp1ZVCX4ZNB50zs6g285CY4QeWci+qsif3nNvXfGFynglAMdr2pY0nLNl0fk4WZpYEGmpyQbLw3v/nNtQ7yN71/2HSvfe1rq/5V7wSrjIFkwPv8888vdZYJONmvsw9l34/Zs2dX/asErTLuUWeZ9LnbbrtVQdm2y/kpEwLqtASb/jlPRIJSuQY3deIqoydZyVnecZNNNpnn8STrZFzjN7/5Tamjf/7zn+Xggw+u+smdiV3dkzPTb06gPFV/0r9og5f8P/buBMzO8Xwc/yOJLEWIJUEtJUolTYhE0Ma+qyXWohWKoghdLA1KlBah1doVqaWUxr4vQUtbpWgioWKppShiXyLI8r/u5/c/852ZTCLbzLzvO5/PdZ0rM2fOzHnm5Mz7vs9z3899r7VWrs5UpHNXLckoEs/jtW4q8bzo8SmBcSihmU1cPvroo/y12I1dZJF9FosOZdjd25TY3RslTQYMGJDKKEq0xO7quPiJnu5RAjjeMy+++GK+aB4zZkwq+usf2X/f/OY38yJbLNQXfRJff0fQFyniYmHVxCJb7N6IUo0xcawl5pShFHkcd2L8RbsgnpU5ydQtesZ12ROLIiknqlXUDyLVdjrFov8jjzySiizOV7/5zW/y+yR2V9bKB37jG99Ihx9+eCH7ujeeGEYCQv0etjGhj8BRZL3/6U9/atXxVV3Zr9+Y9/NM7GqIhaXG1bZaS5XOj2VNTqjK9WEVgpXx+jdOHIv5aVxn1XaGFVUkU8T4o3x6fbEJIBaLYwde0UWgIcrpxjx7+PDheUde/A30798/VUkRA+Nlvz4vu7LPL8qeuErriTXhK6+8cobdyXHsiY0AZTh3RavBCJKXdeNUmc9dVUg8V0q9hU2aNCn3Ud5www3ryhTFbqPYQditW7ccLIwdR8x/Ze/hExP0KNsaIhs/Lpob9yKOk1eU4Ch6YDwO5pFVXSZV6kEUpeh+/OMf5+B4ZM/Fgk/37t1zj+LGk7EiuuWWW/Iiwx133JFL/kQmfhxTYxdt/Fu00jKNWyHUV38nUE1RFwurdB6O0nQR4KzfhytKSMWtftnjIopkkHjfR5nJCM4W8f3+RYv5MfmK81dt91jsEIpElz322CMVXZROj4XDeO1jx3X0h44F25iIRR+rootjzRtvvDHDsT4WnhvvLihqYl2UEfv5z39ed1/05IrFrOjtGeeDIotJ+0EHHZSTK2rHnyeffDKPPapBFFHZr5+rdP3GvAeNIzmiSLstqnR+LPvCe9mvD8su1sCiokq0HKkvgs1FLEPe2FJLLZVefvnlGQLjcV8ZypPHuTLmFlEZ6cEHH8zlUGP9KQLkkUhf1BKoVVH26/OyK/v8ouiJ2RR7x3hUHIhbLbAc10Jx3G+8i7yo4vosKrTEMTSq0dbWWSPhJdZWt9lmm9YeYqWtuOKKecd4WQmMt6C4KI5sv48//jhf9McFcpRaiqzMWKiJxe34+kUXXZQz2Zm/yt7DJ0pwRQCzpnF/yhC/SxkCzvF7xEVn/BvBhcYX+0XcMVulHkSxKL/00kvn90tkd0WGXSRURJ/iCJaXQQSkan1aI0gei5xHHnlkLl0XfX4j+F+k42j9xcJIjordKPG613rERGDk1FNPTbvttlurjrMtiKSQyHyNDPDoh1k2sXsjKhDMrL1G0Y8/0asqMkrrLzwMHDgwnXfeeTmwHLuwi6zsiUV77rlnDgxGy4YYb1wHjRs3LpeKHTp0aCq6CCjHjpr6SaTx/xAJCwceeGDhF66ibH0kB95///353BWifH0kdUWvzyIGXsp+/Vyl6zfmXZG7yJX9/Fj0He1Vvz4su5jHxTXVww8/XFfKPqqYRTn+KKVedLvvvnu+BondpTG/rpXiP+usswrfHz2cfvrpdS0DYldaiOvFuMaN30FgvHmV/fq87Mo4vyh74irFcMwxx+QNmpEUFRVCai2+IoE0NmOUQay1xt/oe++912TSmsD4/PfPCiWeK6XegiL7NbJXfvWrX+VASAQ6N9544/wmiuyccOmll+bFsthFTvMpew+f2H0a41988cVTGTUOHtQvt2dhsnXEgltUrWhqV3NRRUZg9JuLiUwsnMQiRFz0TJw4MQf6I8hcv1xtUWy00Ua5j09t0acmJr9xMVHUPj5VEYs90eevtuhTNl9USi8W0YsszrGRfd+4lHEshMaiaAT+iy6SGyOBK17rUaNG1SUWxW7gxjuFiuiaa67J464FZqPsVSyExs73oovM9QsvvDD3EasvSqxGi5k5WSRqreufyKqOc0D9a6G33norL0gUPTBb9utnKGoZwqqcH8us7NeHVRDXJdFSpH4p+wgYLrPMMqnoYh0jWtr94Q9/qNs5teSSS+Zg/7777luY9g0zE9dVUZUtjo31j5NRJSmqshW9lH0VzgNlvj4vuzLOL5paUy1T4irFEMnCEZP65JNPGpx7o1pR0c9bNTH+CL7G+TYqLP3ud7/LQfLYiBqVH6ItQhUU6dz1tdncEFKG+I4d4y0oTkYR+K6VPY2SXNOmTWvQiD4OPpGRSfOK5ITICKwt6oXlllsuZ0vFxWfRF/bKXiqn7KX2yt6DKALKsTs5yqVFECcu9qN0VCw6xK6Uou96jONoBMQjGB6lDmOyHlnu9RdNoj9d7LgpYmA8qoZEqZ/GPvroo/T555+3ypjaknjPR0m0sip64PuLxKSrqf5PkZjz4YcfpjIYPHhw3cexC2jTTTctVWLRzjvvnMccC7Yhdsj17t07lUH0dI8JfPSJ7tWrV74vdpNFCcSoFlIGtbLIcc1Zhl1kVbp+Lvv1G9VWhfNjmZX9+rAK4twSFbXKKK4BY2dv3KL9XQQXytTvNM6LkaTdeME9Kp2VvRpEWZT5+rzsyji/iPHNbuIqzKqNTJy3IqEi/g4iyNw4QbPoIoErEltWWGGF3A4nNkpFxYcI7I8YMaIygfGiHn/KTmC8hSe7iyyySINAeZTpql9WIMoA0/zK3sMnxj2zBfhY1ItyIXFCK+oiX0x4I6M6shcb71qORdXrr78+FVnZexBFr7C4AIodjvFa13rTR/ZZZNVFf8Mi++Mf/5h7L8b/Q/3F+fpiQlPU/4fI+o5SM1FFpH6ptEiKijJ8NK9ICokS/NFnNHb9Ns6ErR/0LHqP+jImHsWu2Ji0n3baaXVtM+J8HJ+XYeJe9sSiyNiNdhqRUBTHoXDEEUfk41BMKGN3SpHF9UPszI+2MZFcGmI3xA477JCDs0UXf8dRDjlKp8fr/+ijj+ZribIkVZT9+rns129UW9nPj2VXxuvDKoldstHTNHasRRJT2a5vw+jRo2c6/qba4BVJzEt/+tOf5jlpJHBHdaQIrN122205sFB27777bk4yqiUoRhCoSMp+fV52ZZ9flD1xldZ978Q5K6pW3nPPPXmeGsfHrbbaKh+PovVj0UVcIeJtIXa7R9A2AuOxVhPnMZrXZyVPPBeFbUGrrLJKLsUSJ6gISkU5tJjk1naQ1y6mZxboYf4pew+fCGxGYDnGGuV+Yvzjx4/P/Skj03TVVVdN5557br4/Su8VQbzfayW4ohRR9Ar70pe+1OAxL730Unr11VdT0ZWxB1F9//jHP3JAPAI5ccyJzOQo6x2l+SPgXHRRYuziiy/OC4WffvrpDP0i4+84dpLHragTr4UWWij3ko1kkBCZ4XERERNimleUaIxjTSRYNA7kRNCniAuf9c9LL7/8cm63EjtOY6ISF5pPPfVULt0Yk/miO/7443MgKo47iy66aL4vSk6uu+66OUu/6MqeWBQBwdj58KMf/ajuvpgER8/0+NoVV1yRiiwm6rFwHuUB4+843v9xXR27suJ4GsfWMojAePQSPuyww/J1WwTDyqDs189lv36j2sp+fiy7Ml4fVklU+YpNI9HrtIw93o8++uh0++235/NLU++footrw9gtHu0EYi0pEhEiwBDXtY3bfxVxt2DsbI9AZrQsqyUWhbhOid8h1smiLUWIdbSiKfv1edmVfX5R9sRVWlfEpGIjRtwiyBkVOmO9Ptacil4GuzavPvHEE/OxMtoiRDJXtC2OKqPdu3dPVRHn5CIGmU8ueeK5wHgLit5CMeEdO3ZsvkVGSy1zq9YrNy7STHybX2Tsxq7qCLBFBmaILMz4/ylDD59LLrkkXyRvsMEGdffFRVAEOuNgFAutscgXi5RFCYzHQTyCmTE5idvjjz/e4KAeF3MRKC/D4nCMPwKyTd1fhlLYcXEc44/FtpggRpZgiGy62kJckUU2eySCRFWE+lU4yiIqg/z4xz/Ot1pgPJISaBlRZiwmvtGPvizqt1yJ7Ms4Tsb7vyYW0WMhLi4+4yK0yOK9HoHkCRMmpBdeeCH/PUTrg0gerC8uriPwXz95sAjKnlgUk9uYLNY//8auuFiIjl0RRRfv87/97W/59a6fwR5JdfH6R9nJIqufyBULbvG3EBP56IlWBmW/fi779RvzrsgBqrKfH8uujNeHVRIBnEgYK+smkQhixlpeLNCXVawnxTVitPeaOnVqKdYF4nWPOXUcL+MW4481pygFHJsyojx2HFOLfo1S9uvzsiv7/KLsiau0rjjex7pwJBDH+kZUTYjqObFjvAyi2kOsj8UacRwvI7a2yy675PhCtNwsoqjKMrtqiZlFrax7d8kTzwXGW1CcUCP79aabbspZK5GNWcu+/N3vfpduueWWXMJIqbSWUeYePm+99VZaeumlZ7g/fpdIsgixcBm9jIsiMpDjwqx+qaIy9d0qew+i+uIkFceaOB5F2ZnIrI4M90i2qB+AK6q///3v+b3Ut2/fVBZx4RMLbbGI+UUXQXakNK8oo9d4kblMYrE8dnI0dYwtQ8WNmi+q6vD9738/Xy817nXY2sqeWBQB/Wjl0/h1jWS12vVQ0cQxszYRjIWeKPHWOFv6zTffzNc9ZQgs16+WE+eEmMhH9Z+oOlAGZb5+Lvv1G/Mu/uaKHBwv8/mx7Mp+fVh2kfAfSR9lDYzHAnCtVHdZxe7ACCrHWlOIIGFUqCpyGfjYCR7JwnEtFYHkaE0WVeUiQBiBwkjeu+qqq9Jaa62ViqyM1+dlV7X5RZkTV2ndTUf3339//huI+V1UtYwS2FGBoywirhDjrjnjjDNylb9YtyniDusQ56r6/ve//+U5SpwDYsxRuSLWnCLRpejrw9NLnni+wPTGNWhpFRHAvO+++/LJOXYjPfnkk/4nWriHT5wEytLDJy583nvvvXzRH30cQgREIuAcC65RRj0yliOAeM0116Si9hGLBJHY8fvggw/m938sUu66666p6KLaQwT2IxOtqR5ERS+1FH3Douzya6+9lr797W/nBYg49sRxaLfddivsxUNNLF7HztiyLMSHKEsUuyBiwSQ+nplYrC1DD70y+8tf/pIuuuiiPPmNHZuNL/rrl98roqg0E+eqSGSJRbhaCcE49sT7q/FFdln169cvBwqLtvAfGa/RFzoSi2KxJCaS8TdbSyyKUqBFFsGUOH9FFvjXv/71usBgvNYRLIyKBEUT56bIQg5xbROLnY3Ps3HtUysDSvMp+/Vz2a/faFoEceL8VzufR3uRmE9HUGeLLbaYoXVT2RX1/Fh2Zb8+LLuYl8Z1VCR/xvpG4wSW+oveRRTXhnEtuNdeezXZo37ttddORRbrR7E+EP1A4xgT58gIytauuw444IBURJFYGNUeakktcZ7v379/DpTEnClujf8viqiM1+dlV7X5RazxxRpx48RV1WWYlai4ERt4IjmtzO+VWuzjP//5T75+iATTaFUZ54iiO//883OFh7iGiHZ9ISq3RGJL/D3HHLXIjj/++Hy8aSrxPAL78XsVmcB4K4us2AhI3XnnnfmNHwGqyMqsNaynecRrHBcJ0QuqFgSMi//4g41SS0Xv4RMXPNF/KLJK48AZC5LRDyeqDcTvEAfVOHied955hcyOjRNWlHyPC9GYtEQwNvrnxcEzAuMxISuDmfUgKnpZ7FhAjAXs5557LpfNqZ/NFQkL0QO+iIsl9Uu1xOQxFuWrvHB166235iB61RZ0W1v93lv1F93ibyA+L3ofpQjGRruMaMkSO5Rrx/84hp555pl1F9NlV9SF/7InFoVIRoteqrVSvSuuuGJeyI2yk0V3ww035KBsmSfuZVb26+eyX7/RUBx3IxkpAppxzRTH49j9FQlMkTgWCUzRKzH6yzZVaausinp+LLuyXx+W3X777ZeTWuJ6tqke40UPjMdO6yjFX0u6qq8M758oAR+L2o0TuKOsbuzGjkTQov7dRgnsJZZYosExMs4N3/3ud1OZlPn6vOzKPr8oe+IqzIuolBCtyaJKcyQWxRp3lFWPROjYPR5JskUWx/iIkTSumBNB/igJH0lqRfZJyRPPBcZbQezsjUXUCOzELq8oZRwLNFGOU0+rlhFZQ1G6vvGCwssvv5z/eIveQ6YmLpqfeeaZfNCJLNnoQxcmT56cy4YUtVRgZF1G8DtOXCeddFLu/xQnswjIRsD/r3/9aypLD6L6ytKDKDK+470eFwjR0iGyY+NYFAHnKKUTvayKOOmtvZ/rFzqp8sJVJLUolTn/fVG58VoVjqJ79tlncyJLiMluWUtPlm3hv4yJRXN7nogs36jsUjRxjI/3f23iFa9/BL9iQT0mxTSfsl8/l/36jYZOPfXU/P8Z5RLjmikWZtZff/18TowkjUh8iEBPBNBjYawqinp+LLuqXB+WVbQY/OMf/1i326hsBg4cmH7wgx/kXr+xDlM2scs61mNWXnnlBvfHtW3sVo6E3DIFxmO9NQLLVVPk6/OyK/P8oiqJqzA3IqErqv1Eu6/6ItHokksuyQHbItt4443zzv2oGFJfrTVCJKiVwQclTTzXY7wFRRnduECLMktxIRN/vBGYirJKMRFoqmcozaMqPXziQBN9lmuBwtqu2qLvmI0+6DH5CpF9HLvuQuzmKFJf9Kr2IIrATQTEY9IYE8noMR7/H7/73e/SAw88UMjAeFssL67TSfOowsJm7FqOpLo4/tfeK5EoFRN6CXbNK7JeZ5VYVBVxnmiqV1Rri1KHcYtrtbfffjvvCo1khUhS0CO6+ZXx+rlK1280FMfdWPStzSkisTbmEbHDrvZ/HAGdAw880EtHm7g+LLNIaIlF1bKKnaaxuF3GoHiIdYG4ro2qfrXS43FtFUGF2nyjiCIpvqnNIGUon16l6/OyK/v8ItYARowY0eD6Nv4GYl0vElehyqKibsTUmtqJXfRqMyHmKbHj+uGHH85J3DFfjSrAd9xxRynGv3q9xPM+ffqULvFcYLwFxRs9shajL/T222/fkk9NI1FmJv4/4g+0qR4+RRcLP9HH4X//+1+D+8uyYzYykWPHURw4I5i/2Wab5d12MRmrX8auSOKC+JVXXskfP/LII3nX1Mx6EBVdvE9qvYmj0kBkwcai4tZbb50nv0VksQr+n8gY/dnPfpYnAI1FYEdgvHmVMbGoSqLMWOzaiIS6SDC97LLLckuBqDazwgortPbwKq+M189Vun6joYkTJzb4u4/+glFFK1pL1cQid+wkB1LhdzxGCeBIZomdRlFKur7BgwenIovrkFjnGzZsWB5/2QKzMe5o5xjH0dh5Gp588sm8YzbKxBdVUwlvETg+4ogjZkhSuPzyy1thhJRB2ecXZUxchfklzl1x/o3kkG7duuX74tr/ggsuyFVcim733XfP693XXnttrpxTSxaMNaeittK4sUKJ5wLjLSgy2m+77bZ80RlZH7GYGgHB+pN3WkZkzUVQNkprxIGn1sMngoJFPfDUF+XHI3P3/PPPz7vGyyZK/Pzwhz9M77//fj5RRQngyE6+55578smriGIR9dBDD80fx0mrzD2IokRdlOiOcm+17K7YXVNbOAaKK9quRABnn332yYuIEZCNIHmcFw4++ODWHl7llTGxqErefffdXCo5xPkrArSRbBoLV4cddlheCKX5lPH6uUrXbzQUx+Ko2BGVsuLYHL3GY8dILGbXxDEiFoyBYjv33HPzOSUSrRqLxP+iB8Zj/LEI/Oc//7nJrxd940Ksx0RVpJhTRF/TCCpHZb+zzjqrsBsXQu383risPbSl+UUZE1dhfnnsscdye9aIsUUiSwRoo6R3VJGKOcKdd95Z+EqkcfypHYPKYPMKJZ4LjLegyH6NW9TYj5IIt99+e76Q69y5c+7/EWUTYnGpcZYFrXfgKWoPn9dffz1n7pa1t9x6662XMxo//PDDusWrCOhE0kj99/+tt96aMzbjgFokO+64Y6l7EP3kJz/JF89dunTJi9zxXop+JrF7XzULKLYIAkSvobjoj4lv7JiLJLvYmRJZsnGdUQUrrbRSIa+HJBYVJxAWi7hxzo3zVq2HFc2vzNfPZb9+o6G4hv3FL36RDj/88PSPf/wjV9KKa9yaWBT+9a9/Xblr26KeH2Fe3HfffbP1uKKuD5x66qmpzKIH8ZlnnpmrUg0fPjzfF3OLCAhGq6DddtstFVGsp0bCf2ywiOPipptumku3QluaX5QxcRXml1133TXfyiySWC699NLcsu+GG27I5+So9hBz6iJaqEKJ5wLjrSBOWFHqIW4R4IyL+wiSx26vs88+O5/UIkBI6ytqD5+4uImsqLIGxmsTrfo7Opoq8RPl4mPnR9EmvmXvQRS7GyMDfPLkybnUzHXXXZfLMy+22GJ51yNQXNFbvFYWNhbHY+E/AuPRoqIMVR+iP/qoUaPShhtumBcffvvb3+Y+sRFwjkz3OA6FWmmmopFY1Lpi0vvjH/84V2GK931UTojga1Q+KfKOpramqNfPZb9+o6GofPTRRx/lXY6xozR2ddUCIlFS8fe//33ePRKPK4Oynx+hJRR1fWB2dymvtdZaOZBbtHWcOF5GVarok16/yl+tR2tRA+NR8joSg2PjRRxDYx31mWeeydeK0JbmF2VOXIV5TXwus6uuuiqdd955efPa6aefnu+LliZxPIrk7aYqoxTJjiVPPBcYb2VLL7102n///fPtxRdfrAuSC4wzK2uvvXY+uESprqaqDBT9wDm74mBaRGXvQRQi+7VWhj8WhiNRByi+WDCP40+0n1hnnXXyYlAsYt11112lmOTGjpoYa/Tojgvliy66KAczoj93JAjGolyRSSxqXTFhjGvnqHgSi8oR8Irr5ggYxeQRqn79xv+JHVExZ25q3hxll6MaUgSVy6Ls50doy+sDZR9/lJJu6jwYSbiRQFZUV199da4cUiu1H8lEcU6I83okTMHsaCvzi6ImrsKcGjJkSE52jk0j0RZ0Vsf7yy+/vNAvcOwOj4SVSOatXevHhtk4/kQyYNHjO+eUPPFcYLxAvvKVr+Q3fNHf9LS+yFyMErpx0IlbfSYAza/sPYgoh29+85t5cgb1xa6xWAAaP358vmCORfRddtkl75ypZZgWWSwyREZsZN/Hov+gQYNy9noE93ffffdUBhKLWk9kIb/wwgs5ISTOxbWe71tssUWumgCz4vqt7VhttdVS2VTh/AiUUyR+RvXK2B1em39GAO2CCy7IyTpFFeWvY7d4TSS9RWWt6Pce14cwO8wvoFyiSkttg2BsFimzaGkaLRwaiySd9957LxXdNSVPPBcYhxKKjCJaT9l7ENHyIoNudtWSo+bke2g74jgTi1Y1Z5xxRu4F2KlTp1L0HI3FqiWWWCKXO4xdcLVEoliQiN1/FEOUIW6qxUlri/d+7AaK900kCMb7Zty4cemss84qRakxWpfrt2qJxZfZSQiOx0TLoKJzfgRaS+xK23fffXNCTmzYCdHrNK4FI2GnqGI+UX/+EB/HnCiuCauoqNfnZWd+AeVSf85f9vl/tIe58cYb09ChQ+vui+T/kSNHpr59+6aie7fkGwetQEJJRWA2elG89NJLOSgSAYYodRXZvjSvKvQgomU9/PDDdR9HIOexxx7L75m4cIhgZvSJ/t///pc22GAD/zXMUlw0z0qtlGBRRW/F2NkeAf4IAsQxNN7/USZ23XXXbe3hVV4cZyKZIl7z2AnUuKTnvffem/+NHdhFFH05I2mofi/POO9++ctfzpOusk+MaV6u36ql/gJSY5MmTcoLSq+++mqhdzvW5/wItJbY1RVVKx588MHc4jECzBEgj0B5+/bt/cc0s7Jfn5ed+QWU1+eff56uv/76mR4/628qKaLjjjsuV4iKVrmR1BW7r6NC3uTJk9PFF1+ciq5HyTcOCoxDSXvDxIEzsnJi8hIH///85z85QP7rX//aBXMzays9iGieKg8RAIwLhsjMr2W4x8Vb9JYscg83iiF2xtYXvXuipUa8lyKjtOiB8eifFP3Rn3zyyTxJid3j0fcp/j3hhBNae3iVd9RRR6X3338/l7paZJFFUtl07ty5ycoI0V9MKxm+iOu3atlxxx2bvD8CCFESOILjcc6JdiNl4PwItKaOHTumTTfdtHT/CXfccUdegK+fhH7PPfekxRdfvMHjijxHKvv1edmZX0B5xbrqnXfemVtRlvH4ueqqq+bz2M0335wT0z7++OOc1BuJUF/96ldT0e1a8o2DAuNQQrHb7ic/+Un67ne/W7cLIi6m4+ATQROZpM1LDyLmRWQzxq1+2bcI6ET/yJkt8kLNfffdN8OLERfPMSEoQ0/VZZZZJp1//vkN7osyS7SMsWPHpuuuu64Uk6ymxLXOMccck/+N6584jkZ2+C9+8Yu099575x5dNZG1DPW5fqu22B0eweW//OUvaaeddspVJCJptSycH6H6JPHNX3GtF9VB6otk2z/84Q8zvO5FDoyX/fq87MwvoLwiKB4tP9Zbb71URlFN9Ic//GGO82y88cZ5DhObH2PNLO7beuutU5EdVPKNgwLjUMIePs8880zacMMNZ7g/Mnxjx3hVRMZXHFyLRg8i5kUksESlh2h9UF/0zY0LCZhTCy20UC4pu8cee+RqIkX30EMP5b7QUfaqcakrpbCb14orrph3pJRVrUdVTLhqi8u199C///3vdOaZZ+bP42vxOa2jqNfPrt+qKXrMXnLJJXkBKY5xV155ZWnKpzfm/AjlXB+YXY2ve5n/CcNlVPbr87Izv4Dyil3iscZaVhE83mabbXKv8ZjPdOrUKZ/bbrvttrzxseiB8WnTpuXS7yNGjMj9xuM6J8qrx4bNlVdeORWdwDhtUtl7+EQvzQgqNA6iRU+K+FoRRU/Q2VULjMzJ97QkPYiY14lX7JC9//7760rLxN/z+PHjZ9hJC7MrzmdxUVp00TIgSqfHez8C+vXZRdN87VdqYmIVuyIisBzXEI37Rq699tqpyGrXZ7SOsl8/u36rnocffji353jjjTfyboshQ4akdu3apTJyfqStqdL6QK29Ua1Hd+z4ioXiWBSuX9o1zpNlqmRB86nS9XnZtZX5RVETV2FeHHzwwbl6XLSWjeNn2daUnn322dwCKpL+IiAe8+hobTJw4MD8OxXdKaeckjd5xTr317/+9bwmGevbEdSPnulF3/giME6bVPYePrHw89Of/jQfbGICduONN6ZXXnklZxRFlk5RF65q4kAZ5UIiq2v11VfP/UJjkTUWXDfYYINUdHoQMS8233zz/Dcb5dSff/75fN+aa66ZMwVXWGEFLy6ztNdee81wsR+l1CdMmJD7+RRdlAmMxf/tt9++tYfSpt4zjf3sZz+b4b4y7LIuavJfW1H262fXb9USCzAx94njQiwcxe6EmF80pQxBBedH2poqrQ/EOPfbb7/03nvv5fNkVPK7+OKL07/+9a+8A6zW7qhx32varipdn5dd2ecXZU9chTkVmyzqr4nFe37LLbds8rFFP35Gsspzzz2XJk2alJ566qkc6wl///vfc5ulorvpppty0mIE8uv//8RxNeZqAuNQQGXv4ROBtciEin5K8TvEhU6UZY7SgVF+o4iuuOKKuo9POumk1LNnz9wTt9ZnOU5kESx56623UtHpQcS8iGNPXLTF+wjm1DrrrDPDfZFRGhedZeirFDsg+vbt29rDaFNikQTmh7JfP7t+q5Zbb701/xvJwbUyqE0pS1DB+ZG2pkrrA1G5on///jlJZ8CAAfm+aHF37LHHppNPPrnB7wrB9TnzS9kTV2FORQXCqojNLYccckiueNWnT58cYL7gggtysDl2Yxdd586dczJjY127di3F7v0FpmtyQxu03XbbpRNOOKFu0lI2MbmKUoFl3V0aff9it2zjHstRbmzHHXdMY8aMSUVWK38dGvc4rd2nxymzOv689NJLuUfetttumzbZZJNS98qDORFloqLEZCyAfulLX/LitbAoZ/Wb3/wmZ/B+5zvfyfdFuc9vfOMb6fDDD29yUgNVuX52/UaROT/SlpV9fSCqUvzpT3/K44/f5eabb84bGeKaN8YfO8dhZlyfMy8i6bzMiaswP8Su66jIGQHmqNJSpnhJJPC++uqradCgQTnQHNc88W/9uWuRE5XPPffcnKAT1z+R3BiJX1HePtqExJp3zbLLLpuKRil12owq9fCJidbee++dyipKpEX/rcYT3+hL0bhvehG1lR5ENI9bbrklX7Ddcccduaf4cccdlzbccMP0rW99K/8bu39hZoYNGzbbL04RM0wfeeSRvDh45513piWWWGKGQKzja/Mn1kWp0tjZVL8vVwTLJ0+enI9HUNXrZ8cXisz5kbas7OsDsYD99ttvNxnYX3jhhVttXJSD63PmxYorrph3jENbFFVlDjvssPT444+nRRddNLdm+eijj/JGpDPPPLMUVRSihUzcaqLVZlkc8f9X7Yr1gcYbByPgH/8HRd44aMc4bcbsZtoU9Y+1vvPOOy8HFqLkRmTcdOrUqcHXi5iFU98999yTfvSjH+UF1Nr/S/RLHz9+fA4UlqEcMMwvESSPvixRDiiy66JVwq677prWWmstLzIziJKS0TYjsmDjgjkSKeKc9dBDD+X3Tv3FtyIGxm+44Ya6j6MPY4w3zru1AFvsqqH5rLvuuun3v/99g4lX7Rx84IEH5l5WUNXrZ6rXn3V2S/SVoeSi8yNtWdnXB2K31G233ZaTx+L3+O1vf5smTpyYF4RjXhdVeWBmXJ8zL4mr8fG1115b2sRVmBexhvHJJ5+kX/7yl2m55ZbL90WFzmhl0qNHj/SrX/3KC9yMXn311dl+bFQtLBqBcajAImX9rJyyLExGmZMolxZBwRBlf3bbbbdSlTuBefHGG2+ku+66K++EiFI5UQJrm222yYsoV199df57+MlPfuJFpoHoP7TKKqvkRbf6Itj58MMP535ERRYZvDHGyy67LH344Yf5byAWD6OseuxWVjGh+XvUX3jhhTNkIT/xxBNpv/32a7DIAlBk0Xtvdh166KGp6JwfaevKvj4QfcQvueSS9Prrr+fPozJSbGSI66so7Qoz4/qcOSVxFf6fKN8drUwatxKIct577rln3kkOMyMwTptU9h4+X5SRU8QsnPqi/82WW26prBht0qWXXpqDgREMj12/UUI9+q4ss8wyDXYNRalj/eiYkx6MgwcPTmPHji18IKP+jppoLfDyyy+n448/Pm288cZKeTezeJ3juBJ9onv16lU3aYwSjrHAEpnWUNXrZygy50fasiqtD0yaNClNnTq1FOVbKQbX5wBzZ/vtt08HHHBAg17W4b777ku//vWvcw9smBmBcdrshWetx2b//v3zfaNHj86LfVHGqOg9NocMGZIXT7p27drg/nfeeSftv//+OWhSZNttt10ubRI9P+Lktckmm6QuXbq09rCgRcSiT7zvY3d4z549m3zMhAkTcunAnXfe2f8KDUSp8UGDBjWoJhDVQk466aT05JNPpmuuuabQr9imm26ay8FHSbcI8t9888255Nujjz6aA2t/+9vfWnuIlRZlxqKsWCTnxO7EEOX2dthhh3TMMcekhRZaqLWHSIGV/fqZ6vYW7NatW13p0Keeeir94x//SIsvvnjaYostckWSMnB+pC0r4/rAjTfeONuPjeRVmBnX58wLiau0ZVGJMOIju+yyS15fivaUUUU32ihFAndsRqpxLqYxgXHapDL28HnggQdyqdNaD6vvfe97Myz0xGQyHhfldIsuSqTdcccd6c4778w74DfccMO8czb+VUqXKvvggw/SxRdfnHdpfvrppzmoWbY+mLSeCCDHeWrJJZesu8iPgHjsTPnd736XVl111UL/90QJ75tuuimtuOKKDQLjzzzzTPr2t7+tSkILHofimiF2+EYvrtihFcl1EUiCKl0/U10ff/xxThL7y1/+kneDRLJhJAdHgkb0FOzcuXNeLL7yyivT0ksvnYrO+ZG2rmzrAxG8nx3R6u7ee+9t9vFQfq7PmRsSV2nLnIuZFx3m6buhpCIQFQGppu7//PPPUxFF2dwIpsUY4xZ9MuqXrIwJVwTKf/GLX6QyiMWr6PcXt5gER6DkyCOPzNldm2++edp1113TWmut1drDhPnupz/9ad4NvvXWWyuxxxx75ZVXcvnxWGD7z3/+kxf+Y8EwKhHE8f+UU04pfGAt+i/GjtOajz76KJe5iv56NK8IaMau/AiA9+nTp+7+WICOHVraN1C162eq6+yzz87Hrj/84Q9p5ZVXzuWL4zzYt2/f3Os35knRNuKMM87It6JzfqStK9v6QJRphfnB9Tnz4u67754hcXWzzTbLSYKRuKqiE1XmXMy8EBinTYoAws9+9rMme2zGpKuIYkddbSfpsGHDcinUsvfgeuONN3I517iQi37LsZAV5aUnTpyYfvCDH6TddtutQblgqILYURd/y/F+h7lJrIjd1r/97W/TXnvt1aCUbJR0LHpgfPjw4XnBM0plRoDt4IMPTq+99lpadtll0/nnn9/aw6ukeF/UWqxEAPOQQw6ZoRf0m2++mZZaaqlWGiFlUcbrZ6or5g+//OUv68r6//Wvf827yOPcWDvGRQnFWBQuA+dHKPf6QFxLRYWKCOpHJadI2Ilg/le+8pXWHhoF5Pqc+UXiKm1dzEdj00hUimpM+XRmRWCcNqkWWN57772b7LFZdBH4iAlXXAAtssgi6cEHH8xZUrFIGZOvorv00kvzhDcmu1EKOEqknX766WmZZZape0xMIGNHYVEnvjC3IvhU64MJcyOCynvssUc+X5XhmF9flLO99tpr00MPPZQnL1OmTMkVUaJvert27Vp7eJUUAcuoNBAeeeSRXK63cS/xqDgjsEnVr5+plgiUrbDCCg0SD+P9GOeTmmg7Er1by8D5kbas7OsD0ero+9//fh57XGdFYPyf//xnrmgxcuTIugQeqHF9zvwicZW2LKpCRXXdJZZYInXq1KnB16KyrsA4s6LHOG1aWXv4XHPNNXlSGOVyYsyROR3l9yJLKoIkhx9+eCr6hVuUbI3s7yiZ1pQJEybkctM777xzi48P5rfYEVsTOyCiNOBRRx2VjzuNg+SxcxZmJkqkxa64OD7Ge2ijjTbK54MoRx7BgH//+99ePGbqhhtuyIvNRezVSXmU9fqZaokyoVE6PdpwRLJw9BiMINpVV11V95joPX7OOefknsVAcZV9fWCXXXZJ66233gxB+1iwj6D51Vdf3Wpjo/hcnzMvIgEwElcjuaipxNXGCdFQJWuvvXauqljEawOKT2Cc1NZ7+NRXlh6bkV0awe8Y60knnZSeeOKJNGrUqJyV/KMf/SgHTYq+oBoZXRHIj1K6sZhVX61kPFTF1772tZytGOq/32v31e6PzwU2mZ3AeGTExi7gww47LJeMiuDA7rvv7v3DF4pjzLPPPlu3cBLHnngPPfXUU+nEE0/0ClLZ62eq12P8nnvuyXOif/zjH7mveASh4r0YYp4R7Tq233779MMf/rC1hwtUeH1gjTXWyInPjcumv/jiizk4NXbs2FYbG+Xg+px5JXGVtigSYy+44IK06qqrtvZQKCGl1GkzqtTDJ3pv1cpx3X///enb3/52XQm+6K1XdJHNFdneW2+9dS4FD1V37733tvYQqIj6C4WxUzN2oEQwc5999mnVcVEOsXMyblFe+O233049evTI/emj5KdS6lT9+plqiX7DUS0ldkNFYmEkitWC4qeddlqurBVVVeJxQLGVfX3gy1/+ct6s0DgwHgHxuOaCWXF9zvxKXO3Tp0/d/RJXaQuOPvroXEEx5gFRfbNxez4VOZkVgXHajCr18Fl55ZXTLbfcki98okRzlBL8/PPPc/+q2JladNEDMLK++/bt29pDgRZbLIH54dBDD83nqpooiR27xeOcdvPNN3uR+cJWLJFIEQl1kV192WWXpUUXXTRXm6nfqxeqeP1MtXTo0CH3vY9bY9FPcLvttku9evVqlbEBbWt9YP/9908nnHBC+s9//lP3O0RQPCpZ/PjHP27t4VFwrs+ZUxJX4f+ZPHlyevLJJ9OQIUNU5GSOCYzTZsQiXgQUakGqMvfYjIyoKAn4/vvvpz333DP34YoMqSgnGCVEii52FTXuqwzAF6udxxrbdddd8w1m5d13303rr79+3e6CKH0dZYYjMB5Z1kcccYQXkMpeP9M2DB8+PB/P9LyH8ij7+sBOO+2U//3DH/6Qq1V06tQprbTSSjl5NXbBw6y4PmdOSVyF/+f0009Pu+22W7517tzZy8Ic0WOcNqvsPXxi3B9++GHe6RWiFGp8XL+85a233pp3hNXfXdhaYmd7zd133517cB111FG5FHDjSbBSJwAw/2266abpl7/8ZVpnnXXSr3/963zdE+VLowdm7LAcM2aMl51KXz9TfWuttVaeZyy//PKtPRRgFqwPwP/j+px5ccMNN0hcpc0aOHBguu6661z3M1fsGKdNqkIPn+ibUQuKh6Z6Vx1//PFpjTXWKERgPAL00f+vfo/c733ve0qdAEALiaoCUdIzguPRhiV603fv3j33pStDKxZaVxWun6m+2jwDKLaqrQ+MGjUql8R+/vnn81rNaqutlr773e+mbbbZprWHRsG5Pmde7LjjjhJXabP23XffdOGFF6af/exnuVoLzAmBcdqkttLDp0gLQ/fee29rDwEA2rSDDjooLb300qlLly45q/oHP/hBuv3229Niiy2Wg+UwK23l+hmA5lel9YFoZ3fxxRenvffeOx1yyCE5YWzcuHF5of69997L7e9gZlyfMy8krtKWRYJ/VL278cYbc/J244q0VbrWYP4TGKdN0sOn5UVfSgCg9UT56xdeeCGNGDEiXwtFAl3s+t1iiy3Syiuv7L+GWXL9TBlEsk8c14Biq9L6QPQWP+2003JJ7JqozNOrV690yimnCIwzS67PmRcSV2nLdtppp3xryueff97i46FcBMZpk2Kx5L///W/uZd2zZ8/cF3H77bdPCy+8cHrnnXdae3gAAPNdLM7efffd6Ygjjkhf//rX80Jc7Gg666yzcp/oQw891KvOTLl+pmjiuBX9xLfccsvUtWvXfN8yyyyTrrjiitShQ4e08847p44dO7b2MIGKi8X3pgL9kXT48ccft8qYKA/X58wLiau0ZYMGDUq/+93v0nPPPZertYRI/o/zcrQ22W233Vp7iBRYu9YeALRmD5+//OUvOZP3T3/6Uxo5cmQ66aST9NgEACopAkinn356zqpeddVV8zVPXBPFLqerrrqqtYdHwbl+pkjef//99J3vfCe3gXjxxRcbfO2tt95Kv/rVr9KQIUPShx9+2GpjBNqGSCw87rjj0jPPPFN332uvvZZOPfXUXFodZsX1OfMjcTXUNn4FG79oC4499tj04IMPpj59+qTHH388rbHGGmmJJZZITzzxRBo6dGhrD4+Cs2OcNkkPHwCgrencuXNacMEFZ7g/dlousMACrTImysP1M0Vy9tln56oXo0ePzgtg9UXf++jpu//++6fzzjsvHX300a02TqD6or/422+/nXbYYYf0pS99KVes+OCDD/Kutb///e85AbHm3//+d6uOleJxfc78SFyNRMHY+LXPPvuk7t27597LkQQNVfbPf/4zb3Ts169ffs9vtNFGqX///nkX+QMPPJCTZGFmBMZpk/TwAQDamqOOOiodc8wx+d+YPMbC7dNPP51+8YtfpL333jvvbqqJdjNQn+tniuS+++7LwabGQfH6O6iibcTPf/5zgXGgWUU1Hphbrs+ZFxJXacsiAS2u+cMqq6ySKyZEYHzrrbdOl1xySWsPj4ITGKdNais9fL75zW+mLl26tPYwAIACiOue8IMf/KBuh3hMJms7mM4888z8eXzNjiba6vUz5fDOO+/kfuKzEiVFYxcnQHMaOHCgF5i55vqceSFxlbasV69euR1FrG+svvrqedf4XnvtlV555ZXWHholIDBOmxQHzXPOOafBBCZKzHz5y1/OF6VFXNiL8c6u2vjn5HsAgGq79957W3sIlFgZr5+prhVXXDGNHz8+LbfccjN9THx96aWXbtFxAW3Dpptumq699trUrVu3tMkmm8yyJY3rL2bF+4N5IXGVtuwnP/lJrpoQmwKjnUm0Ntluu+1yJbztt9++tYdHwQmM0yaVsYfPww8/3CAj8LHHHst9YyIjKn6XKIX6v//9L22wwQatOk4AoJgigAlt6fqZ6tppp51y+eKoXtBUcDx2ipxxxhlp8ODBrTI+oNoiGWyhhRbKHw8dOnSmj3N+5Iu4PmdeSFylLYuy6ffff3+aPHlyTlS77rrr0ujRo9Niiy2Wy6nDrAiM0yaVsYfPFVdcUffxSSedlEsDHn/88XnsIUqfnnrqqemtt95qxVECAFBFZbx+prqGDBmSHn300bTNNtukzTffPPXp0yctvPDC6YMPPkhPPvlkXhRbe+210wEHHNDaQwUq6Prrr0833HDDbD1Wgg7QXCSu0tbF9X/cQvQb/853vtPaQ6IkFpheaywIbUiUfaxp3GOzdl+Re2zGYmRMxFZaaaUG97/wwgtpxx13TGPGjGm1sQEAUD1lv36mmm6++eZ044035iSNCIrHDpHYRR7lEyNo/uabb+YqWwDzU/22de+++2665ppr0mabbZaTdKK6SpwHb7/99rxAHwllAM3h1ltvTeeee26TiauxY3bbbbete6zEVYD/IzBOm/Tqq6+WuqzRlltumSdYsVOivgsvvDBfFN1yyy2tNjYAAKqn7NfPtB2ffvpp3jEeuzkfeuihvIMcoLlE1ZRYo9lzzz0b3B+bGSJgHjeA5iBxFWDuCIxDCd1zzz3pRz/6US4PWLsIGjduXBo/fnw6//zz03rrrdfaQwQAAGgxjz32WN49fuedd6aPPvoot57aY489lFQEmtWaa66ZE3EaV/R7/vnn084776yiH9BsJK4CzB2BcSip5557Lmcgx2QrfPWrX0277bZbWmGFFVp7aAAAAC2yIBzB8Jtuuin997//TV27ds0l1X/1q1/lUuoAzS12iq+44opp+PDhqVOnTvm+SM459thj0/vvv58uvfRS/wkAAAUiMA4ldN111+VSXQsvvHBrDwUAAKDF50MREH/00UdzD/FNNtkkbbHFFrmi1hprrJED5aussor/FaDZxWaFAw44IAfBI0A+ffr09OKLL+Z+vtHuTnsRAIBiERiHEtpuu+3SSy+9lL75zW+mbbfdNi8EdenSpbWHBQAA0OyinVQEoA455JC0/fbbN/ha7969BcaBFvXZZ5+lv//97w0q+n3jG99IHTp08D8BAFAwAuNQUjHhuuOOO3IPvSghuOGGG6Zvfetb+d+OHTu29vAAAACaRbSUuu2229I//vGPXD59o402SptttlkaNGhQWmuttQTGAQAAaJLAOFQkSB7lAi+//PKckbz55punXXfdNS8KAQAAVNE777yTk4Vvv/329Pjjj6fOnTunyZMnp+OOOy7ttttuacEFF2ztIQIAAFAgAuNQYm+88Ua666670t13353GjBmT+vbtm7bZZps0ceLEdPXVV+fFoJ/85CetPUwAAIBm9frrr6dbb701B8mfeuqptNhii6UddtghDRs2zCsPAABAJjAOJXTppZfmgHgEw1dbbbVcQj16jS+zzDJ1j7nhhhvSz3/+8/Svf/2rVccKAADQkl588cW6IHncAAAAIAiMQwltueWWORAeu8N79uzZ5GMmTJiQxo8fn3beeecWHx8AAAAAAAAUicA4lNAHH3yQLr744vT000+nTz/9NE2fPr3B16PXOAAAAAAAAPD/dPj//wVK5Kc//WneDb711lunRRZZpLWHAwAAAAAAAIVmxziU0Jprrpl3hfft27e1hwIAAAAAAACF1661BwDMuaWWWiq1b9/eSwcAAAAAAACzwY5xKInXXnut7uO777473XTTTemoo45Kyy233AxB8mWXXbYVRggAAAAAAADFJDAOJfG1r30tLbDAAvnj6dOn191fu692f3z+73//u1XGCAAAAABFttpqq+V/77///hk2l/zxj39Mw4cPT4ceemgaOnRo3f233XZbuvTSS9MzzzyTvvSlL6UBAwakQw45JK/X1fz0pz9NN9xwQ5PP2fjn1bzzzjvpsMMOS2PHjk3bbLNNOu200+bpd3vooYdS9+7dU8+ePefp5wBAVXVo7QEAs+fee+/1UgEAAADAPFpwwQXTfffdl7773e82uH/06NENNqGEs88+O40cOTL98Ic/TBtvvHH66KOP0tVXX5123333dP7556f11luv7rFbb711OvbYY2d4vgimN+Xmm29OL774YrrxxhtTt27d5vn/dZ999kmXX365wDgAzITAOJTEl7/85dYeAgAAAACUXuz4bhwYj4D3v/71r9SrV6+6+5588sl03nnnpUsuuSR94xvfqLv/5z//eerYsWMaNmxYuuuuu1KnTp3y/Z07d05LLbXUbI8jnvMrX/mKQDYAtJB2LfVEAAAAAADQ2jbddNP0yCOP5MB0zZ///OccMF9ooYXq7rv22mtT7969GwTFaw4++OD0xhtvpAcffHCuxhA70eP2z3/+M5d3f/jhh3ObxHPPPTcNGjQoj+Wggw5Kr732Wt33PPfcc2m//fZL/fr1S3369El77rlnev755/PXNtlkk/zvkCFD8s+9/vrr6+6r2WuvvfLXaqXf47b99tvnXe+xc/2DDz5IRx55ZFprrbXyGE466aQ0efLkufr9AKCIBMYBAAAAAGgzVl111dSjR4/0wAMP1N13zz33pM0226zB48aPH58D0E1ZfPHF827vJ554Yq7GsO++++ZbBLn/+te/5n//8Ic/pFtuuSX96le/Stdcc01aYokl8mM+//zzNG3atBwoj6qSN910Uy7nPnXq1HT66afXBfFDBL7je2ZH/JwoEX/hhRfm3yXKwH/44Ye513rslB83blzeHQ8AVSEwDgAAAABAm9s1HuXUw2effZb+9re/5fvqe//991PXrl1n+jMWXXTR9N5779V9HkHtCHA3vtXf9V0TO9Oj93j0O4/y61Ga/eKLL05HHXVUWmeddXJ59QhKxxhiV3rs3I6+5rHLe4UVVsg72Xfccce8i7wWqK+Nqf6u91mJoH/sKu/bt296+eWXc4/1CLTHDva4L3aM33DDDTlYDgBVoMc4AAAAAABtSgTBDzvssDRlypT00EMP5V3ksUO7vggyv/XWWzP9GW+++WYaOHBg3ecRZD7iiCNmeFz37t2/cDwff/xxev3119OPfvSj1K7d/+1ni4B4lDmPn73HHnukG2+8Me9k/89//pOeeuqptOSSS6a5FbvPa6Ike+xK32CDDRo8Ju576aWX0te//vW5fh4AKAqBcQAogcjWDvfff39adtllG3wtSpwNHz48HXrooWno0KF19992223p0ksvTc8880zOQo/+ZIccckj62te+VveYyDSP7O+mNP55Ne+8805ePBg7dmzaZptt0mmnnTZPv1ssQMQiQWTDAwAAQEvo379//vexxx7LO6U333zzGR6zxhpr5K83ZeLEiTmQXb/UeuzUXnHFFedqPFEWPfz2t79NK6200gwB+gic77LLLqlbt245SL7tttvm4PjIkSOb/HkLLLDADPdFEkB9nTp1avD8iyyySLruuutm+L4oOw8AVaCUOgCURJRXq5V5qy8m8I0nvNFT7LjjjssT5Sjldskll+TJc5Rdi0B0fVtvvXXuZ9b4NrOeZDfffHPOVo8s9aOPPnqef6999tlnlhn4AAAAML916NAhbbjhhnmeHUnojfuLhwhET5gwIc+7Gzv//PPzbu3GO6znVpRsjx3rEXCP4HrclllmmVza/IUXXkiPPPJI3qF++eWXp/333z994xvfyCXap0+fPtM1hAim18TjXnnllZk+fwTjo2R6rC/Unj92q48YMSKXmgeAKrBjHABKInZ8x4T9u9/9bt19H330UfrXv/6VevXqVXffk08+mc4777w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+      "text/plain": [
+       "<Figure size 2000x2000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_top_k_corr_to_target(df_corr, ax, target, k=10, mode = \"plusminus\"):\n",
+    "  df_corr_target = df_corr[target].sort_values(ascending=False)\n",
+    "  if mode == \"plusminus\":\n",
+    "    mid_k = int(k/2)\n",
+    "    df_corr_target = pd.concat([df_corr_target[1:mid_k], df_corr_target[-mid_k:]], axis=0)\n",
+    "  elif mode == \"plus\":\n",
+    "    df_corr_target = df_corr_target[1:k+1]\n",
+    "  else:\n",
+    "    df_corr_target = df_corr_target[-k:]\n",
+    "  sns.barplot(x=df_corr_target.index, y=df_corr_target.values, ax=ax)\n",
+    "  return ax\n",
+    "\n",
+    "fig, axs = plt.subplots(3,2, figsize=(20,20))\n",
+    "fig.suptitle('Correlation of MOE features with target properties')\n",
+    "for i, property in enumerate(target_features):\n",
+    "  ax = axs[i//2, i%2]\n",
+    "  ax = plot_top_k_corr_to_target(df_corr, ax, property, k = 20)\n",
+    "  ax.set_title(property)\n",
+    "  ax.set_xlabel('MOE feature')\n",
+    "  ax.set_ylabel('Correlation')\n",
+    "  # center and rorate ticks\n",
+    "  ax.tick_params(axis='x', rotation=90, bottom=False)\n",
+    "  # ax.set_ylim(-1,1)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "EwTDIsQnLvQx"
+   },
+   "source": [
+    "# Linear Feature Selection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eBsumfymLy9b"
+   },
+   "source": [
+    "**Inspiration**: Stabl (see J. Hédou et al, 2024, Nature Biotechnology).\n",
+    "\n",
+    "**Idea**: Single cell proteomics has for long had to deal with low sample datasets in high-dimensional predictive configurations (notably where $n << p$). Although, we're not fully concerned with this dimensionality issue here ($n > p$), we believe that this algorithm would provide a powerful performance-agnostic selection method to identify the features that are most stringent for target prediction. \n",
+    "\n",
+    "**Steps**: Train fold with $n$ observations/$p$ parameters. For evey hyperparameter $\\lambda \\in [\\lambda_{min}, \\lambda_{max}]$, we train set of LASSO estimators. We fix $\\lambda$ in the interval:\n",
+    "\n",
+    "> 1. Generate $n_{bootstraps}$ bootstraps of the train fold\n",
+    "> 2. Then generate $p$ artificial (uninformative) features from actual features permutated\n",
+    "> 3. On every bootstrap with $2p$ features, run $LASSO(\\lambda)$.\n",
+    "> 4. Report every selected features frequency in each boostrap: $(f_i^{(\\lambda)})_i$\n",
+    "> 5. Repear steps 1-4 for every $\\lambda \\in [\\lambda_{min}, \\lambda_{max}]$.\n",
+    "\n",
+    "The previous algorithm gives a series of hyperparameters-frequency pairs $(\\lambda_i, f_{j}^{(i)})$. For every frequency threshold $t$ one could apply stability selection and decide to keep all the \"real\" features that have a maximum selection frequency overall hyperparameters and folds $\\geq t$.  At this point, let's remember that Stabl was engineered to rely on artifical uninformative features to allow for data-driven feature selection.\n",
+    "\n",
+    "More specifically, let's choose a given threshold $t \\in [0,1]$. Applying this selection rule, gives us a set of selected features $O_t$, which contains a possibly empty set $A_t$ of artificial features. We compute:\n",
+    "$$FDP_{+}(t) = \\frac{1+\\#A_t}{ \\max(\\#O_t,1)}$$\n",
+    "\n",
+    "The Stabl paper gives nice theoretical guarantees that this quantity can serve as an estimator of the false discovery rate in this set of selected features. Moreover, we choose $t_{opt} = \\arg \\min_{t \\in [0,1]} FDP_{+}(t)$ and derive our final selection method:\n",
+    "\n",
+    "$$\\text{Choose feature across all real and artificial features only if their selection frequency is larger than }t_{opt}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "id": "6vK465zQLx4v"
+   },
+   "outputs": [],
+   "source": [
+    "# import LASSO regression\n",
+    "from sklearn.linear_model import Lasso, LinearRegression\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from tqdm import tqdm\n",
+    "# low variance threshold\n",
+    "from sklearn.feature_selection import VarianceThreshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "id": "XDkNBePYRFfL"
+   },
+   "outputs": [],
+   "source": [
+    "def generate_bootstrap_from_dataset(df_main_dataset, n_bootstraps):\n",
+    "  df_bootstrapped = df_main_dataset.sample(frac=1, replace=True).reset_index(drop=True)\n",
+    "  for i in range(n_bootstraps-1):\n",
+    "    bootstrap_i = df_main_dataset.sample(frac=1, replace=True).reset_index(drop=True)\n",
+    "    df_bootstrapped = pd.concat([df_bootstrapped, bootstrap_i])\n",
+    "  return df_bootstrapped\n",
+    "\n",
+    "def generate_artificial_permuted_features(df_bootstrapped,  target_prop = \"HIC\", fraction = 1.):\n",
+    "  #sampling on columns not row\n",
+    "  df_old = df_bootstrapped.drop(columns = [target_prop])\n",
+    "  d_old_sampled = df_old.sample(frac=fraction, axis=1)\n",
+    "  # shuffle rows\n",
+    "  df_artificial_shuffled = d_old_sampled.sample(frac=1, axis=0).reset_index(drop=True)\n",
+    "  df_artificial_shuffled.columns = [col + \"_artificial\" for col in df_artificial_shuffled.columns]\n",
+    "  df_artificial_suffled = pd.concat([df_bootstrapped.reset_index(drop=True), df_artificial_shuffled], axis=1)\n",
+    "  return df_artificial_suffled\n",
+    "\n",
+    "def fit_lasso_on_one_bootstrap(df_artificial_one, df_target_one, alpha_param, max_iter = 1000):\n",
+    "    selected_features = []\n",
+    "    X_train = df_artificial_one.values\n",
+    "    y_train = df_target_one.values\n",
+    "    # fit lasso with params alpha\n",
+    "    model = Lasso(alpha=alpha_param, max_iter = max_iter)\n",
+    "    model.fit(X_train, y_train)\n",
+    "    selected_features = df_artificial_one.columns[model.coef_ != 0]\n",
+    "    return selected_features\n",
+    "\n",
+    "def train_stabl_proxy_one_alpha(df_main_dataset, target_property, n_bootstraps, alpha_param, fraction = 1., max_iter = 1000):\n",
+    "  df_bootstrapped = generate_bootstrap_from_dataset(df_main_dataset, n_bootstraps)\n",
+    "  df_artificial = generate_artificial_permuted_features(df_bootstrapped, target_property, fraction)\n",
+    "  df_artificial_no_target = df_artificial.drop(columns = [target_property])\n",
+    "  df_artificial_target = df_artificial[target_property]\n",
+    "  # print(df_artificial_target.isna().sum())\n",
+    "  n_main_dataset = df_main_dataset.shape[0]\n",
+    "  all_selected_features = {}\n",
+    "  # for i in tqdm(range(n_bootstraps)):\n",
+    "  for i in range(n_bootstraps):\n",
+    "      df_artificial_one = df_artificial_no_target.iloc[n_main_dataset*i:n_main_dataset*(i+1)]\n",
+    "      df_target_one = df_artificial_target.iloc[n_main_dataset*i:n_main_dataset*(i+1)]\n",
+    "      selected_features = fit_lasso_on_one_bootstrap(df_artificial_one, df_target_one, alpha_param, max_iter = max_iter)\n",
+    "      all_selected_features[\"bootstrap_\"+str(i)] = selected_features\n",
+    "  return all_selected_features\n",
+    "\n",
+    "def compute_fdr_and_feature_freq(all_selected_features, n_bootstraps):\n",
+    "  n_artif_features = 0\n",
+    "  n_tot = 0\n",
+    "  feature_occ = {}\n",
+    "  for bootstrap in all_selected_features:\n",
+    "    features = all_selected_features[bootstrap]\n",
+    "    for feature in features:\n",
+    "      if \"artificial\" in feature:\n",
+    "        n_artif_features += 1\n",
+    "      n_tot += 1\n",
+    "      if feature in feature_occ:\n",
+    "        feature_occ[feature] += 1\n",
+    "      else:\n",
+    "        feature_occ[feature] = 1\n",
+    "  feature_freq = {}\n",
+    "  for feature in feature_occ:\n",
+    "    feature_freq[feature] = feature_occ[feature]/n_bootstraps\n",
+    "  return feature_freq\n",
+    "\n",
+    "def merge_all_feature_freq(every_feature_freq):\n",
+    "  merged_feature_freq = {}\n",
+    "  alpha_params = list(every_feature_freq.keys())\n",
+    "  for alpha in alpha_params:\n",
+    "    for feature in every_feature_freq[alpha]:\n",
+    "      if feature not in merged_feature_freq:\n",
+    "        merged_feature_freq[feature] = {\"1/alpha\":[1/alpha], \"freq\":[every_feature_freq[alpha][feature]]}\n",
+    "      else:\n",
+    "        merged_feature_freq[feature][\"freq\"].append(every_feature_freq[alpha][feature])\n",
+    "        merged_feature_freq[feature][\"1/alpha\"].append(1/alpha)\n",
+    "  return merged_feature_freq\n",
+    "\n",
+    "def get_fdp_curve(merged_feature_freq, n = 1000):\n",
+    "  t = np.linspace(0,1,n)\n",
+    "  max_freqs = {}\n",
+    "  for feature in merged_feature_freq:\n",
+    "    max_freqs[feature] = np.max(merged_feature_freq[feature][\"freq\"])\n",
+    "  arr_freq_artif = np.array([max_freqs[feature] for feature in merged_feature_freq if \"artificial\" in feature])\n",
+    "  arr_freq_all = np.array([max_freqs[feature] for feature in merged_feature_freq])\n",
+    "  fdp = np.zeros(len(t))\n",
+    "  for i in range(len(t)):\n",
+    "    selected_all = np.sum(1*(arr_freq_all >= t[i]))\n",
+    "    selected_artif = np.sum(1*(arr_freq_artif >= t[i]))\n",
+    "    fdp[i] = (1+selected_artif)/np.max([selected_all, 1])\n",
+    "  return fdp, t"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_PQnJPj4QRAG"
+   },
+   "source": [
+    "## Feature selection on HIC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 88,
+     "status": "ok",
+     "timestamp": 1761929782413,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "hQ1zPi6AQU1y",
+    "outputId": "670716d4-b82b-4e98-f6d6-9842b72c8ccd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features with low variance: Index(['debye'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "alpha_params = list(np.linspace(0.01, 1, 100))\n",
+    "# n_bootstraps = 100\n",
+    "fraction = 1.0\n",
+    "target_property = \"HIC\"\n",
+    "df_target_no_nan = df_target[target_property].dropna()\n",
+    "df_main_dataset = df_master_filtered\n",
+    "df_main_dataset = df_main_dataset.loc[df_target_no_nan.index]\n",
+    "\n",
+    "variance_threshold = 0.\n",
+    "selector = VarianceThreshold(threshold=variance_threshold)\n",
+    "df_main_dataset_high_v = pd.DataFrame(selector.fit_transform(df_main_dataset), columns=selector.get_feature_names_out())\n",
+    "df_main_dataset_high_v.index = df_main_dataset.index\n",
+    "features_low_var = df_main_dataset.columns[~selector.get_support()]\n",
+    "print(f\"Features with low variance: {features_low_var}\")\n",
+    "\n",
+    "df_data = (df_main_dataset_high_v - df_main_dataset_high_v.mean()) / df_main_dataset_high_v.std()\n",
+    "df_data[target_property] = df_target_no_nan\n",
+    "# n_bootstraps = df_data.shape[0]\n",
+    "n_bootstraps = 500\n",
+    "every_fdr = np.zeros(len(alpha_params))\n",
+    "every_feature_freq = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 686586,
+     "status": "ok",
+     "timestamp": 1761930470795,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "8GVCOyKPdQjs",
+    "outputId": "479021f3-b973-4d70-e89d-c0bc424bc0f8"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [08:56<00:00,  5.36s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tqdm(range(len(alpha_params))):\n",
+    "  alpha = alpha_params[i]\n",
+    "  all_selected_features = train_stabl_proxy_one_alpha(df_data, target_property, n_bootstraps, alpha, fraction)\n",
+    "  feature_freq = compute_fdr_and_feature_freq(all_selected_features, n_bootstraps)\n",
+    "  every_feature_freq[alpha] = feature_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "id": "R0Y0h-qL-_HT"
+   },
+   "outputs": [],
+   "source": [
+    "merged_feature_freq = merge_all_feature_freq(every_feature_freq)\n",
+    "fdp, t = get_fdp_curve(merged_feature_freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 311,
+     "status": "ok",
+     "timestamp": 1761931302809,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "9RbNkB_JBG2T",
+    "outputId": "7f182796-7997-40c8-ddba-8b0f34a0e095"
+   },
+   "outputs": [
+    {
+     "data": {
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1VAo7UqiUHvdPfvKTrOXSF7/4xfHZL//1X/8VS5Ysyap00if/07FJ15dt6L4+8pGPZNeneRSpiiK9mZ7aK02cX1GWHn+6PAVVaT2pBdRk0nFPj3d9p/VJ61+Yfs8mSK2fHn300fXePlUwpOvSzyvN/EhBUflN+cn2ld7Q35T7SvtOs0NScJXCrPRY77nnnvHvTW24Wltbx783/b6kn9GKFSs2ad8tLS1ZK7GnP/3p8Q//8A9ZW6zyvpOJ35/2naTv39jjSmFEarWVKm3SutOx+f3vfz8etqU1Tvz+5ubmrD1aed8belxp3Smk+fKXvxx/93d/l815ufDCC8dvm479ZI97Y48rBafpPtL+UoiYfkfT736elVxaewEAAAAAhZYGiSezZ89e7/Xly9Obr0l6gze1Ekpv3KY2TmkmSQo40pvJqTIgvXE9Wcug1H4ovXGbpEHZqe1TakGU9pkCljTvIkkBQppHkS5LUlXA0572tKz90YakipryjIqJa0iBSqruSFJwkQZ7pzfCU9XIxIHkSQou0mP7p3/6p+zr9Mn99Gn+FJqkipW03dnZma0pzZVILZMmmuy+0n7SsPLUzikNAU9SZU+qWrn88svXqMpIjzNVPqTWUqnSIIVMaZ5Gaoe1Pmk96bSpUnAx8U38JH1dDsvWltaf2lulORzpDff//M//zKpp0rHa2L42dn3ad/odTBVHKZhIxycNj//5z38+6fcm6fs3tu8UyKR9p0H36fcs/Z6mdlvp9y7NPpm4v83dd1p32kf6vU8zYFJAlobPp4CxHFxM9v2jo6MbfFzlapc0syiFP+n/sVS5lY7Pc5/73Ox+J9v3xh5X2neSfse/9KUvZaFoCjDT/zvpuOdBkAIAANSPNIDypptWbwMAm25C66d1rNVOK8Y+6b9eaw98v/ferf4plIOH9En17bbbbp3ry5UH6dP0ya677pq9CVv25Cc/Oauu2BQT2ySlweIpJCi3fEpfp9ZHSQod0pu/6Y3eFCqkU2rT9JKXvGSLHuPE9kuzZs3KztOn/8t23HHH8e30RnOqMpnYdim1aiq3PUutw1JAkgaMp1Nq/ZUGqW/svtLQ8xQQlcOb8jFNAUqqQJgYpJSHu6fjU5YqhiYLUlLlQnnQ+NrKragmSq2u1g5N0tcTh71PlEKN9HMqX5+qclKAkFpJbWxfG7s+VUOkn3s5CErhUmp9lkKkyb43Sd+fri8HfOvbd5r9k4KF8pD2M888M6699tos0ErBXPn25dZf5X2ngGxj606/A6mKpvz/Txr6nqq7UliTKnYm7q8sfZ32nX6fNvS4UmCXQsfy/3Np3+n3J1VBpSBlsrWlfU8MTdb3uFK1VJotkypikhQgLlu2LNu3IAUAAGBrpTduDjjAcQSALbEZ1QK53XYS6Q36FBKkiof1BSnp8vRma/kN2tSiaKL0pnDj2gHPJNb+3sm+LwUZaRZLaj2UZkGkN3g31NpqY9ae/ZJMbGU0cYZGareVApIPf/jD691Xui61cEozXC699NLsdqmFVgoANnRf65vTUT5+62vttXarpVTpM5lXvOIV45U+m2LRokVZcDZR+nrtdlFl6Q34iVKQlt7kT22uJttXuSJoY/eVfq8mVk+k45QCt7TvNEtl+fLl2c+k/LuTWlelsCFVSqV9p9Bpsn2n7ymHKEm5yqO87vL+ygFfuS1WWvvGHlf63S2HKGVp3+UQLD2OdPs999wzuy49hhT6pO9PP9sNPa60znKIMnHfKQDZ0DEtr3tDjysphyhlaY3llmV5MCMFAAAAACi01LIptdlKcz7Sm/oTpaHVaX5JucVWkgbETxz6noKW1OIrKVeXbK1UMZCGZf+f//N/sjkpaeh7ut9NmeOwtWtIlSGpJVR6EzpV36RTGl7+3//939n13/jGN7LKgzQAPrX1+uQnPxm/+tWvNrrfVKmS3jSfONA9vZmeHtfaw9bT8UwtqdJ1ZakF02TSm+7lta59Wp9UFZMqVcrHM52nSo2J1TJlafB5+lmU38RP0pvuae3pzf00RD21O5s4X+Waa67JLt/YfaXt9LuXZuaU9fT0ZI877Xu//fZb55ilfafwLwUZaR/pZ1FuZ1W+vvw4UsXIOeecM35dCqxuu+22bN8pcEgVP+n2E783XZaCmI09rn/913/NWp2tHQCmfae1pTVO3Pd1112XPZZUXbKxx5V+r9auDinvu3xMJ+47/X+aTunyjT2u1I4tVVFN/H8p/W6V950HQQoAAFA/UkuAM88snSbpnw0AFNMHP/jB7I37NOj7L3/5Szz88MPZzIfUxuqoo47KwoyJb3SnoempHVVqY5TaTZWvT5ULqRXYgw8+uFXrScFAesM7DQZPoUYaAp+G3E82w2N91RMp4JnYvmtTpXkp6Y35VGmSHmOqPvn4xz8+3s4svbH+sY99LHsTPLVb+uUvfxn777//RvebWlelWR2p3dRVV12VvTF+xhlnZFVAaRD62hUCqfIlDapPt7vkkkviW9/6VkyVE044IRuInh5XqqBI52kmSLmqJT3+chVDqug4/PDDs8Ao/TxScJFaV6Wh86lSKQVEqc1XeixpremN+p/97Gfx6le/eqP3lUKvNCPm85//fHZMUgu3NLw9HZPU3iv9LFObq9SSK913Og5p5kz6vUzS7+b222+fBRrpe7/yla9kt0uD35NU0ZSCr1Q9lFq2pZ/bqlWrshAsSVVPqZIo3Xc6peCuvO/y40rruf3227NAceLjSvv+6U9/Gj/60Y+y4CcFNimwSDNNkvT/RGpbdskll2RrSo8hBZLpMW3scaW2XmkuSvr++++/P77zne9k9/OGN7xhfN0pbEzHOh3ztMZ0HMtt5Tb0uFJLs/SzPeuss7J1p7ZtaS5Nea5PHsxIAQAA6kfqWf7Rj5a2zzgj9WGo9IoAgCmSPsWeQpFUlfKe97wnm5mQ3pRNLaPScO6JLbjSG9epRVB6szqdf/rTn87eaE/S/IY0L+XEE0+M3/72t1u8nlRJcMstt2Sfyk8tklJFRBpEn9703Zj05n4KJtLa/+///b+bfd/lgeef+MQnsje7U6iT3jx/y1vekl3/rne9K3szPg0WT6FSWls6Bpvife97X/YGdhp8nkKh9KZ2eqN/7cHhSRrongaMp8eRqgnSMZlYubE10mNMM1VSIJZ+7umYpRCio6Mjuz4NY0/hRAqzkrTmFGaloepp3c9+9rPjQx/60Pj+zj777CyMS0FB+p1Ixy5VEW3KfaUAJlVnvPvd786qX44++ujs+nKLtLSOFDik38O0r3e84x1x/PHHZ9el26Tf2XTfL33pS7MKnC984Qvjs2XS708K09Iw9dT6KlVsfP3rXx9v9/XGN74xHn/88Tj99NOzfaXf6YmVIOXHle577ceV1pAeU5rjk4LHJz3pSfG1r31tvJ1W+n8gVbR8+MMfzo5Zun16rGUbelzpPlJVSppFk87TDJ8UhpTn9qTzFAql61MAmn7fU0BXtqHHlfaVjm/6nU1zUVJAmP6ff8ELXhB5aRjdlFqyGpBK+lLCmsqW1tfjr145LoDnGaDovJ5hs6QWHuUe02lg7hT0ZKf2eZ4B6uk5Jn2KP1VPpDZNkw3tLrr0Rn765P3WhCRQJCkCSIFZCn6mqnVdkWzoeW1Tn4O19gIAAAAAAJiEIAUAAAAAAGASghQAAAAAoG6kORTaegGbQ5ACAAAAAAAwCUEKAAAAALDOcGqAWjA6Bc9nVRGkDAwMxAtf+MK46qqrJr3NLbfcEqecckocfPDB8bKXvSxuuummaV0jAABQA9rbI66+unRK2wDAGlpaWrLznp4eRwaoCT1jz2fl57ct0RwV1t/fH+9+97vjjjvu2OADffOb3xwvetGL4lOf+lR897vfjbe85S3x61//Ojo6OqZ1vQAAQIE1NUUceWSlVwEAVaupqSnmzJkTixcvzr5O7701NDRUelnAVlZkpPfhGxsb6+r/59HR0SxbSM9n6XktPb8VMki58847sxBlY6U1F110UbS1tcV73/ve7Af9wQ9+MP7whz/ExRdfnA2HAgAAAACmxnbbbZedl8MUoNjS+++Dg4NZRUY9BSllKUQpP68VMki5+uqr4ylPeUr88z//cxxyyCGT3u7666+Pww8/fPyHnM4PO+ywuO666wQpAADAphsYiPjc50rb73pXRGurowcAa0nvvW2//faxcOHC7M1XoPot7x6Imx56IsUm61w3MjoafcseiecctddWVWUUUQqPpuIxVzRIedWrXrVJt1uyZEnstddea1w2f/78DbYDm8zw8PBmf08tKx8PxwXwPAMUldczbJa+vmh673tLvztveUup1Rd4ngEqqNpfy2zNTAFg+rzlO3+JG7IgZXK/O2Aodplff3MChzfw/Lqpz70Vn5GyKXp7e6N1rU+Kpa/TkPrNdeONN07hymqH4wJ4ngGKzusZNkVjb28cOrZ9ww03xMiMGQ4cnmeAquC1DLA17n98VXa++5zmaGtat33XdjObYvF9d8SyB+qvtddUKESQkuajrB2apK/b2zc/PTvwwAPrrnxpQ1Lilv6hdlwAzzNAUXk9w2bp7h7fPOiggyI6Ox1APM8AFeW1DDAVmn/x2/SueZzzmqfEvtvN8jyzmc/BNRGkLFq0KJYuXbrGZenr1Kdxc6UQRZDiuADTz/Mv4HmGqjDhQ1XZ3wU+ZMVm/fr4exLIj+cYYGqeSxonff/b88yWa4wCOPjgg+Ovf/1rjI6WBuWk82uvvTa7HAAAAAAA6tnYW+fREFp31VWQkgbM9/X1ZdsnnHBCrFy5Mj7+8Y/HnXfemZ2nuSnPf/7zK71MAAAAAACoqLEcJRrkKPUVpBxzzDFx0UUXZdszZ86Mc889N6655pp46UtfGtdff3185StfiY6OjkovEwAAAAAAKqrczUmOko+qmZFy2223bfDrNAjywgsvnOZVAQAANaW9PeJ3v1u9DQAANUBFSp0EKQAAALlLgzePPdaBBgCgRqlJqavWXgAAAAAAwKYPmycfKlIAAID6MTgY8ZWvlLbf/OaIlpZKrwgAAKZuRoqClFwIUgAAgPoxMBBx+uml7de9TpACAEBtzUip8DpqldZeAAAAAABQA0lKg5KUXAhSAAAAAACgwFSk5EuQAgAAAAAABWZGSr4EKQAAAAAAUBMVKaak5EGQAgAAAAAABTY6PiOl0iupTYIUAAAAAAAosNHxmhTy0JzLXgEAAKpRW1vEz362ehsAAGqAipR8CVIAAID60dwcceKJlV4FAADkMyNFb69caO0FAAAAAABFVp6RUul11CgVKQAAQP0YHIz49rdL269+dURLS6VXBAAAUzYjRUFKPgQpAABA/RgYiHj960vbp5wiSAEAoLZmpKhJyYXWXgAAAAAAUBMzUiq8kBolSAEAAAAAgAIbHStJkaPkQ5ACAAAAAAA1UJEiScmHIAUAAAAAAArMjJR8CVIAAAAAAKAGmJGSD0EKAAAAAAAUfD5KYkZKPppz2i8AAED1aWuL+P73V28DAEDBTchRokFJSi4EKQAAQP1obo445ZRKrwIAAKbMhBxFRUpOtPYCAAAAAIBaaO2lt1cuVKQAAAD1Y2go4sILS9snn1yqUAEAgJqpSJGk5MFfDQAAQP3o7494+ctL211dghQAAGpqRoocJR9aewEAAAAAQEGNTqhJ0dorH4IUAAAAAACASQhSAAAAAACgBlp7mZCSD0EKAAAAAADUgAa9vXIhSAEAAAAAgIJSkZI/QQoAAAAAABSUYfP5a56G+wAAAKgOra0RX//66m0AAKipihRTUvIgSAEAAOpHS0vE615X6VUAAMCUmZCjhBEp+dDaCwAAAAAACmp0YkkKuVCRAgAA1I+hoYhf/rK0/bznRTT7kwgAgGJTkZI/fzUAAAD1o78/4oUvLG13dQlSAAAoPDNS8qe1FwAAAAAAFNXEYfNmzedCkAIAAAAAAAU1OiFJkaPkQ5ACAAAAAAC10NpLSUouBCkAAAAAAFALw+YruI5aJkgBAAAAAICCGp1QkqIgJR+CFAAAAAAAqIWKFElKLprz2S0AAEAVam2NOOec1dsAAFBDM1LIhyAFAACoHy0tEaedVulVAADAlBkdq0lRjJIfrb0AAAAAAKCoxipSDJrPj4oUAACgfgwPR1x2WWn7Gc+IaGqq9IoAAGCrlDt7mY+SH0EKAABQP/r6Io47rrTd1RXR2VnpFQEAwJTMSFGRkh+tvQAAAAAAoKDMSMmfIAUAAAAAAApfkaImJS+CFAAAAAAAKPiMFDlKfgQpAAAAAABQUKNjJSnqUfIjSAEAAAAAgKK39pKk5EaQAgAAAAAABWdGSn6ac9w3AABAdWlpiTj77NXbAAAAGyFIAQAA6kdra8QZZ1R6FQAAMGW09sqf1l4AAAAAAFBQo2HYfN5UpAAAAPVjeDji2mtL24cdFtHUVOkVAQDAFFWkmDafF0EKAABQP/r6Io46qrTd1RXR2VnpFQEAwFYZy1FCjJIfrb0AAAAAAKCgRsdLUiq9ktolSAEAAAAAgIJSkZI/QQoAAAAAABSUGSn5E6QAAAAAAEDBa1LMms+PIAUAAAAAAArKiJT8CVIAAAAAAKDoM1KUpOSmOb9dAwAAVJmWloiPfGT1NgAAFJyKlPwJUgAAgPrR2hpx5pmVXgUAAEyZUTNScqe1FwAAAAAAFLwiJaKhsgupYSpSAACA+jEyEvG3v5W299svotFnywAAqJHWXnKU3AhSAACA+tHbG/HkJ5e2u7oiOjsrvSIAAJia1l6OY258/AoAAAAAAApKRUr+BCkAAAAAAFBwDWpSciNIAQAAAACAglKRkj9BCgAAAAAAFJQZKfkTpAAAAAAAQOErUoybz4sgBQAAAAAACmosRyFHzXnuHAAAoKq0tES85z2rtwEAoOBGx0pSFKTkR5ACAADUj9bWiE9/utKrAACAKa9IEaTkR2svAAAAAAAo+oyUMCMlLypSAACA+jEyEnH//aXtXXaJaPTZMgAAik5rr7wJUgAAgPrR2xux++6l7a6uiM7OSq8IAACmhHqU/Pj4FQAAAAAAFLy1F/kRpAAAAAAAQOGHzatJyYsgBQAAAAAACj9snrwIUgAAAAAAoKBGJSm5E6QAAAAAAEDRW3tVeB21TJACAAAAAAAFNV6QYkZKbprz2zUAAECVaW6OePvbV28DAEDBjY7VpKhIyY+/HAAAgPrR1hbxhS9UehUAADB1xitSHNS8aO0FAAAAAACFn5EiScmLihQAAKC+GkgvXVraXrDAx/YAAKihGSmVXkntEqQAAAD1o6cnYuHC0nZXV0RnZ6VXBAAAUzIjhfxo7QUAAAAAAIWvSFGSkhdBCgAAAAAAFH5GCnkRpAAAAAAAQEGNjpWkKEjJjyAFAAAAAACKXpGiJCU3ghQAAAAAACiq8owUzb1yI0gBAAAAAICCGh1LUlSk5Kc5x30DAABUl+bmiFNPXb0NAAAFNzYiRT1KjvzlAAAA1I+2tohvfKPSqwAAgCkPUpSk5EdrLwAAAAAAKKjxHKXC66hlKlIAAID6+rheT09pu6PDp/YAACi80bGSFDNS8qMiBQAAqB8pRJk5s3QqByoAAFBgKlLyJ0gBAAAAAICiD5tXkpIbQQoAAAAAABTWWGuvSi+jhglSAAAAAACg8BUplV5J7RKkAAAAAABA4WekSFLyIkgBAAAAAICik6PkRpACAAAAAAAFb+1Ffppz3DcAAEB1aWqK+Pu/X70NAAAFN2rYfO4EKQAAQP1ob4+44IJKrwIAAKaMYfP509oLAAAAAAAKyrD5/AlSAAAAAACgoEbHSlIaDJvPjSAFAACoH93dpb8w0yltAwBAjRCk5EeQAgAAAAAARZ+REkpS8iJIAQAAAACAghodm5KiIiU/ghQAAAAAACh4RQr5EaQAAAAAAEDRW3spScmNIAUAAAAAAAqqXJBiQkp+BCkAAAAAAFBQo2MlKQpS8tOc474BAACqS1NTxAtesHobAAAKTkVK/gQpAABA/Whvj/j5zyu9CgAAmDpmpOROay8AAAAAACio0bEkxYyU/AhSAAAAAACgoMZGpJiRkiNBCgAAUD+6uyM6O0untA0AADUyI0VNSn7MSAEAAOpLT0+lVwAAAFNGRUr+VKQAAAAAAEBBmZGSP0EKAAAAAAAUlIqU/AlSAAAAAACg4DNSGqKhwiupXYIUAAAAAAAoeElKgxwlN4IUAAAAAAAoekWKICU3zfntGgAAoMo0NkY885mrtwEAoFZmpGjtlRtBCgAAUD9mzIi49NJKrwIAAKbM6OokhZz4CBYAAAAAABR+2Dx5EaQAAAAAAABMQpACAADUj+7uiG23LZ3SNgAAFNx4Zy/T5nNjRgoAAFBfli6t9AoAAGDKaO2VPxUpAAAAAABQ8GHzClJqNEjp7++PD3zgA3HEEUfEMcccE+edd96kt/31r38dz3/+8+PQQw+NV77ylXHzzTdP61oBAAAAAKBaGTZfo0HK2WefHTfddFOcf/758ZGPfCTOOeecuPjii9e53R133BHvfve74y1veUv8+Mc/jv322y/b7u3trci6AQAAAACgGpiRUsNBSk9PT1xwwQXxwQ9+MA444IB47nOfG29605vi29/+9jq3vfzyy2OvvfaKk046KXbZZZf4l3/5l1iyZEnceeedFVk7AAAAAABUg9GxKSkqUmowSLn11ltjaGgoa9VVdvjhh8f1118fIyMja9x2zpw5WWhyzTXXZNf98Ic/jJkzZ2ahCgAAAAAA1HtFiiQlP81RIamiZO7cudHa2jp+2YIFC7K5KStWrIh58+aNX/6CF7wgfvvb38arXvWqaGpqisbGxjj33HNjm2222ez7HR4enrLHUAvKx8NxATzPAEXl9QybZXQ0Go84ItscSX9x+vsAzzNAhXktA2z180i5MGF0dL3v83qemdymvi9esSAlzTeZGKIk5a8HBgbWuHz58uVZ8PLhD384Dj744Pjud78b73//++PCCy+M+fPnb9b93njjjVOw+trjuACeZ4Ci83qGTfblL5fOb7vNQcPzDFA1vJYBttTDD3eNv49+3XXXeZ7JQcWClLa2tnUCk/LX7e3ta1z+mc98Jvbee+949atfnX397//+7/H85z8/fvCDH8Sb3/zmzbrfAw88MKtqYXXilv6hdlyAvHieAfLmeQbwPAMUmdcywNb60xN3R9x4e8yfNy8OOeRAzzNb8BxctUHKokWLsoQszUlpbi4tI1WdpBBl9uzZa9z25ptvjte85jXjX6fWXvvuu288/PDDm32/KUQRpDguwPTz/At4ngGKzusZwHMMUJUaSmPmGxsbNvjet9cyBRw2v99++2UBysRSozRMPlVGpKBkooULF8Zdd921xmX33HNP7LTTTtO2XgAAoAb09ETstlvplLYBAKBGNJg2X3tByowZM+Kkk06KM888M2644Ya45JJL4rzzzovXvva149UpfX192fbLX/7y+P73vx8/+tGP4r777stafaVqlJNPPrlSywcAAIooDZi/777SKW0DAEDBjY69rh0rTCEHFWvtlaSB8SlIOfXUU2PmzJnxjne8I44//vjsumOOOSY++clPxktf+tJ4wQteEN3d3XHuuefGo48+mlWznH/++Zs9aB4AAAAAAGpJ+fNBgpQaDVJSVcpZZ52VndZ22223rfH1Kaeckp0AAAAAAICS1XXWSlJqrrUXAAAAAACwdVSk5E+QAgAAAAAABTU6VpOiHiU/ghQAAAAAACgoFSk1PiMFAABgWqUJnPvvv3obAABqZEZKg5qU3AhSAACA+tHREXHzzZVeBQAATHlJis8J5UdrLwAAAAAAKHxFCnkRpAAAAAAAQOFnpIhS8iJIAQAA6kdPT8QBB5ROaRsAAApudLwmhbyYkQIAANTXx/VuuWX1NgAA1ExFSqVXUrtUpAAAAAAAQOFnpEhS8iJIAQAAAACAglORkh9BCgAAAAAAFJSOtfkTpAAAAAAAQMGHzWvslR9BCgAAAAAAFJVh87lrzv8uAAAAqqhx9K67rt4GAIBaGTbv9W1uBCkAAED96OiIuPfeSq8CAACmzOjYkBQfE8qP1l4AAAAAAFD0YfOSlNwIUgAAAAAAoKBW5yiSlLwIUgAAgPrR2xtx5JGlU9oGAIAaqUgxIiU/ZqQAAAD1Y2Qk4i9/Wb0NAAAFNzpWk6IeJT8qUgAAAAAAoKBUpORPkAIAAAAAAAVnRkp+BCkAAAAAAFBQo2MlKWak5EeQAgAAAAAABTU2a96MlBwJUgAAAAAAoOAzUpSk5Kc5x30DAABUnwULKr0CAACYMqNjNSkNjmluBCkAAED96OyMWLKk0qsAAIApr0gxIyU/WnsBAAAAAEDhZ6SoScmLIAUAAAAAAApKRUr+BCkAAED96O2NOPbY0iltAwBA4ZmRkjczUgAAgPoxMhLx+9+v3gYAgIJTkZI/FSkAAAAAAFD4IMWMlLwIUgAAAAAAoKBGx8fNkxdBCgAAAAAAFJTWXvkTpAAAAAAAQEGV61EaQmuvvAhSAAAAAACgoFSk5K95Gu4DAACgenR0VHoFAAAw5TNS1KPkR5ACAADUj87OiO7uSq8CAACmXIMkJTdaewEAAAAAQNGHpJAbQQoAAAAAABSUYfP5E6QAAAD1o68v4sQTS6e0DQAABTc6Nm1ea6/8mJECAADUj+HhiIsuWr0NAAAFp7NX/lSkAAAAAABAQY0VpESDkpTcCFIAAAAAAKDwM1LIiyAFAAAAAAAKyoyU/AlSAAAAAACgoFSk5E+QAgAAAAAARWVGSu4EKQAAAAAAUFCjY0mKWfP5ac5x3wAAANWlszM1ka70KgAAYMqUX94aNp8fFSkAAAAAAFBQ458TUpKSG0EKAAAAAAAUvbVXpRdSwwQpAABA/ejrizjllNIpbQMAQK209pKk5EaQAgAA1I/h4Yj//d/SKW0DAEDBjXf2UpOSG0EKAAAAAAAUlIqU/AlSAAAAAACgsMxIyZsgBQAAAAAACkpFSv4EKQAAAAAAUFBmpORPkAIAAAAAAAU1Ol6SUumV1C5BCgAAAAAAFL4ihbw057ZnAACAatPREdHVtXobAABqZkaKKCUvghQAAKB+pD8uOzsrvQoAAJgyKlLyp7UXAAAAAAAUfEaKgpT8CFIAAID60d8f8brXlU5pGwAAaoQgJT+CFAAAoH4MDUWcf37plLYBAKBWZqQYN58bQQoAAAAAABScipT8CFIAAAAAAKCgRsfHzZMXQQoAAAAAABS8tRf5EaQAAAAAAEDRZ6To7ZUbQQoAAAAAABS8tVdDpRdSwwQpAAAAAABQ+IqUSq+kdjVXegEAAADTpqMjYvHi1dsAAFBw5REpDWpSciNIAQAA6kf6mN6221Z6FQAAMHVUpOROay8AAAAAACgoM1LyJ0gBAADqR39/xGmnlU5pGwAACs6MlPwJUgAAgPoxNBTxxS+WTmkbAABqZEZKmpJCPgQpAAAAAABQUKNjJSlpHCD5EKQAAAAAAEDBK1LkKPkRpAAAAAAAQOFnpIhS8iJIAQAAAACAglKRkj9BCgAAAAAAFJUZKbkTpAAAAAAAQNErUnT2yk1zfrsGAACoMjNmRNxzz+ptAAColRkpxs3nRpACAADUj8bGiN12q/QqAABgyoyWa1JUpORGay8AAAAAACh8RQp5EaQAAAD1Y2Ag4owzSqe0DQAAtRKkGJKSG0EKAABQPwYHIz7zmdIpbQMAQK0Mm6/wOmqZIAUAAAAAAApqdKwkRUFKfgQpAAAAAABQcA1qUnIjSAEAAAAAgMLPSKn0SmqXIAUAAAAAAApqdGxKihwlP4IUAAAAAAAoeEWKJCU/ghQAAAAAACg4M1Ly05zjvgEAAKrLjBkRN920ehsAAAquXJBCfgQpAABA/WhsjDjggEqvAgAApszoWG8vw+bzo7UXAAAAAAAUlBEp+VORAgAA1I+BgYhPfKK0/YEPRLS2VnpFAAAwJUlKg5KU3AhSAACA+jE4GPHRj5a2zzhDkAIAQO1UpDRUeCE1TGsvAAAAAAAo+oyUSi+khglSAAAAAACgoFSk5E+QAgAAAAAABTVWkKImJUeCFAAAAAAAKKjRsZoUM1LyI0gBAAAAAICCKlekmJGSH0EKAAAAAAAUPUhRkpKb5vx2DQAAUGXa2yOuvnr1NgAA1AgVKfkRpAAAAPWjqSniyCMrvQoAAJgyo2MlKQpS8qO1FwAAAAAAFNRYZ69oUJOSGxUpAABA/RgYiPjc50rb73pXRGtrpVcEAABTNCPFgcyLIAUAAKgfg4MR731vafvtbxekAABQeKPjNSnkRWsvAAAAAAAoKBUp+ROkAAAAAABAQZmRkj9BCgAAAAAAFJSKlPwJUgAAAAAAoOA1KYbN50eQAgAAAAAARa9IiYZKL6VmCVIAAAAAAKDoM1LkKLlpzm/XAAAAVaa9PeJ3v1u9DQAABTc6VpIiR8mPIAUAAKgfTU0Rxx5b6VUAAMCUUZGSP629AAAAAACg4DNS1KTkR0UKAABQPwYHI77yldL2m98c0dJS6RUBAMDUtPbS2ys3ghQAAKB+DAxEnH56aft1rxOkAAAAG6W1FwAAAAAAFH1GSoXXUcsEKQAAAAAAUPAkpUFvr9wIUgAAAAAAoKBUpORPkAIAAAAAAAVl2Hz+BCkAAAAAAFD4ihRTUvIiSAEAAAAAgIIaHZ+RUumV1K7mSi8AAABg2rS1RfzsZ6u3AQCg4EbHa1LIiyAFAACoH83NESeeWOlVAADAlFGRkj+tvQAAAAAAoOgzUvT2yo2KFAAAoH4MDkZ8+9ul7Ve/OqKlpdIrAgCArVOekeI45kaQAgAA1I+BgYjXv760fcopghQAAGpmRoqClPxo7QUAAAAAAEWfkaImJTeCFAAAAAAAKPyMlAovpIYJUgAAAAAAoKBGx0pS5Cj5EaQAAAAAAEDBK1IkKfkRpAAAAAAAQEGZkZI/QQoAAAAAABScGSn5ac5x3wAAANWlrS3i+99fvQ0AADUwHyUxIyU/ghQAAKB+NDdHnHJKpVcBAABTYkKOEg1KUmqztVd/f3984AMfiCOOOCKOOeaYOO+88ya97W233RavfOUr46CDDooXvehF8ac//Wla1woAAAAAANVkQo6iIqVWg5Szzz47brrppjj//PPjIx/5SJxzzjlx8cUXr3O7VatWxRve8IbYa6+94qc//Wk897nPjdNPPz0ef/zxiqwbAAAoqKGhiAsuKJ3SNgAA1EprL729aq+1V09PT1xwwQXx1a9+NQ444IDsdMcdd8S3v/3tOOGEE9a47YUXXhgdHR1x5plnRlNTU7zzne+M3//+91kI88xnPrNSDwEAACia/v6Il7+8tN3VVWr1BQAANVGRIknJS8X+arj11ltjaGgoDj300PHLDj/88Pjyl78cIyMj0di4uljm6quvjmc/+9lZiFL2gx/8YNrXDAAAAAAA1TgjRY5Sg0HKkiVLYu7cudHa2jp+2YIFC7K5KStWrIh58+aNX/7AAw9ks1H+7d/+LX7729/GjjvuGO973/uy4GVzDQ8PT9ljqAXl4+G4AJ5ngKLyeobN/IWJpom/O/4+wPMMUGFeywBbY2h4ZHx7dGQ4hofXnebheWZym/q+eMWClN7e3jVClKT89cDAwDptwL7yla/Ea1/72qwV2M9//vN44xvfGL/4xS9i++2336z7vfHGG6dg9bXHcQE8zwBF5/UMm6KxtzfKNfE33HBDjMyY4cDheQaoCl7LAFtiYHh0jeeRjpbJx6J7ntlyFQtS2tra1glMyl+3t7evcXlq6bXffvtls1GS/fffPy6//PL48Y9/HG9961s3634PPPDANVqE1buUuKX/gRwXwPMMUFRez7BZurvHN1PVe3R2OoB4ngEqymsZYGv0Dw5H/PDX469vZ7at+5a/55nJlY9N1QYpixYtiuXLl2dzUprHBjymdl8pRJk9e/Yat912221jjz32WOOy3XbbLR555JHNvt8UoghSHBdg+nn+BTzPUBUmfKgq+7vAh6zYrF8ff08C+fEcA2yJxpFNfx7xPLPlJq/zyVmqMEkBynXXXTd+2TXXXJNVRkwcNJ8ccsghcdttt61x2d13353NSgEAAAAAgHofNt9QyYXUuIoFKTNmzIiTTjopzjzzzKw38SWXXBLnnXdeNgelXJ3S19eXbb/iFa/IgpTPf/7zcd9998XnPve5bAD9S17ykkotHwAAKKI0l/HrXy+d1prZCAAARTMaq5OUBklK7QUpyfvf//444IAD4tRTT42PfvSj8Y53vCOOP/747LpjjjkmLrroomw7VZ587Wtfi9/97nfxwhe+MDtPw+dTezAAAIBN1tIS8brXlU5pGwAAaqYiRZKSl4rNSClXpZx11lnZaW1rt/I6/PDD44c//OE0rg4AAAAAAKrXhBxFRUqtBikAAADTamgo4pe/LG0/73kRzf4kAgCguEYnlqSQG381AAAA9aO/P+KFLyxtd3UJUgAAKDQVKXUwIwUAAAAAANgyZqRMD0EKAAAAAAAU0cRh82bN50aQAgAAAAAABTQ6IUmRo+RHkAIAAAAAAEVv7aUkJTeCFAAAAAAAKPqw+Qquo9YJUgAAAAAAoIBGJ5SkKEjJT3OO+wYAAKgura0R55yzehsAAGqlIkWSkhtBCgAAUD9aWiJOO63SqwAAgCmfkUJ+tPYCAAAAAIACGh2rSVGMki8VKQAAQP0YHo647LLS9jOeEdHUVOkVAQDAlhurSDFoPl+CFAAAoH709UUcd1xpu6srorOz0isCAIAtVu7sZT5KvrT2AgAAAACAAs9IUZGSL0EKAAAAAAAUkBkp00OQAgAAAAAAha5IUZOSJ0EKAAAAAAAUeEaKHCVfghQAAAAAACig0bGSFPUo+RKkAAAAAABAkVt7SVJy1Zzv7gEAAKpIS0vE2Wev3gYAgBpgRkq+BCkAAED9aG2NOOOMSq8CAACmhIqU6aG1FwAAAAAAFJjOXvlSkQIAANSP4eGIa68tbR92WERTU6VXBAAAW2w0xoakkCtBCgAAUD/6+iKOOqq03dUV0dlZ6RUBAMAUtPZSk5Inrb0AAAAAAKCAyvUoYpR8CVIAAAAAAKCARsdLUiq9ktomSAEAAAAAgAJSkTI9BCkAAAAAAFBAZqRMD0EKAAAAAAAUuCbFrPl8CVIAAAAAAKCAjEiZHs3TdD8AAACV19IS8ZGPrN4GAIBamJGiJCVXghQAAKB+tLZGnHlmpVcBAABTQkVKlQYpt956a7S0tMQee+wh5aIiHlrRG5fetjhGynHrVmhrboznHbBdbDPDpxEBAAAAgGIZNSOluoKUu+++O972trfF/fffn3295557xmc+85nYd99981wfrOP071wbf71/xZQdmVseXhlnvvgARxoAoB6MjET87W+l7f32i2g0NhIAgOJXpEQ0VHYhNW6Tg5TPfvazsWjRojj77LOjsbExPve5z8UHPvCB+OEPf5jvCimMu5d0xZcuvSseXdk36W1S9cc/HL3rFt/Hsu6B8RDlhAO2i61p/bd4VX9cc9/yuOmhJ7Z8JwAAFEtvb8STn1za7uqK6Oys9IoAAGDrW3vJUaojSLniiivie9/7Xuy1117Z1x//+Mfj2GOPja6urpg5c2aea6RK/OT6h+PMn9wcA0Mj672+Z2Boo+22rrp7WbziyJ2juWnLPvl3+Z1Ls/N9t5sVX37N4bE1bn74iTjxv/4Ydy3p2qr9AAAAAABUtLWXw18dQUp3d3fMmTNn/OtUndLa2horVqwQpNSJH//1oawiZEOeve/COPGg7aNxPRHo+394Y/QODsd9y3piz223LHz74x2lIOWYvRbE1tpjQWkNy3sGs8c1r7N1i/YzOjoan/rFrXHLIys36/tamhrjtOP2jMN3nbdF9wsAAAAA1DcVKVUWpKQ3ixvWenM8tfhKl1MfUius5FMvPTCeuuf8da6f0dIUC2e3T/r9/++P98SNDz0RdzzWtcEgJYUaj3eV7mttfxyrSDnmSVsfpMxobYod58zIhtenqpR5nVsWaFz/4BNx7h/u3qLv7eofiu+/5alb9L0AAAAAAEmDmpTqCFJSiLJ2kLL219S2x8Zmnxywwzax6/zN7yX9pIUzsyBlQ6200nUnfPYPMTg8eUDX2tQYR+0+NVUce2zbmQUpab7Lkbtt2T6vuKsU7hy+69z4h6N32aTv6e4fjg/96KZsRssTPYOxTUfLFt03AAAAAFC/VKRUYUXKy172sqwKpay3tzde85rXRFNT0xq3/c1vfjO1q6TihkdGY+lYlcii2W1btI89F5aqUO54bNWkt7n4pkezEKWtuTE629b99UzR3cuP3Dk6Wjf5V3fDa9p2Zlx2x9K4a0n3Fu/jijsfz85fdND2cfKhO23y933zynvj9se64tLbF8dLDtkxiujS2xbH9Q88scZlO8xpj78/fCdBKwAAAADkzIyU6bHJ70Z/8pOfzHclVLXHu/uzQfKNDRHzZ25ZkJIqUpI7Fk9ekfKH25dk5x86cb94zVN3i7yVw527NrCmDekbHI4/37ss2376Zs5tefZ+i7Ig5Td/WxwvPniH2FJbWhn2tcvujt+PHe+Neda+C+P1T999jctSsPam8/8SQ+kXYy3bbzNjStqvAQAAAACbUpGie1RVBCknn3xyrguhui1eWapGWTCzLZpSmrIFnrRo1nj7rpGR0Whcaz9pXkhqdZX83d7bxnTYc0GpRVkaFH/xTY9s9vffs7Qn+odGYuGstthrLJTZVM/ed2F86dK74ifXP5ydtkRrc2N85pSDNzuISXNo/uPnf9vk26fZNCcdsmPM7Wwdv+yqu5dlIUp67M/df1F22V/vX5Edy9/dtliQAgBUp5aWiPe8Z/U2AAAUmAnm02Oz+yPdcsst8etf/zruuuuu6O7ujs7Ozth7773jhBNOiL322iufVVJxi1eV5qMs3MK2XsnOc2dk8036Bkfi9sWrskHvE/3h9qXZG/O7zu/YohksW1OR8sgTffHWb127xft52p7zNzv1PXSXubHf9rPjb4+s3OL7HRgaiW9ded9mBylX3V1qR5aO9T8/Z+8N3vazl9we9z7eE5fduXSN+7nqntI+nv/k7eKjL3lytv2zGx6O07/z17jsjk2rdAEAmHatrRGf/rQDDwBATUgjORIFKVUSpAwNDcW//du/xYUXXhi77LJL7LHHHjFv3rzo6urKLvvCF74Qp5xySnz0ox9VRlSDHhurSFk0q32L99Hc1JgNd7/10VVxwmcvm/R2z5jGllCLZrfH6cftFX8aCxa2xIzWpnjz3+252d+XKnt+/o5jYkXv4Bbd76NP9MUL/uuyuOb+zR9Yf+XY4z12723jpEM3PJ8lVZh85Q93Z/NQ1ghS7i61NHvKHvPHLztmrwVZ+7fUsuyRJ3qzFl8AAAAAQL4VKYKUKglSvvjFL8Zll10WX//61+OpT33qOtdfeeWVccYZZ2RVKa997Wunep1USWuvralISV508A5x22O3jffuW1tHa1O87LBNH9g+Fd7zvH2iUlJ7s3kT2mVtjvR9qZ3YnYu74rI7l8QLD9r0qpQr7yoFKU/dc3UIMpkUtqQgJVUMlVuypdZgtz22Krv+qN3njd92TkdrHLTTnLjugRXxhd/dmW3Xi21ntWXHSj9KAKhyIyMR999f2t5ll/SCrNIrAgCArZ+REmakVEWQ8pOf/CQ++MEPrjdESdLl73nPe7KgRZBSex4rt/baioqU5LTj9op/fMYeMTJJktLc2JBVrrBpjttn2yxIuejGR7I2YZtiVd9Q3LG4K9t+yu4bD1KO2G1eFnCl4fKnnHtltDQ1ZPtIUpCT5uZM9HdPWpAFKd/6U3qDYuxNijrxzTccNW3zfQCALdTbG7H77qXtrq6IzulpKQsAAPnQ2quqgpRHH300DjrooA3e5ogjjoiPfexjU7EuarQipTwgnalx3L4L46uX3RMX3fhodtoc+243a43h8Rv6eT1r34XxsxseiWvuW77m/e+zbmjwmqfuFvcv64mVY2FLPbhnaXd2+u2tiwUpAAAAAFSgIoWqmZHS3r7haoR0fW/6hBc1O2x+a2akMPWO3G1eNlPmhgef2KzvS1Ulbzxm7JOYm+ATLz0wXnjQ9jE0srqSqK25KZuJsr4WV599xaFRTy6+6dF467euicvuWFLppQAAAABQlzNSRClVEaSkH4QfRv2ayooUpk5LU2P89xufkvshnd3eEic8efvc76eo0qyZxoaIu5Z0xyNP9Mb228yo9JIAAAAAqCNilCoJUkZHR+O0006LlpaWSW8zODg4Vetimjy0vDcuva837hl9KBrHBm3O6WiJ4/ZZmAVn9y7tjjN/evPqipTZKlJgbdvMaImDdpqTzYZJ1SknPHm7LTpIc2a0xozWJgcYAAAAgE0yyShqKhWknH766Zt0u6c//elbsx6m2T/+9zVx22NdEVffuMbln/3/DomTDt0xvnLZ3XHpbaV2RfM7W7MTsK7U5iwFKR/96S3ZaUvMamuO37z7mbFQYAkAAADAJhZAZJSkVEeQcuWVV8aXvvSlmD179vhlfX19G52bQnV7w9N3i+9efnvMmj07q0BZvLIvbn10Vfzwrw/Fiw/eIX59y2PZ7c543j7Z181NBsXD+px82I5xwTUPxPLuLavMGxgeiVX9Q3HzwysFKQAAAABs3owUx6s6gpRrr712ndZdT3va0+LHP/5x7LzzznmsjWnw94fvFHs1LY1DDjkkmpqa4p6l3XHcZy6Ny+9cGr+9dXEsWdWffUr+H5+xR7Q2C1FgMntuOzOu+sBztvgAvfa8q+MPty+Jx7sHHGQAyFNzc8Tb3756GwAACmy8IMWw+eqZkbIpl1Fsuy/ojAN33CZufOiJ+NCPbsouO3bfhUIUyFm5bd7jXf2ONQDkqa0t4gtfcIwBAKgJo2M1KSpS8qXEgHW86ODts/NHV5YGzB+//yJHCaYpSFmmIgUAAACATTVekeKQ5UktO+t4+RE7x+9vXxKLV/bHrvM747mCFMjdvJmlIGVpl9ZeAJCrVFW/dGlpe8ECf3ECAFAjM1IkKVUTpPziF7+ImTNnjn89MjISv/71r2PevHlr3O6kk06auhUy7eZ0tMa333S0Iw/TaEFnW3b+eLfWXgCQq56eiIULS9tdXRGdnQ44AAA1MCOl0iupbZscpOywww5x3nnnrXHZ/Pnz41vf+tYal6WhNoIUgM0zT2svAAAAALZwRgpVEqT89re/zXclAHVs/lhrr8e19gIAAABgsytSlKTkybB5gCowf0Jrr9Hyv4AAAAAAsEkzUsiTYfMAVVSR0jc4Ej0Dw9HZtv6n5yWr+uNvj6yMjtamOHSXudHU6J9JAAAAgHpV/kCugpR8CVIAqkAKRtqaG6N/aCSWdQ+sN0gZGRmNk75weTy0ojf7+sMv3D/ecMzuFVgtAAAAAFVVkeKztrnS2gugCqQ+lgtmltp7Le3qX+9tbl+8ajxESX532+JpWx8AAAAAVag8I0Vzr1ypSAGoEvM6W7OgJFWkrM+f71mWnS+Y2RpLuwbi2vuWx9DwSDQ3ycQBYJM1N0eceurqbQAAKLDRsSRFRUq+vPsGUGVzUh7vmiRIuXd5dv6qp+was9qbo3tgOP72yKppXSMAFF5bW8Q3vlE6pW0AACiwsREp6lFyJkgBqBLzO0tv5jy+noqUNDjsz/eWKlKO3n1eHLHr3Gy7fBkAAAAA9RukKEnJl1p2gCqrSLn2/uVx8U2PrHHdyr6heOSJvmhubIhDdpkTR+4+L35325K4+OZHY4c57Vt0f7PaW+LoPeZHU+Pk08ie6B2Mq+5+PEbG/1XefCMjI3HPg33xWMuj0di46fn9wTvPie23mbHF9wsA65X+TevpKW13dPiDEwBgiqT3EP509+PZh0GZPtc/+ER2btZ8vgQpAFUizT5Jfn3LY9lpfQ7YcZvoaG2OI3ebl3199T3LstOWOvvvD4qXH7HzpNe/+/vXxyV/W/9aNtuV123WzXeaOyMue+9x0aDJJwBTKYUoM2eWtru6Ijo7HV8AgCnwT9/7a/ahTyqjpUmUkidBCkCVeNHBO2ShyIqewfVe39zUEG87dq9s+/Bd5sarn7JL3Pbols1IWdLVH/c93hOX3bF00iAlDbK/4q6l49UhLRuoXNnY0LPuru7onNkZDZv4+YjrHlgRDy7vzda42wJvcAEAAEC1S500kr0XzYzZ7S2VXk5daWxsiDcds3ull1HTBCkAVSK1sfraqUdu8j+QHz/5wC2+ryvuXBqv+tpVcc0GZqykQfY9A8Mxu705Lnzb07L73BLDw8Nx3XXXxSGHHBJNTU2b9D0v/eLlce39K7I2Z4IUAAAAqH7DI6WWXme++IB42p4LKr0cmFKGzQPUoTRnJc1GefiJvnh4Re96b3PNfaWQ5bBd525xiLKlDttlbnaeghQAAACg+g2PzUZp0qKbGiRIAahDac7K/tvPzrb/ct/6w4ry5UfsWgo1plMKb5Jr71sx7fcNAAAAbL6RsYqU9MFNqDVaewHUqcN3nRs3PvREXHrr4thn0ax1rv/LvcvXCDWm06G7zMnOb310ZXT3D0Vnm3+uAAAAoAgVKdPd1QKmg3emAOrUEbvNjW9ccW/88K8PZaf1SZ8iOWTnUqgx3fNitt+mPRtUd+jHfh3lquD2lqY462UHxQlP3m7a1wQAAABMbmSkdK61F7VIkAJQp56597ZZ5ccDy3omvc1Jh+yYtQGrhBccuH38vz/eEwPDY6/EIqJ/aCQu+MsDghQAtlxTU8Tf//3qbQAApnTYvNZe1CJBCkCdmtXeEhe+/elRrT504n7xj8/YI4bGPtJy6yOr4k3f/Etc98CKGB0djQbD6wDYEu3tERdc4NgBAEyxkXJrL3+vU4MEKQBUpRSUbLdN+/jXC2a2RUtTQzzePRAPLu+Nned1VHR9AAAAwHqClEZHhdrj1xqAQkjzUfbffna2/dcHVlR6OQAAAMD6WnupSKEGCVIAKIzy4Pvr7hekALCFurtT2WPplLYBAJjSIKWxscERpeZo7QVAYRyyy5w4/8r74g93LIn/+fP945fvNLcjnr7XgoquDQAAAOrZWI6iIoWaJEgBoDAO3Xludn7n4q543w9uXOO6i975jNh/h1Lrr7IvXnpnfO2ye7JBdx94wb7x0sN2mtb1AgAAQN219lKRQg0SpABQGLst6Iz3HL93XDdhRspND62MR1f2xdX3PL5GkDI6OpqFKMu6B7Kvf3Dtg4IUAAAAyMnw+LB5rb2oPYIUAArl9Gc9aY2vP3vJ7fHZS+6IGx58Yo3L73u8ZzxESRav7J+2NQIAAEC9GTFsnhpm2DwAhXbwTqUB9Nc/uOYA+mvvX56dz2ovfWZg8SpBCgAAAORfkeIYU3v8WgNQaAfttE12fvfS7ljVN7hOkHLCAdtl50/0Dkbf4HCFVgkAAAC1K7XXHstRsjmlUGsEKQAU2vyZbbHjnBnZC7YbH1rd3uva+0oVKsftuzBam0v/3C1RlQJAU1PEC15QOqVtAAC22lhXr0yTIIUaZEYKAIV38M7bxEMreuNzl9wRv7jx0RiN0bj10ZXZdYftMje2ndmWXb94VV/sPK+j0ssFoJLa2yN+/nM/AwCAKTQ8IUkxbJ5aJEgBoPAO33VeXHTjo3HVPcuyU9nO82bEdtu0x8LZY0GKgfMAAAAw5UbKfb1SRUqj1l7UHkEKAIX3qqN2yc5X9q6ekZIqiZ+z36Jse9Gs9uzcwHkAAADItyJFay9qkSAFgMKb0doUbzxm90mvTxUpSWrtBUCd6+6OWLiwtL14cURnZ6VXBABQeMMTKlIaTeWmBglSAKh5C2eNBSlaewGQ9PQ4DgAAU2hERQo1Tj4IQM1bqLUXAAAATE9rLzNSqEGCFABq3rbjrb36K70UAAAAqNnWXmleaUP6D9QYQQoAddPaa4kZKQAAADDlyiNSGoUo1CgzUgCom9Zej3cPxMd/fsv4p2MWzGyNNzx992hu8rkCAAAA2NrWXk2CFGqUIAWAmje/szU6W5uie2A4vnrZPWtct+OcjjjxoO0rtjYAAAColSCl0ecUqVGCFABqXmNjQ3zpHw6PP965dPyyq+5+PK5/8Im44aEVghSAepL+un/mM1dvAwCw1UbGenupSKFWCVIAqAt/t/e22ansO1fdH9c/eGPc8vDKiq4LgGk2Y0bEpZc67AAAuVSkGDRPbfIRLADq0n7bz8rO//bIqkovBQAAAGqjIkWQQo0SpABQl/bZblakGXhLu/pj8aq+Si8HAAAACmt4pHSutRe1SpACQF3qaG2O3Rd0ZtuqUgDqSHd3xLbblk5pGwCAraa1F7VOkAJA3dpv+9nZ+d8eMScFoK4sXVo6AQAwJQybp9YZNg9A3dp/+9nx8xseif/zq9viC7+9c/zy5x6wKP7vyw+p6NoAAACgaEGKESnUKhUpANStZ+69bTYIb3B4NFb1D42ffnjtQ/FE72CllwcAAACFoLUXtU5FCgB168k7bhPXfOg5saJndWjyqq/+KR5+oi9ue3RVHLX7vIquDwAAAArV2ktJCjVKkAJAXZvT0ZqdJs5NSUHKrY+uFKQAAADAJhgeKZ03NTQ4XtQkrb0AYIJ9t5+Vnf/tkVWOCwAAAGwCrb2odSpSAGCCfbebnZ3f9uhKxwWgFjU2RhxxxOptAACmrrWXihRqlCAFACbYd7tSRUqakTIyMhqN+rsC1JYZMyL+/OdKrwIAoKaoSKHW+QgWAEyw+4LOaG1qjO6B4bhrSZdjAwAAABsxPD5s3qGiNvnVBoAJmpsaY6+FM7Pt5/7nH+LjP7/F8QEAAIANSB0dEq29qFWCFABYy0mH7jC+/ZtbFzs+ALWkpydit91Kp7QNAMBWG8tRosGMFGqUIAUA1vLmv9szfvaOY7LtZd0Djg9ALUltJ+67r3Qaa0EBAMDUzEhpMmeUGmXYPACsx/bbtGfnK3oGY3B4JFqmqNHrZXcsyQbZz+tsjZccsqMXmQAAABTeSHlGiooUapQgBQDWY05Ha6TXf+m14PKegVg4qxSsbI0Hl/fEqeddPV7yPKOlKZ5/4PaOPwAAADVRkdKo/xE1yq82AKxHKkee19GabT/eNTXtva65b/l4iJLc9PATjj0AAAC1U5GitRc1SpACAJNI7bemck7K9Q+sGZzctbjbsQcAAKB2KlK09qJGCVIAYBLzZ5aClKVd/VNyjG54cEV2/pJDdsjO71rS5dgDAABQeIbNU+sEKQAwifmdbVNWkZIG1pdbeb3ssJ2y83sf746h4RHHH2A6pU9J7r9/6eQTkwAAU8KweWqdYfMAsJGKlKmYkXL7Y6uib3AkZrU3x9P3WhDtLY3Z1/cv64k9tp3pZwAwXTo6Im6+2fEGAJhC5c8INpqRQo1SkQIAG6lIebx761p7nffHe+Kf/+e6bPvgneZkw/f2WFAKT+5aYk4KAAAAtVGRIkehVglSAGAS86agIqVnYCg+ftHf4vbHSvNQnrrn/Ox8z4XlIMWcFAAAAGqktZckhRolSAGASSzoHAtStmJGyo0PPpEN3Vswsy3Oe90R8Y/P2CO7fK+xdl4X3/Ro/PyGR/wMAKZLT0/EAQeUTmkbAIApGzbfaAYdNcqMFACYxLyxIGVrhs3/9YEV2fmRu82NZ+27aPzyfbYrBSnXPbAiTvvOtdHWfEQ8Z//V1wOQk/RpyVtuWb0NAMCUBSkqUqhVKlIAYBLzZ5ZmpCzt2vIZKX+9f3l2fuguc9a4/Nn7LYrTj9srjtp9Xvb1Jy76WwwMjU3nAwAAgCK29lKRQo1SkQIAk5g/VpGyqm8oCzlamzfv8wejo6Nx7f2lipRDd5m7xnUtTY3xnuftE6v6BuO4z1wady/tjn3/7RfRMPaiM7WVfeMxe8S/Pn9fPx8AAACq2vDY5wIbzUihRglSAGAS28xoycqSU4nyj/76UMyesXn/bK7sG4olq/qjubEhnrzDNuu9zaz2lvjgifvFP//P9ZFVQo99imc4In5w7YOCFAAAAKqeihRqnSAFACaRPkmzYGZrPLayP977gxu2+Djtt/3smNHaNOn1Jx+6UzY/pX8wxScRXf1D8az/8/sshOnuH4rONv9cAwAAUIBh8ypSqFHemQGADXj3c/eJC655YIvnEaeKlrc+c89Nqn6JdIqIhWOD7tOQ+3sf744DJqlmAQAAgKqqSDGRmxolSAGADXj5kTtnp+m26/yOLEi57/EeQQrAVEqzqHbddfU2AABbbaRckeL1FTVKRggAVWj3+Z3Z+T1Luyu9FIDa0tERce+9pVPaBgBgqw2PVaQIUqhVghQAqEK7jgUp9wpSAAAAqHLDI6vbW0MtEqQAQBXabUHpU9KptRcAAAAUY0aKIIXaJEgBgCq0+4Kx1l6Pa+0FMKV6eyOOPLJ0StsAAGy1YTNSqHGGzQNAFbf2WrKqP257dFW0t2z+Zx9Sb9qd5s6IBsP+AFYbGYn4y19WbwMAMGVBSpOP7VOjBCkAUIW2mdES8zpbY1n3QDzvs3/Y4v289qm7xsde8uQpXRsAAACst7WXD/JRowQpAFClXv2UXeIbl98bpZejm/9poN7B4fjjnUtzWBkAAACs+Tdo0mhGCjVKkAIAVerdx++TnbbEA8t64hln/y4eXNabvaCt9MC/Wx5eGTc//ETM7WiNY/fZNprVewMAANSMsRxFRQo1q6JBSn9/f3z0ox+NX/3qV9He3h5veMMbstOGPPjgg/GiF70ovvzlL8dTnvKUaVsrABTJDnNmREtTQwwMj8SjK/tixzkz1nu7vz2yMt753b/Gqr6h8ctSJfY/HL1rnHbcXlOyllV9g/GyL12RVcgk//XKQ+PFB+8wJfsGAACg8kZUpFDjKhqknH322XHTTTfF+eefHw8//HC8733vix122CFOOOGESb/nzDPPjJ6enmldJwAUTapA2WluR9yztDvue7x70iDlB9c8GHcs7lrn8m9eee+UBSm3PrpqPEQpV8sAAABQO4bHZqQ0mpFCjapYkJLCkAsuuCC++tWvxgEHHJCd7rjjjvj2t789aZDyk5/8JLq7u6d9rQBQRLvMKwUp9z/eE0/bc/23ufGhJ7Lz9xy/dxy7z8JY3jMQr/l/V8fjXQPZJ4qmor9tClIm6upfXf0CUBELFjjwAAA5VKTo4kytaqzUHd96660xNDQUhx566Phlhx9+eFx//fUxMjKyzu2XL18en/70p+NjH/vYNK8UAIpp1/kd2fl9k1SApBe6Nz+8Mtt+zv6L4sk7bhNH7zE/+3poZDRW9A5OyTpue7R0HxNbfQFUTGdnxJIlpVPaBgBgq6lIodZVrCJlyZIlMXfu3GhtbR2/bMGCBdnclBUrVsS8efPWuP2nPvWpOPnkk+NJT3rSVt3v8PDq1iKsPh6OC5AXzzOVs/PcUjuv+5Z2r/d5/u4lXVl1SHtLY+w+b0Z2m/QJi3kdLbGsZzAefaIntmlv2up13PpIqSLlgO1nx82PrIxVvYP+3WFKeZ4B8uZ5BvAcAxs2NFz6YHxDjPp7rwp5LTO5TX1fvGJBSm9v7xohSlL+emBgYI3Lr7jiirjmmmviZz/72Vbf74033rjV+6hFjgvgeab2DD/Rl53f+uDSuO6669a5/rL7e7PzXWY1xU033jB++czm0VgWEX+67pboXdS2VWsYHR2NWx5eUbqfzqG4OSIeWrxsveuBreX1DJA3zzOA5xhYv2XLl2fnjzz8UFx3XelvQKqP1zJbrmJBSltb2zqBSfnr9vb28cv6+vriwx/+cHzkIx9Z4/ItdeCBB0ZT09Z/uraWErf0P5DjAnieqT0d26+KT11+eSzujTj44IOjYa2hfxc9fGtEPBFPedL2ccgh+49fvvO1f477Vz4e2yzaOQ45ZIetWsPDK3qjZ/CxaG5siGcfsmf84s4bo6m9Iw455JCt2i9M5PUMm6W3NxpPPDHbHPn5zyNmlKr3YEM8zwB58hxDLZh1818j4rHYdef0d+QulV4Oa/E8s/FjU7VByqJFi7K5J2lOSnNz83i7rxSWzJ49e/x2N9xwQzzwwAPxzne+c43v/8d//Mc46aSTNntmSgpRBCmOCzD9PP9Ov923nZWdp/ZdJ/zX5bH22PhHxipWDtx5zhr/Nm47q1SF8nj3wDr/Zl5z3/JsgP2mumNxqa3XHtt2xvxZpQ9EdPUP+7eYXHieYZOkUPkPfyj9zqRtH7LC8wxQJbyWocjGZs1Hc7P3XquZ55ktV7EgZb/99ssClNTa44gjjsguS+27UmVEY2Pq0F5y0EEHxa9+9as1vvf444+P//iP/4inP/3p075uACiK9pam2H/72XHLIyvjzsVd671NqhQ5evfSgPm1g5Qlq/rXuPyBZT1xypevGH+BvDn23W52zG5vHg92AAAAqB2prfP4B1WgBlUsSJkxY0ZWUXLmmWfGJz7xiVi8eHGcd9558clPfnK8OmXWrFlZhcquu+663oqW+fPXfOMHAFjT995ydNz00BOTHpYd58yIXeZ3bFKQcu39y7MQZV5naxy00zabfKjbmhvjrc/cM5oaSy+oV/UJUgAAAGrJ8FiQIkehVlUsSEne//73Z0HKqaeeGjNnzox3vOMdWbVJcswxx2Shyktf+tJKLhEACm12e0s8bc8Fm/U940FK15pBSqpsSV5w4HbxHycduNlreWhFabh9lyAFAACgpgyPtS4of4AOak1Fg5RUlXLWWWdlp7Xddtttk37fhq4DALbOtjPb11uR8rdHSvNO9t9+06tRJprZVnrZMTA8Ev1Dw9HWvOb8FQAAAIpppNzaS5BCjVo9jAQAYEJFytKugTWOxy0PlypS9tu+NMR+S4OURFUKAABA7VWkNOrtRY0SpAAA6w1SlnUPxODwSLa9eFVfLO3qj/ThojQ4fkukTyZ1tJaqUAycByqqo6N0AgBgSoyU/nRUkULNEqQAAGuYM6MlmsfKsR8fq0opt/XabUFnzBgLQ7bErPZSVYqB80DFdHZGdHeXTmkbAIApGzavIoVaVdEZKQBA9WlsbIgFM9vi0ZV98bqvXx3tLU3xeHdpXsr+229ZNcrE9l6PRb+KFAAAgBpi2Dy1TpACAKxjn+1mZUHKrY+WKlHKnrL7vK06WjPbW7JzFSkAAAC1OGy+0iuBfAhSAIB1fP5Vh8Zf7l023uc26WxrjqO2MkiZNTZwvqt/0FEHKqOvL+JlLytt/+AHEe3tfhIAAFMUpGjtRa0SpAAA65jd3hLP2nfRlB+Z1Nor6eobctSByhgejrjootXbAABs/UussQ/hCVKoVYqtAIBpM7M8bL5fkAIAAFArRkbKrb0aKr0UyIUgBQCYNrPGghQVKQAAALVjWGsvapwgBQCYNqtnpKhIAQAAqBUqUqh1ghQAYPpbe5mRAgAAUHMVKU3ebaZG+dUGAKbNzLaW7FyQAgAAUDuGx2akGDZPrRKkAADTXpHS1T/oqAMAANQIrb2odaV3MwAApnFGyjX3LY+jP/GbDd62uakhznjePvGSQ3b0swGmTmdnxFjrCQAApoZh89Q6QQoAMG323m5WtDQ1xODwaDy6sm+jt//OVfcLUgAAAKrcWGevaGpsqPRSIBeCFABg2uw4Z0Zc+f5nx6NPbDhEueXhlfHeH9wQS1b1T9vaAAAA2LrWXmakUKsEKQDAtFowsy07bcjMsRZgj21C1QrAZunri3jNa0rb//3fEe3tDiAAwBS19moykZsa5VcbAKg6C2eXgpbugeFY1WcwPTCFhocj/vd/S6e0DQDA1r/EUpFCjROkAABVp6O1OWa1l6tStPcCAAAoQmsvM1KoVYIUAKAqLZpdarezWHsvAACAQrT2MiOFWiVIAQCq0qKx9l6PrTInBQAAoJqNjJTOVaRQqwQpAEBVWjSrVJGitRcAAEBRhs03VHopkAtBCgBQlRaOtfZ6TGsvAACAqmbYPLVOkAIAVHVrr8WGzQMAAFSt0bFqlERFCrWqudILAADY0LB5FSnAlOroiOjqWr0NAMCUVKMkOntRqwQpAEBVMmweyEVDQ0Rnp4MLADDF81GSRkkKNUqQAgBUpYUThs33Dw1HQzSMl4orFwcAAKgOIyOrt5vSh1agBglSAICqtHBsRsrA0Ejs86GLxy+f09ESP3zb02KPbWdWcHVAYfX3R7zlLaXtc8+NaCs91wAAsPUVKT70Rq0ybB4AqEptzU3xd3tvu87lK3oG45r7lldkTUANGBqKOP/80iltAwAwhTNSVKRQm1SkAABV6/zXHxmr+le/0fme718fv7rlsegZGK7ougAAACgZmRCkqEihVglSAICq1dDQELPbW8a/3mZGabt7wKfIAQAAqm7YvIIUapTWXgBAYXS0NmXnPf0qUgAAAKqpIiWFKOnDcFCLBCkAQGF0tJWKabX2AgAAqA7lzl7aelHLBCkAQGF0litStPYCAACoqtZeqlGoZYIUAKAwOlpLFSndhs0DAABUVWuvJm29qGGGzQMAhdHZVp6RYtg8sIU6OiIWL169DQDAVhkuBykmzVPDBCkAQAErUgQpwBZKn5TcdluHDwBgilt7yVGoZVp7AQDFq0jR2gsAAKC6WntJUqhhghQAoHgVKVp7AVuqvz/itNNKp7QNAMCUVKQIUqhlghQAoDA6x4IUFSnAFhsaivjiF0untA0AwJTMSGk0bJ4aJkgBAAqjY6y1l4oUAACA6jAyUjpXkUItM2weACiMjlYzUgAAAKbLPUu747ZHV27wNvc+3pOdq0ihlglSAIDCzUgZGhmNgaGRaG1WXAsAAJCHnoGhOPG/Ltvk1sr+PqOWCVIAgMJVpJRf1Lc2t1Z0PQAAALVqRc9gFqKk0SeH7zJ3g7dNt3nFkbtM29pguglSAIDCaGlqzD7llKpRugeGY05HpVcEAABQmwaHS8NPOlub43/f9rRKLwcqSj8MAKBQOstzUvqHKr0UAACAmg9SmpsaKr0UqDgVKQBA4eakLO8ZzCpSADbbjBkR99yzehsAgPUaGBod7wwA9U6QAgAUSmebihRgKzQ2Ruy2m0MIALARQyOlipRWQQpo7QUAFK8iJVGRAgAAkH9rrxatvUCQAgAUtCJlwIwUYAsMDESccUbplLYBANhga69mFSkgSAEAClqR0m9GCrAFBgcjPvOZ0iltAwCwkYoUM1LA/wUAQKF0tKpIAQAAmL4ZKQ0ONnVPkAIAFLIipWdARQoAAEDerb1UpIDWXgBAwXSOVaR0m5ECAACQG629YDUVKQBAoXS0jVWkmJECAACQe5DSrLUXCFIAgGJRkQIAADB9QUqrYfMgSAEAikVFCgAAQP4Gh81IgbJSbwwAgIJVpPz53mXxpvP/nMt9NDU2xOufvnscvcf8XPYPVNCMGRE33bR6GwCADc9IaTYdAgQpAECh7Din9Mbn490DccnfFud2P0/0Dsb33vzU3PYPVEhjY8QBBzj8AACbGqQ0NjhW1D1BCgBQKEftPi++/roj47GVfbns/57Hu+Pc398di1f157J/AACAItDaC1YTpAAAhdLQ0BDH7bswt/3f/tiqLEhZ1j2Q230AFTQwEPGJT5S2P/CBiNZWPw4AgA229lKRAoIUAIAJ5neW3lRd0TOY/eHQ0qQfMNSUwcGIj360tH3GGYIUAICNBSn+JoLwzgAAwARzOlqj3AJ4uaoUAACgzlt7tQpSQJACADBRU2NDzO1oHR9oDwAAUI8GhkoVKc1NWnuBihQAgLXMnzkWpHQJUgAAgPo0NKK1F5QJUgAA1jK/sy07f7y737EBAADq0uBQqbWXGSkQhs0DABSlIuX6B1bEI0/0RrXYZ7vZsfuCzkovAwAAyHHYvBkpIEgBAFjH/M7WqqtIufnhJ+IlX7g8qklna1P8+UPPiY7W5kovBQAAmGIDY0GKGSkgSAEAWMf8mW1VV5Fy9T3LxkOeaqgCue6BFdE9MByPPtEXe2w7s9LLgU3X3h5x9dWrtwEA2GBFitZeIEgBAJi8tVd39QQpNz20Mjt/zVN3jX96zt6VXk783dm/i/uX9cTynuo5RrBJmpoijjzSwQIA2Iih4dKMFK29wLB5AIDJW3t1VVdrr+TJO2wT1WDu2DFa1j1Y6aUAAAA5tvZqaW5wfKl7GloDAEzW2qtKKlL6BofjjsVd2fYBO86OajCvoyU7X14lxwg22cBAxOc+V9p+17siWkuhIAAAa9LaC1YTpAAATFKRsqxKZqTc+uiqGB4Zzda13ez2qqpI0dqLwhkcjHjve0vbb3+7IAUAYLKXTWOtvZobGx0j6p4gBQBgLfM7SxUpq/qHYsmq/mhtruwfDtfctzw7P2DHbaKhoTrK6ud1jIVNZqQAAEBNGhpr7dWqtRcIUgAA1jZ7RnM0NzbE0MhoHPnxS6rmAD15h+po67VGRYrWXgAAUJMGxipSWppUpID/CwAA1pKqPp5/4PZVdVxmtjXH8w7YLqrFPMPmAQCgppmRAqtp7QUAsB6ff+Wh8X9OObhqjk1TY0N2qhZzx1p7mZECAAC1HqRUz98hUCmCFACASVR6Nko1K1ekaO0FAAC1aXCoHKT4uwj8XwAAwGab19mSnRs2DwAAtWlwxIwUKFORAgDAFrf2eqJ3MIaGR6LZp9Qoivb2iN/9bvU2AADrZUYKrCZIAQBgs20zoyUaGiJGR0thyvyZbY4ixdDUFHHssZVeBQBAgVp7mZECWnsBALDZUgVKClMSA+cBAKD2DA5r7QVlghQAALbIvLH2Xsu6Bx1BimNwMOILXyid0jYAAOsYHR2NwRHD5qFMay8AALbInI6xgfPdA44gxTEwEHH66aXt170uoqX0ewwAwGrDI6NZG9+k1TxEEKQAALBl5nWWKlJufGhFLJpdmpHS3NgY++8wO5oa9VEGAICit/VKWpq9tgcVKQAAbJG5Y629vvC7u7JT2aueskt84uQDHVUAACiogeFSW6/yh6Wg3glSAADYIi87fKe4/sEV0Ts4nH3dOzASS7v645aHVzqiAABQYIMTgpSWJhUpIEgBAGCLHL3H/PjVPz9z/Our71kWLz/3ynii1wBvAAAosqGx1l4pRGloEKSAuiwAAKbE3LHh88t7DJ8HAIBaqEhpMWgeMoIUAACmxDZjQUqqSBkeWT2cEgAAKOaMlOZG1SiQ/b/gMAAAMBXmzCgNnx8djVjVNxhzxobRQ1Vpa4v42c9WbwMAMGlFSmuzz+FDIkgBAGBKpD+yOlubontgOJb3CFKoUs3NESeeWOlVAAAUZEaKIAUS/ycAADBlylUoK8xJAQCAwrf2EqRAiSAFAIApM7ezNCdlRc+go0p1GhyM+MY3Sqe0DQDAui+ZhspBihkpkGjtBQDAlM9JWa4ihWo1MBDx+teXtk85JaKlFP4BALDaoNZesAYVKQAATJk5HSpSAACg6AZHtPaCiQQpAADkEKQMOKoAAFBQWnvBmrT2AgBgyswtD5vvnXz2xL1Lu+PMn94cXX1DU3KfozEa3V3d0Xn1n6IhitvDubmpId5+7F7xd3tvW+mlAABQ57T2gjUJUgAAmDJzxoKU5RsYNv+tP90Xl962ZOqP+uMrouhamu4WpAAAUHGDw1p7wUSCFAAApsycGRtv7XXDQ09k5288Zvc4cre5W32fIyMjcc8998buu+8WjY3F7Fx788Mr4/O/vTOWa4kGAEAVGBgPUopb8Q1TSZACAMCUmdu54WHzwyOjcfNYkPLyI3aOfbabtdX3OTw8HNcNPhqHHLBdNDU1RRFtO6stC1Ke2EBLNAAAmC5Dw6PZeUtTMT+oBFNNkAIAQA6tvdZfkXLP0q7oHhiOGS1Nsee2nY78mG3GKnme2EBLNKZIW1vE97+/ehsAgMlbezULUiARpAAAkENrr/UHAjeOVaMcsMPsaPbptnGzx47bqv6hGBkZjcZGLRRy09wcccop+e0fAKCGgpRWr9khI1IEAGDKzB2rSOnqHxr/42uiGx4sBSkH7rSNo76eipTR0VKYAgAA1TAjpdkHfCCjIgUAgCmtrGhoKAUCL/jcZdG01h9eDy7vzc4PEqSsoa25KdpbGqNvcCRW9g6OByvkYGgo4sILS9snn1yqUAEAqHMX3/RINrMvzTRMlnb1Z+dae0GJvxoAAJgyKTh50sKZcftjXXHH4q713qalqSGO2n2+o76WFJ70DfZnA+d3dnTy098f8fKXl7a7ugQpAAARcf4V98XND69c51jsOq/D8QFBCgAAU+2CtzxtfBbK+uw8b0bsOGeGA7+W2e0t8djKUpACAADTqWdwODt/93P3jkN3mZttz2htikN3nuMHAYIUAACm2jYdLXHMkxY4sJt73MbaeQlSAACYbn0DpSDlsF3nxtP38loe1mbYPAAAVAFBCgAAldI7VpHS3tLkhwDrIUgBAIAqIEgBAKBS+saDFG8Xw/r4PwMAAKrAbK29AACocEXKDBUpsF6CFAAAqAIqUgAAqHRFShowD6yreT2XAQAA00yQMk1aWyO+/vXV2wAAdW5oeCQGh0ez7fZmQQqsjyAFAACqKEhZ2TtY6aXUtpaWiNe9rtKrAACoGn1DI+PbKlJg/bT2AgCAKqAiBQCASugdKLX1StqavV0M66MiBQAAqsA2HSpSpsXQUMQvf1naft7zIpr9SQQA1LfyfJT2lsZoaGio9HKgKvmrAQAAqoCKlGnS3x/xwheWtru6BCkAQN0bHzTfYj4KTEatFgAAVNOMlL6hGB0tDfsEAIC89Q2WZqS0C1JgUipSAACgCsxuLwUpwyOj8c0r74vW9fSn3mNBZzxlj/kVWB0AALWqV0UKbJQgBQAAqkDqSd3R2hQ9A8PxkZ/cvN7bpJbVf3zfs2LHOTOmfX0AANR2kKIiBSYnSAEAgCqQBnt+7CVPjl/e/Oh6r7/izqXRPTAcD6/oFaQAADBlJg6bB9ZPkAIAAFXi7w/fKTutz4vP+WPc8OATsbJ3cNrXBQBAHQybbzVsHiYjZgQAgALNUFnVN1TppQAAUEN6B8YqUpoFKTAZFSkAAFAAs9pLL91X9qlI2SqtrRHnnLN6GwCgzo239lKRApMSpAAAQIEqUrT22kotLRGnnTYVPxIAgJrQOziSnc9oUZECk9HaCwAAClSRorUXAABTqdewedgoFSkAAFAAs2eMVaRo7bV1hocjLrustP2MZ0Q0+eQlAFDf+svD5lWkwKQEKQAAUACzyzNSeg2b3yp9fRHHHVfa7uqK6Oycgp8OAEDxK1IEKTA5rb0AAKAAZpVnpKhIAQBgCvUOlIKUNhUpMClBCgAAFKq1l4oUAACmTt+QYfOwMYIUAAAoUGuvVSpSAADIoSKlXUUKTEqQAgAARWrtZUYKAABTqH9obEZKq7eKYTL+7wAAgAKYPWNs2LyKFAAAcqhIMWweJidIAQCAAlWkDAyNRN9g6Y9dAADYWr1jry0Nm4fJlT7WBgAAVLVZbc3R0BAxOprmpAzpYb2lWloizj579TYAQJ0rf0hHRQpMTpACAAAF0NjYEDPbmrMQJbX32nZWW6WXVEytrRFnnFHpVQAAVI2+wZHs3LB5mJzWXgAAUBCzx9p7pTAFAACmsrWXihSYnIoUAAAoiFntYwPnewcrvZTiGh6OuPba0vZhh0U0NVV6RQAAFaW1F1R5RUp/f3984AMfiCOOOCKOOeaYOO+88ya97aWXXhoveclL4tBDD40XvehF8Zvf/GZa1woAANVSkZJae7GF+voijjqqdErbAAB1bHR0dLwipb1F8yKYTEX/7zj77LPjpptuivPPPz8+8pGPxDnnnBMXX3zxOre79dZb4/TTT4+Xvexl8aMf/She8YpXxLve9a7scgAAqBezZ5QqUrT2AgBgKvQPjcToaGm7vVWlLlRda6+enp644IIL4qtf/WoccMAB2emOO+6Ib3/723HCCSescduf/exncfTRR8drX/va7Otdd901fvvb38YvfvGL2HfffSv0CAAAoEIVKVp7AQAwBfrHBs0nZqRAFQYpqZpkaGgoa9VVdvjhh8eXv/zlGBkZicbG1cUyJ598cgwOrtu+YNWqVdO2XgAAqJYZKd//ywPxwoN3iB3nzKj0kgCocU/0DMaf7nk8a/9DPtL7YPc82BePtTy6xvthMB2eGPuATlNjQ7Q0+f2DqgtSlixZEnPnzo3W1tbxyxYsWJDNTVmxYkXMmzdv/PI999xzje9NlStXXnll1uJrcw2n4ZKsczwcFyAvnmeAvNXT88zssSDlriXdcfIXLo8r3ndsNDQ0VHpZxTI8HOWmFdnvTB383rD16ul5Btb2ru/9NS69fYkDMx2uvM5xpmI6Wpv8O1fDvJaZ3Ka+vqtYkNLb27tGiJKUvx4YGJj0+5YtWxbveMc74rDDDotnP/vZm32/N9544xastvY5LoDnGaDo6uH1zD7tQ3HAti1x85LBWLyqP/50zV9jRrNPDm6Oxt7eKNfE33DDDTEyQ1UPm64enmdgbbc+vCw733Wb5pjRLLyHWvX0XdrjuuuEebXOa5ktV7Egpa2tbZ3ApPx1e3v7er9n6dKl8frXvz4rJ/2v//qvLSp3PPDAA6OpyeCkiYlb+h/IcQHy4nkGyFs9Pc8cEhEnPH009vnwr2J4ZDT22Hv/WDR7/a+dmUR39/jmQQcdFNHZ6VCxUfX0PANrG7zot+n/gvjia58S+243ywHKgecYIG+eZzZ+bKo2SFm0aFEsX748m5PS3Nw83u4rhSizZ89e5/aPPfbY+LD5b37zm2u0/toc6UWvF76OCzD9PP8Cnmemzsy25qyfdc/giNe2myt9aOsjHyn925S2vSnOZvB6hnq0qn8oO589o9W/OTnzHAN4nqleFQtS9ttvvyxASSVjRxxxRHbZNddck33CZ+1Kk56ennjTm96UXZ5ClG233bZCqwYAgOoJUlb1ld7cYjOkdsJnnumQAWyC/qHhGBgaybZnt7c4ZgDUrYo1VJ4xY0acdNJJceaZZ2a9iS+55JI477zzxqtOUnVKX19ftn3uuefG/fffH2edddb4dem0atWqSi0fAAAqZtbY0HlBCgB56poQ2He2aWsHQP2qWEVK8v73vz8LUk499dSYOXNmNkT++OOPz6475phj4pOf/GS89KUvjV/+8pdZqHLKKaes8f0nn3xyfOpTn6rQ6gEAoLJBStdYuxU2w8hIxN/+Vtreb7+ILZi7CFAvyv/OdLQ2RXOT50sA6ldFg5RUlZKqTMqVJhPddttt49sXX3zxNK8MAACqu7XX2p8UZhP19kY8+cml7a4uw+YBNqBc+Vj+dwcA6pWPEwAAQMHMGutTv7JvsNJLAaAOgpRyJSQA1CtBCgAAFMxMrb0AmMbWXjMNmgegzglSAACgYAybB2A6rBqrfJyltRcAdU6QAgAABVN+Q8uMFACmoyJFay8A6p0gBQAACqY89HdVvxkpAOTHsHkAKBGkAABAQYfNl9/gAoB8h82X/t0BgHpV+igbAABQuGHzgpQt0NIS8Z73rN4GYKMzUsr/7gBAvfIvIQAAFEy5V325dz2bobU14tOfdsgANkH535nZghQA6pzWXgAAUDCz2sqtvcxIASA/ZqQAQImKFAAAKGpFihkpm29kJOL++0vbu+wS0eizZQCTKf87Y0YKAPVOkAIAAAVT7lXfPTAcwyOj0dTYUOklFUdvb8Tuu5e2u7oiOjsrvSKAqrVqrLWXGSkA1DtBCgAAFLQipdy/fpsZq4em/+evb48r7348Zre3xJkv3j92mttRoVUCUHTlFpIT/90BgHrkX0IAACiYtuamaG1qjIHhkTWClAeX98TnfnPH+O0O33VuvO3YPSu4UgBqYdj8rDZvHwFQ3zQEBgCAAiq3WZk4cP6KOx9f4zaG0QOwpUZHR1cPm1eRAkCdE6QAAECNDJy/4q6la9yme+yTxACwufoGR7I5XKV/c1a3kASAeiRIAQCAApo51mal/Gnh9MnhK+4qVaQ8c+9ts/Ou/uEKrhCAIitXNTY0RHS0NFV6OQBQUZpcAgBAgStSLr9zafQPDcfj3QOxeFV/tDU3xjOetCB+f/uSqq5ISX33r7zr8RgeGVnv9XM6WuOo3eZFY2PDtK8NoB6l6pOr7nk8VvaWApT0b0o5uPdcDEC9E6QAAEABlQfMf+2P92SnsiN2mxvzOluz7e6B6g1SPnjhjfHj6x7e4G2+/A+HxQlP3n5q77i5OeLtb1+9DUDmp9c/HP/0P9etczRma+sFAIIUAAAootc/ffesrdfA0OqKjtbmxnjXs/eO5T0D2dfVWpGS2pClSprkyTvOjvbmNVvG3LO0O6uwuX9Zz9TfeVtbxBe+MPX7BSi4Oxd3ZeeLZrfFznM7xtt6vfyInSu8MgCoPB/BAgCAAjp6j/nZaX3KIUV3lc5IeXB5byztGojmxob437c+LdrX6r3/gQtvjO9cdX826BiA6bFsLIR/xZG7xD8/d2+HHQAmEKQAAECN6RwbRJ/mkFSj6x5YkZ3vt/3sdUKUpFyh0juYQxA0OhqxtBQ0xYIFpY9bAxDLu0tBSrk9JACwmiAFAABqzMy2pqqekXL9WJByyM5z1nv9jNbG7Lx3IIcgpacnYuHC0nZXV0Rn59TfB0ABLROkAMCkSn+hAAAANaOjtbmqZ6Rct7EgZaxKpS+PihQA1kuQAgCTU5ECAAA12tprcHg0+oeGo22tYe55+cof7orf3bpko7e7/sGxIGWX9Qcp5XZfubT2AmC9lo/NSJnbobUXAKxNkAIAADWms3V1cNLTPz1BSmrD9clf3JqNINkU22/THrvPX39brRlj68+ltRcA6xgZGY3lPYPZthkpALAuQQoAANSY5qbGaG9pjL7BkWzg/NxpGBz80IqeLERJIc4nX3bQRm9/6M5zorGxYcOtvYZGpnydAKxrVd9QDI+UkvC5nS0OEQCsRZACAAA1aGZbc/QNDkzbwPkHlvdm5zvP64gXH7zDVu2r3NqrT0UKwLRYNtbWK/3bMV3tIAGgSAybBwCAGjTdA+cfHAtSdprbsdX7KlekmJECMD2Wdfdn56pRAGD9VKQAAEAND5zv6p+eOSMPLu/JzneaO2Or95XrsPnm5ohTT129DUAs6x6bj2LQPACsl78cAACgBs1sa6pQRcrWBym5Dptva4v4xjemfr8ABba8u9Tay6B5AFg/rb0AAKCmK1KK29qrf2h6qmkA6l15RsrcztZKLwUAqpKKFAAAqOEgpWeagpSHprS1V2N+FSmjoxE9pbVGR0dEQ8PU3wdAUStStPYCgPVSkQIAADVoZnnYfB5hxFr6BodjaVfpTbidp3jY/GgKPqZSClFmzvz/27sXGLvqOg/gvzt33jN90CdCaWtLaXkW5OmGDSJLIiq2BSGAayGAGp8kKhpFEEVCUeJGNNkVs+yqYFYbBZQA8YGwu0BpeL+7hUKHV0tpKS3Tzkxn5m7OmQ60wKWPuXfunXs+n2Ryzn30zH/+MP8593zP//8b+BoMVAAybt3WIMWMFAB4d4IUAACoQa1ba6QMx9Jeg8t6jWqqj9EtQ5/03ry1Rkp/IaKnr3/IxwNg54KU8Zb2AoB3ZWkvAACoQe1bl/Yqd7H5Z9a8Ef9x17Pp/t57tESuBEtlDc5ISXT19EdT/VuPAcotCaBvePDFso+f1WTZ6o3p1owUAHh3ghQAAKhBw1Vs/gvXPfDmBbjp49tKcsyGfF3U1+Wit7+QLu81JhpKclyAnfHre1bGlbc9lcnOmjy6udJNAICqJEgBAIAaDlLKeUd1x9pNaYiShB6nHbFPnPMP00t27OaGfBoCJfVXAIbTijVvpNtDpoyJ/SaPykznTx/fGnOnjKl0MwCgKglSAACgBrVvrZGyqYzF5v++7JV0e8T0PeKKUw4u6bEHg5RkRgrAcFq1oSvdfvqYaWlIDACg2DwAANSg1sbyL+01GKQcP3tSyY/d0jjwUUWQAgy31VuDlD3HWOYKABhgRgoAANRwsflnX+2Mb9/waFm+x93PrE23x88pQ5CyteB8V6ln1OTzEZ/85Fv7AG+z6vWtQYp6IQDAVoIUAACoQZNHN6Xb9Zu2xG/u7Sjb99lnXEvMmtRetiCl5DNSmpsjFi8u7TGBmrG5py82dA3M5JtsRgoAsJUgBQAAatC+k0bF1WceFs+u6Szb98jlIv5p/8mRS3ZKLKmRkuja0l/yYwPsqD5Ka2M+Rm2d2QcA4KwAAABq1Cfm7hUj1WCQokYKUKllvcoREgMAI5Ni8wAAQNUp29JenZ0DU2mSr2Qf4F0KzU9WHwUA2IYgBQAAqDotjWUqNg+wE0t7DdaZAgBICFIAAICqY2kvoKIzUhSaBwC2IUgBAACys7QXwE4EKUmNFACAQYrNAwAAVae5YeCer66tQUqyHdwfkk09MXboRwFKaEtff3R291ZFn764XpACALyTIAUAAKjaGSlJeLJkxdpY+O9Lo6evf+jH7emKJ0vQPqA0NnRtiRN/fGes3tBdVV1qaS8AYFuW9gIAAKq22Pzmnr646aEXSxKiANXnkedfr7oQZebEtth/z9GVbgYAUEXMSAEAAKq62PyDz69P93+x8Ig4fvbE3T5mZ09fHHXxzXH7jCPiuP0mRj4/8D2Aylm5rjPdJr/bye94NcjX5SKXy1W6GQBAFRGkAAAAVbu014o1nbFy7ab0wuYxM8ZFfX73J9W3NUZ01zfGuaddGg9dcmKMbW4sYYuB3dGxblO6nTa+bUi/3wAA5eQsBQAAqNqlvZa/8ka6PWjvMTGquWFIx0wu0iaBTKK711JhUA061g4EKVPHtVa6KQAARQlSAACAqtPcsP1HlX+YOb4kx22qHzhu9xZBClSDZMZZYtp4QQoAUL0EKQAAQNUZ19a03eNj951QkuOO6e+JJ358akzZZ2JE50BtBqAyCoVCPP/m0l6CFACgeqmRAgAAVJ25U8bE5QsOihde2xx7j20p2YyUZKZL65buiC0lORwwBK9t2hIbu3vT/Sl7CFIAgOolSAEAAKpOLpeLTx09reTHHVzaC6i8lWsHZoXtObo5mhsG6iIBAFQjnyIAAIDMaMy7WAvVomPrsl5TLesFAFQ5M1IAAIDMaHpbEXtg59z33Lq4bsnK6CuUrsdWrHkj3U4bZ1kvAKC6CVIAAIDMaK43IwV2x+W3PBkPdqwvS+fN3nNUWY4LAFAqghQAACAzzEiBXdffX4hlqzam+xecMCvGtjaUrBvbm+rj5Ll7+c8CAFQ1QQoAAJAZDQ35WLLPQTFjYntMqrPMF+yMF9dvjk09fdGYr4svfXjfaMj73QEAssXZDwAAkBl1rW1xxlmL4parfxPR0lLp5sCIsPyVgdkoMya2CVEAgEwSpAAAAJlb2qu7t7/STYERY9mqgaLwsyarZQIAZJMgBQAAyIymrcXmBSmw85avHpiRMntyu24DADJJjRQAACAz2nu74v6rz4qWf8tHvPh8RFtbpZsEVW/Z1iDFjBQAIKsEKQAAQGY0N+Rj/OYNEZsr3RIoj/tXros1G7tLesynXxlY2mu2pb0AgIwSpAAAAJnRmLe6MbVryYq1ccY1S8py7OaGuthnXGtZjg0AUO0EKQAAQOaKzUMtuvuZtel28uim2GeP0oYeJ8/dK/J1uZIeEwBgpBCkAAAAmSs2D7XokRfWp9svHr9vLPzg9Eo3BwCgZrgdCwAAyAwzUqhVhUIhHnnh9XT/kCljK90cAICaIkgBAAAyw4wUatULr22OdZ090ZDPxf7vG1Xp5gAA1BRLewEAAJnR2JCPh/ecFe1N+ZhZ574yasfgbJQ5e44WGAIAlJggBQAAyIyG9raYd/a/xGFTx8YNLS2Vbg674KX1m+OGB1+Mnt7+Ye+3QqE/Vq3aGHeuXR65XHUGcEufXZduD5kyptJNAQCoOYIUAAAgM5obBorNd20Z/ovxDM1lNz8Rtz62qrLd+MQzUe0Om7pHpZsAAFBzBCkAAEBmNNUPzCbo7u2rdFPYBf39hbj7mbXp/vxD94pRzQ3DXsj91VdfjQkTJkQul4tqNa6tMU6e+75KNwMAoOYIUgAAgMxo7u2O//3XcyNfl4v4/IqI1tZKN4md8NSqjfH65i3R1pi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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "thr_opt = t[np.argmin(fdp)]\n",
+    "plt.plot(t, fdp)\n",
+    "plt.axvline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"t\")\n",
+    "plt.ylabel(\"FDP\")\n",
+    "plt.title(\"FDP vs t\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 1560,
+     "status": "ok",
+     "timestamp": 1761931308946,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "ODMCXcIqY8rY",
+    "outputId": "8f4e2d21-8f20-405f-be19-43bab6b411a2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "for feature in merged_feature_freq:\n",
+    "    alphas = merged_feature_freq[feature][\"1/alpha\"]\n",
+    "    freqs = merged_feature_freq[feature][\"freq\"]\n",
+    "    plt.plot(alphas, freqs, label=feature)\n",
+    "plt.xlabel(\"1/alpha\")\n",
+    "plt.axhline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.ylabel(\"Feature frequency\")\n",
+    "plt.title(\"Feature frequency vs alpha\")\n",
+    "# plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 51,
+     "status": "ok",
+     "timestamp": 1761931312394,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "5U9FF5yuow-Z",
+    "outputId": "351ab573-1523-4c99-e657-580289f023d5"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ens_charge', 'patch_pos', 'r_gyr', 'dipole_moment', 'hyd_moment', 'helicity', 'amphipathicity', 'BSA_LC_HC', 'DRT', 'hyd_idx', 'hyd_strength_cdr', 'Packing Score', 'patch_cdr_hyd', 'ASPmax', 'coeff_280']\n",
+      "15\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimal_features = [feature for feature in merged_feature_freq if max(merged_feature_freq[feature][\"freq\"]) >= thr_opt]\n",
+    "print(optimal_features)\n",
+    "print(len(optimal_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 3261,
+     "status": "ok",
+     "timestamp": 1761931335409,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "WwlqYr17Cujv",
+    "outputId": "6a06bf00-d727-49ad-97ba-32022e48c804"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "# colormap adapted\n",
+    "from matplotlib import colormaps\n",
+    "cmap = colormaps.get_cmap('tab20')\n",
+    "for i,feature in enumerate(merged_feature_freq):\n",
+    "    alphas = merged_feature_freq[feature][\"1/alpha\"]\n",
+    "    freqs = merged_feature_freq[feature][\"freq\"]\n",
+    "    if feature in optimal_features:\n",
+    "      plt.plot(alphas, freqs, color=\"darkred\")\n",
+    "    else:\n",
+    "      plt.plot(alphas, freqs, color = \"gray\")\n",
+    "# plt.axhline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"1/alpha\")\n",
+    "plt.ylabel(\"Feature frequency\")\n",
+    "plt.title(\"Feature frequency vs alpha\")\n",
+    "# plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bsgrVd9ERgrX"
+   },
+   "source": [
+    "## Feature selection on CHO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 56,
+     "status": "ok",
+     "timestamp": 1761930620958,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "xcGr8IHxRmU0",
+    "outputId": "cb8c8129-07ea-4f18-d1f7-1bbbfcad95be"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features with low variance: Index(['debye'], dtype='object')\n",
+      "(197, 46)\n"
+     ]
+    }
+   ],
+   "source": [
+    "alpha_params = list(np.linspace(0.01, 1, 100))\n",
+    "# n_bootstraps = 100\n",
+    "fraction = 1.0\n",
+    "target_property = \"PR_CHO\"\n",
+    "df_target_no_nan = df_target[target_property].dropna()\n",
+    "df_main_dataset = df_master_filtered\n",
+    "df_main_dataset = df_main_dataset.loc[df_target_no_nan.index]\n",
+    "\n",
+    "variance_threshold = 0.\n",
+    "selector = VarianceThreshold(threshold=variance_threshold)\n",
+    "df_main_dataset_high_v = pd.DataFrame(selector.fit_transform(df_main_dataset), columns=selector.get_feature_names_out())\n",
+    "df_main_dataset_high_v.index = df_main_dataset.index\n",
+    "features_low_var = df_main_dataset.columns[~selector.get_support()]\n",
+    "print(f\"Features with low variance: {features_low_var}\")\n",
+    "\n",
+    "df_data = (df_main_dataset_high_v - df_main_dataset_high_v.mean()) / df_main_dataset_high_v.std()\n",
+    "df_data[target_property] = df_target_no_nan\n",
+    "print(df_data.shape)\n",
+    "# n_bootstraps = df_data.shape[0]\n",
+    "n_bootstraps = 500\n",
+    "every_fdr = np.zeros(len(alpha_params))\n",
+    "every_feature_freq_cho = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 585227,
+     "status": "ok",
+     "timestamp": 1761931211702,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "069tcsdITSaC",
+    "outputId": "2aa840fa-1516-4415-f650-95e8862f319d"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [09:45<00:00,  5.85s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tqdm(range(len(alpha_params))):\n",
+    "  alpha = alpha_params[i]\n",
+    "  all_selected_features = train_stabl_proxy_one_alpha(df_data, target_property, n_bootstraps, alpha, fraction)\n",
+    "  feature_freq = compute_fdr_and_feature_freq(all_selected_features, n_bootstraps)\n",
+    "  every_feature_freq_cho[alpha] = feature_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RtVzyAovUSqS"
+   },
+   "outputs": [],
+   "source": [
+    "merged_feature_freq = merge_all_feature_freq(every_feature_freq_cho)\n",
+    "fdp, t = get_fdp_curve(merged_feature_freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 350,
+     "status": "ok",
+     "timestamp": 1761931356906,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "YYX0eKVAUV2w",
+    "outputId": "9e7c1b89-26c3-414b-dd99-918d012a0583"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "thr_opt = t[np.argmin(fdp)]\n",
+    "plt.plot(t, fdp)\n",
+    "plt.axvline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"t\")\n",
+    "plt.ylabel(\"FDP\")\n",
+    "plt.title(\"FDP vs t\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 2031,
+     "status": "ok",
+     "timestamp": 1761931367708,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "tB4duzqQUW2E",
+    "outputId": "a5002995-714f-4af9-a7c3-79e4503219e8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "for feature in merged_feature_freq:\n",
+    "    alphas = merged_feature_freq[feature][\"1/alpha\"]\n",
+    "    freqs = merged_feature_freq[feature][\"freq\"]\n",
+    "    plt.plot(alphas, freqs, label=feature)\n",
+    "plt.xlabel(\"1/alpha\")\n",
+    "plt.axhline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.ylabel(\"Feature frequency\")\n",
+    "plt.title(\"Feature frequency vs alpha\")\n",
+    "# plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 17,
+     "status": "ok",
+     "timestamp": 1761931369845,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "If7a9v7KUdFR",
+    "outputId": "654bfa02-7bfc-4e02-a1dd-706062982ea2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Fv_chml', 'patch_cdr_pos', 'patch_cdr_neg', 'asa_hyd', 'mobility', 'sed_const', 'zeta', 'zquadrupole', 'helicity', 'strand', 'E', 'HI', 'hyd_idx', 'patch_ion', 'dipole_moment', 'hyd_moment', 'coeff_280', 'zdipole', 'affinity_VL_VH', 'Packing Score']\n",
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimal_features = [feature for feature in merged_feature_freq if max(merged_feature_freq[feature][\"freq\"]) >= thr_opt]\n",
+    "print(optimal_features)\n",
+    "print(len(optimal_features))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 2433,
+     "status": "ok",
+     "timestamp": 1761931385329,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "NoIkvPAuUd7g",
+    "outputId": "56e265f2-b4ed-4084-ce6c-2e35b4f7aeb8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "# colormap adapted\n",
+    "from matplotlib import colormaps\n",
+    "cmap = colormaps.get_cmap('tab20')\n",
+    "for i,feature in enumerate(merged_feature_freq):\n",
+    "    alphas = merged_feature_freq[feature][\"1/alpha\"]\n",
+    "    freqs = merged_feature_freq[feature][\"freq\"]\n",
+    "    if feature in optimal_features:\n",
+    "      plt.plot(alphas, freqs, label=feature, color=\"darkred\")\n",
+    "    else:\n",
+    "      plt.plot(alphas, freqs, label=feature, color = \"gray\")\n",
+    "# plt.axhline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"1/alpha\")\n",
+    "plt.ylabel(\"Feature frequency\")\n",
+    "plt.title(\"Feature frequency vs alpha\")\n",
+    "# plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g6TCf_5FVEnj"
+   },
+   "source": [
+    "## Feature selection on AC-SINS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 62,
+     "status": "ok",
+     "timestamp": 1761931412096,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "Ni6fDz5tVO4Z",
+    "outputId": "230a027b-fdf4-427a-9199-2729376f212d"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features with low variance: Index(['debye'], dtype='object')\n",
+      "(242, 46)\n"
+     ]
+    }
+   ],
+   "source": [
+    "alpha_params = list(np.linspace(0.01, 1, 100))\n",
+    "# n_bootstraps = 100\n",
+    "fraction = 1.0\n",
+    "target_property = \"AC-SINS_pH7.4\"\n",
+    "df_target_no_nan = df_target[target_property].dropna()\n",
+    "df_main_dataset = df_master_filtered\n",
+    "df_main_dataset = df_main_dataset.loc[df_target_no_nan.index]\n",
+    "\n",
+    "variance_threshold = 0.\n",
+    "selector = VarianceThreshold(threshold=variance_threshold)\n",
+    "df_main_dataset_high_v = pd.DataFrame(selector.fit_transform(df_main_dataset), columns=selector.get_feature_names_out())\n",
+    "df_main_dataset_high_v.index = df_main_dataset.index\n",
+    "features_low_var = df_main_dataset.columns[~selector.get_support()]\n",
+    "print(f\"Features with low variance: {features_low_var}\")\n",
+    "\n",
+    "df_data = (df_main_dataset_high_v - df_main_dataset_high_v.mean()) / df_main_dataset_high_v.std()\n",
+    "df_data[target_property] = df_target_no_nan\n",
+    "print(df_data.shape)\n",
+    "# n_bootstraps = df_data.shape[0]\n",
+    "n_bootstraps = 500\n",
+    "every_fdr = np.zeros(len(alpha_params))\n",
+    "every_feature_freq_acs = {}\n",
+    "max_iter = 2000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 786232,
+     "status": "ok",
+     "timestamp": 1761932210504,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "854_ROJGVWIO",
+    "outputId": "e50adf61-19b0-46e3-a63d-1104082c0b77"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r  0%|          | 0/100 [00:00<?, ?it/s]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.919e+01, tolerance: 1.394e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.923e+00, tolerance: 1.623e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.563e+01, tolerance: 1.506e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.780e+00, tolerance: 1.614e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.285e+02, tolerance: 1.667e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.151e+01, tolerance: 1.816e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.998e+00, tolerance: 1.784e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.106e+01, tolerance: 1.741e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.272e+00, tolerance: 1.783e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.565e+00, tolerance: 1.711e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.443e+02, tolerance: 1.832e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.017e+00, tolerance: 1.685e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.840e+01, tolerance: 1.772e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.402e+00, tolerance: 1.730e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.466e+01, tolerance: 1.599e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.828e+02, tolerance: 1.906e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.945e+01, tolerance: 1.758e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.735e+00, tolerance: 1.767e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.009e+01, tolerance: 1.824e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.127e+00, tolerance: 1.645e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.532e+02, tolerance: 1.496e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.991e+01, tolerance: 1.653e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+02, tolerance: 1.685e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.010e+02, tolerance: 1.606e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.419e+00, tolerance: 1.610e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.809e+00, tolerance: 1.636e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.403e+01, tolerance: 1.693e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.883e+02, tolerance: 1.796e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.177e+02, tolerance: 1.519e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.199e+02, tolerance: 1.833e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.166e+00, tolerance: 1.695e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.329e+01, tolerance: 1.777e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.698e+01, tolerance: 1.516e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.918e+01, tolerance: 1.923e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.265e+01, tolerance: 1.640e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.919e+00, tolerance: 1.778e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.970e+01, tolerance: 1.663e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.993e+02, tolerance: 1.631e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.386e+01, tolerance: 1.732e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.313e+01, tolerance: 1.676e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.354e+00, tolerance: 1.453e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.459e+01, tolerance: 1.419e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.820e+00, tolerance: 1.260e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.553e+01, tolerance: 1.605e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.776e+00, tolerance: 1.650e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.685e+00, tolerance: 1.657e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.091e+00, tolerance: 1.361e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+01, tolerance: 1.348e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.597e+00, tolerance: 1.595e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.995e+01, tolerance: 1.664e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.661e+00, tolerance: 1.456e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.280e+00, tolerance: 1.581e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.836e+01, tolerance: 1.592e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.406e+01, tolerance: 1.493e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.974e+00, tolerance: 1.653e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.162e+00, tolerance: 1.449e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.725e+00, tolerance: 1.586e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.224e+02, tolerance: 1.591e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.459e+02, tolerance: 1.831e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.402e+02, tolerance: 1.798e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.037e+01, tolerance: 1.673e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.801e+02, tolerance: 1.920e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.799e+00, tolerance: 1.461e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+01, tolerance: 1.572e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.930e+00, tolerance: 1.403e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.596e+00, tolerance: 1.557e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.460e+00, tolerance: 1.837e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.297e+01, tolerance: 1.853e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.128e+01, tolerance: 1.785e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.519e+02, tolerance: 1.814e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.699e+00, tolerance: 1.727e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.110e+01, tolerance: 1.858e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.960e+02, tolerance: 1.800e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.720e+01, tolerance: 1.687e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.896e+00, tolerance: 1.770e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.966e+01, tolerance: 1.500e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.904e+00, tolerance: 1.353e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.980e+00, tolerance: 1.511e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.674e+01, tolerance: 1.796e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.018e+02, tolerance: 1.423e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.306e+01, tolerance: 1.714e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.337e+01, tolerance: 1.759e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.104e+01, tolerance: 1.732e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.096e+00, tolerance: 1.767e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.675e+00, tolerance: 1.213e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.168e+01, tolerance: 1.637e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.846e+01, tolerance: 1.670e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.903e+02, tolerance: 1.797e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.682e+02, tolerance: 1.547e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.808e+01, tolerance: 1.753e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.469e+02, tolerance: 1.829e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.958e+01, tolerance: 1.501e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.357e+00, tolerance: 1.419e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.787e+00, tolerance: 1.565e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.213e+00, tolerance: 1.542e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.553e+01, tolerance: 1.723e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.692e+01, tolerance: 1.675e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.892e+01, tolerance: 1.666e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.562e+02, tolerance: 1.559e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.416e+00, tolerance: 1.472e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.788e+02, tolerance: 1.559e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.175e+00, tolerance: 1.396e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.099e+02, tolerance: 1.580e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.282e+00, tolerance: 1.437e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.676e+01, tolerance: 1.783e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.302e+02, tolerance: 1.816e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.952e+00, tolerance: 1.570e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.669e+00, tolerance: 1.598e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+00, tolerance: 1.792e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+02, tolerance: 1.702e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.653e+00, tolerance: 1.450e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.202e+01, tolerance: 1.681e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.432e+00, tolerance: 1.616e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.994e+02, tolerance: 1.622e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+01, tolerance: 1.562e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.717e+00, tolerance: 1.571e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.357e+01, tolerance: 1.597e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.927e+01, tolerance: 1.650e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.031e+02, tolerance: 1.588e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.356e+01, tolerance: 1.336e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.521e+00, tolerance: 1.601e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.426e+01, tolerance: 1.691e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.376e+02, tolerance: 1.533e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.996e+00, tolerance: 1.785e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.696e+00, tolerance: 1.770e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.532e+00, tolerance: 1.825e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.815e+00, tolerance: 1.720e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.433e+01, tolerance: 1.688e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.610e+00, tolerance: 1.724e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.694e+01, tolerance: 1.603e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.657e+02, tolerance: 1.839e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.321e+01, tolerance: 1.750e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.736e+01, tolerance: 1.464e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.554e+00, tolerance: 1.564e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.715e+01, tolerance: 1.578e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.074e+00, tolerance: 1.841e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.152e+01, tolerance: 1.658e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.326e+01, tolerance: 1.403e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.422e+01, tolerance: 1.808e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+01, tolerance: 1.688e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.646e+01, tolerance: 1.615e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.067e+02, tolerance: 1.628e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.211e+01, tolerance: 1.656e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.609e+01, tolerance: 1.831e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.079e+00, tolerance: 1.724e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.664e+01, tolerance: 1.715e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.137e+01, tolerance: 1.954e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.631e+02, tolerance: 1.903e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+01, tolerance: 1.832e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.256e+02, tolerance: 1.765e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.885e+02, tolerance: 1.696e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.363e+01, tolerance: 1.631e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.578e+01, tolerance: 1.569e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.315e+00, tolerance: 1.738e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.064e+00, tolerance: 1.574e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.257e+00, tolerance: 1.643e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.513e+01, tolerance: 1.599e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.997e+01, tolerance: 1.777e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.229e+01, tolerance: 2.095e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.703e+01, tolerance: 1.578e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.472e+01, tolerance: 1.684e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.336e+02, tolerance: 1.730e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.140e+00, tolerance: 1.481e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.127e+02, tolerance: 1.748e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.520e+00, tolerance: 1.826e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.628e+01, tolerance: 1.577e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.385e+01, tolerance: 1.299e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.953e+02, tolerance: 1.767e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.850e+02, tolerance: 1.622e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.740e+00, tolerance: 1.276e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.313e+01, tolerance: 1.912e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.334e+01, tolerance: 1.795e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.637e+00, tolerance: 1.806e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.230e+00, tolerance: 1.565e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.155e+01, tolerance: 1.922e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.084e+01, tolerance: 1.718e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.838e+00, tolerance: 1.709e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.490e+00, tolerance: 1.407e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.200e+01, tolerance: 1.698e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.880e+01, tolerance: 1.513e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.972e+01, tolerance: 1.613e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.907e+01, tolerance: 1.699e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.736e+00, tolerance: 1.479e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.548e+02, tolerance: 1.809e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.313e+00, tolerance: 1.536e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.514e+01, tolerance: 1.676e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.087e+00, tolerance: 1.710e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.649e+01, tolerance: 1.989e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.654e+00, tolerance: 1.785e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.557e+01, tolerance: 1.786e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.903e+02, tolerance: 1.598e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.103e+02, tolerance: 1.626e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.126e+00, tolerance: 1.654e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.006e+00, tolerance: 1.653e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.169e+02, tolerance: 1.596e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.912e+00, tolerance: 1.293e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.666e+00, tolerance: 1.576e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.630e+01, tolerance: 1.684e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.250e+01, tolerance: 1.583e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.133e+00, tolerance: 1.805e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.514e+01, tolerance: 1.616e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.621e+00, tolerance: 1.324e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.991e+01, tolerance: 1.514e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+01, tolerance: 1.390e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.602e+00, tolerance: 1.707e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.978e+00, tolerance: 1.583e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.298e+00, tolerance: 1.657e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.079e+00, tolerance: 1.639e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.458e+02, tolerance: 1.538e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.272e+00, tolerance: 1.921e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.172e+02, tolerance: 1.895e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.757e+01, tolerance: 1.797e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.090e+01, tolerance: 1.475e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.973e+02, tolerance: 1.795e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.653e+00, tolerance: 1.675e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.462e+02, tolerance: 1.775e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.326e+02, tolerance: 1.704e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.769e+00, tolerance: 1.555e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.853e+01, tolerance: 1.681e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.419e+02, tolerance: 1.657e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.677e+02, tolerance: 1.948e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.411e+01, tolerance: 1.406e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.101e+01, tolerance: 1.739e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.513e+00, tolerance: 1.699e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.508e+01, tolerance: 1.925e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.856e+02, tolerance: 1.337e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.554e+01, tolerance: 1.423e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.095e+02, tolerance: 1.577e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.074e+01, tolerance: 1.685e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.090e+01, tolerance: 1.653e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.753e+00, tolerance: 1.565e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.240e+02, tolerance: 1.440e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.605e+02, tolerance: 1.650e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.160e+01, tolerance: 1.776e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.573e+00, tolerance: 1.895e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.309e+00, tolerance: 1.552e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.738e+01, tolerance: 1.237e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.244e+01, tolerance: 1.627e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.265e+01, tolerance: 1.569e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.014e+00, tolerance: 1.622e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.823e+00, tolerance: 1.770e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.112e+01, tolerance: 1.583e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.962e+00, tolerance: 1.764e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.746e+00, tolerance: 1.704e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.709e+00, tolerance: 1.691e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.174e+00, tolerance: 1.704e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.565e+00, tolerance: 1.539e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.123e+01, tolerance: 1.792e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.253e+00, tolerance: 1.297e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+02, tolerance: 1.845e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.960e+01, tolerance: 1.867e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.213e+01, tolerance: 1.543e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.551e+00, tolerance: 1.556e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.739e+00, tolerance: 1.521e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.994e+01, tolerance: 1.585e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.869e+00, tolerance: 1.723e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.106e+02, tolerance: 1.630e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.644e+02, tolerance: 1.593e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.759e+02, tolerance: 1.875e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.667e+02, tolerance: 1.647e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.309e+00, tolerance: 1.791e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.341e+00, tolerance: 1.743e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.451e+01, tolerance: 1.647e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.643e+02, tolerance: 1.576e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.624e+02, tolerance: 1.635e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.976e+02, tolerance: 1.787e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.267e+01, tolerance: 1.471e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.020e+00, tolerance: 1.460e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.182e+00, tolerance: 1.730e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.985e+02, tolerance: 1.687e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.271e+00, tolerance: 1.782e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.447e+02, tolerance: 1.869e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.210e+01, tolerance: 1.817e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.078e+01, tolerance: 1.646e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.932e+01, tolerance: 1.681e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+01, tolerance: 1.704e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.206e+01, tolerance: 1.482e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.862e+01, tolerance: 1.610e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.771e+01, tolerance: 1.662e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.003e+02, tolerance: 1.728e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.946e+01, tolerance: 1.699e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.023e+00, tolerance: 1.928e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.253e+01, tolerance: 1.534e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.099e+01, tolerance: 1.577e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.671e+00, tolerance: 1.419e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.006e+02, tolerance: 1.637e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.081e+00, tolerance: 1.520e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.527e+01, tolerance: 1.462e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.237e+02, tolerance: 1.935e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.006e+01, tolerance: 1.712e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.195e+02, tolerance: 1.717e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.029e+02, tolerance: 1.834e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.981e+02, tolerance: 1.680e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.525e+01, tolerance: 1.963e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.949e+00, tolerance: 1.701e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.224e+01, tolerance: 1.667e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.255e+02, tolerance: 1.699e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.824e+00, tolerance: 1.519e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.526e+02, tolerance: 1.615e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.751e+01, tolerance: 1.661e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.517e+00, tolerance: 1.620e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.429e+01, tolerance: 1.728e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.122e+01, tolerance: 2.030e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.419e+01, tolerance: 1.967e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.718e+01, tolerance: 1.854e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.121e+00, tolerance: 1.795e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+01, tolerance: 1.757e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.397e+00, tolerance: 1.460e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.126e+02, tolerance: 1.913e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.467e+02, tolerance: 1.808e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.136e+02, tolerance: 1.621e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.435e+02, tolerance: 1.663e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.576e+00, tolerance: 1.739e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.185e+00, tolerance: 1.477e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.041e+01, tolerance: 1.973e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.211e+01, tolerance: 1.779e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.979e+02, tolerance: 1.635e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.711e+01, tolerance: 1.672e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+00, tolerance: 1.634e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.315e+00, tolerance: 1.544e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.062e+01, tolerance: 1.694e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.579e+01, tolerance: 1.659e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.062e+02, tolerance: 1.781e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.641e+00, tolerance: 1.870e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.691e+01, tolerance: 1.628e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.464e+00, tolerance: 1.495e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.452e+02, tolerance: 1.760e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.951e+01, tolerance: 1.587e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.782e+00, tolerance: 1.665e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.071e+02, tolerance: 1.868e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.376e+00, tolerance: 1.332e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.058e+01, tolerance: 1.493e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.122e+00, tolerance: 1.558e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.646e+01, tolerance: 1.884e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.384e+00, tolerance: 1.679e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.884e+00, tolerance: 1.596e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.115e+00, tolerance: 1.540e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.953e+00, tolerance: 1.425e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.988e+00, tolerance: 1.650e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.331e+01, tolerance: 1.576e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.566e+00, tolerance: 1.326e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.516e+00, tolerance: 1.506e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.046e+01, tolerance: 1.588e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.986e+01, tolerance: 1.772e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.406e+00, tolerance: 1.421e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.796e+01, tolerance: 1.425e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.880e+02, tolerance: 1.715e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.825e+01, tolerance: 1.809e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.991e+00, tolerance: 1.558e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.833e+01, tolerance: 1.829e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.673e+02, tolerance: 1.699e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.674e+00, tolerance: 1.385e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.623e+02, tolerance: 1.650e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.123e+00, tolerance: 1.920e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.001e+02, tolerance: 1.770e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.165e+00, tolerance: 1.397e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.785e+01, tolerance: 1.753e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.085e+01, tolerance: 1.820e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.370e+02, tolerance: 1.834e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.931e+01, tolerance: 2.038e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.471e+01, tolerance: 1.860e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.542e+01, tolerance: 1.762e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.022e+00, tolerance: 1.473e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.078e+00, tolerance: 1.852e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.011e+00, tolerance: 1.559e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.602e+00, tolerance: 1.479e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.156e+02, tolerance: 1.579e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.862e+00, tolerance: 1.931e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.700e+01, tolerance: 1.657e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.710e+02, tolerance: 1.787e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.351e+02, tolerance: 1.658e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.645e+00, tolerance: 1.609e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.727e+02, tolerance: 1.699e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.260e+01, tolerance: 1.374e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.932e+01, tolerance: 1.903e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.799e+01, tolerance: 1.676e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.720e+02, tolerance: 1.302e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.369e+00, tolerance: 1.708e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+00, tolerance: 1.812e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.990e+00, tolerance: 1.720e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.088e+02, tolerance: 1.559e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.663e+00, tolerance: 1.510e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.047e+01, tolerance: 1.620e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.667e+02, tolerance: 1.701e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e+00, tolerance: 1.719e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.787e+01, tolerance: 1.578e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  1%|          | 1/100 [00:36<1:00:47, 36.85s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.856e+00, tolerance: 1.900e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.117e+00, tolerance: 1.617e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.003e+00, tolerance: 1.663e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.264e+00, tolerance: 1.904e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.520e+00, tolerance: 1.570e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.886e+00, tolerance: 1.661e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.900e+00, tolerance: 1.702e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.858e+01, tolerance: 1.729e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.812e+00, tolerance: 1.656e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.009e+01, tolerance: 1.582e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.062e+00, tolerance: 1.609e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.369e+00, tolerance: 1.587e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.603e+00, tolerance: 1.654e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.681e+00, tolerance: 2.073e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.174e+00, tolerance: 1.562e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.415e+00, tolerance: 1.862e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.454e+00, tolerance: 1.402e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.261e+00, tolerance: 1.710e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.766e+00, tolerance: 1.491e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.797e+00, tolerance: 1.805e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.722e+00, tolerance: 1.676e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+00, tolerance: 1.492e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.896e+00, tolerance: 1.607e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.588e+00, tolerance: 1.464e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.147e+00, tolerance: 1.645e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.188e+00, tolerance: 1.612e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.515e+00, tolerance: 1.775e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.366e+00, tolerance: 1.651e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.587e+00, tolerance: 1.466e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.256e+00, tolerance: 1.702e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.447e+00, tolerance: 1.424e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.543e+00, tolerance: 1.319e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.126e+00, tolerance: 1.417e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.872e+00, tolerance: 1.491e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.214e+00, tolerance: 1.903e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.404e+00, tolerance: 1.528e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.717e+00, tolerance: 1.211e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.732e+00, tolerance: 1.707e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.328e+00, tolerance: 1.500e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.369e+00, tolerance: 1.562e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.116e+00, tolerance: 1.658e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.222e+01, tolerance: 1.727e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.618e+00, tolerance: 1.495e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+01, tolerance: 1.611e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.969e+00, tolerance: 1.701e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.764e+00, tolerance: 1.649e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.033e+00, tolerance: 1.445e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.797e+00, tolerance: 1.825e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.127e+00, tolerance: 1.778e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.805e+00, tolerance: 1.739e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.265e+00, tolerance: 1.414e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.414e+00, tolerance: 2.180e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.387e+00, tolerance: 1.576e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+00, tolerance: 1.544e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.635e+00, tolerance: 1.513e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.404e+01, tolerance: 1.508e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.252e+00, tolerance: 1.435e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.061e+00, tolerance: 1.477e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.148e+00, tolerance: 1.879e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.610e+00, tolerance: 1.687e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.963e+00, tolerance: 1.823e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.299e+00, tolerance: 1.980e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+00, tolerance: 1.603e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.141e+00, tolerance: 1.342e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.551e+00, tolerance: 1.661e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.754e+00, tolerance: 1.589e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.203e+00, tolerance: 1.409e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.214e+00, tolerance: 1.763e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.670e+01, tolerance: 1.678e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.124e+00, tolerance: 1.363e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.451e+00, tolerance: 1.733e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.104e+00, tolerance: 1.338e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.604e+01, tolerance: 1.498e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.045e+00, tolerance: 1.638e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.926e+00, tolerance: 1.539e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.680e+00, tolerance: 1.684e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.978e+00, tolerance: 1.782e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.145e+00, tolerance: 1.903e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.479e+00, tolerance: 1.778e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.799e+00, tolerance: 1.459e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.151e+00, tolerance: 1.683e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.280e+01, tolerance: 1.678e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.412e+00, tolerance: 1.593e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.832e+00, tolerance: 1.648e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.221e+00, tolerance: 1.883e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.626e+01, tolerance: 1.699e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.626e+00, tolerance: 1.494e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.357e+00, tolerance: 1.653e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.910e+00, tolerance: 1.493e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.607e+01, tolerance: 1.810e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.428e+00, tolerance: 1.932e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.332e+00, tolerance: 1.624e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.067e+00, tolerance: 1.641e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.775e+01, tolerance: 1.914e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.443e+00, tolerance: 1.846e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+00, tolerance: 1.481e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.763e+00, tolerance: 1.541e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+00, tolerance: 1.706e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.370e+00, tolerance: 1.600e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.775e+00, tolerance: 1.720e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.313e+00, tolerance: 1.538e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.502e+00, tolerance: 1.809e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.247e+01, tolerance: 1.839e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+01, tolerance: 1.956e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.896e+00, tolerance: 1.628e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.536e+00, tolerance: 1.695e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.946e+00, tolerance: 1.564e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.737e+00, tolerance: 1.657e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.129e+00, tolerance: 1.685e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.798e+01, tolerance: 1.628e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.241e+00, tolerance: 1.470e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.570e+00, tolerance: 1.440e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.452e+00, tolerance: 1.376e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.924e+00, tolerance: 1.556e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.249e+00, tolerance: 1.758e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.442e+00, tolerance: 1.434e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.534e+00, tolerance: 2.074e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.784e+00, tolerance: 1.744e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+00, tolerance: 1.527e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.788e+00, tolerance: 1.562e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.717e+00, tolerance: 1.667e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.659e+00, tolerance: 1.522e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.466e+00, tolerance: 1.580e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.821e+00, tolerance: 1.605e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.201e+00, tolerance: 1.558e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.251e+01, tolerance: 1.688e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.552e+00, tolerance: 1.567e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.332e+00, tolerance: 1.342e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.913e+00, tolerance: 1.675e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.806e+00, tolerance: 1.720e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.489e+00, tolerance: 1.663e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.821e+00, tolerance: 1.745e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.130e+01, tolerance: 1.514e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.668e+00, tolerance: 2.215e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.689e+00, tolerance: 1.696e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.628e+00, tolerance: 1.744e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.972e+00, tolerance: 1.634e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.236e+01, tolerance: 1.923e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.064e+00, tolerance: 1.587e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.637e+00, tolerance: 1.881e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.001e+00, tolerance: 1.626e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.581e+00, tolerance: 1.374e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.698e+00, tolerance: 1.635e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.521e+00, tolerance: 1.524e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.631e+00, tolerance: 1.389e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.958e+00, tolerance: 1.663e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.421e+00, tolerance: 1.528e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.333e+00, tolerance: 1.563e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.151e+00, tolerance: 1.829e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.523e+00, tolerance: 1.480e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.611e+00, tolerance: 1.591e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.021e+00, tolerance: 1.869e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e+00, tolerance: 1.802e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.919e+00, tolerance: 1.476e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.537e+00, tolerance: 1.452e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.068e+00, tolerance: 1.838e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.640e+00, tolerance: 1.445e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.771e+00, tolerance: 1.835e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.843e+00, tolerance: 1.592e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.646e+00, tolerance: 1.595e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.008e+00, tolerance: 1.849e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.122e+00, tolerance: 1.592e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.484e+00, tolerance: 1.564e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.375e+00, tolerance: 1.629e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+01, tolerance: 1.645e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  2%|▏         | 2/100 [01:07<54:07, 33.14s/it]  /usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.792e+00, tolerance: 1.705e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.146e+00, tolerance: 1.663e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.148e+00, tolerance: 1.444e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.985e+00, tolerance: 1.623e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.112e+01, tolerance: 1.654e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.260e+00, tolerance: 1.631e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.745e+00, tolerance: 1.644e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.681e+00, tolerance: 1.720e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+00, tolerance: 1.662e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+01, tolerance: 1.467e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.031e+01, tolerance: 1.492e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.052e+00, tolerance: 1.633e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.456e+00, tolerance: 1.608e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.810e+00, tolerance: 1.540e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.902e+00, tolerance: 1.578e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.204e+00, tolerance: 1.759e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.217e+00, tolerance: 1.567e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.991e+00, tolerance: 1.622e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.423e+00, tolerance: 1.805e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.578e+01, tolerance: 1.491e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.270e+00, tolerance: 1.503e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.173e+00, tolerance: 1.780e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.570e+00, tolerance: 1.788e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.673e+00, tolerance: 1.714e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.360e+00, tolerance: 1.561e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.675e+00, tolerance: 1.617e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.345e+00, tolerance: 1.676e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.397e+00, tolerance: 1.523e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.464e+00, tolerance: 1.637e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.699e+00, tolerance: 1.871e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.522e+00, tolerance: 1.313e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.368e+00, tolerance: 1.774e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.996e+00, tolerance: 1.513e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.592e+00, tolerance: 1.978e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.553e+00, tolerance: 1.825e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+00, tolerance: 1.754e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.339e+00, tolerance: 1.575e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.625e+00, tolerance: 1.847e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.553e+00, tolerance: 1.472e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.283e+00, tolerance: 1.772e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.745e+00, tolerance: 1.874e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.983e+00, tolerance: 1.753e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.846e+00, tolerance: 1.475e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.879e+00, tolerance: 1.886e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.069e+00, tolerance: 1.803e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.827e+00, tolerance: 1.671e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.045e+00, tolerance: 1.486e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.363e+00, tolerance: 1.757e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.277e+00, tolerance: 1.561e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.527e+00, tolerance: 1.868e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.102e+00, tolerance: 1.805e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.611e+00, tolerance: 1.818e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.979e+00, tolerance: 1.715e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.093e+00, tolerance: 1.784e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.920e+00, tolerance: 1.512e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.811e+00, tolerance: 1.581e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.216e+00, tolerance: 1.570e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.106e+00, tolerance: 1.709e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+01, tolerance: 1.766e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.228e+00, tolerance: 1.734e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.827e+00, tolerance: 1.620e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.038e+00, tolerance: 1.886e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.971e+00, tolerance: 1.878e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.487e+00, tolerance: 1.592e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.870e+00, tolerance: 1.629e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.169e+00, tolerance: 1.584e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.580e+00, tolerance: 1.516e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.965e+00, tolerance: 1.775e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.627e+00, tolerance: 1.423e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.615e+00, tolerance: 1.722e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.743e+00, tolerance: 1.342e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.446e+00, tolerance: 1.471e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.852e+00, tolerance: 1.512e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.047e+00, tolerance: 1.697e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.919e+00, tolerance: 1.455e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.729e+00, tolerance: 1.651e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.329e+00, tolerance: 1.549e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.909e+00, tolerance: 1.991e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.985e+00, tolerance: 1.658e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.520e+00, tolerance: 1.448e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.070e+00, tolerance: 1.756e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.056e+00, tolerance: 1.395e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.288e+01, tolerance: 1.354e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.698e+01, tolerance: 1.786e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.510e+00, tolerance: 1.412e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.738e+00, tolerance: 1.771e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+00, tolerance: 1.319e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.220e+00, tolerance: 1.549e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.627e+00, tolerance: 1.437e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.014e+00, tolerance: 1.430e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  3%|▎         | 3/100 [01:24<42:01, 25.99s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.400e+00, tolerance: 1.669e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.928e+00, tolerance: 1.784e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.239e+00, tolerance: 1.588e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+00, tolerance: 1.825e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+00, tolerance: 1.739e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.688e+00, tolerance: 1.547e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.868e+00, tolerance: 1.469e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.511e+00, tolerance: 1.275e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.591e+00, tolerance: 1.376e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.682e+00, tolerance: 1.429e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.415e+00, tolerance: 1.706e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.673e+00, tolerance: 1.646e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.182e+00, tolerance: 1.728e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.904e+00, tolerance: 1.537e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.319e+00, tolerance: 1.562e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.950e+00, tolerance: 1.746e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.452e+00, tolerance: 1.552e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.190e+00, tolerance: 1.838e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.093e+00, tolerance: 1.918e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.725e+00, tolerance: 1.658e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+00, tolerance: 1.424e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.460e+00, tolerance: 1.579e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+00, tolerance: 1.553e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.686e+00, tolerance: 1.496e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.746e+00, tolerance: 1.947e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.501e+00, tolerance: 1.609e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.815e+00, tolerance: 1.485e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.723e+00, tolerance: 1.604e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.058e+00, tolerance: 1.455e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.432e+00, tolerance: 1.830e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.023e+00, tolerance: 1.970e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.866e+00, tolerance: 1.652e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.897e+00, tolerance: 1.745e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.210e+00, tolerance: 1.532e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.072e+01, tolerance: 1.615e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.765e+00, tolerance: 1.691e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.811e+00, tolerance: 1.751e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.087e+00, tolerance: 1.838e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.174e+00, tolerance: 1.808e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.488e+00, tolerance: 1.609e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.337e+00, tolerance: 1.680e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.941e+00, tolerance: 1.384e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+00, tolerance: 1.419e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.752e+00, tolerance: 1.539e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.824e+00, tolerance: 1.522e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.834e+00, tolerance: 1.996e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.222e+00, tolerance: 1.704e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.269e+00, tolerance: 1.889e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.815e+00, tolerance: 1.882e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.930e+00, tolerance: 1.672e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.262e+00, tolerance: 1.732e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.794e+00, tolerance: 1.277e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.655e+00, tolerance: 1.905e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.183e+00, tolerance: 1.649e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.908e+00, tolerance: 1.536e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.863e+00, tolerance: 1.595e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.465e+00, tolerance: 1.815e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.069e+00, tolerance: 1.523e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.068e+01, tolerance: 1.556e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.680e+00, tolerance: 1.413e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.052e+00, tolerance: 1.780e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.926e+00, tolerance: 1.657e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+01, tolerance: 1.917e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.803e+00, tolerance: 1.854e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+00, tolerance: 1.412e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.283e+00, tolerance: 1.400e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.107e+00, tolerance: 1.483e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.496e+00, tolerance: 1.657e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.964e+00, tolerance: 1.350e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.512e+00, tolerance: 1.696e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.197e+00, tolerance: 1.788e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.212e+00, tolerance: 1.387e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.890e+01, tolerance: 1.684e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.025e+00, tolerance: 1.748e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.839e+00, tolerance: 1.536e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.830e+00, tolerance: 1.633e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.036e+00, tolerance: 1.809e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.649e+00, tolerance: 1.594e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.279e+00, tolerance: 1.651e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.757e+00, tolerance: 1.563e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.181e+00, tolerance: 1.722e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.676e+00, tolerance: 1.747e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.167e+00, tolerance: 1.609e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.524e+00, tolerance: 1.607e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.343e+00, tolerance: 1.741e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.979e+00, tolerance: 1.713e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.464e+00, tolerance: 1.528e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.687e+00, tolerance: 1.780e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.538e+00, tolerance: 1.551e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.445e+00, tolerance: 1.728e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+00, tolerance: 1.536e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.288e+00, tolerance: 1.653e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.726e+00, tolerance: 1.367e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.975e+00, tolerance: 1.709e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.531e+00, tolerance: 1.427e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.903e+00, tolerance: 1.505e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.663e+00, tolerance: 1.696e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  4%|▍         | 4/100 [01:42<36:00, 22.50s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.183e+00, tolerance: 1.551e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.819e+00, tolerance: 1.490e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.068e+00, tolerance: 1.615e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+00, tolerance: 1.495e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.166e+00, tolerance: 1.793e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.383e+00, tolerance: 1.577e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.999e+00, tolerance: 1.635e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.635e+00, tolerance: 1.773e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.595e+00, tolerance: 1.839e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.698e+00, tolerance: 1.563e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.535e+00, tolerance: 1.708e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.520e+00, tolerance: 1.353e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.235e+00, tolerance: 1.802e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+00, tolerance: 1.758e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.133e+01, tolerance: 1.639e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.234e+00, tolerance: 1.597e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.181e+00, tolerance: 1.547e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.437e+00, tolerance: 1.728e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.249e+00, tolerance: 1.618e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.513e+00, tolerance: 1.651e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.873e+00, tolerance: 1.839e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.667e+00, tolerance: 1.750e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+01, tolerance: 1.736e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+00, tolerance: 1.759e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+00, tolerance: 1.522e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.818e+00, tolerance: 2.130e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.310e+00, tolerance: 1.497e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.835e+00, tolerance: 1.738e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.300e+00, tolerance: 1.609e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.792e+00, tolerance: 1.443e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.087e+00, tolerance: 1.743e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.630e+00, tolerance: 1.618e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.205e+00, tolerance: 1.707e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.575e+00, tolerance: 1.833e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.436e+00, tolerance: 1.868e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.660e+00, tolerance: 1.698e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.219e+00, tolerance: 1.728e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.219e+00, tolerance: 1.619e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.922e+00, tolerance: 1.605e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.753e+01, tolerance: 1.785e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.240e+00, tolerance: 1.617e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.917e+00, tolerance: 1.717e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.210e+00, tolerance: 1.563e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.915e+00, tolerance: 1.811e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.769e+00, tolerance: 1.529e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+00, tolerance: 1.463e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.003e+00, tolerance: 1.794e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.740e+00, tolerance: 1.694e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.718e+00, tolerance: 1.747e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.756e+00, tolerance: 1.615e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.489e+00, tolerance: 1.860e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.836e+00, tolerance: 1.686e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  5%|▌         | 5/100 [01:56<30:59, 19.57s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.231e+00, tolerance: 1.928e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.471e+00, tolerance: 1.729e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.331e+00, tolerance: 1.565e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+00, tolerance: 1.527e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.106e+00, tolerance: 1.492e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.917e+00, tolerance: 1.674e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.884e+00, tolerance: 1.782e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.525e+00, tolerance: 1.399e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.244e+00, tolerance: 1.681e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.930e+00, tolerance: 1.344e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.413e+00, tolerance: 1.538e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.232e+00, tolerance: 1.659e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.564e+00, tolerance: 1.677e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.682e+00, tolerance: 1.694e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.740e+00, tolerance: 1.481e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.691e+00, tolerance: 1.583e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.818e+00, tolerance: 1.662e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.815e+00, tolerance: 1.817e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.297e+00, tolerance: 1.741e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.123e+00, tolerance: 2.035e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.147e+00, tolerance: 1.512e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.654e+00, tolerance: 1.604e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.670e+00, tolerance: 1.895e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.674e+00, tolerance: 1.521e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.084e+00, tolerance: 1.903e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.428e+00, tolerance: 1.630e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.118e+00, tolerance: 1.722e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.968e+00, tolerance: 1.835e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.767e+00, tolerance: 1.777e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.039e+00, tolerance: 1.585e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.965e+00, tolerance: 1.749e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.245e+00, tolerance: 1.571e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.782e+00, tolerance: 1.582e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.684e+00, tolerance: 1.479e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.369e+00, tolerance: 1.405e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.610e+00, tolerance: 1.697e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.597e+00, tolerance: 1.682e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.760e+00, tolerance: 1.673e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.602e+00, tolerance: 1.504e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.530e+00, tolerance: 1.848e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  6%|▌         | 6/100 [02:08<26:47, 17.11s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.836e+00, tolerance: 1.786e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.594e+00, tolerance: 1.511e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.757e+00, tolerance: 1.565e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.132e+00, tolerance: 1.855e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.359e+00, tolerance: 1.907e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.455e+00, tolerance: 1.902e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+00, tolerance: 1.593e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  7%|▋         | 7/100 [02:17<22:17, 14.39s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.068e+00, tolerance: 1.491e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.956e+00, tolerance: 1.494e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.971e+00, tolerance: 1.643e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.063e+00, tolerance: 1.616e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+00, tolerance: 1.610e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.485e+00, tolerance: 1.765e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.583e+00, tolerance: 1.645e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.113e+00, tolerance: 1.565e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.850e+00, tolerance: 1.743e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.295e+00, tolerance: 1.391e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.658e+00, tolerance: 1.716e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.238e+00, tolerance: 1.354e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.483e+00, tolerance: 1.611e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.899e+00, tolerance: 1.822e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  8%|▊         | 8/100 [02:28<20:13, 13.19s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+00, tolerance: 1.743e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.392e+00, tolerance: 1.600e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.689e+00, tolerance: 1.507e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.097e+00, tolerance: 1.731e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.019e+00, tolerance: 1.678e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.701e+00, tolerance: 1.709e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.727e+00, tolerance: 1.900e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.852e+00, tolerance: 1.216e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.047e+00, tolerance: 1.569e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.228e+00, tolerance: 1.705e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  9%|▉         | 9/100 [02:37<18:25, 12.15s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.269e+00, tolerance: 1.644e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.601e+00, tolerance: 1.400e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.585e+00, tolerance: 1.882e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e+00, tolerance: 1.573e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.487e+00, tolerance: 1.682e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 10%|█         | 10/100 [02:46<16:37, 11.08s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.730e+00, tolerance: 1.842e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.055e+00, tolerance: 1.718e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.906e+00, tolerance: 1.694e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 11%|█         | 11/100 [02:54<15:10, 10.23s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.900e+00, tolerance: 1.909e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.801e+00, tolerance: 1.932e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.805e+00, tolerance: 1.502e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.502e+00, tolerance: 1.649e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 12%|█▏        | 12/100 [03:03<14:27,  9.86s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.824e+00, tolerance: 1.733e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.235e+00, tolerance: 1.565e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 13%|█▎        | 13/100 [03:11<13:14,  9.13s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.257e+00, tolerance: 1.666e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 15%|█▌        | 15/100 [03:28<12:26,  8.79s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.964e+00, tolerance: 1.561e+00\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "100%|██████████| 100/100 [13:05<00:00,  7.86s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tqdm(range(len(alpha_params))):\n",
+    "  alpha = alpha_params[i]\n",
+    "  all_selected_features = train_stabl_proxy_one_alpha(df_data, target_property, n_bootstraps, alpha, fraction, max_iter = max_iter)\n",
+    "  feature_freq = compute_fdr_and_feature_freq(all_selected_features, n_bootstraps)\n",
+    "  every_feature_freq_acs[alpha] = feature_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "vqfeUSGLVo8Z"
+   },
+   "outputs": [],
+   "source": [
+    "merged_feature_freq = merge_all_feature_freq(every_feature_freq_acs)\n",
+    "fdp, t = get_fdp_curve(merged_feature_freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 342,
+     "status": "ok",
+     "timestamp": 1761932229624,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "jpkHwX5OVbSQ",
+    "outputId": "5c08b825-92fa-459c-adde-8f07d897b26f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "thr_opt = t[np.argmin(fdp)]\n",
+    "plt.plot(t, fdp)\n",
+    "plt.axvline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"t\")\n",
+    "plt.ylabel(\"FDP\")\n",
+    "plt.title(\"FDP vs t\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 2481,
+     "status": "ok",
+     "timestamp": 1761932238477,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "9Z6uUfPfcZk4",
+    "outputId": "47cbfecd-086e-4145-e1be-68787b1bc14b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "# colormap adapted\n",
+    "from matplotlib import colormaps\n",
+    "cmap = colormaps.get_cmap('tab20')\n",
+    "for i,feature in enumerate(merged_feature_freq):\n",
+    "    alphas = merged_feature_freq[feature][\"1/alpha\"]\n",
+    "    freqs = merged_feature_freq[feature][\"freq\"]\n",
+    "    if feature in optimal_features:\n",
+    "      plt.plot(alphas, freqs, label=feature, color=\"darkred\")\n",
+    "    else:\n",
+    "      plt.plot(alphas, freqs, label=feature, color = \"gray\")\n",
+    "# plt.axhline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"1/alpha\")\n",
+    "plt.ylabel(\"Feature frequency\")\n",
+    "plt.title(\"Feature frequency vs alpha\")\n",
+    "# plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 54,
+     "status": "ok",
+     "timestamp": 1761932242470,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "DOOEytl6cOYr",
+    "outputId": "19aee7af-6a46-4ad8-818c-788664c7b7ff"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['ens_charge', 'Fv_chml', 'patch_cdr_neg', 'patch_pos', 'patch_neg', 'r_gyr', 'dipole_moment', 'coeff_280', 'zquadrupole', 'helicity', 'amphipathicity', 'sed_const']\n",
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimal_features = [feature for feature in merged_feature_freq if max(merged_feature_freq[feature][\"freq\"]) >= thr_opt]\n",
+    "print(optimal_features)\n",
+    "print(len(optimal_features))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DVbzIrklcovi"
+   },
+   "source": [
+    "## Feature selection on Titer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 37,
+     "status": "ok",
+     "timestamp": 1761932283741,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "gGnekjV7cqmt",
+    "outputId": "a9f8634e-c3a8-4ccf-e591-8e0319f1b08a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features with low variance: Index(['debye'], dtype='object')\n",
+      "(239, 46)\n"
+     ]
+    }
+   ],
+   "source": [
+    "alpha_params = list(np.linspace(0.01, 1, 100))\n",
+    "# n_bootstraps = 100\n",
+    "fraction = 1.0\n",
+    "target_property = \"Titer\"\n",
+    "df_target_no_nan = df_target[target_property].dropna()\n",
+    "df_main_dataset = df_master_filtered\n",
+    "df_main_dataset = df_main_dataset.loc[df_target_no_nan.index]\n",
+    "\n",
+    "variance_threshold = 0.\n",
+    "selector = VarianceThreshold(threshold=variance_threshold)\n",
+    "df_main_dataset_high_v = pd.DataFrame(selector.fit_transform(df_main_dataset), columns=selector.get_feature_names_out())\n",
+    "df_main_dataset_high_v.index = df_main_dataset.index\n",
+    "features_low_var = df_main_dataset.columns[~selector.get_support()]\n",
+    "print(f\"Features with low variance: {features_low_var}\")\n",
+    "\n",
+    "df_data = (df_main_dataset_high_v - df_main_dataset_high_v.mean()) / df_main_dataset_high_v.std()\n",
+    "df_data[target_property] = df_target_no_nan\n",
+    "print(df_data.shape)\n",
+    "# n_bootstraps = df_data.shape[0]\n",
+    "n_bootstraps = 500\n",
+    "every_fdr = np.zeros(len(alpha_params))\n",
+    "every_feature_freq_titer = {}\n",
+    "max_iter = 3000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 2187369,
+     "status": "ok",
+     "timestamp": 1761934495274,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "SOR7tYUNcwRc",
+    "outputId": "c51a619e-89bf-4db0-eb97-d98735400be2"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[1;30;43mLe flux de sortie a été tronqué et ne contient que les 5000 dernières lignes.\u001b[0m\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.159e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.189e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.340e+02, tolerance: 1.208e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.271e+02, tolerance: 1.378e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.801e+02, tolerance: 1.513e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.777e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+02, tolerance: 1.189e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.487e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.584e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.578e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.430e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.503e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.127e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.414e+02, tolerance: 1.349e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.691e+02, tolerance: 1.470e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.735e+02, tolerance: 1.603e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.313e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.611e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.886e+02, tolerance: 1.604e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.677e+02, tolerance: 1.546e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.894e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.132e+02, tolerance: 1.591e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.866e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.978e+02, tolerance: 1.578e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.492e+02, tolerance: 1.510e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.788e+02, tolerance: 1.484e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.681e+02, tolerance: 1.375e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.027e+02, tolerance: 1.335e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.574e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.285e+02, tolerance: 1.561e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.902e+02, tolerance: 1.560e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.685e+02, tolerance: 1.502e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.537e+02, tolerance: 1.445e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.158e+02, tolerance: 1.278e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.773e+02, tolerance: 1.551e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.766e+02, tolerance: 1.476e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.474e+02, tolerance: 1.317e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.244e+02, tolerance: 1.519e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.125e+02, tolerance: 1.596e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.843e+02, tolerance: 1.572e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.860e+02, tolerance: 1.388e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.351e+02, tolerance: 1.316e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.965e+02, tolerance: 1.500e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.079e+02, tolerance: 1.306e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.234e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.145e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.652e+02, tolerance: 1.578e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+02, tolerance: 1.348e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.300e+02, tolerance: 1.447e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.522e+02, tolerance: 1.455e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.858e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.941e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.579e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.575e+02, tolerance: 1.501e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.424e+02, tolerance: 1.376e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.048e+02, tolerance: 1.473e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.649e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.168e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.032e+02, tolerance: 1.318e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.944e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.830e+02, tolerance: 1.530e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.711e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.670e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.073e+02, tolerance: 1.503e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.835e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.100e+02, tolerance: 1.331e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.801e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.416e+02, tolerance: 1.324e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+02, tolerance: 1.401e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.601e+02, tolerance: 1.298e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.441e+02, tolerance: 1.285e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.917e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.786e+02, tolerance: 1.349e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.441e+02, tolerance: 1.495e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 34%|███▍      | 34/100 [17:04<24:32, 22.31s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.683e+02, tolerance: 1.271e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.619e+02, tolerance: 1.601e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.400e+02, tolerance: 1.538e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.908e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.750e+02, tolerance: 1.502e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.160e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.318e+03, tolerance: 1.558e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.401e+02, tolerance: 1.276e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.766e+02, tolerance: 1.537e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.273e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.252e+02, tolerance: 1.373e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.439e+02, tolerance: 1.419e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.900e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.651e+02, tolerance: 1.339e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.781e+02, tolerance: 1.409e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.293e+02, tolerance: 1.540e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.836e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+02, tolerance: 1.356e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.480e+02, tolerance: 1.308e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.334e+02, tolerance: 1.313e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.377e+02, tolerance: 1.310e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.943e+02, tolerance: 1.493e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.486e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.681e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.020e+02, tolerance: 1.355e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.303e+02, tolerance: 1.505e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.994e+02, tolerance: 1.454e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.309e+02, tolerance: 1.574e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.501e+02, tolerance: 1.439e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.888e+02, tolerance: 1.374e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.108e+02, tolerance: 1.335e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.910e+02, tolerance: 1.638e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.240e+02, tolerance: 1.237e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.913e+02, tolerance: 1.615e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.624e+02, tolerance: 1.611e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+02, tolerance: 1.635e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.104e+02, tolerance: 1.558e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.934e+02, tolerance: 1.326e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.662e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+02, tolerance: 1.432e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.440e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.464e+02, tolerance: 1.420e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.303e+02, tolerance: 1.473e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.689e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.690e+02, tolerance: 1.283e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.474e+02, tolerance: 1.290e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.710e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.217e+02, tolerance: 1.483e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.106e+02, tolerance: 1.627e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.930e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.550e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.686e+02, tolerance: 1.509e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.358e+02, tolerance: 1.352e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.032e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.340e+02, tolerance: 1.313e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.529e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.542e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.676e+02, tolerance: 1.570e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.460e+02, tolerance: 1.249e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.025e+02, tolerance: 1.358e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.366e+02, tolerance: 1.387e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.435e+02, tolerance: 1.288e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.652e+02, tolerance: 1.547e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.620e+02, tolerance: 1.440e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.086e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.843e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.852e+02, tolerance: 1.703e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.699e+02, tolerance: 1.684e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.073e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.406e+02, tolerance: 1.251e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.755e+02, tolerance: 1.521e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.829e+02, tolerance: 1.672e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.290e+02, tolerance: 1.250e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.595e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+02, tolerance: 1.533e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.329e+02, tolerance: 1.469e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.516e+02, tolerance: 1.331e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.427e+02, tolerance: 1.555e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.000e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.924e+02, tolerance: 1.561e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.182e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.952e+02, tolerance: 1.672e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.681e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.348e+02, tolerance: 1.423e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.726e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.340e+02, tolerance: 1.550e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.826e+02, tolerance: 1.497e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.466e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.977e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.781e+02, tolerance: 1.211e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+02, tolerance: 1.423e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e+02, tolerance: 1.627e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.055e+02, tolerance: 1.502e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.145e+02, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.071e+02, tolerance: 1.473e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.176e+02, tolerance: 1.564e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.218e+02, tolerance: 1.175e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.076e+02, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.296e+02, tolerance: 1.280e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.781e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.961e+02, tolerance: 1.443e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.925e+02, tolerance: 1.496e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.989e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.707e+02, tolerance: 1.416e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.269e+03, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.358e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.297e+02, tolerance: 1.481e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.993e+02, tolerance: 1.518e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.383e+02, tolerance: 1.546e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.970e+02, tolerance: 1.605e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.647e+02, tolerance: 1.310e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.073e+02, tolerance: 1.633e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.382e+02, tolerance: 1.294e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.451e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.120e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.899e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.632e+02, tolerance: 1.439e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.049e+02, tolerance: 1.292e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.743e+02, tolerance: 1.297e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.272e+02, tolerance: 1.391e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.925e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.429e+02, tolerance: 1.206e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.077e+02, tolerance: 1.490e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 35%|███▌      | 35/100 [17:28<24:32, 22.65s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.452e+02, tolerance: 1.321e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.674e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.731e+02, tolerance: 1.601e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.587e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.839e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.755e+02, tolerance: 1.363e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e+02, tolerance: 1.241e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.707e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.376e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.506e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.680e+02, tolerance: 1.457e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.774e+02, tolerance: 1.269e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+02, tolerance: 1.501e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.665e+02, tolerance: 1.469e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.685e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.578e+02, tolerance: 1.286e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.583e+02, tolerance: 1.440e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.058e+02, tolerance: 1.447e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.173e+02, tolerance: 1.321e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.324e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.481e+02, tolerance: 1.672e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.036e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+02, tolerance: 1.722e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.791e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+02, tolerance: 1.598e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.267e+02, tolerance: 1.353e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.540e+02, tolerance: 1.260e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.154e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.401e+02, tolerance: 1.296e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.559e+02, tolerance: 1.289e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.622e+02, tolerance: 1.326e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.884e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.674e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.868e+02, tolerance: 1.274e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.668e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.885e+02, tolerance: 1.443e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.519e+02, tolerance: 1.227e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.608e+02, tolerance: 1.519e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.080e+02, tolerance: 1.451e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.852e+02, tolerance: 1.190e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.656e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.412e+02, tolerance: 1.214e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+02, tolerance: 1.304e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+02, tolerance: 1.535e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.339e+02, tolerance: 1.592e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.395e+02, tolerance: 1.369e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.458e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.242e+02, tolerance: 1.616e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+02, tolerance: 1.619e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.585e+02, tolerance: 1.319e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.314e+02, tolerance: 1.363e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.586e+02, tolerance: 1.593e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.148e+02, tolerance: 1.536e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.056e+02, tolerance: 1.239e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.488e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.347e+02, tolerance: 1.208e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.612e+02, tolerance: 1.516e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.160e+02, tolerance: 1.454e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.705e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.840e+02, tolerance: 1.539e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.292e+02, tolerance: 1.272e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.786e+02, tolerance: 1.373e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.898e+02, tolerance: 1.531e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.992e+02, tolerance: 1.644e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.648e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.077e+02, tolerance: 1.586e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.725e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.768e+02, tolerance: 1.232e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.564e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.730e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.500e+02, tolerance: 1.221e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.563e+02, tolerance: 1.287e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.669e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.423e+02, tolerance: 1.512e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.085e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.594e+02, tolerance: 1.285e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.856e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.038e+02, tolerance: 1.539e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.451e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.497e+02, tolerance: 1.243e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.138e+02, tolerance: 1.317e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.898e+02, tolerance: 1.598e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+02, tolerance: 1.349e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.381e+02, tolerance: 1.302e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.831e+02, tolerance: 1.674e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.992e+02, tolerance: 1.264e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 36%|███▌      | 36/100 [17:48<23:31, 22.05s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.381e+02, tolerance: 1.631e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.734e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.173e+03, tolerance: 1.665e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.724e+03, tolerance: 1.595e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.048e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.393e+02, tolerance: 1.655e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.865e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.634e+02, tolerance: 1.682e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.753e+02, tolerance: 1.274e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.882e+02, tolerance: 1.611e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.311e+02, tolerance: 1.599e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+02, tolerance: 1.419e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.684e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.612e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.818e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.502e+02, tolerance: 1.278e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.473e+02, tolerance: 1.285e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.240e+02, tolerance: 1.470e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.709e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.234e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.615e+02, tolerance: 1.224e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.205e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.675e+02, tolerance: 1.287e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.585e+02, tolerance: 1.449e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.740e+02, tolerance: 1.357e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+02, tolerance: 1.587e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.671e+02, tolerance: 1.558e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.124e+03, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.996e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.954e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.484e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.229e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.990e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.816e+02, tolerance: 1.756e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.564e+02, tolerance: 1.676e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.787e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.817e+02, tolerance: 1.398e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.749e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.719e+02, tolerance: 1.306e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.218e+02, tolerance: 1.511e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.019e+02, tolerance: 1.509e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.642e+02, tolerance: 1.579e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.323e+02, tolerance: 1.231e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+02, tolerance: 1.349e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.823e+02, tolerance: 1.398e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.751e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.882e+02, tolerance: 1.345e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.590e+02, tolerance: 1.652e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.662e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.718e+02, tolerance: 1.276e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.118e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.649e+02, tolerance: 1.512e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.530e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.728e+02, tolerance: 1.321e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.402e+02, tolerance: 1.544e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.854e+02, tolerance: 1.470e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.579e+02, tolerance: 1.445e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.400e+02, tolerance: 1.211e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.228e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.621e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+02, tolerance: 1.265e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.242e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.173e+02, tolerance: 1.495e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.971e+02, tolerance: 1.560e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.383e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.624e+02, tolerance: 1.637e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.385e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.710e+02, tolerance: 1.405e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.985e+02, tolerance: 1.324e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.974e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.313e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.677e+02, tolerance: 1.249e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.560e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.377e+02, tolerance: 1.357e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.994e+02, tolerance: 1.517e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.962e+02, tolerance: 1.611e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.610e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.566e+02, tolerance: 1.451e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.455e+02, tolerance: 1.536e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.886e+02, tolerance: 1.365e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.662e+02, tolerance: 1.264e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.921e+02, tolerance: 1.381e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.451e+02, tolerance: 1.429e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.376e+02, tolerance: 1.526e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.915e+02, tolerance: 1.552e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.073e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.823e+02, tolerance: 1.333e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.679e+02, tolerance: 1.342e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.865e+02, tolerance: 1.328e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.469e+02, tolerance: 1.225e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.433e+02, tolerance: 1.265e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.402e+03, tolerance: 1.554e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.454e+02, tolerance: 1.451e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.731e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.602e+02, tolerance: 1.574e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.915e+02, tolerance: 1.292e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.976e+02, tolerance: 1.570e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 37%|███▋      | 37/100 [18:11<23:26, 22.32s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.234e+02, tolerance: 1.369e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.067e+03, tolerance: 1.647e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.618e+02, tolerance: 1.366e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.658e+02, tolerance: 1.280e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.115e+02, tolerance: 1.389e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.839e+02, tolerance: 1.484e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.211e+02, tolerance: 1.618e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.144e+02, tolerance: 1.515e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+02, tolerance: 1.464e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.857e+02, tolerance: 1.533e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.609e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.923e+02, tolerance: 1.222e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.463e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.662e+02, tolerance: 1.487e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.206e+02, tolerance: 1.538e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.987e+02, tolerance: 1.187e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.877e+02, tolerance: 1.546e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.683e+02, tolerance: 1.480e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.741e+02, tolerance: 1.573e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.466e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.479e+02, tolerance: 1.582e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.100e+02, tolerance: 1.384e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.922e+02, tolerance: 1.567e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.829e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.579e+02, tolerance: 1.519e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.190e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.396e+02, tolerance: 1.439e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.405e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.659e+02, tolerance: 1.587e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.363e+02, tolerance: 1.365e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.268e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.442e+02, tolerance: 1.314e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.934e+02, tolerance: 1.433e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.798e+02, tolerance: 1.556e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.757e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.054e+02, tolerance: 1.416e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.511e+02, tolerance: 1.266e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.167e+02, tolerance: 1.328e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.591e+02, tolerance: 1.423e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.024e+02, tolerance: 1.544e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.572e+02, tolerance: 1.432e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.625e+02, tolerance: 1.339e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.296e+02, tolerance: 1.497e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.411e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.663e+02, tolerance: 1.439e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.341e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.920e+02, tolerance: 1.596e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.198e+02, tolerance: 1.283e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.469e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.997e+02, tolerance: 1.515e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.260e+02, tolerance: 1.355e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.520e+02, tolerance: 1.307e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.554e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.696e+02, tolerance: 1.445e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+02, tolerance: 1.367e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.389e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.946e+02, tolerance: 1.470e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.631e+02, tolerance: 1.432e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.512e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.771e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.864e+02, tolerance: 1.278e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.335e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.478e+02, tolerance: 1.517e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.836e+02, tolerance: 1.245e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.205e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.085e+02, tolerance: 1.480e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.666e+02, tolerance: 1.510e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.804e+02, tolerance: 1.423e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.351e+02, tolerance: 1.515e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.604e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.644e+02, tolerance: 1.313e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.296e+02, tolerance: 1.490e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.584e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.647e+02, tolerance: 1.487e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.953e+02, tolerance: 1.412e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.310e+02, tolerance: 1.563e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.950e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.930e+02, tolerance: 1.325e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.853e+02, tolerance: 1.335e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.179e+02, tolerance: 1.709e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.212e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.697e+02, tolerance: 1.177e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.755e+02, tolerance: 1.382e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.792e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.315e+02, tolerance: 1.291e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.783e+02, tolerance: 1.612e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.753e+02, tolerance: 1.563e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.483e+02, tolerance: 1.290e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.674e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.744e+02, tolerance: 1.174e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.642e+02, tolerance: 1.385e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.372e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.118e+02, tolerance: 1.443e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.137e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.016e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.932e+02, tolerance: 1.535e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.646e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.636e+02, tolerance: 1.483e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.098e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.482e+02, tolerance: 1.348e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.823e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.666e+02, tolerance: 1.385e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.660e+02, tolerance: 1.553e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.083e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.449e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.583e+02, tolerance: 1.449e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 38%|███▊      | 38/100 [18:33<22:47, 22.05s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.060e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.813e+02, tolerance: 1.269e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.620e+02, tolerance: 1.572e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.203e+02, tolerance: 1.198e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.443e+02, tolerance: 1.524e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.824e+02, tolerance: 1.412e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.800e+02, tolerance: 1.405e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.348e+02, tolerance: 1.543e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.392e+02, tolerance: 1.625e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.554e+02, tolerance: 1.366e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+02, tolerance: 1.265e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.474e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.669e+02, tolerance: 1.272e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+02, tolerance: 1.350e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.903e+02, tolerance: 1.510e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.370e+02, tolerance: 1.346e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.066e+02, tolerance: 1.457e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.629e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.076e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.378e+02, tolerance: 1.438e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.827e+02, tolerance: 1.352e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.704e+02, tolerance: 1.556e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.694e+02, tolerance: 1.591e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.739e+02, tolerance: 1.657e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.462e+02, tolerance: 1.367e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.068e+03, tolerance: 1.432e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.993e+02, tolerance: 1.431e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.497e+02, tolerance: 1.298e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.145e+02, tolerance: 1.526e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.677e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+02, tolerance: 1.281e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.425e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.288e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.300e+02, tolerance: 1.561e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.592e+02, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.605e+02, tolerance: 1.319e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.357e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.945e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.271e+02, tolerance: 1.635e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.958e+02, tolerance: 1.606e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.942e+02, tolerance: 1.516e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.727e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.817e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.623e+02, tolerance: 1.333e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.607e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.199e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.327e+02, tolerance: 1.374e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.993e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.213e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.599e+02, tolerance: 1.570e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.625e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.744e+02, tolerance: 1.371e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.662e+02, tolerance: 1.595e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.664e+02, tolerance: 1.294e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.889e+02, tolerance: 1.200e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.688e+02, tolerance: 1.408e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.751e+02, tolerance: 1.503e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.143e+02, tolerance: 1.757e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.301e+02, tolerance: 1.276e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.133e+02, tolerance: 1.290e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.256e+02, tolerance: 1.574e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.186e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.909e+02, tolerance: 1.385e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.823e+02, tolerance: 1.475e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.704e+02, tolerance: 1.535e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.967e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.818e+02, tolerance: 1.529e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.307e+02, tolerance: 1.225e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.077e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.363e+02, tolerance: 1.418e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.378e+02, tolerance: 1.593e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.980e+02, tolerance: 1.448e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.329e+02, tolerance: 1.264e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.097e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.345e+02, tolerance: 1.427e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.908e+02, tolerance: 1.408e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.211e+02, tolerance: 1.317e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.610e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.882e+02, tolerance: 1.382e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.342e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.328e+02, tolerance: 1.302e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.784e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.572e+02, tolerance: 1.313e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+02, tolerance: 1.558e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.171e+02, tolerance: 1.373e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 39%|███▉      | 39/100 [18:53<21:45, 21.41s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.502e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.093e+02, tolerance: 1.630e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.878e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.941e+02, tolerance: 1.269e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+02, tolerance: 1.331e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.498e+02, tolerance: 1.233e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.378e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.427e+02, tolerance: 1.259e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.549e+02, tolerance: 1.379e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.150e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.822e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.616e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.043e+02, tolerance: 1.570e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.273e+02, tolerance: 1.232e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.904e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.960e+02, tolerance: 1.486e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.937e+02, tolerance: 1.416e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.958e+02, tolerance: 1.500e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.235e+02, tolerance: 1.524e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+02, tolerance: 1.366e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.494e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.775e+02, tolerance: 1.647e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.675e+02, tolerance: 1.486e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.913e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.251e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.009e+02, tolerance: 1.394e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.178e+02, tolerance: 1.674e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.022e+02, tolerance: 1.515e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.223e+02, tolerance: 1.530e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.866e+02, tolerance: 1.300e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.783e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.693e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.556e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.989e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.892e+02, tolerance: 1.443e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.630e+02, tolerance: 1.336e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.682e+02, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.046e+02, tolerance: 1.221e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.402e+02, tolerance: 1.431e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.962e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.626e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.746e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.662e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.535e+02, tolerance: 1.309e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.253e+02, tolerance: 1.156e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e+03, tolerance: 1.519e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.864e+02, tolerance: 1.529e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.842e+02, tolerance: 1.527e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.300e+02, tolerance: 1.260e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.426e+02, tolerance: 1.355e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.088e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.528e+02, tolerance: 1.418e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.498e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.334e+02, tolerance: 1.331e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.881e+02, tolerance: 1.649e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.440e+02, tolerance: 1.249e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.667e+02, tolerance: 1.405e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.460e+02, tolerance: 1.387e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.325e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.376e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.506e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.910e+02, tolerance: 1.469e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.252e+02, tolerance: 1.291e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.454e+02, tolerance: 1.376e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.952e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.881e+02, tolerance: 1.327e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.722e+02, tolerance: 1.359e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.771e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.701e+02, tolerance: 1.138e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.561e+02, tolerance: 1.513e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.953e+02, tolerance: 1.560e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.796e+02, tolerance: 1.661e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.744e+02, tolerance: 1.431e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.940e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.807e+02, tolerance: 1.339e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.380e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.392e+02, tolerance: 1.249e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.967e+02, tolerance: 1.401e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.426e+02, tolerance: 1.387e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.908e+02, tolerance: 1.278e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.818e+02, tolerance: 1.284e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.963e+02, tolerance: 1.476e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.125e+02, tolerance: 1.212e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.178e+02, tolerance: 1.517e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.033e+02, tolerance: 1.388e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.641e+02, tolerance: 1.237e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.764e+02, tolerance: 1.439e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.802e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.716e+02, tolerance: 1.257e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.973e+02, tolerance: 1.476e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.374e+02, tolerance: 1.323e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 40%|████      | 40/100 [19:13<21:11, 21.20s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.412e+02, tolerance: 1.497e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.210e+02, tolerance: 1.497e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.925e+02, tolerance: 1.295e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.592e+02, tolerance: 1.420e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.718e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.075e+02, tolerance: 1.490e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.347e+02, tolerance: 1.493e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.758e+02, tolerance: 1.464e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.984e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.142e+02, tolerance: 1.440e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.995e+02, tolerance: 1.388e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.645e+02, tolerance: 1.585e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.191e+02, tolerance: 1.485e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.277e+02, tolerance: 1.437e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.444e+02, tolerance: 1.333e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.380e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.402e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.517e+02, tolerance: 1.394e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.695e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.701e+02, tolerance: 1.500e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.028e+02, tolerance: 1.326e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.908e+02, tolerance: 1.568e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.751e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.636e+02, tolerance: 1.345e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.022e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.731e+02, tolerance: 1.457e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.053e+02, tolerance: 1.324e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.728e+02, tolerance: 1.529e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.196e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.512e+02, tolerance: 1.501e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.677e+02, tolerance: 1.575e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.770e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.608e+02, tolerance: 1.375e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.243e+02, tolerance: 1.521e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.359e+02, tolerance: 1.234e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.139e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.706e+02, tolerance: 1.487e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.990e+02, tolerance: 1.341e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.131e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.027e+02, tolerance: 1.544e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.363e+02, tolerance: 1.517e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.649e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.553e+02, tolerance: 1.372e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.681e+02, tolerance: 1.670e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.261e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.498e+02, tolerance: 1.496e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.764e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.397e+02, tolerance: 1.157e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.026e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.391e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.095e+02, tolerance: 1.457e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.854e+02, tolerance: 1.598e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.871e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.820e+02, tolerance: 1.337e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.646e+02, tolerance: 1.462e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.151e+02, tolerance: 1.406e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.371e+02, tolerance: 1.341e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+02, tolerance: 1.615e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.314e+02, tolerance: 1.233e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.347e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.826e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.368e+02, tolerance: 1.279e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.620e+02, tolerance: 1.449e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.001e+02, tolerance: 1.377e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+02, tolerance: 1.489e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.303e+02, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.580e+02, tolerance: 1.490e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.044e+02, tolerance: 1.346e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.371e+02, tolerance: 1.546e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.149e+03, tolerance: 1.696e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.284e+02, tolerance: 1.688e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.367e+02, tolerance: 1.616e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.471e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.514e+02, tolerance: 1.431e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.271e+02, tolerance: 1.564e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.529e+02, tolerance: 1.334e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.769e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.036e+02, tolerance: 1.511e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.840e+02, tolerance: 1.475e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.913e+02, tolerance: 1.359e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.389e+02, tolerance: 1.457e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.644e+02, tolerance: 1.286e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.404e+02, tolerance: 1.538e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+02, tolerance: 1.369e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.141e+02, tolerance: 1.537e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 41%|████      | 41/100 [19:35<20:52, 21.23s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.643e+02, tolerance: 1.557e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.282e+02, tolerance: 1.213e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.422e+02, tolerance: 1.318e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.828e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.731e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.832e+02, tolerance: 1.579e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.120e+03, tolerance: 1.228e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.036e+02, tolerance: 1.524e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.925e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.921e+02, tolerance: 1.304e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.525e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.741e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.641e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.974e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.184e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.444e+02, tolerance: 1.317e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.182e+02, tolerance: 1.719e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.251e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.800e+02, tolerance: 1.274e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.016e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.775e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.181e+02, tolerance: 1.664e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.450e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.978e+02, tolerance: 1.591e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.045e+02, tolerance: 1.381e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.359e+02, tolerance: 1.374e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.050e+02, tolerance: 1.357e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.802e+02, tolerance: 1.175e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.256e+02, tolerance: 1.464e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.955e+02, tolerance: 1.285e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.443e+02, tolerance: 1.447e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.642e+02, tolerance: 1.320e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.652e+02, tolerance: 1.406e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.550e+02, tolerance: 1.191e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.865e+02, tolerance: 1.532e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.556e+02, tolerance: 1.536e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.842e+02, tolerance: 1.724e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.991e+02, tolerance: 1.591e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.455e+02, tolerance: 1.232e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.928e+02, tolerance: 1.569e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.845e+02, tolerance: 1.357e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.803e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.723e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.831e+02, tolerance: 1.342e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.276e+02, tolerance: 1.373e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.979e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.784e+02, tolerance: 1.318e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+02, tolerance: 1.216e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.380e+02, tolerance: 1.286e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.108e+03, tolerance: 1.655e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.621e+02, tolerance: 1.540e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.387e+02, tolerance: 1.389e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.526e+02, tolerance: 1.178e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.560e+02, tolerance: 1.529e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+02, tolerance: 1.571e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.041e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.968e+02, tolerance: 1.366e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.599e+02, tolerance: 1.378e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.131e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.592e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.460e+02, tolerance: 1.266e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.480e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.074e+02, tolerance: 1.295e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.220e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.339e+02, tolerance: 1.480e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.110e+02, tolerance: 1.522e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.973e+02, tolerance: 1.503e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.909e+02, tolerance: 1.647e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.436e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.597e+02, tolerance: 1.439e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.890e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.226e+02, tolerance: 1.554e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.462e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.416e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.659e+02, tolerance: 1.545e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.534e+02, tolerance: 1.571e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 42%|████▏     | 42/100 [19:56<20:29, 21.20s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.493e+02, tolerance: 1.363e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.063e+02, tolerance: 1.250e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.978e+02, tolerance: 1.483e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.603e+02, tolerance: 1.429e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.429e+02, tolerance: 1.389e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.028e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.713e+02, tolerance: 1.350e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e+02, tolerance: 1.475e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.255e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.914e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.379e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.231e+02, tolerance: 1.443e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.985e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.702e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.240e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.372e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.823e+02, tolerance: 1.451e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.696e+02, tolerance: 1.186e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.899e+02, tolerance: 1.365e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.365e+02, tolerance: 1.266e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.317e+02, tolerance: 1.306e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.156e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.435e+02, tolerance: 1.332e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.497e+02, tolerance: 1.312e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.747e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.516e+02, tolerance: 1.723e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.168e+02, tolerance: 1.413e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.620e+02, tolerance: 1.276e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.919e+02, tolerance: 1.306e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.906e+02, tolerance: 1.391e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.088e+02, tolerance: 1.323e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.487e+02, tolerance: 1.229e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.010e+02, tolerance: 1.433e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.696e+02, tolerance: 1.252e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.198e+02, tolerance: 1.413e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.095e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.645e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.724e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.305e+02, tolerance: 1.451e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.603e+02, tolerance: 1.439e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.567e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.443e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.737e+02, tolerance: 1.360e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.130e+02, tolerance: 1.423e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.664e+02, tolerance: 1.378e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.482e+02, tolerance: 1.306e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.156e+02, tolerance: 1.411e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.495e+02, tolerance: 1.453e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.045e+02, tolerance: 1.455e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.402e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.631e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.890e+02, tolerance: 1.270e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+02, tolerance: 1.405e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.117e+02, tolerance: 1.353e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.048e+02, tolerance: 1.342e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.469e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.286e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.770e+02, tolerance: 1.745e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.576e+02, tolerance: 1.735e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.500e+02, tolerance: 1.601e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.048e+02, tolerance: 1.537e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.196e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.412e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.275e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.220e+02, tolerance: 1.457e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.382e+02, tolerance: 1.387e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.537e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.757e+02, tolerance: 1.455e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.043e+02, tolerance: 1.600e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.825e+02, tolerance: 1.588e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.999e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.812e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.966e+02, tolerance: 1.406e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.362e+02, tolerance: 1.554e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.672e+02, tolerance: 1.367e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+02, tolerance: 1.449e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.507e+02, tolerance: 1.349e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.308e+02, tolerance: 1.566e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.273e+02, tolerance: 1.297e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.622e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.274e+02, tolerance: 1.391e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.388e+02, tolerance: 1.341e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.446e+02, tolerance: 1.377e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.641e+02, tolerance: 1.384e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.741e+02, tolerance: 1.333e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.598e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.761e+02, tolerance: 1.342e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.483e+02, tolerance: 1.455e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.880e+02, tolerance: 1.509e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.566e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.548e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.105e+02, tolerance: 1.496e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.712e+03, tolerance: 1.550e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.311e+02, tolerance: 1.304e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.156e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.692e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.640e+02, tolerance: 1.332e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.638e+02, tolerance: 1.431e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.695e+02, tolerance: 1.578e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.152e+02, tolerance: 1.339e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.963e+02, tolerance: 1.484e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.592e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.568e+02, tolerance: 1.591e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.663e+02, tolerance: 1.608e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.507e+02, tolerance: 1.381e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.002e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.155e+02, tolerance: 1.245e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 43%|████▎     | 43/100 [20:17<20:07, 21.18s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.533e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.410e+02, tolerance: 1.496e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.504e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.068e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.496e+02, tolerance: 1.528e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.032e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.113e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.635e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.156e+02, tolerance: 1.604e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.570e+02, tolerance: 1.646e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.528e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.044e+02, tolerance: 1.295e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.673e+02, tolerance: 1.348e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.566e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.891e+02, tolerance: 1.236e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.888e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.651e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.076e+02, tolerance: 1.382e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.402e+02, tolerance: 1.278e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.982e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.612e+02, tolerance: 1.656e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.957e+02, tolerance: 1.239e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.699e+02, tolerance: 1.284e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.741e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.897e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.094e+02, tolerance: 1.445e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.814e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.466e+02, tolerance: 1.434e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.293e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.788e+02, tolerance: 1.609e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.533e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.539e+02, tolerance: 1.279e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.061e+02, tolerance: 1.497e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.623e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.208e+02, tolerance: 1.604e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.581e+02, tolerance: 1.540e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.781e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.135e+02, tolerance: 1.660e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.901e+02, tolerance: 1.627e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.908e+02, tolerance: 1.570e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.221e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.454e+02, tolerance: 1.304e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.983e+02, tolerance: 1.412e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.182e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.573e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.946e+02, tolerance: 1.522e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.624e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.862e+02, tolerance: 1.229e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 1.585e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.721e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.724e+02, tolerance: 1.286e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.895e+02, tolerance: 1.121e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.800e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.668e+02, tolerance: 1.359e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.622e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.211e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.157e+02, tolerance: 1.432e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.032e+02, tolerance: 1.664e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.054e+02, tolerance: 1.451e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.169e+02, tolerance: 1.479e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.846e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.493e+02, tolerance: 1.381e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.898e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.693e+02, tolerance: 1.265e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.735e+02, tolerance: 1.571e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+02, tolerance: 1.429e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.556e+02, tolerance: 1.433e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.764e+02, tolerance: 1.333e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.922e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.445e+02, tolerance: 1.448e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.335e+02, tolerance: 1.551e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 44%|████▍     | 44/100 [20:36<19:18, 20.68s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.459e+02, tolerance: 1.603e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.803e+02, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.998e+02, tolerance: 1.513e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.236e+02, tolerance: 1.621e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.949e+02, tolerance: 1.532e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.002e+02, tolerance: 1.391e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.773e+02, tolerance: 1.513e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.621e+02, tolerance: 1.329e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.495e+02, tolerance: 1.329e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.519e+02, tolerance: 1.480e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.470e+02, tolerance: 1.232e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.953e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.895e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.416e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.855e+02, tolerance: 1.264e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.038e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.935e+02, tolerance: 1.427e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.267e+02, tolerance: 1.488e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.558e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.806e+02, tolerance: 1.448e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.705e+02, tolerance: 1.573e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.430e+02, tolerance: 1.376e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.995e+02, tolerance: 1.287e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.539e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.601e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.915e+02, tolerance: 1.517e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.128e+02, tolerance: 1.453e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.677e+02, tolerance: 1.316e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.613e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.861e+02, tolerance: 1.500e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.961e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.947e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.362e+02, tolerance: 1.418e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.252e+02, tolerance: 1.736e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.087e+02, tolerance: 1.559e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.078e+02, tolerance: 1.651e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.804e+02, tolerance: 1.601e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.546e+02, tolerance: 1.329e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e+02, tolerance: 1.384e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.903e+02, tolerance: 1.411e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.332e+02, tolerance: 1.522e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.848e+02, tolerance: 1.551e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.072e+02, tolerance: 1.577e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.032e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.439e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.604e+02, tolerance: 1.416e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+02, tolerance: 1.589e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.689e+02, tolerance: 1.298e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.678e+02, tolerance: 1.185e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.811e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.971e+02, tolerance: 1.510e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.024e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.039e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.712e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.194e+02, tolerance: 1.485e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.218e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.359e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.787e+02, tolerance: 1.509e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.159e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.158e+02, tolerance: 1.713e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.497e+02, tolerance: 1.382e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.168e+02, tolerance: 1.555e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.042e+03, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.253e+02, tolerance: 1.372e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.576e+02, tolerance: 1.557e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.671e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.414e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.816e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.661e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.415e+02, tolerance: 1.534e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.994e+02, tolerance: 1.388e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.746e+02, tolerance: 1.332e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.040e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.675e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.293e+02, tolerance: 1.362e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.891e+02, tolerance: 1.434e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.820e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.949e+02, tolerance: 1.157e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.848e+02, tolerance: 1.398e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.496e+02, tolerance: 1.434e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+02, tolerance: 1.489e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 45%|████▌     | 45/100 [20:57<18:54, 20.63s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+02, tolerance: 1.326e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+02, tolerance: 1.359e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.315e+02, tolerance: 1.287e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.593e+02, tolerance: 1.429e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.221e+02, tolerance: 1.554e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.850e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.260e+02, tolerance: 1.795e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.536e+02, tolerance: 1.391e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.322e+02, tolerance: 1.518e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.669e+02, tolerance: 1.510e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.408e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.733e+02, tolerance: 1.582e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.345e+02, tolerance: 1.255e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.322e+02, tolerance: 1.298e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.978e+02, tolerance: 1.406e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.480e+02, tolerance: 1.295e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.540e+02, tolerance: 1.325e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.464e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.776e+02, tolerance: 1.342e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.266e+02, tolerance: 1.302e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.967e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.429e+02, tolerance: 1.769e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.388e+02, tolerance: 1.465e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.253e+02, tolerance: 1.372e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.967e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.581e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.109e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.657e+02, tolerance: 1.590e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.088e+02, tolerance: 1.445e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.144e+02, tolerance: 1.332e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.144e+02, tolerance: 1.322e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.728e+02, tolerance: 1.337e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.590e+02, tolerance: 1.355e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.213e+02, tolerance: 1.546e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.541e+02, tolerance: 1.438e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.920e+02, tolerance: 1.445e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.048e+02, tolerance: 1.454e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.450e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.000e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.725e+02, tolerance: 1.341e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.215e+02, tolerance: 1.349e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.029e+02, tolerance: 1.651e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.020e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.947e+02, tolerance: 1.526e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.927e+02, tolerance: 1.211e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.615e+02, tolerance: 1.337e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.369e+02, tolerance: 1.313e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.715e+02, tolerance: 1.473e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.518e+02, tolerance: 1.379e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.912e+02, tolerance: 1.594e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.602e+02, tolerance: 1.369e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.403e+02, tolerance: 1.224e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.982e+02, tolerance: 1.713e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.876e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.042e+02, tolerance: 1.307e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.515e+02, tolerance: 1.341e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.249e+02, tolerance: 1.539e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.989e+02, tolerance: 1.518e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.813e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.535e+02, tolerance: 1.308e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.574e+02, tolerance: 1.337e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.176e+02, tolerance: 1.449e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.540e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.604e+02, tolerance: 1.235e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.954e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.289e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.141e+02, tolerance: 1.490e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.480e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.879e+02, tolerance: 1.495e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.351e+02, tolerance: 1.530e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.505e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.921e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 46%|████▌     | 46/100 [21:18<18:45, 20.84s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.499e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.890e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.965e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+02, tolerance: 1.373e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.559e+02, tolerance: 1.593e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.180e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.146e+02, tolerance: 1.376e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.519e+02, tolerance: 1.278e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.508e+02, tolerance: 1.462e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.560e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.627e+02, tolerance: 1.518e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.706e+02, tolerance: 1.639e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.589e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+02, tolerance: 1.522e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.648e+02, tolerance: 1.239e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.825e+02, tolerance: 1.496e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.426e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.630e+02, tolerance: 1.526e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.947e+02, tolerance: 1.573e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.400e+02, tolerance: 1.302e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.651e+02, tolerance: 1.503e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.571e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.747e+02, tolerance: 1.431e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.262e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.918e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.621e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.475e+02, tolerance: 1.561e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.584e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.620e+02, tolerance: 1.245e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.026e+02, tolerance: 1.365e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.622e+02, tolerance: 1.554e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.364e+02, tolerance: 1.297e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.236e+02, tolerance: 1.545e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.556e+02, tolerance: 1.385e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.082e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.404e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.127e+02, tolerance: 1.469e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.456e+02, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.985e+02, tolerance: 1.625e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.240e+02, tolerance: 1.365e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+02, tolerance: 1.313e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.925e+02, tolerance: 1.333e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.676e+02, tolerance: 1.284e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.922e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.430e+02, tolerance: 1.413e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.854e+02, tolerance: 1.601e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.577e+02, tolerance: 1.359e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.340e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.743e+02, tolerance: 1.550e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.553e+02, tolerance: 1.420e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e+02, tolerance: 1.643e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.577e+02, tolerance: 1.630e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.895e+02, tolerance: 1.643e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.366e+02, tolerance: 1.322e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.696e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.438e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.558e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.329e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.531e+02, tolerance: 1.363e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.080e+02, tolerance: 1.502e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.476e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.777e+02, tolerance: 1.419e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.762e+02, tolerance: 1.363e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.640e+02, tolerance: 1.317e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.857e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.414e+02, tolerance: 1.206e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.190e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.844e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.602e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.157e+02, tolerance: 1.418e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.247e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.594e+02, tolerance: 1.532e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.612e+02, tolerance: 1.560e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.454e+02, tolerance: 1.438e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.524e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 47%|████▋     | 47/100 [21:39<18:28, 20.91s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.564e+02, tolerance: 1.505e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.372e+02, tolerance: 1.249e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.657e+02, tolerance: 1.371e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.950e+02, tolerance: 1.536e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.722e+02, tolerance: 1.516e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.613e+02, tolerance: 1.606e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.718e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.271e+02, tolerance: 1.333e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.375e+02, tolerance: 1.517e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.139e+02, tolerance: 1.515e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.572e+02, tolerance: 1.717e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.775e+02, tolerance: 1.486e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.459e+02, tolerance: 1.221e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.111e+02, tolerance: 1.519e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.464e+02, tolerance: 1.566e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.685e+02, tolerance: 1.587e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.955e+02, tolerance: 1.346e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.514e+02, tolerance: 1.378e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.775e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.726e+02, tolerance: 1.401e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.343e+02, tolerance: 1.505e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.222e+02, tolerance: 1.652e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.269e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.314e+02, tolerance: 1.277e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.871e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.320e+02, tolerance: 1.267e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.939e+02, tolerance: 1.311e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.137e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+02, tolerance: 1.592e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.629e+02, tolerance: 1.375e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.092e+02, tolerance: 1.551e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.178e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.672e+02, tolerance: 1.585e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.463e+02, tolerance: 1.324e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.778e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.921e+02, tolerance: 1.556e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.066e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 48%|████▊     | 48/100 [21:56<17:09, 19.80s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.076e+02, tolerance: 1.291e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.379e+02, tolerance: 1.319e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.980e+02, tolerance: 1.437e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.184e+02, tolerance: 1.563e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.866e+02, tolerance: 1.694e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.583e+02, tolerance: 1.455e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.441e+02, tolerance: 1.401e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.466e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.351e+02, tolerance: 1.617e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.253e+02, tolerance: 1.662e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+02, tolerance: 1.547e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.971e+02, tolerance: 1.826e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.647e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.719e+02, tolerance: 1.288e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.963e+02, tolerance: 1.411e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.612e+02, tolerance: 1.563e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.421e+02, tolerance: 1.553e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.592e+02, tolerance: 1.346e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.063e+02, tolerance: 1.529e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.647e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.859e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.651e+02, tolerance: 1.271e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.892e+02, tolerance: 1.366e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.568e+02, tolerance: 1.434e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.528e+02, tolerance: 1.379e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.479e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.523e+02, tolerance: 1.518e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.862e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.436e+02, tolerance: 1.375e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.439e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.767e+02, tolerance: 1.327e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.881e+02, tolerance: 1.663e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.934e+02, tolerance: 1.348e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.936e+02, tolerance: 1.483e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+02, tolerance: 1.577e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.674e+02, tolerance: 1.637e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.600e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.476e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.187e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.684e+02, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.049e+02, tolerance: 1.335e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.615e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.890e+02, tolerance: 1.409e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.536e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.983e+02, tolerance: 1.408e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.684e+02, tolerance: 1.433e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.925e+02, tolerance: 1.513e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.554e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.433e+02, tolerance: 1.242e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.842e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.103e+02, tolerance: 1.479e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.474e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.787e+02, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.740e+02, tolerance: 1.516e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.776e+02, tolerance: 1.457e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.942e+02, tolerance: 1.391e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.163e+02, tolerance: 1.319e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.773e+02, tolerance: 1.770e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.480e+02, tolerance: 1.612e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 49%|████▉     | 49/100 [22:14<16:16, 19.15s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.733e+02, tolerance: 1.524e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.595e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.572e+02, tolerance: 1.554e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.383e+02, tolerance: 1.611e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.586e+02, tolerance: 1.503e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.158e+02, tolerance: 1.356e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.695e+02, tolerance: 1.337e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.538e+02, tolerance: 1.535e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.590e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.300e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.854e+02, tolerance: 1.588e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.843e+02, tolerance: 1.287e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.275e+02, tolerance: 1.454e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.289e+02, tolerance: 1.448e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.013e+02, tolerance: 1.708e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.039e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.997e+02, tolerance: 1.381e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+02, tolerance: 1.292e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.559e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.628e+02, tolerance: 1.565e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.910e+02, tolerance: 1.596e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.860e+02, tolerance: 1.601e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.684e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.457e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.524e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+03, tolerance: 1.578e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.826e+02, tolerance: 1.351e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.960e+02, tolerance: 1.555e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+02, tolerance: 1.431e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.538e+02, tolerance: 1.501e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.130e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.643e+02, tolerance: 1.302e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+02, tolerance: 1.406e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.742e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.672e+02, tolerance: 1.528e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+02, tolerance: 1.661e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.048e+02, tolerance: 1.376e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.993e+02, tolerance: 1.208e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.445e+02, tolerance: 1.304e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.661e+02, tolerance: 1.496e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.205e+02, tolerance: 1.432e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.618e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.360e+02, tolerance: 1.473e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.438e+02, tolerance: 1.314e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.284e+02, tolerance: 1.758e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.817e+02, tolerance: 1.529e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.878e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.767e+02, tolerance: 1.500e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.421e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.335e+02, tolerance: 1.267e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.410e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.922e+02, tolerance: 1.135e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.828e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.923e+02, tolerance: 1.398e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.263e+02, tolerance: 1.182e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.568e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.872e+02, tolerance: 1.448e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.656e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.609e+02, tolerance: 1.238e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.889e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.787e+02, tolerance: 1.605e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.789e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.822e+02, tolerance: 1.574e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.426e+02, tolerance: 1.570e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.385e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.862e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.829e+02, tolerance: 1.511e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.893e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.012e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.466e+02, tolerance: 1.208e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.981e+02, tolerance: 1.540e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.598e+02, tolerance: 1.512e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 50%|█████     | 50/100 [22:35<16:20, 19.60s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.711e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.182e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.608e+02, tolerance: 1.361e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.543e+02, tolerance: 1.309e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.015e+02, tolerance: 1.394e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.596e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.001e+02, tolerance: 1.325e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.720e+02, tolerance: 1.419e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.682e+02, tolerance: 1.295e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.914e+02, tolerance: 1.291e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.668e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.647e+02, tolerance: 1.573e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.370e+02, tolerance: 1.275e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.389e+02, tolerance: 1.629e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.681e+02, tolerance: 1.519e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.717e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.647e+02, tolerance: 1.603e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.161e+02, tolerance: 1.300e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.739e+02, tolerance: 1.294e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.517e+02, tolerance: 1.536e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.507e+02, tolerance: 1.346e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.967e+02, tolerance: 1.312e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+02, tolerance: 1.419e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.402e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.636e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.287e+02, tolerance: 1.591e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.771e+02, tolerance: 1.469e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.521e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.368e+02, tolerance: 1.311e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.585e+02, tolerance: 1.578e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.600e+02, tolerance: 1.522e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+02, tolerance: 1.342e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.305e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.001e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.713e+02, tolerance: 1.572e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.671e+02, tolerance: 1.268e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.681e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.196e+02, tolerance: 1.501e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.205e+02, tolerance: 1.264e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.055e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.387e+02, tolerance: 1.434e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+02, tolerance: 1.591e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.096e+02, tolerance: 1.590e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.488e+02, tolerance: 1.241e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.443e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.422e+02, tolerance: 1.260e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.686e+02, tolerance: 1.669e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.096e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.531e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.700e+02, tolerance: 1.394e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.656e+02, tolerance: 1.612e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.346e+02, tolerance: 1.760e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.789e+02, tolerance: 1.253e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.762e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.829e+02, tolerance: 1.669e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.618e+02, tolerance: 1.538e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.110e+02, tolerance: 1.597e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 51%|█████     | 51/100 [22:54<15:58, 19.56s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.843e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.523e+02, tolerance: 1.432e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.500e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.521e+02, tolerance: 1.381e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.985e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.506e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.906e+02, tolerance: 1.511e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.970e+02, tolerance: 1.598e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.316e+02, tolerance: 1.362e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.259e+02, tolerance: 1.207e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.234e+02, tolerance: 1.496e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.045e+02, tolerance: 1.548e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.136e+02, tolerance: 1.373e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.910e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.822e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.464e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.900e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.048e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.069e+02, tolerance: 1.230e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.446e+02, tolerance: 1.687e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.917e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.234e+02, tolerance: 1.101e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.875e+02, tolerance: 1.385e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.622e+02, tolerance: 1.440e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.988e+02, tolerance: 1.239e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.981e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.648e+02, tolerance: 1.336e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.759e+02, tolerance: 1.268e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.674e+02, tolerance: 1.451e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.224e+02, tolerance: 1.299e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.553e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+02, tolerance: 1.659e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.516e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.752e+02, tolerance: 1.337e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.456e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 52%|█████▏    | 52/100 [23:12<15:13, 19.03s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.800e+02, tolerance: 1.527e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.951e+02, tolerance: 1.355e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.716e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.596e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.935e+02, tolerance: 1.475e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.411e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.209e+02, tolerance: 1.249e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.690e+02, tolerance: 1.306e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.927e+02, tolerance: 1.500e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.252e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.102e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.639e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.814e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.482e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.731e+02, tolerance: 1.406e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.957e+02, tolerance: 1.432e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.760e+02, tolerance: 1.539e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.819e+02, tolerance: 1.457e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.193e+02, tolerance: 1.470e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.542e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.277e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.732e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.853e+02, tolerance: 1.538e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.586e+02, tolerance: 1.293e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.120e+02, tolerance: 1.351e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.502e+02, tolerance: 1.313e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.682e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.911e+02, tolerance: 1.409e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.945e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.870e+02, tolerance: 1.348e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.692e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.600e+02, tolerance: 1.307e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.192e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+02, tolerance: 1.431e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.553e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.650e+02, tolerance: 1.434e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.694e+02, tolerance: 1.401e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+02, tolerance: 1.582e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.998e+02, tolerance: 1.481e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+02, tolerance: 1.377e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.825e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.342e+02, tolerance: 1.297e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.243e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.227e+02, tolerance: 1.503e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.955e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.034e+02, tolerance: 1.470e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.246e+02, tolerance: 1.647e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.194e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.406e+02, tolerance: 1.619e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.153e+02, tolerance: 1.495e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.537e+02, tolerance: 1.341e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.696e+02, tolerance: 1.566e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.910e+02, tolerance: 1.480e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.420e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.003e+02, tolerance: 1.646e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.365e+02, tolerance: 1.448e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 53%|█████▎    | 53/100 [23:30<14:45, 18.84s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.579e+02, tolerance: 1.485e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.555e+02, tolerance: 1.214e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.805e+02, tolerance: 1.485e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.806e+02, tolerance: 1.120e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.558e+02, tolerance: 1.325e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.133e+02, tolerance: 1.479e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.161e+02, tolerance: 1.638e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.338e+02, tolerance: 1.453e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.384e+02, tolerance: 1.418e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.685e+02, tolerance: 1.254e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.816e+02, tolerance: 1.384e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.955e+02, tolerance: 1.328e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.943e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.798e+02, tolerance: 1.269e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.790e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.438e+02, tolerance: 1.753e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.913e+02, tolerance: 1.493e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.491e+02, tolerance: 1.285e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.066e+02, tolerance: 1.576e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.577e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.803e+02, tolerance: 1.379e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.118e+02, tolerance: 1.579e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.525e+02, tolerance: 1.427e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.158e+02, tolerance: 1.537e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+02, tolerance: 1.550e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.627e+02, tolerance: 1.602e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e+02, tolerance: 1.678e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.490e+02, tolerance: 1.597e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.209e+02, tolerance: 1.483e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.951e+02, tolerance: 1.630e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.614e+02, tolerance: 1.551e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.605e+02, tolerance: 1.516e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 54%|█████▍    | 54/100 [23:47<14:00, 18.27s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.680e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.327e+02, tolerance: 1.241e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.332e+02, tolerance: 1.378e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.933e+02, tolerance: 1.537e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.537e+02, tolerance: 1.489e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+02, tolerance: 1.326e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.812e+02, tolerance: 1.233e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.422e+02, tolerance: 1.352e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.013e+02, tolerance: 1.745e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.130e+02, tolerance: 1.429e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.887e+02, tolerance: 1.378e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.910e+02, tolerance: 1.278e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.218e+02, tolerance: 1.377e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.504e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.486e+02, tolerance: 1.353e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.079e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.186e+02, tolerance: 1.233e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.506e+02, tolerance: 1.409e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.821e+02, tolerance: 1.516e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.599e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.674e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.969e+02, tolerance: 1.270e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.821e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.568e+02, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.763e+02, tolerance: 1.389e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.238e+02, tolerance: 1.250e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.914e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.860e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+02, tolerance: 1.357e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.557e+02, tolerance: 1.727e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.662e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.805e+02, tolerance: 1.642e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.430e+02, tolerance: 1.319e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e+02, tolerance: 1.293e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.084e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.489e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.436e+02, tolerance: 1.526e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.642e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.540e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.390e+02, tolerance: 1.341e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.833e+02, tolerance: 1.547e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.489e+02, tolerance: 1.285e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.420e+02, tolerance: 1.310e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.724e+02, tolerance: 1.320e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.381e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 55%|█████▌    | 55/100 [24:05<13:35, 18.11s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.644e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.314e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.424e+02, tolerance: 1.322e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.780e+02, tolerance: 1.367e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.544e+02, tolerance: 1.326e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.419e+02, tolerance: 1.495e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.761e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.814e+02, tolerance: 1.635e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.415e+02, tolerance: 1.770e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.748e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.436e+02, tolerance: 1.340e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.734e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.845e+02, tolerance: 1.532e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.047e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.901e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+02, tolerance: 1.300e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.812e+02, tolerance: 1.622e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.169e+02, tolerance: 1.361e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.493e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.910e+02, tolerance: 1.750e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.765e+02, tolerance: 1.546e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.595e+02, tolerance: 1.382e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.390e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.803e+02, tolerance: 1.326e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.869e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.165e+02, tolerance: 1.535e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.031e+02, tolerance: 1.681e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.749e+02, tolerance: 1.545e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.774e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.441e+02, tolerance: 1.409e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.448e+02, tolerance: 1.540e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.850e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.796e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.517e+02, tolerance: 1.560e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.567e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.167e+02, tolerance: 1.628e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.670e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.987e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.583e+02, tolerance: 1.511e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.030e+02, tolerance: 1.324e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.609e+02, tolerance: 1.526e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.639e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.851e+02, tolerance: 1.587e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.021e+02, tolerance: 1.401e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.620e+02, tolerance: 1.564e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.771e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 56%|█████▌    | 56/100 [24:23<13:08, 17.93s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.037e+02, tolerance: 1.462e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.261e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.317e+02, tolerance: 1.596e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.008e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.516e+02, tolerance: 1.310e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.108e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.555e+02, tolerance: 1.193e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.517e+02, tolerance: 1.485e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.293e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.778e+02, tolerance: 1.547e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.052e+02, tolerance: 1.667e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.877e+02, tolerance: 1.462e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.802e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.679e+02, tolerance: 1.634e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.628e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.729e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.748e+02, tolerance: 1.367e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.723e+02, tolerance: 1.501e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.232e+02, tolerance: 1.330e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.407e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.044e+02, tolerance: 1.566e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.602e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.112e+02, tolerance: 1.472e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.507e+02, tolerance: 1.283e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.150e+02, tolerance: 1.411e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.744e+02, tolerance: 1.484e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.362e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.516e+02, tolerance: 1.421e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.731e+02, tolerance: 1.312e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.621e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.496e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.753e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.511e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.424e+02, tolerance: 1.497e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.407e+02, tolerance: 1.307e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.577e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.836e+02, tolerance: 1.484e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.104e+02, tolerance: 1.527e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.598e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.929e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.221e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.009e+03, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+02, tolerance: 1.262e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.898e+02, tolerance: 1.288e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.770e+02, tolerance: 1.473e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.654e+02, tolerance: 1.324e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 57%|█████▋    | 57/100 [24:42<13:15, 18.50s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.956e+02, tolerance: 1.396e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.015e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.576e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.527e+02, tolerance: 1.309e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.046e+02, tolerance: 1.438e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.661e+02, tolerance: 1.297e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.840e+02, tolerance: 1.570e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+02, tolerance: 1.530e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.415e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.400e+02, tolerance: 1.568e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.623e+02, tolerance: 1.598e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.565e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.766e+02, tolerance: 1.462e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.840e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.389e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.580e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+02, tolerance: 1.391e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.996e+02, tolerance: 1.794e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.584e+02, tolerance: 1.557e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.751e+02, tolerance: 1.502e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.510e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.479e+02, tolerance: 1.405e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.475e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.378e+02, tolerance: 1.401e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.408e+02, tolerance: 1.239e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.819e+02, tolerance: 1.307e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.408e+02, tolerance: 1.557e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.731e+02, tolerance: 1.609e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.179e+02, tolerance: 1.366e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.582e+02, tolerance: 1.379e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.801e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.219e+02, tolerance: 1.654e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.428e+02, tolerance: 1.352e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.740e+02, tolerance: 1.582e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.667e+02, tolerance: 1.447e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 58%|█████▊    | 58/100 [25:00<12:43, 18.19s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.702e+02, tolerance: 1.633e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.084e+02, tolerance: 1.573e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.502e+02, tolerance: 1.453e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.601e+02, tolerance: 1.511e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.927e+02, tolerance: 1.729e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.439e+02, tolerance: 1.534e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.445e+02, tolerance: 1.405e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.475e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.407e+02, tolerance: 1.479e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.434e+02, tolerance: 1.334e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.801e+02, tolerance: 1.385e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.880e+02, tolerance: 1.585e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.030e+02, tolerance: 1.642e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.311e+02, tolerance: 1.588e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.895e+02, tolerance: 1.275e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.437e+02, tolerance: 1.356e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.894e+02, tolerance: 1.469e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.563e+02, tolerance: 1.343e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.904e+02, tolerance: 1.571e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.767e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.640e+02, tolerance: 1.254e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.440e+02, tolerance: 1.359e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.378e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.681e+02, tolerance: 1.389e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.488e+02, tolerance: 1.553e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.933e+02, tolerance: 1.427e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.460e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.221e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.555e+02, tolerance: 1.418e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 59%|█████▉    | 59/100 [25:17<12:15, 17.95s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.695e+02, tolerance: 1.505e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e+02, tolerance: 1.586e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.698e+02, tolerance: 1.455e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.584e+02, tolerance: 1.407e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.870e+02, tolerance: 1.567e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.647e+02, tolerance: 1.578e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.809e+02, tolerance: 1.465e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.269e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.131e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.551e+02, tolerance: 1.271e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.594e+02, tolerance: 1.564e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.804e+02, tolerance: 1.593e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.114e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.598e+02, tolerance: 1.513e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.574e+02, tolerance: 1.555e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.750e+02, tolerance: 1.395e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.341e+02, tolerance: 1.680e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.470e+02, tolerance: 1.629e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 60%|██████    | 60/100 [25:33<11:28, 17.21s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.207e+02, tolerance: 1.263e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.625e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.339e+02, tolerance: 1.247e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.067e+02, tolerance: 1.455e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.789e+02, tolerance: 1.768e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.669e+02, tolerance: 1.548e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.955e+02, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.402e+02, tolerance: 1.515e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.037e+02, tolerance: 1.284e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.921e+02, tolerance: 1.415e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.324e+02, tolerance: 1.516e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.220e+02, tolerance: 1.501e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.932e+02, tolerance: 1.646e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.622e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+02, tolerance: 1.643e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.066e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.611e+02, tolerance: 1.486e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.187e+02, tolerance: 1.576e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.477e+02, tolerance: 1.458e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.098e+02, tolerance: 1.665e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.500e+02, tolerance: 1.445e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.149e+02, tolerance: 1.634e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.419e+02, tolerance: 1.249e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.652e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.520e+02, tolerance: 1.359e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.153e+02, tolerance: 1.588e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.751e+02, tolerance: 1.481e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.437e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+02, tolerance: 1.433e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.449e+02, tolerance: 1.318e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.336e+02, tolerance: 1.604e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.335e+02, tolerance: 1.521e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.430e+02, tolerance: 1.416e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.341e+02, tolerance: 1.627e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.569e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.759e+02, tolerance: 1.324e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.626e+02, tolerance: 1.264e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.215e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.371e+02, tolerance: 1.302e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.575e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.552e+02, tolerance: 1.622e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.814e+02, tolerance: 1.358e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.716e+02, tolerance: 1.413e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.897e+02, tolerance: 1.572e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.147e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.444e+02, tolerance: 1.385e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.572e+02, tolerance: 1.379e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.310e+02, tolerance: 1.682e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.644e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.617e+02, tolerance: 1.381e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.525e+02, tolerance: 1.394e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 61%|██████    | 61/100 [25:50<11:15, 17.31s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.982e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.709e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.686e+02, tolerance: 1.391e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.705e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.799e+02, tolerance: 1.362e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 1.420e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.544e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.330e+02, tolerance: 1.312e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.216e+02, tolerance: 1.602e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.811e+02, tolerance: 1.513e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.310e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.836e+02, tolerance: 1.561e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.490e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.307e+02, tolerance: 1.433e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.841e+02, tolerance: 1.453e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.044e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 1.802e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.585e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.030e+02, tolerance: 1.565e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.615e+02, tolerance: 1.558e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.363e+02, tolerance: 1.494e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.729e+02, tolerance: 1.416e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.489e+02, tolerance: 1.519e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.830e+02, tolerance: 1.578e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.806e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.236e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.476e+02, tolerance: 1.334e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.726e+02, tolerance: 1.488e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.458e+02, tolerance: 1.279e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.671e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.762e+02, tolerance: 1.465e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.483e+02, tolerance: 1.329e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.169e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.088e+02, tolerance: 1.551e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.113e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.759e+02, tolerance: 1.503e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.145e+02, tolerance: 1.606e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e+02, tolerance: 1.488e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.858e+02, tolerance: 1.294e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.545e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.128e+02, tolerance: 1.454e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.950e+02, tolerance: 1.446e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.748e+02, tolerance: 1.532e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 62%|██████▏   | 62/100 [26:06<10:41, 16.87s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.570e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.670e+02, tolerance: 1.320e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.733e+02, tolerance: 1.480e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.251e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.699e+02, tolerance: 1.616e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.060e+02, tolerance: 1.258e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.600e+02, tolerance: 1.411e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.918e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.556e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.976e+02, tolerance: 1.553e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.909e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.440e+02, tolerance: 1.397e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.664e+02, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.978e+02, tolerance: 1.599e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.591e+02, tolerance: 1.296e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.388e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.343e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.081e+02, tolerance: 1.587e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.133e+02, tolerance: 1.559e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.913e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.166e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.620e+02, tolerance: 1.422e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.748e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.239e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.318e+02, tolerance: 1.272e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.914e+02, tolerance: 1.512e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.585e+02, tolerance: 1.418e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.795e+02, tolerance: 1.584e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.808e+02, tolerance: 1.272e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.322e+02, tolerance: 1.483e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.004e+02, tolerance: 1.544e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.631e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.541e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 63%|██████▎   | 63/100 [26:23<10:29, 17.01s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.639e+02, tolerance: 1.440e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.266e+02, tolerance: 1.283e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.725e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.567e+02, tolerance: 1.454e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.346e+02, tolerance: 1.295e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.435e+02, tolerance: 1.304e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.841e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.153e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.460e+02, tolerance: 1.346e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.810e+02, tolerance: 1.575e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.607e+02, tolerance: 1.578e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.860e+02, tolerance: 1.481e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.260e+02, tolerance: 1.530e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.565e+02, tolerance: 1.692e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.776e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.539e+02, tolerance: 1.316e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.985e+02, tolerance: 1.352e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.368e+02, tolerance: 1.248e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.636e+02, tolerance: 1.429e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.639e+02, tolerance: 1.577e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.912e+02, tolerance: 1.459e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.708e+02, tolerance: 1.548e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.777e+02, tolerance: 1.367e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.664e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.371e+02, tolerance: 1.480e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.117e+02, tolerance: 1.486e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.837e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.501e+02, tolerance: 1.371e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.689e+02, tolerance: 1.364e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.528e+02, tolerance: 1.376e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.025e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.459e+02, tolerance: 1.463e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.660e+02, tolerance: 1.522e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.406e+02, tolerance: 1.449e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.536e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.451e+02, tolerance: 1.216e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 64%|██████▍   | 64/100 [26:41<10:22, 17.28s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.652e+02, tolerance: 1.423e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.761e+02, tolerance: 1.592e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.130e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.451e+02, tolerance: 1.352e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.026e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.754e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.937e+02, tolerance: 1.249e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.472e+02, tolerance: 1.521e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.772e+02, tolerance: 1.533e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.642e+02, tolerance: 1.410e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.862e+02, tolerance: 1.434e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.822e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.672e+02, tolerance: 1.654e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.586e+02, tolerance: 1.242e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.576e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.720e+02, tolerance: 1.533e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.736e+02, tolerance: 1.316e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.275e+02, tolerance: 1.626e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.167e+02, tolerance: 1.600e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.614e+02, tolerance: 1.450e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.635e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.393e+02, tolerance: 1.314e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.887e+02, tolerance: 1.182e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 65%|██████▌   | 65/100 [26:58<09:58, 17.11s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.220e+02, tolerance: 1.571e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.927e+02, tolerance: 1.312e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.845e+02, tolerance: 1.489e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.906e+02, tolerance: 1.424e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.734e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.527e+02, tolerance: 1.299e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.019e+02, tolerance: 1.314e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.313e+02, tolerance: 1.608e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.955e+02, tolerance: 1.449e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.129e+02, tolerance: 1.535e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.015e+02, tolerance: 1.269e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.615e+02, tolerance: 1.640e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.661e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+02, tolerance: 1.497e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.136e+02, tolerance: 1.318e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.444e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.082e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.956e+02, tolerance: 1.517e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.269e+02, tolerance: 1.338e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.667e+02, tolerance: 1.605e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.756e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.920e+02, tolerance: 1.466e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.496e+02, tolerance: 1.492e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.592e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.716e+02, tolerance: 1.555e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.577e+02, tolerance: 1.529e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.134e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.640e+02, tolerance: 1.319e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.934e+02, tolerance: 1.599e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.756e+02, tolerance: 1.579e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.203e+02, tolerance: 1.438e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.406e+02, tolerance: 1.538e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 66%|██████▌   | 66/100 [27:15<09:37, 16.98s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.265e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.545e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.256e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.382e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.603e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.273e+02, tolerance: 1.287e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.422e+02, tolerance: 1.438e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.497e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.384e+02, tolerance: 1.475e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.740e+02, tolerance: 1.290e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.940e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.094e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.929e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.774e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.443e+02, tolerance: 1.416e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.219e+02, tolerance: 1.500e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.626e+02, tolerance: 1.439e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.708e+02, tolerance: 1.599e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.537e+02, tolerance: 1.485e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.532e+02, tolerance: 1.319e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.501e+02, tolerance: 1.382e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.662e+02, tolerance: 1.358e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.733e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+02, tolerance: 1.629e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.162e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.002e+02, tolerance: 1.672e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.473e+02, tolerance: 1.448e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.977e+02, tolerance: 1.378e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.564e+02, tolerance: 1.493e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.755e+02, tolerance: 1.521e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.677e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.979e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.397e+02, tolerance: 1.264e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.509e+02, tolerance: 1.593e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.326e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.457e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.863e+02, tolerance: 1.482e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+02, tolerance: 1.316e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.951e+02, tolerance: 1.272e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 67%|██████▋   | 67/100 [27:33<09:28, 17.21s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.769e+02, tolerance: 1.504e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.166e+02, tolerance: 1.606e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.742e+02, tolerance: 1.475e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.258e+02, tolerance: 1.647e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.615e+02, tolerance: 1.537e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.647e+02, tolerance: 1.568e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.200e+02, tolerance: 1.299e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.333e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.694e+02, tolerance: 1.588e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.156e+02, tolerance: 1.339e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.307e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.726e+02, tolerance: 1.523e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.677e+02, tolerance: 1.348e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.140e+02, tolerance: 1.518e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.425e+02, tolerance: 1.598e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.411e+02, tolerance: 1.503e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.505e+02, tolerance: 1.628e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.692e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.488e+02, tolerance: 1.286e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.393e+02, tolerance: 1.484e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.364e+02, tolerance: 1.302e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.385e+02, tolerance: 1.256e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.584e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.677e+02, tolerance: 1.550e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.886e+02, tolerance: 1.484e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.942e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.584e+02, tolerance: 1.309e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.217e+02, tolerance: 1.427e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.803e+02, tolerance: 1.635e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.127e+02, tolerance: 1.516e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.483e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 68%|██████▊   | 68/100 [27:48<08:55, 16.74s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.464e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.543e+02, tolerance: 1.400e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.745e+02, tolerance: 1.597e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.565e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.502e+02, tolerance: 1.411e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.059e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.369e+02, tolerance: 1.302e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.808e+02, tolerance: 1.569e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.498e+02, tolerance: 1.199e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.136e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.051e+02, tolerance: 1.301e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.815e+02, tolerance: 1.537e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.317e+02, tolerance: 1.324e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.664e+02, tolerance: 1.559e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.827e+02, tolerance: 1.356e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.998e+02, tolerance: 1.216e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.573e+02, tolerance: 1.470e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.271e+02, tolerance: 1.365e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.854e+02, tolerance: 1.398e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.951e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.960e+02, tolerance: 1.690e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.766e+02, tolerance: 1.435e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.544e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 69%|██████▉   | 69/100 [28:03<08:20, 16.14s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.662e+02, tolerance: 1.568e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.716e+02, tolerance: 1.398e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.646e+02, tolerance: 1.427e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.783e+02, tolerance: 1.409e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.554e+02, tolerance: 1.485e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.741e+02, tolerance: 1.256e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.642e+02, tolerance: 1.640e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.949e+02, tolerance: 1.358e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.757e+02, tolerance: 1.427e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.058e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.522e+02, tolerance: 1.412e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.325e+02, tolerance: 1.237e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.835e+02, tolerance: 1.644e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.791e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.789e+02, tolerance: 1.489e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.061e+02, tolerance: 1.498e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.077e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.943e+02, tolerance: 1.633e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 70%|███████   | 70/100 [28:18<07:58, 15.94s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.729e+02, tolerance: 1.374e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.784e+02, tolerance: 1.594e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.381e+02, tolerance: 1.335e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.809e+02, tolerance: 1.538e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.258e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.113e+02, tolerance: 1.287e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.509e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.991e+02, tolerance: 1.419e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.542e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.892e+02, tolerance: 1.333e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.697e+02, tolerance: 1.309e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.645e+02, tolerance: 1.357e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.674e+02, tolerance: 1.274e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.975e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.556e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.874e+02, tolerance: 1.369e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.964e+02, tolerance: 1.371e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.342e+02, tolerance: 1.342e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.450e+02, tolerance: 1.349e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.118e+02, tolerance: 1.475e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.571e+02, tolerance: 1.534e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 71%|███████   | 71/100 [28:36<07:56, 16.42s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.479e+02, tolerance: 1.469e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.397e+02, tolerance: 1.233e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.180e+02, tolerance: 1.509e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.592e+02, tolerance: 1.489e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.491e+02, tolerance: 1.447e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.881e+02, tolerance: 1.515e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.468e+02, tolerance: 1.612e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.052e+02, tolerance: 1.353e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.792e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.841e+02, tolerance: 1.490e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.326e+02, tolerance: 1.199e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.429e+02, tolerance: 1.321e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.915e+02, tolerance: 1.403e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.506e+02, tolerance: 1.472e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.511e+02, tolerance: 1.352e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.692e+02, tolerance: 1.510e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.074e+02, tolerance: 1.440e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 72%|███████▏  | 72/100 [28:53<07:47, 16.70s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.646e+02, tolerance: 1.383e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.525e+02, tolerance: 1.374e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.584e+02, tolerance: 1.562e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.552e+02, tolerance: 1.284e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.440e+02, tolerance: 1.256e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.795e+02, tolerance: 1.643e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.106e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.971e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.266e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.658e+02, tolerance: 1.472e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.498e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.030e+02, tolerance: 1.715e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.205e+02, tolerance: 1.505e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 73%|███████▎  | 73/100 [29:11<07:38, 17.00s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.558e+02, tolerance: 1.465e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.121e+02, tolerance: 1.353e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.525e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.434e+02, tolerance: 1.394e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.345e+02, tolerance: 1.343e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.613e+02, tolerance: 1.305e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.580e+02, tolerance: 1.406e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.212e+02, tolerance: 1.585e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.867e+02, tolerance: 1.426e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.632e+02, tolerance: 1.402e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.581e+02, tolerance: 1.485e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.658e+02, tolerance: 1.451e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.431e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.951e+02, tolerance: 1.511e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.269e+02, tolerance: 1.538e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.399e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.595e+02, tolerance: 1.465e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.373e+02, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.282e+02, tolerance: 1.234e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.055e+02, tolerance: 1.657e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 74%|███████▍  | 74/100 [29:27<07:14, 16.71s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.608e+02, tolerance: 1.588e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.180e+02, tolerance: 1.277e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.973e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.767e+02, tolerance: 1.294e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e+02, tolerance: 1.454e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.016e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.168e+02, tolerance: 1.673e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.278e+02, tolerance: 1.353e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+02, tolerance: 1.691e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.914e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.499e+02, tolerance: 1.244e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.441e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.080e+02, tolerance: 1.626e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.752e+02, tolerance: 1.408e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.524e+02, tolerance: 1.310e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 75%|███████▌  | 75/100 [29:43<06:53, 16.53s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.506e+02, tolerance: 1.404e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.060e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.116e+02, tolerance: 1.373e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.330e+02, tolerance: 1.565e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.100e+02, tolerance: 1.337e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.953e+02, tolerance: 1.493e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.760e+02, tolerance: 1.567e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.716e+02, tolerance: 1.354e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.778e+02, tolerance: 1.539e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.801e+02, tolerance: 1.392e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.011e+02, tolerance: 1.312e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 76%|███████▌  | 76/100 [29:59<06:32, 16.35s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.187e+02, tolerance: 1.369e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.754e+02, tolerance: 1.387e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.738e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.554e+02, tolerance: 1.595e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.831e+02, tolerance: 1.447e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.333e+02, tolerance: 1.477e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.868e+02, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.360e+02, tolerance: 1.490e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.167e+02, tolerance: 1.377e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.822e+02, tolerance: 1.448e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.556e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.721e+02, tolerance: 1.356e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.698e+02, tolerance: 1.544e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.856e+02, tolerance: 1.464e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.662e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.224e+02, tolerance: 1.470e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.803e+02, tolerance: 1.496e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.774e+02, tolerance: 1.502e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.980e+02, tolerance: 1.557e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.757e+02, tolerance: 1.481e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.330e+02, tolerance: 1.319e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 77%|███████▋  | 77/100 [30:16<06:21, 16.59s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.756e+02, tolerance: 1.582e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.064e+02, tolerance: 1.583e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.120e+02, tolerance: 1.599e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.004e+02, tolerance: 1.474e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.377e+02, tolerance: 1.315e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.525e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.579e+02, tolerance: 1.493e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.709e+02, tolerance: 1.371e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.406e+02, tolerance: 1.371e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.753e+02, tolerance: 1.344e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.110e+02, tolerance: 1.545e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.362e+02, tolerance: 1.348e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.927e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 78%|███████▊  | 78/100 [30:33<06:04, 16.56s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.740e+02, tolerance: 1.453e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.905e+02, tolerance: 1.554e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.220e+02, tolerance: 1.569e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.692e+02, tolerance: 1.274e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.519e+02, tolerance: 1.515e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.585e+02, tolerance: 1.575e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.089e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.940e+02, tolerance: 1.436e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+02, tolerance: 1.444e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.739e+02, tolerance: 1.372e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.037e+02, tolerance: 1.659e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.615e+02, tolerance: 1.530e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.940e+02, tolerance: 1.376e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.047e+02, tolerance: 1.506e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.875e+02, tolerance: 1.305e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 79%|███████▉  | 79/100 [30:50<05:50, 16.69s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.628e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.453e+02, tolerance: 1.207e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.635e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.169e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.314e+02, tolerance: 1.605e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.651e+02, tolerance: 1.416e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.201e+02, tolerance: 1.412e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.590e+02, tolerance: 1.545e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.451e+02, tolerance: 1.393e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.752e+02, tolerance: 1.569e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.606e+02, tolerance: 1.603e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.655e+02, tolerance: 1.320e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e+02, tolerance: 1.420e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 80%|████████  | 80/100 [31:06<05:34, 16.72s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.784e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.064e+02, tolerance: 1.281e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.627e+02, tolerance: 1.334e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.080e+02, tolerance: 1.419e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.123e+02, tolerance: 1.467e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.713e+02, tolerance: 1.491e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.783e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.258e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.039e+02, tolerance: 1.381e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.637e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.821e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.317e+02, tolerance: 1.534e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.032e+02, tolerance: 1.532e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.435e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.334e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.235e+02, tolerance: 1.592e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.189e+02, tolerance: 1.399e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.562e+02, tolerance: 1.443e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.590e+02, tolerance: 1.520e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.403e+02, tolerance: 1.630e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.509e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 81%|████████  | 81/100 [31:24<05:20, 16.88s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e+02, tolerance: 1.560e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.373e+02, tolerance: 1.341e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.636e+02, tolerance: 1.587e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.983e+02, tolerance: 1.376e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.635e+02, tolerance: 1.563e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.749e+02, tolerance: 1.502e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.538e+02, tolerance: 1.461e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.653e+02, tolerance: 1.380e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.649e+02, tolerance: 1.293e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.705e+02, tolerance: 1.679e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.640e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.738e+02, tolerance: 1.698e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.834e+02, tolerance: 1.489e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.215e+02, tolerance: 1.265e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.420e+02, tolerance: 1.552e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 82%|████████▏ | 82/100 [31:41<05:03, 16.86s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.866e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.901e+02, tolerance: 1.339e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.824e+02, tolerance: 1.518e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.546e+02, tolerance: 1.513e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.310e+02, tolerance: 1.200e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.760e+02, tolerance: 1.510e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.475e+02, tolerance: 1.588e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.830e+02, tolerance: 1.697e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.152e+02, tolerance: 1.581e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.812e+02, tolerance: 1.531e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.490e+02, tolerance: 1.471e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 83%|████████▎ | 83/100 [31:56<04:41, 16.58s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.321e+02, tolerance: 1.430e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.793e+02, tolerance: 1.597e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.350e+02, tolerance: 1.479e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.636e+02, tolerance: 1.547e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.827e+02, tolerance: 1.555e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.781e+02, tolerance: 1.469e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.321e+02, tolerance: 1.456e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.380e+02, tolerance: 1.438e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.169e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 84%|████████▍ | 84/100 [32:13<04:25, 16.61s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.071e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.723e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.689e+02, tolerance: 1.387e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 85%|████████▌ | 85/100 [32:28<04:01, 16.09s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.873e+02, tolerance: 1.339e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.705e+02, tolerance: 1.238e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.108e+02, tolerance: 1.525e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.239e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.426e+02, tolerance: 1.317e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.352e+02, tolerance: 1.577e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.740e+02, tolerance: 1.252e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.739e+02, tolerance: 1.558e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.010e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.649e+02, tolerance: 1.534e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.260e+02, tolerance: 1.266e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.416e+02, tolerance: 1.489e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 86%|████████▌ | 86/100 [32:43<03:40, 15.75s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.475e+02, tolerance: 1.441e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.160e+02, tolerance: 1.428e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.070e+02, tolerance: 1.532e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.760e+02, tolerance: 1.285e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.788e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.420e+02, tolerance: 1.568e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.621e+02, tolerance: 1.316e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.819e+02, tolerance: 1.269e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.742e+02, tolerance: 1.529e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.202e+02, tolerance: 1.350e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.299e+02, tolerance: 1.256e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.924e+02, tolerance: 1.579e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 87%|████████▋ | 87/100 [33:00<03:29, 16.15s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.291e+02, tolerance: 1.386e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.237e+02, tolerance: 1.614e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.433e+02, tolerance: 1.370e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.994e+02, tolerance: 1.414e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.759e+02, tolerance: 1.674e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.649e+02, tolerance: 1.487e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.613e+02, tolerance: 1.490e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.499e+02, tolerance: 1.488e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.996e+02, tolerance: 1.528e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.513e+02, tolerance: 1.438e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 88%|████████▊ | 88/100 [33:17<03:16, 16.40s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.669e+02, tolerance: 1.580e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.168e+02, tolerance: 1.745e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.769e+02, tolerance: 1.563e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.292e+02, tolerance: 1.243e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.840e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.715e+02, tolerance: 1.548e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.847e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.680e+02, tolerance: 1.546e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.084e+02, tolerance: 1.468e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 89%|████████▉ | 89/100 [33:34<03:00, 16.43s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.839e+02, tolerance: 1.599e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.451e+02, tolerance: 1.307e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.754e+02, tolerance: 1.460e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.486e+02, tolerance: 1.301e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.120e+02, tolerance: 1.445e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 90%|█████████ | 90/100 [33:50<02:43, 16.35s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.752e+02, tolerance: 1.709e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.844e+02, tolerance: 1.318e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.027e+02, tolerance: 1.452e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.919e+02, tolerance: 1.502e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.385e+02, tolerance: 1.369e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.536e+02, tolerance: 1.571e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.319e+02, tolerance: 1.508e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.226e+02, tolerance: 1.290e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.704e+02, tolerance: 1.558e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.154e+02, tolerance: 1.602e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.024e+02, tolerance: 1.531e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.752e+02, tolerance: 1.325e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.652e+02, tolerance: 1.454e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.149e+02, tolerance: 1.549e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 91%|█████████ | 91/100 [34:06<02:26, 16.24s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.302e+02, tolerance: 1.272e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.850e+02, tolerance: 1.360e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.529e+02, tolerance: 1.351e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.622e+02, tolerance: 1.519e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.055e+02, tolerance: 1.241e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.879e+02, tolerance: 1.591e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.618e+02, tolerance: 1.412e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.793e+02, tolerance: 1.340e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 92%|█████████▏| 92/100 [34:22<02:09, 16.20s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.259e+02, tolerance: 1.413e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.462e+02, tolerance: 1.547e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.732e+02, tolerance: 1.413e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.974e+02, tolerance: 1.405e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.495e+02, tolerance: 1.642e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.339e+02, tolerance: 1.717e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 93%|█████████▎| 93/100 [34:37<01:50, 15.75s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.855e+02, tolerance: 1.368e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.782e+02, tolerance: 1.447e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.606e+02, tolerance: 1.586e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.226e+02, tolerance: 1.507e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.065e+02, tolerance: 1.347e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.607e+02, tolerance: 1.512e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 94%|█████████▍| 94/100 [34:52<01:34, 15.70s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.514e+02, tolerance: 1.369e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.754e+02, tolerance: 1.412e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.529e+02, tolerance: 1.493e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.425e+02, tolerance: 1.480e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.764e+02, tolerance: 1.536e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.779e+02, tolerance: 1.497e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.114e+02, tolerance: 1.541e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.453e+02, tolerance: 1.394e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.591e+02, tolerance: 1.542e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 95%|█████████▌| 95/100 [35:08<01:19, 15.83s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.926e+02, tolerance: 1.533e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.510e+02, tolerance: 1.372e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.619e+02, tolerance: 1.594e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 96%|█████████▌| 96/100 [35:25<01:03, 15.97s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.519e+02, tolerance: 1.475e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.617e+02, tolerance: 1.706e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.832e+02, tolerance: 1.352e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.494e+02, tolerance: 1.440e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.415e+02, tolerance: 1.390e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.304e+02, tolerance: 1.281e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.079e+02, tolerance: 1.790e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 97%|█████████▋| 97/100 [35:41<00:48, 16.18s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.576e+02, tolerance: 1.483e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.437e+02, tolerance: 1.270e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.400e+02, tolerance: 1.351e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.329e+02, tolerance: 1.218e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.278e+02, tolerance: 1.495e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.883e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.648e+02, tolerance: 1.408e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 98%|█████████▊| 98/100 [35:56<00:31, 15.88s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e+02, tolerance: 1.478e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.720e+02, tolerance: 1.388e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.817e+02, tolerance: 1.425e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.118e+02, tolerance: 1.717e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.622e+02, tolerance: 1.509e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      " 99%|█████████▉| 99/100 [36:11<00:15, 15.63s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.467e+02, tolerance: 1.499e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.743e+02, tolerance: 1.417e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.803e+02, tolerance: 1.514e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.887e+02, tolerance: 1.442e+02\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "100%|██████████| 100/100 [36:27<00:00, 21.87s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tqdm(range(len(alpha_params))):\n",
+    "  alpha = alpha_params[i]\n",
+    "  all_selected_features = train_stabl_proxy_one_alpha(df_data, target_property, n_bootstraps, alpha, fraction, max_iter = max_iter)\n",
+    "  feature_freq = compute_fdr_and_feature_freq(all_selected_features, n_bootstraps)\n",
+    "  every_feature_freq_titer[alpha] = feature_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "REGC3qnyc3ei"
+   },
+   "outputs": [],
+   "source": [
+    "merged_feature_freq = merge_all_feature_freq(every_feature_freq_titer)\n",
+    "fdp, t = get_fdp_curve(merged_feature_freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 732
+    },
+    "executionInfo": {
+     "elapsed": 375,
+     "status": "ok",
+     "timestamp": 1761934500785,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "9_LZqXRcc4HR",
+    "outputId": "f3c8478b-b9cf-4928-f73a-da2345e9d660"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "thr_opt = t[np.argmin(fdp)]\n",
+    "plt.plot(t, fdp)\n",
+    "plt.axvline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"t\")\n",
+    "plt.ylabel(\"FDP\")\n",
+    "plt.title(\"FDP vs t\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 2269,
+     "status": "ok",
+     "timestamp": 1761934507335,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "1O4bGYeBc7bP",
+    "outputId": "08bd5f02-befe-451e-f67f-6ee382aeda9f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "# colormap adapted\n",
+    "from matplotlib import colormaps\n",
+    "cmap = colormaps.get_cmap('tab20')\n",
+    "for i,feature in enumerate(merged_feature_freq):\n",
+    "    alphas = merged_feature_freq[feature][\"1/alpha\"]\n",
+    "    freqs = merged_feature_freq[feature][\"freq\"]\n",
+    "    if feature in optimal_features:\n",
+    "      plt.plot(alphas, freqs, label=feature, color=\"darkred\")\n",
+    "    else:\n",
+    "      plt.plot(alphas, freqs, label=feature, color = \"gray\")\n",
+    "# plt.axhline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"1/alpha\")\n",
+    "plt.ylabel(\"Feature frequency\")\n",
+    "plt.title(\"Feature frequency vs alpha\")\n",
+    "# plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 21,
+     "status": "ok",
+     "timestamp": 1761934511272,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "t64V6_k7dBCk",
+    "outputId": "1ce3549c-396b-4676-ebd4-e8dd62cc026a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['patch_pos', 'r_gyr', 'dipole_moment', 'hyd_moment', 'helicity', 'E', 'amphipathicity', 'hyd_strength', 'Packing Score', 'strand_artificial', 'dipole_moment_artificial', 'Packing Score_artificial', 'helicity_artificial']\n",
+      "13\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimal_features = [feature for feature in merged_feature_freq if max(merged_feature_freq[feature][\"freq\"]) >= thr_opt]\n",
+    "print(optimal_features)\n",
+    "print(len(optimal_features))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "i0-uALwNdC0c"
+   },
+   "source": [
+    "## Feature selection on theromostability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 32,
+     "status": "ok",
+     "timestamp": 1761934693683,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "Kcm7xlFBdIiP",
+    "outputId": "f7b6f819-bf04-485d-b81e-91949d4c8385"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Features with low variance: Index(['debye'], dtype='object')\n",
+      "(193, 46)\n"
+     ]
+    }
+   ],
+   "source": [
+    "alpha_params = list(np.linspace(0.01, 1, 100))\n",
+    "# n_bootstraps = 100\n",
+    "fraction = 1.0\n",
+    "target_property = \"Tm2\"\n",
+    "df_target_no_nan = df_target[target_property].dropna()\n",
+    "df_main_dataset = df_master_filtered\n",
+    "df_main_dataset = df_main_dataset.loc[df_target_no_nan.index]\n",
+    "\n",
+    "variance_threshold = 0.\n",
+    "selector = VarianceThreshold(threshold=variance_threshold)\n",
+    "df_main_dataset_high_v = pd.DataFrame(selector.fit_transform(df_main_dataset), columns=selector.get_feature_names_out())\n",
+    "df_main_dataset_high_v.index = df_main_dataset.index\n",
+    "features_low_var = df_main_dataset.columns[~selector.get_support()]\n",
+    "print(f\"Features with low variance: {features_low_var}\")\n",
+    "\n",
+    "df_data = (df_main_dataset_high_v - df_main_dataset_high_v.mean()) / df_main_dataset_high_v.std()\n",
+    "df_data[target_property] = df_target_no_nan\n",
+    "# n_bootstraps = df_data.shape[0]\n",
+    "print(df_data.shape)\n",
+    "n_bootstraps = 500\n",
+    "every_fdr = np.zeros(len(alpha_params))\n",
+    "every_feature_freq_tm = {}\n",
+    "max_iter = 2000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 604900,
+     "status": "ok",
+     "timestamp": 1761935301494,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "SL3qDVjGdOm9",
+    "outputId": "1122eefd-7f1e-4797-c138-57a06396c700"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r  0%|          | 0/100 [00:00<?, ?it/s]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.810e+00, tolerance: 2.224e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+00, tolerance: 1.777e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.464e-01, tolerance: 2.025e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.777e-01, tolerance: 2.140e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.762e-01, tolerance: 1.945e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.557e+00, tolerance: 2.084e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.870e-01, tolerance: 1.722e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.940e-01, tolerance: 1.974e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.307e+00, tolerance: 1.983e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.113e-01, tolerance: 1.982e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.247e+00, tolerance: 1.815e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.186e-01, tolerance: 1.833e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.165e+00, tolerance: 1.803e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.644e-01, tolerance: 1.962e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.424e-01, tolerance: 1.678e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.606e-01, tolerance: 1.802e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.050e-01, tolerance: 1.996e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.599e-01, tolerance: 1.701e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.714e-01, tolerance: 1.665e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.121e+00, tolerance: 1.853e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.265e-01, tolerance: 2.002e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.849e+00, tolerance: 1.740e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.560e-01, tolerance: 1.967e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.971e-01, tolerance: 2.202e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.293e-01, tolerance: 2.002e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.600e-01, tolerance: 2.140e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.102e-01, tolerance: 1.953e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.722e-01, tolerance: 2.363e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.090e-01, tolerance: 2.090e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.856e-01, tolerance: 1.902e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.754e-01, tolerance: 2.248e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.993e-01, tolerance: 1.777e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.013e-01, tolerance: 2.193e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.808e-01, tolerance: 1.974e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.534e-01, tolerance: 1.665e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.361e-01, tolerance: 1.894e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.465e-01, tolerance: 1.992e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.058e-01, tolerance: 2.062e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.148e-01, tolerance: 1.850e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.535e+00, tolerance: 1.803e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.440e-01, tolerance: 1.768e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.262e-01, tolerance: 1.984e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.020e-01, tolerance: 2.059e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.590e+00, tolerance: 1.980e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.986e-01, tolerance: 1.683e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.443e-01, tolerance: 2.124e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.987e-01, tolerance: 1.862e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.199e+00, tolerance: 1.959e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.115e-01, tolerance: 1.775e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.601e-01, tolerance: 1.910e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.446e-01, tolerance: 1.873e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.934e-01, tolerance: 1.841e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.478e-01, tolerance: 2.143e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.054e-01, tolerance: 1.871e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.336e-01, tolerance: 1.665e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.927e-01, tolerance: 2.153e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.990e-01, tolerance: 2.008e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.512e-01, tolerance: 1.601e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.689e-01, tolerance: 1.836e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.140e-01, tolerance: 2.255e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.260e-01, tolerance: 2.123e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.075e+00, tolerance: 1.968e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.374e-01, tolerance: 2.088e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.461e-01, tolerance: 1.740e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.906e-01, tolerance: 2.081e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.088e-01, tolerance: 2.083e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.870e+00, tolerance: 1.878e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.230e+00, tolerance: 1.879e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.222e-01, tolerance: 1.936e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.951e-01, tolerance: 1.901e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.280e-01, tolerance: 1.656e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.256e-01, tolerance: 1.795e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.002e-01, tolerance: 1.719e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.596e-01, tolerance: 1.947e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.966e+00, tolerance: 1.842e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.491e-01, tolerance: 2.043e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.381e+00, tolerance: 1.741e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.928e-01, tolerance: 1.890e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.466e-01, tolerance: 2.179e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.300e+00, tolerance: 2.148e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.741e-01, tolerance: 2.129e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.731e-01, tolerance: 1.935e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.014e+00, tolerance: 2.117e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.568e+00, tolerance: 2.191e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.276e-01, tolerance: 1.886e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.416e-01, tolerance: 2.157e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.758e-01, tolerance: 2.008e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.094e-01, tolerance: 1.977e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.978e-01, tolerance: 1.715e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.151e-01, tolerance: 2.024e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.341e-01, tolerance: 1.986e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.035e-01, tolerance: 1.684e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.594e+00, tolerance: 1.643e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.349e-01, tolerance: 1.852e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.172e-01, tolerance: 1.597e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.008e-01, tolerance: 1.502e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.394e-01, tolerance: 1.897e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.569e-01, tolerance: 2.011e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.188e-01, tolerance: 1.829e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.553e+00, tolerance: 2.183e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.859e-01, tolerance: 2.147e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 9.252e-01, tolerance: 1.974e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.204e-01, tolerance: 1.953e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.768e-01, tolerance: 2.046e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.052e-01, tolerance: 2.326e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.420e-01, tolerance: 2.057e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.664e-01, tolerance: 2.120e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.517e+00, tolerance: 1.849e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.288e-01, tolerance: 1.895e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.151e-01, tolerance: 2.102e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.818e-01, tolerance: 1.662e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.238e-01, tolerance: 1.925e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.938e-01, tolerance: 2.133e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.554e-01, tolerance: 1.858e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.909e+00, tolerance: 1.898e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.831e-01, tolerance: 1.658e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.017e-01, tolerance: 1.855e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.482e-01, tolerance: 1.815e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.070e+00, tolerance: 1.791e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.742e+00, tolerance: 2.124e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.396e-01, tolerance: 2.084e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.985e-01, tolerance: 1.850e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.549e-01, tolerance: 1.977e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.040e+00, tolerance: 1.888e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.114e+00, tolerance: 2.006e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.520e-01, tolerance: 2.126e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.790e-01, tolerance: 2.032e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.725e-01, tolerance: 1.991e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.097e-01, tolerance: 1.802e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.710e-01, tolerance: 2.045e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.184e+00, tolerance: 1.695e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.379e-01, tolerance: 1.922e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.420e-01, tolerance: 2.248e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.567e+00, tolerance: 2.132e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.424e-01, tolerance: 2.127e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.570e-01, tolerance: 1.482e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.221e-01, tolerance: 2.099e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.364e+00, tolerance: 1.771e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.981e-01, tolerance: 1.951e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.974e-01, tolerance: 2.075e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.747e-01, tolerance: 1.872e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.697e-01, tolerance: 2.173e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.141e-01, tolerance: 1.874e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.884e-01, tolerance: 1.615e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.423e-01, tolerance: 2.110e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.312e-01, tolerance: 1.993e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.253e-01, tolerance: 2.035e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.010e-01, tolerance: 1.961e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.091e-01, tolerance: 1.830e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.538e-01, tolerance: 1.808e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.878e-01, tolerance: 1.630e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.067e-01, tolerance: 1.866e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.096e-01, tolerance: 2.000e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.730e-01, tolerance: 1.729e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  1%|          | 1/100 [00:17<29:20, 17.78s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.560e-01, tolerance: 1.832e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.729e-01, tolerance: 1.999e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.248e-01, tolerance: 1.958e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.353e-01, tolerance: 2.126e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.472e-01, tolerance: 2.281e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.962e-01, tolerance: 1.843e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.991e-01, tolerance: 2.085e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.544e-01, tolerance: 2.003e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.929e-01, tolerance: 1.983e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.649e-01, tolerance: 1.781e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.939e-01, tolerance: 1.908e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.979e-01, tolerance: 2.063e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.117e-01, tolerance: 2.185e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.472e-01, tolerance: 2.005e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.254e-01, tolerance: 1.755e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.987e-01, tolerance: 2.068e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.533e-01, tolerance: 1.877e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.379e-01, tolerance: 1.870e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.226e-01, tolerance: 1.909e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.936e-01, tolerance: 2.239e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.638e-01, tolerance: 2.025e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.897e-01, tolerance: 2.233e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.582e-01, tolerance: 1.776e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.039e-01, tolerance: 2.048e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.965e-01, tolerance: 1.958e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.024e+00, tolerance: 1.867e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.636e-01, tolerance: 1.623e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.233e-01, tolerance: 2.097e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.397e-01, tolerance: 1.903e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.619e-01, tolerance: 2.091e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.004e-01, tolerance: 1.838e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.135e-01, tolerance: 1.805e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.933e-01, tolerance: 1.569e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.598e-01, tolerance: 2.095e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.392e-01, tolerance: 2.045e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.580e-01, tolerance: 2.006e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.929e-01, tolerance: 1.820e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.271e-01, tolerance: 1.557e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.502e-01, tolerance: 1.785e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.008e-01, tolerance: 1.739e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.821e-01, tolerance: 1.991e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.313e+00, tolerance: 1.914e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.868e-01, tolerance: 1.897e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.108e-01, tolerance: 1.767e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.413e-01, tolerance: 2.089e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.866e-01, tolerance: 2.087e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.381e-01, tolerance: 2.111e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.936e-01, tolerance: 2.107e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.392e-01, tolerance: 1.778e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.466e-01, tolerance: 1.943e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.388e-01, tolerance: 2.091e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.627e-01, tolerance: 1.768e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.131e-01, tolerance: 1.852e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.276e-01, tolerance: 2.022e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.750e-01, tolerance: 2.046e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.596e-01, tolerance: 1.959e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.353e-01, tolerance: 1.763e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.268e+00, tolerance: 1.782e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.353e-01, tolerance: 2.134e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.046e-01, tolerance: 2.152e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.648e-01, tolerance: 1.995e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.089e+00, tolerance: 2.270e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.205e-01, tolerance: 2.118e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.907e-01, tolerance: 2.047e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.668e-01, tolerance: 1.592e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.304e-01, tolerance: 2.132e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.447e-01, tolerance: 1.895e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.610e-01, tolerance: 1.901e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.207e-01, tolerance: 2.009e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.283e-01, tolerance: 1.775e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.939e-01, tolerance: 1.736e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.828e-01, tolerance: 1.521e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.200e-01, tolerance: 2.449e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.350e-01, tolerance: 1.766e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.390e-01, tolerance: 2.077e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.168e-01, tolerance: 2.188e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.293e-01, tolerance: 2.103e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.260e-01, tolerance: 1.590e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.350e-01, tolerance: 1.940e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.169e-01, tolerance: 2.071e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.328e-01, tolerance: 1.887e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.474e-01, tolerance: 1.655e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.450e-01, tolerance: 2.188e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.810e-01, tolerance: 1.860e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.433e-01, tolerance: 1.655e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.623e+00, tolerance: 2.118e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.109e-01, tolerance: 2.055e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.339e-01, tolerance: 1.915e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.980e-01, tolerance: 2.247e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.104e-01, tolerance: 2.144e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.488e-01, tolerance: 1.762e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.846e-01, tolerance: 1.968e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 6.251e-01, tolerance: 2.019e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.203e-01, tolerance: 1.920e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.273e-01, tolerance: 1.866e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.085e-01, tolerance: 2.072e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.367e-01, tolerance: 2.139e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  2%|▏         | 2/100 [00:30<23:56, 14.66s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.262e-01, tolerance: 1.829e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.046e-01, tolerance: 1.836e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.226e-01, tolerance: 1.879e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.056e-01, tolerance: 2.033e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.218e-01, tolerance: 1.844e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.486e-01, tolerance: 2.074e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.447e-01, tolerance: 2.102e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.319e-01, tolerance: 2.026e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.474e-01, tolerance: 1.851e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.351e-01, tolerance: 1.968e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.065e+00, tolerance: 1.860e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.328e-01, tolerance: 1.714e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.360e-01, tolerance: 1.802e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 7.714e-01, tolerance: 1.938e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.137e-01, tolerance: 2.048e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.652e-01, tolerance: 1.566e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.125e-01, tolerance: 2.178e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.721e-01, tolerance: 1.958e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.358e-01, tolerance: 1.982e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.678e-01, tolerance: 2.207e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.934e-01, tolerance: 1.850e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.981e-01, tolerance: 1.887e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.192e-01, tolerance: 2.005e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.595e-01, tolerance: 1.894e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 5.028e-01, tolerance: 2.007e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.856e-01, tolerance: 1.789e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.864e-01, tolerance: 1.702e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.404e-01, tolerance: 1.865e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.923e-01, tolerance: 1.820e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  3%|▎         | 3/100 [00:40<20:32, 12.70s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 4.422e-01, tolerance: 2.247e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.447e-01, tolerance: 1.647e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.984e-01, tolerance: 1.902e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.712e-01, tolerance: 2.208e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  4%|▍         | 4/100 [00:48<17:26, 10.90s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.122e-01, tolerance: 1.926e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "  5%|▌         | 5/100 [00:56<15:37,  9.87s/it]/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.800e-01, tolerance: 1.792e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.395e-01, tolerance: 1.818e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "/usr/local/lib/python3.12/dist-packages/sklearn/linear_model/_coordinate_descent.py:695: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.176e-01, tolerance: 1.696e-01\n",
+      "  model = cd_fast.enet_coordinate_descent(\n",
+      "100%|██████████| 100/100 [10:04<00:00,  6.05s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in tqdm(range(len(alpha_params))):\n",
+    "  alpha = alpha_params[i]\n",
+    "  all_selected_features = train_stabl_proxy_one_alpha(df_data, target_property, n_bootstraps, alpha, fraction, max_iter = max_iter)\n",
+    "  feature_freq = compute_fdr_and_feature_freq(all_selected_features, n_bootstraps)\n",
+    "  every_feature_freq_tm[alpha] = feature_freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "HWlvtUCqdQ4l"
+   },
+   "outputs": [],
+   "source": [
+    "merged_feature_freq = merge_all_feature_freq(every_feature_freq_tm)\n",
+    "fdp, t = get_fdp_curve(merged_feature_freq)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 830,
+     "status": "ok",
+     "timestamp": 1761935392847,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "-ZAEE0rvdZfD",
+    "outputId": "6814c959-713e-4e16-9052-8c1053a3e48f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "thr_opt = t[np.argmin(fdp)]\n",
+    "plt.plot(t, fdp)\n",
+    "plt.axvline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"t\")\n",
+    "plt.ylabel(\"FDP\")\n",
+    "plt.title(\"FDP vs t\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 736
+    },
+    "executionInfo": {
+     "elapsed": 2385,
+     "status": "ok",
+     "timestamp": 1761935398501,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "1LwTV3dHdWLI",
+    "outputId": "3082d19c-970e-42c1-e45c-5327a366c799"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(20,10))\n",
+    "# colormap adapted\n",
+    "from matplotlib import colormaps\n",
+    "cmap = colormaps.get_cmap('tab20')\n",
+    "for i,feature in enumerate(merged_feature_freq):\n",
+    "    alphas = merged_feature_freq[feature][\"1/alpha\"]\n",
+    "    freqs = merged_feature_freq[feature][\"freq\"]\n",
+    "    if feature in optimal_features:\n",
+    "      plt.plot(alphas, freqs, label=feature, color=\"darkred\")\n",
+    "    else:\n",
+    "      plt.plot(alphas, freqs, label=feature, color = \"gray\")\n",
+    "# plt.axhline(thr_opt, color='r', linestyle='--', label = \"Optimal threshold = \"+str(thr_opt))\n",
+    "plt.xlabel(\"1/alpha\")\n",
+    "plt.ylabel(\"Feature frequency\")\n",
+    "plt.title(\"Feature frequency vs alpha\")\n",
+    "# plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 9,
+     "status": "ok",
+     "timestamp": 1761935401169,
+     "user": {
+      "displayName": "Valentin BADEA",
+      "userId": "12919603136597084511"
+     },
+     "user_tz": 240
+    },
+    "id": "Vf5-WtWOdaPP",
+    "outputId": "f71ec1c2-4352-47cd-dff8-496bb88dd2c8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['hyd_moment', 'zquadrupole', 'helicity', 'strand', 'E', 'amphipathicity', 'hyd_idx_cdr', 'Packing Score']\n",
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "optimal_features = [feature for feature in merged_feature_freq if max(merged_feature_freq[feature][\"freq\"]) >= thr_opt]\n",
+    "print(optimal_features)\n",
+    "print(len(optimal_features))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "UYVw7fo4kJUS"
+   },
+   "source": [
+    "# Conclusions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yzGuqvTukRcx"
+   },
+   "source": [
+    "> * Dimensionality reduction shows that HIC's variations seems to nicely correlate with data point position in the 2D PCA plane.\n",
+    "> * Correlation analysis show that some MD features are highly correlated with the outcomes. Dataset seems to have a hierarchical structure where some targets will correlate with others (ex: HIC with Tm2 or CHO with Titer)\n",
+    "> * We have applied the methodology described in *Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data, Jean Hédou et al. 2024, Nature Biotechnology* to produce subsets of reliable MD features that are likely to play an important role in further feature engineering, model selection or model performance. This set of features is not selected from the model performance on the 5-fold split given for this competition but on a multitude of bootstraps with data-driven hyperparameter tuning."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "collapsed_sections": [
+    "Vtj14B4TF6d7"
+   ],
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "molml_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/outputs/evaluation/saprot_vh_vl_cv.csv b/outputs/evaluation/saprot_vh_vl_cv.csv
new file mode 100644
index 0000000..7ebdfa1
--- /dev/null
+++ b/outputs/evaluation/saprot_vh_vl_cv.csv
@@ -0,0 +1,71 @@
+spearman,top_10_recall,fold,dataset,assay,model,split
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diff --git a/outputs/predictions/GDPa1_cross_validation/saprot_vh_vl/predictions.csv b/outputs/predictions/GDPa1_cross_validation/saprot_vh_vl/predictions.csv
new file mode 100644
index 0000000..dea8946
--- /dev/null
+++ b/outputs/predictions/GDPa1_cross_validation/saprot_vh_vl/predictions.csv
@@ -0,0 +1,247 @@
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+bleselumab,2,QLQLQESGPGLLKPSETLSLTCTVSGGSISSPGYYGGWIRQPPGKGLEWIGSIYKSGSTYHNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCTRPVVRYFGWFDPWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPTFGQGTKVEIK,2.819282,82.6467,246.47905,0.25371036,5.317379
+blosozumab,0,QVQLVQSGAEVKKPGASVKVSCKVSGFPIKDTFQHWVRQAPGKGLEWMGWSDPEIGDTEYASKFQGRVTMTEDTSTDTAYMELSSLRSEDTAVYYCATGDTTYKFDFWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVHTAVAWYQQKPGKAPKLLIYWASTRWTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSDYPWTFGGGTKVEIK,2.8106937,82.74251,207.78487,0.143351,0.61407375
+bococizumab,0,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYYMHWVRQAPGQGLEWMGEISPFGGRTNYNEKFKSRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARERPLYASDLWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQRYSLWRTFGQGTKLEIK,2.759393,82.58634,254.58745,0.21350868,6.532382
+brazikumab,4,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVIWYDGSNEYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGYTSSWYPDAFDIWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSSSNTGAGYDVHWYQQVPGTAPKLLIYGSGNRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDSSLSGWVFGGGTRLTVL,2.9184425,81.74377,328.912,0.1315682,11.814361
+brentuximab,4,QIQLQQSGPEVVKPGASVKISCKASGYTFTDYYITWVKQKPGQGLEWIGWIYPGSGNTKYNEKFKGKATLTVDTSSSTAFMQLSSLTSEDTAVYFCANYGNYWFAYWGQGTQVTVSA,DIVLTQSPASLAVSLGQRATISCKASQSVDFDGDSYMNWYQQKPGQPPKVLIYAASNLESGIPARFSGSGSGTDFTLNIHPVEEEDAATYYCQQSNEDPWTFGGGTKLEIK,2.8088477,82.709694,184.24472,0.16906387,2.939931
+briakinumab,4,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAFIRYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCKTHGSHDNWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCSGSRSNIGSNTVKWYQQLPGTAPKLLIYYNDQRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDRYTHPALLFGTGTKVTVL,2.7559948,81.39998,323.992,0.14014295,14.442716
+brodalumab,2,QVQLVQSGAEVKKPGASVKVSCKASGYTFTRYGISWVRQAPGQGLEWMGWISTYSGNTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARRQLYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVSSNLAWFQQKPGQAPRPLIYDASTRATGVPARFSGSGSGTDFTLTISSLQSEDFAVYYCQQYDNWPLTFGGGTKVEIK,2.822659,81.20329,285.8033,0.21959522,8.500259
+brontictuzumab,4,QVQLVQSGAEVKKPGASVKISCKVSGYTLRGYWIEWVRQAPGKGLEWIGQILPGTGRTNYNEKFKGRVTMTADTSTDTAYMELSSLRSEDTAVYYCARFDGNYGYYAMDYWGQGTTVTVSS,QAVVTQEPSLTVSPGGTVTLTCRSSTGAVTTSNYANWFQQKPGQAPRTLIGGTNNRAPGVPARFSGSLLGGKAALTLSGAQPEDEAEYYCALWYSNHWVFGGGTKLTVL,2.9346325,80.69258,281.94336,0.25310892,10.035311
+budigalimab,4,EIQLVQSGAEVKKPGSSVKVSCKASGYTFTHYGMNWVRQAPGQGLEWVGWVNTYTGEPTYADDFKGRLTFTLDTSTSTAYMELSSLRSEDTAVYYCTREGEGLGFGDWGQGTTVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSIVHSHGDTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHIPVTFGQGTKLEIK,2.892438,82.14256,175.97864,0.1805748,0.86771345
+burosumab,1,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNHYMHWVRQAPGQGLEWMGIINPISGSTSNAQKFQGRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARDIVDAFDFWGQGTMVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALVWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNDYFTFGPGTKVDIK,2.7652209,81.41944,270.91098,0.1851778,6.6737146
+cabiralizumab,3,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDNYMIWVRQAPGQGLEWMGDINPYNGGTTFNQKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARESPYFSNLYVMDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCKASQSVDYDGDNYMNWYQQKPGQAPRLLIYAASNLESGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCHLSNEDLSTFGGGTKVEIK,2.8142161,81.90048,200.43164,0.1534187,3.5577116
+camidanlumab,3,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSRYIINWVRQAPGQGLEWMGRIIPILGVENYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARKDWFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPLTFGGGTKVEIK,2.827734,81.10983,256.7398,0.2727087,9.65197
+camrelizumab,1,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYMMSWVRQAPGKGLEWVATISGGGANTYYPDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARQLYYFDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCLASQTIGTWLTWYQQKPGKAPKLLIYTATSLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQVYSIPWTFGGGTKVEIK,2.7518148,83.3015,302.64508,0.055333525,10.209062
+carlumab,3,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYGISWVRQAPGQGLEWMGGIIPIFGTANYAQKFQGRVTITADESTSTAYMELSSLRSEDTAVYYCARYDGIYGELDFWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSDAYLAWYQQKPGQAPRLLIYDASSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCHQYIQLHSFTFGQGTKVEIK,2.8793876,81.36633,221.16353,0.23345363,5.0780363
+cemiplimab,1,EVQLLESGGVLVQPGGSLRLSCAASGFTFSNFGMTWVRQAPGKGLEWVSGISGGGRDTYFADSVKGRFTISRDNSKNTLYLQMNSLKGEDTAVYYCVKWGNIYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDSITITCRASLSINTFLNWYQQKPGKAPNLLIYAASSLHGGVPSRFSGSGSGTDFTLTIRTLQPEDFATYYCQQSSNTPFTFGPGTVVDFR,2.7691426,82.407776,267.85495,0.10811654,7.839448
+certolizumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYVFTDYGMNWVRQAPGKGLEWMGWINTYIGEPIYADSVKGRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCARGYRSYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQNVGTNVAWYQQKPGKAPKALIYSASFLYSGVPYRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNIYPLTFGQGTKVEIK,2.8216002,82.078445,244.44447,0.11560607,1.9476676
+cetrelimab,3,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGGIIPIFDTANYAQKFQGRVTITADESTSTAYMELSSLRSEDTAVYYCARPGLAAAYDTGSLDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVRSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRNYWPLTFGQGTKVEIK,2.874748,81.323,237.53949,0.22803979,5.2769833
+cetuximab,1,QVQLKQSGPGLVQPSQSLSITCTVSGFSLTNYGVHWVRQSPGKGLEWLGVIWSGGNTDYNTPFTSRLSINKDNSKSQVFFKMNSLQSNDTAIYYCARALTYYDYEFAYWGQGTLVTVSA,DILLTQSPVILSVSPGERVSFSCRASQSIGTNIHWYQQRTNGSPRLLIKYASESISGIPSRFSGSGSGTDFTLSINSVESEDIADYYCQQNNNWPTTFGAGTKLELK,2.9383786,82.850044,170.38266,0.23120749,5.1947722
+cixutumumab,4,EVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGGIIPIFGTANYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARAPLRFLEWSTQDHYYYYYMDVWGKGTTVTVSS,SSELTQDPAVSVALGQTVRITCQGDSLRSYYATWYQQKPGQAPILVIYGENKRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCKSRDGSGQHLVFGGGTKLTVL,2.9345915,80.88077,246.75311,0.2827685,7.4366007
+clazakizumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFSLSNYYVTWVRQAPGKGLEWVGIIYGSDETAYATSAIGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDDSSDWDAKFNLWGQGTLVTVSS,AIQMTQSPSSLSASVGDRVTITCQASQSINNELSWYQQKPGKAPKLLIYRASTLASGVPSRFSGSGSGTDFTLTISSLQPDDFATYYCQQGYSLRNIDNAFGGGTKVEIK,2.7759695,82.299164,256.31296,0.08881229,2.90452
+codrituzumab,4,QVQLVQSGAEVKKPGASVKVSCKASGYTFTDYEMHWVRQAPGQGLEWMGALDPKTGDTAYSQKFKGRVTLTADKSTSTAYMELSSLTSEDTAVYYCTRFYSYTYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLVHSNRNTYLHWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQNTHVPPTFGQGTKLEIK,2.7552383,82.078156,178.08105,0.22398245,2.594615
+coltuximab,2,QVQLVQPGAEVVKPGASVKLSCKTSGYTFTSNWMHWVKQAPGQGLEWIGEIDPSDSYTNYNQNFQGKAKLTVDKSTSTAYMEVSSLRSDDTAVYYCARGSNPYYYAMDYWGQGTSVTVSS,EIVLTQSPAIMSASPGERVTMTCSASSGVNYMHWYQQKPGTSPRRWIYDTSKLASGVPARFSGSGSGTDYSLTISSMEPEDAATYYCHQRGSYTFGGGTKLEIK,2.9481578,82.53295,232.13704,0.15665674,5.067498
+concizumab,4,EVQLVESGGGLVKPGGSLRLSCAASGFTFSNYAMSWVRQTPEKRLEWVATISRSGSYSYFPDSVQGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARLGGYDEGDAMDSWGQGTTVTVSS,DIVMTQTPLSLSVTPGQPASISCKSSQSLLESDGKTYLNWYLQKPGQSPQLLIYLVSILDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQATHFPQTFGGGTKVEIK,2.6162646,83.54695,216.22414,0.076305434,3.899561
+crenezumab,4,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYGMSWVRQAPGKGLELVASINSNGGSTYYPDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCASGDYWGQGTTVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSLVYSNGDTYLHWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHVPWTFGQGTKVEIK,2.7960215,83.48113,212.4142,0.046978116,5.0819693
+crizanlizumab,0,QVQLVQSGAEVKKPGASVKVSCKVSGYTFTSYDINWVRQAPGKGLEWMGWIYPGDGSIKYNEKFKGRVTMTVDKSTDTAYMELSSLRSEDTAVYYCARRGEYGNYEGAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQSVDYDGHSYMNWYQQKPGKAPKLLIYAASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSDENPLTFGGGTKVEIK,2.7674844,82.39126,210.60265,0.15880674,0.6435752
+cusatuzumab,4,EVQLVESGGGLVQPGGSLRLSCAASGFTFSVYYMNWVRQAPGKGLEWVSDINNEGGTTYYADSVKGRFTISRDNSKNSLYLQMNSLRAEDTAVYYCARDAGYSNHVPIFDSWGQGTLVTVSS,QAVVTQEPSLTVSPGGTVTLTCGLKSGSVTSDNFPTWYQQTPGQAPRLLIYNTNTRHSGVPDRFSGSILGNKAALTITGAQADDEAEYFCALFISNPSVEFGGGTQLTVL,2.8698833,82.37921,239.93835,0.06291485,6.437975
+dacetuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYSFTGYYIHWVRQAPGKGLEWVARVIPNAGGTSYNQKFKGRFTLSVDNSKNTAYLQMNSLRAEDTAVYYCAREGIYWWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSLVHSNGNTFLHWYQQKPGKAPKLLIYTVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYFCSQTTHVPWTFGQGTKVEIK,2.6802707,81.909996,277.55463,0.15252522,9.171395
+daclizumab,1,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTSYRMHWVRQAPGQGLEWIGYINPSTGYTEYNQKFKDKATITADESTNTAYMELSSLRSEDTAVYYCARGGGVFDYWGQGTLVTVSS,DIQMTQSPSTLSASVGDRVTITCSASSSISYMHWYQQKPGKAPKLLIYTTSNLASGVPARFSGSGSGTEFTLTISSLQPDDFATYYCHQRSTYPLTFGQGTKVEVK,2.7006545,81.70427,267.90808,0.2032533,7.5203085
+dalotuzumab,3,QVQLQESGPGLVKPSETLSLTCTVSGYSITGGYLWNWIRQPPGKGLEWIGYISYDGTNNYKPSLKDRVTISRDTSKNQFSLKLSSVTAADTAVYYCARYGRVFFDYWGQGTLVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSIVHSNGNTYLQWYLQKPGQSPQLLIYKVSNRLYGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPWTFGQGTKVEIK,2.8603604,81.768906,160.19392,0.26194048,5.319029
+daratumumab,2,EVQLLESGGGLVQPGGSLRLSCAVSGFTFNSFAMSWVRQAPGKGLEWVSAISGSGGGTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCAKDKILWFGEPVFDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPTFGQGTKVEIK,2.863234,81.893295,204.25528,0.13673817,5.9592338
+denosumab,4,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSGITGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDPGTTVIMSWFDPWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVRGRYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVFYCQQYGSSPRTFGQGTKVEIK,2.7639868,82.71992,219.13055,0.0823776,8.252889
+depatuxizumab,3,QVQLQESGPGLVKPSQTLSLTCTVSGYSISSDFAWNWIRQPPGKGLEWMGYISYSGNTRYQPSLKSRITISRDTSKNQFFLKLNSVTAADTATYYCVTAGRGFPYWGQGTLVTVSS,DIQMTQSPSSMSVSVGDRVTITCHSSQDINSNIGWLQQKPGKSFKGLIYHGTNLDDGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCVQYAQFPWTFGGGTKLEIK,2.8924127,82.10158,230.09155,0.1875867,5.4592175
+dinutuximab,4,EVQLLQSGPELEKPGASVMISCKASGSSFTGYNMNWVRQNIGKSLEWIGAIDPYYGGTSYNQKFKGRATLTVDKSSSTAYMHLKSLTSEDSAVYYCVSGMEYWGQGTSVTVSS,EIVMTQSPATLSVSPGERATLSCRSSQSLVHRNGNTYLHWYLQKPGQSPKLLIHKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPPLTFGAGTKLELK,2.8144703,82.39316,105.582085,0.27173603,4.526019
+domagrozumab,0,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSTISSGGSYTSYPDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKQDYAMNYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVSTAVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQHYSTPWTFGGGTKVEIK,2.7283823,82.278305,293.94238,0.03917268,8.657586
+dostarlimab,1,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYDMSWVRQAPGKGLEWVSTISGGGSYTYYQDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCASPYYAMDYWGQGTTVTVSS,DIQLTQSPSFLSAYVGDRVTITCKASQDVGTAVAWYQQKPGKAPKLLIYWASTLHTGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCQHYSSYPWTFGQGTKLEIK,2.8032353,83.17552,250.62923,0.058006063,8.677631
+duligotuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFTLSGDWIHWVRQAPGKGLEWVGEISAAGGYTDYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARESRVSFEAAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQNIATDVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSEPEPYTFGQGTKVEIK,2.81226,82.534256,221.46649,0.07656762,3.467863
+dupilumab,4,EVQLVESGGGLEQPGGSLRLSCAGSGFTFRDYAMTWVRQAPGKGLEWVSSISGSGGNTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDRLSITIRPRYYGLDVWGQGTTVTVSS,DIVMTQSPLSLPVTPGEPASISCRSSQSLLYSIGYNYLDWYLQKSGQSPQLLIYLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGFYYCMQALQTPYTFGQGTKLEIK,2.7496297,82.91269,228.22147,0.06522517,3.4444356
+durvalumab,2,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRYWMSWVRQAPGKGLEWVANIKQDGSEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGGWFGELAFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQRVSSSYLAWYQQKPGQAPRLLIYDASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSLPWTFGQGTKVEIK,2.823079,81.59433,210.56183,0.13192014,7.9442253
+eculizumab,0,QVQLVQSGAEVKKPGASVKVSCKASGYIFSNYWIQWVRQAPGQGLEWMGEILPGSGSTEYTENFKDRVTMTRDTSTSTVYMELSSLRSEDTAVYYCARYFFGSSPNWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCGASENIYGALNWYQQKPGKAPKLLIYGATNLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQNVLNTPLTFGQGTKVEIK,2.851741,82.37253,221.70552,0.22737983,3.7057638
+efalizumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYSFTGHWMNWVRQAPGKGLEWVGMIHPSDSETRYNQKFKDRFTISVDKSKNTLYLQMNSLRAEDTAVYYCARGIYFYGTTYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASKTISKYLAWYQQKPGKAPKLLIYSGSTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQHNEYPLTFGQGTKVEIK,2.6707666,82.13534,225.39258,0.1795497,4.5488043
+eldelumab,2,QMQLVESGGGVVQPGRSLRLSCTASGFTFSNNGMHWVRQAPGKGLEWVAVIWFDGMNKFYVDSVKGRFTISRDNSKNTLYLEMNSLRAEDTAIYYCAREGDGSGIYYYYGMDVWGQGTTVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPIFTFGPGTKVDIK,2.9840424,81.835785,246.9251,0.14730205,6.601766
+elezanumab,4,EVQLVQSGAEVKKPGASVKVSCKASGYTFTSHGISWVRQAPGQGLDWMGWISPYSGNTNYAQKLQGRVTMTTDTSTSTAYMELSSLRSEDTAVYYCARVGSGPYYYMDVWGQGTLVTVSS,QSALTQPRSVSGSPGQSVTISCTGTSSSVGDSIYVSWYQQHPGKAPKLMLYDVTKRPSGVPDRFSGSKSGNTASLTISGLQAEDEADYYCYSYAGTDTLFGGGTKVTVL,2.912371,80.57805,321.85138,0.26987177,9.638965
+elotuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFDFSRYWMSWVRQAPGKGLEWIGEINPDSSTINYAPSLKDKFIISRDNAKNSLYLQMNSLRAEDTAVYYCARPDGNYWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVGIAVAWYQQKPGKVPKLLIYWASTRHTGVPDRFSGSGSGTDFTLTISSLQPEDVATYYCQQYSSYPYTFGQGTKVEIK,2.7206497,82.74231,231.208,0.07453492,2.6225977
+emactuzumab,1,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYDISWVRQAPGQGLEWMGVIWTDGGTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARDQRLYFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASEDVNTYVSWYQQKPGKAPKLLIYAASNRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSFSYPTFGQGTKLEIK,2.7519436,81.53604,277.36652,0.18041411,6.01548
+emapalumab,4,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAISGSGGSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKDGSSGWYVPHWFDPWGQGTLVTVSS,NFMLTQPHSVSESPGKTVTISCTRSSGSIASNYVQWYQQRPGSSPTTVIYEDNQRPSGVPDRFSGSIDSSSNSASLTISGLKTEDEADYYCQSYDGSNRWMFGGGTKLTVL,2.7782552,81.73815,311.8641,0.07345036,9.909086
+emibetuzumab,0,QVQLVQSGAEVKKPGASVKVSCKASGYTFTDYYMHWVRQAPGQGLEWMGRVNPNRRGTTYNQKFEGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARANWLDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCSVSSSVSSIYLHWYQQKPGKAPKLLIYSTSNLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQVYSGYPLTFGGGTKVEIK,2.7156603,82.185585,263.50082,0.212193,9.817235
+enavatuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYWMSWVRQAPGKGLEWVAEIRLKSDNYATHYAESVKGRFTISRDDSKNSLYLQMNSLRAEDTAVYYCTGYYADAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSVSTSSYSYMHWYQQKPGKAPKLLIKYASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHSWEIPYTFGGGTKVEIK,2.7861223,81.61929,267.96475,0.08133082,7.4854965
+enokizumab,1,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSYYWIEWVRQAPGQGLEWMGEILPGSGTTNPNEKFKGRVTITADESTSTAYMELSSLRSEDTAVYYCARADYYGSDYVKFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQHVITHVTWYQQKPGKAPKLLIYGTSYSYSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFYEYPLTFGGGTKVEIK,2.8782482,81.1724,237.23001,0.21255124,4.0263424
+epratuzumab,1,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTSYWLHWVRQAPGQGLEWIGYINPRNDYTEYNQNFKDKATITADESTNTAYMELSSLRSEDTAFYFCARRDITTFYWGQGTTVTVSS,DIQLTQSPSSLSASVGDRVTMSCKSSQSVLYSANHKNYLAWYQQKPGKAPKLLIYWASTRESGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCHQYLSSWTFGGGTKLEIK,2.7665164,81.417015,251.47841,0.21822014,6.98308
+eptinezumab,0,EVQLVESGGGLVQPGGSLRLSCAVSGIDLSGYYMNWVRQAPGKGLEWVGVIGINGATYYASWAKGRFTISRDNSKTTVYLQMNSLRAEDTAVYFCARGDIWGQGTLVTVSS,QVLTQSPSSLSASVGDRVTINCQASQSVYHNTYLAWYQQKPGKVPKQLIYDASTLASGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCLGSYDCTNGDCFVFGGGTKVEIK,2.797247,82.16305,300.03027,0.14268053,6.1799192
+erenumab,3,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSFGMHWVRQAPGKGLEWVAVISFDGSIKYSVDSVKGRFTISRDNSKNTLFLQMNSLRAEDTAVYYCARDRLNYYDSSGYYHYKYYGMAVWGQGTTVTVSS,QSVLTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKLLIYDNNKRPSGIPDRFSGSKSGTSTTLGITGLQTGDEADYYCGTWDSRLSAVVFGGGTKLTVL,3.0767965,81.76549,332.64868,0.19897799,11.461688
+etaracizumab,2,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYDMSWVRQAPGKGLEWVAKVSSGGGSTYYLDTVQGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARHLHGSFASWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCQASQSISNFLHWYQQRPGQAPRLLIRYRSQSISGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQSGSWPLTFGGGTKVEIK,2.7517803,81.23133,231.44902,0.1657915,10.857851
+etrolizumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFFITNNYWGWVRQAPGKGLEWVGYISYSGSTSYNPSLKSRFTISRDTSKNTFYLQMNSLRAEDTAVYYCARTGSSGYFDFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASESVDDLLHWYQQKPGKAPKLLIKYASQSISGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGNSLPNTFGQGTKVEIK,2.6933348,82.2164,234.7059,0.12692593,6.6143923
+evinacumab,1,EVQLVESGGGVIQPGGSLRLSCAASGFTFDDYAMNWVRQGPGKGLEWVSAISGDGGSTYYADSVKGRFTISRDNSKNSLYLQMNSLRAEDTAFFYCAKDLRNTIFGVVIPDAFDIWGQGTMVTVSS,DIQMTQSPSTLSASVGDRVTITCRASQSIRSWLAWYQQKPGKAPKLLIYKASSLESGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQYNSYSYTFGQGTKLEIK,2.813222,82.519264,213.16498,0.06787674,3.4792547
+evolocumab,3,EVQLVQSGAEVKKPGASVKVSCKASGYTLTSYGISWVRQAPGQGLEWMGWVSFYNGNTNYAQKLQGRGTMTTDPSTSTAYMELRSLRSDDTAVYYCARGYGMDVWGQGTTVTVSS,ESALTQPASVSGSPGQSITISCTGTSSDVGGYNSVSWYQQHPGKAPKLMIYEVSNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCNSYTSTSMVFGGGTKLTVL,3.02948,81.05181,330.30826,0.24290934,11.523221
+farletuzumab,1,EVQLVESGGGVVQPGRSLRLSCSASGFTFSGYGLSWVRQAPGKGLEWVAMISSGGSYTYYADSVKGRFAISRDNAKNTLFLQMDSLRPEDTGVYFCARHGDDPAWFAYWGQGTPVTVSS,DIQLTQSPSSLSASVGDRVTITCSVSSSISSNNLHWYQQKPGKAPKPWIYGTSNLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSYPYMYTFGQGTKVEIK,2.7243114,83.27904,311.6993,0.078663796,9.000205
+fasinumab,0,QVQLVQSGAEVKKPGASVKVSCKVSGFTLTELSIHWVRQAPGKGLEWMGGFDPEDGETIYAQKFQGRVTMTEDTSTDTAYMELTSLRSEDTAVYYCSTIFGVVTNFDNWGQGTLVTVSS,DIQMTQSPSSLSASAGDRVTITCRASQAIRNDLGWYQQKPGKAPKRLIYAAFNLQSGVPSRFSGSGSGTEFTLTISSLQPEDLASYYCQQYNRYPWTFGQGTKVEIK,2.7814507,81.6315,219.02815,0.24425663,2.393836
+fezakinumab,4,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYYMHWVRQAPGQGLEWVGWINPYTGSAFYAQKFRGRVTMTRDTSISTAYMELSRLRSDDTAVYYCAREPEKFDSDDSDVWGRGTLVTVSS,QAVLTQPPSVSGAPGQRVTISCTGSSSNIGAGYGVHWYQQLPGTAPKLLIYGDSNRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCQSYDNSLSGYVFGGGTQLTVL,2.7926958,80.79584,292.10776,0.28819555,10.498387
+ficlatuzumab,4,QVQLVQPGAEVKKPGTSVKLSCKASGYTFTTYWMHWVRQAPGQGLEWIGEINPTNGHTNYNQKFQGRATLTVDKSTSTAYMELSSLRSEDTAVYYCARNYVGSIFDYWGQGTLLTVSS,DIVMTQSPDSLAMSLGERVTLNCKASENVVSYVSWYQQKPGQSPKLLIYGASNRESGVPDRFSGSGSATDFTLTISSVQAEDVADYHCGQSYNYPYTFGQGTKLEIK,2.7435834,82.65952,198.2438,0.15294133,3.25417
+figitumumab,2,EVQLLESGGGLVQPGGSLRLSCTASGFTFSSYAMNWVRQAPGKGLEWVSAISGSGGTTFYADSVKGRFTISRDNSRTTLYLQMNSLRAEDTAVYYCAKDLGWSDSYYYYYGMDVWGQGTTVTVSS,DIQMTQFPSSLSASVGDRVTITCRASQGIRNDLGWYQQKPGKAPKRLIYAASRLHRGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQHNSYPCSFGQGTKLEIK,2.788281,82.124756,180.51022,0.09601997,4.3799057
+fletikumab,0,QVQLVQSGAEVKRPGASVKVSCKASGYTFTNDIIHWVRQAPGQRLEWMGWINAGYGNTQYSQNFQDRVSITRDTSASTAYMELISLRSEDTAVYYCAREPLWFGESSPHDYYGMDVWGQGTTVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.9377837,81.68915,208.60747,0.21958515,5.453018
+foralumab,2,QVQLVESGGGVVQPGRSLRLSCAASGFKFSGYGMHWVRQAPGKGLEWVAVIWYDGSKKYYVDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARQMGYWHFDLWGRGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPLTFGGGTKVEIK,2.839444,81.81594,260.83972,0.19905728,10.706441
+fremanezumab,4,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYWISWVRQAPGKGLEWVAEIRSESDASATHYAEAVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCLAYFDYGLAIQNYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCKASKRVTTYVSWYQQKPGQAPRLLIYGASNRYLGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCSQSYNYPYTFGQGTKLEIK,2.822254,83.52609,192.92876,0.071933,5.4982195
+fresolimumab,3,QVQLVQSGAEVKKPGSSVKVSCKASGYTFSSNVISWVRQAPGQGLEWMGGVIPIVDIANYAQRFKGRVTITADESTSTTYMELSSLRSEDTAVYYCASTLGLVLDAMDYWGQGTLVTVSS,ETVLTQSPGTLSLSPGERATLSCRASQSLGSSYLAWYQQKPGQAPRLLIYGASSRAPGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYADSPITFGQGTRLEIK,2.8625827,81.22071,212.74794,0.19864336,4.1069717
+fulranumab,2,EVQLVESGGGLVQPGGSLRLSCAASGFTLRSYSMNWVRQAPGKGLEWVSYISRSSHTIFYADSVKGRFTISRDNAKNSLYLQMDSLRDEDTAMYYCARVYSSGWHVSDYFDYWGQGILVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.806219,82.222984,217.74052,0.16146137,6.039706
+galcanezumab,0,QVQLVQSGAEVKKPGSSVKVSCKASGYTFGNYWMQWVRQAPGQGLEWMGAIYEGTGKTVYIQKFADRVTITADKSTSTAYMELSSLRSEDTAVYYCARLSDYVSGFGYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASKDISKYLNWYQQKPGKAPKLLIYYTSGYHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGDALPPTFGGGTKVEIK,2.7824233,82.50134,248.68863,0.18974136,4.6536837
+galiximab,4,QVQLQESGPGLVKPSETLSLTCAVSGGSISGGYGWGWIRQPPGKGLEWIGSFYSSSGNTYYNPSLKSQVTISTDTSKNQFSLKLNSMTAADTAVYYCVRDRLFSVVGMVYNNWFDVWGPGVLVTVSS,ESALTQPPSVSGAPGQKVTISCTGSTSNIGGYDLHWYQQLPGTAPKLLIYDINKRPSGISDRFSGSKSGTAASLAITGLQTEDEADYYCQSYDSSLNAQVFGGGTRLTVL,2.9391112,81.67523,282.262,0.2632468,7.6585283
+ganitumab,3,QVQLQESGPGLVKPSGTLSLTCAVSGGSISSSNWWSWVRQPPGKGLEWIGEIYHSGSTNYNPSLKSRVTISVDKSKNQFSLKLSSVTAADTAVYYCARWTGRTDAFDIWGQGTMVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLLHSNGYNYLDWYLQKPGQSPQLLIYLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQGTHWPLTFGQGTKVEIK,2.9183729,81.63039,200.61424,0.27248466,8.035423
+gantenerumab,2,QVELVESGGGLVQPGGSLRLSCAASGFTFSSYAMSWVRQAPGKGLEWVSAINASGTRTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARGKGNTHKPYGYVRYFDVWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGVPARFSGSGSGTDFTLTISSLEPEDFATYYCLQIYNMPITFGQGTKVEIK,2.7958164,81.39752,247.2516,0.14725192,12.340707
+gatipotuzumab,1,EVQLVESGGGLVQPGGSMRLSCVASGFPFSNYWMNWVRQAPGKGLEWVGEIRLKSNNYTTHYAESVKGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCTRHYYFDYWGQGTLVTVSS,DIVMTQSPLSNPVTPGEPASISCRSSKSLLHSNGITYFFWYLQKPGQSPQLLIYQMSNLASGVPDRFSGSGSGTDFTLRISRVEAEDVGVYYCAQNLELPPTFGQGTKVEIK,2.802785,82.73398,214.21365,0.14757246,7.781661
+gemtuzumab,0,EVQLVQSGAEVKKPGSSVKVSCKASGYTITDSNIHWVRQAPGQSLEWIGYIYPYNGGTDYNQKFKNRATLTVDNPTNTAYMELSSLRSEDTAFYYCVNGNPWLAYWGQGTLVTVSS,DIQLTQSPSTLSASVGDRVTITCRASESLDNYGIRFLTWFQQKPGKAPKLLMYAASNQGSGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQTKEVPWSFGQGTKVEVK,2.8089788,82.14719,186.84459,0.23521653,3.4187098
+gevokizumab,1,QVQLQESGPGLVKPSQTLSLTCSFSGFSLSTSGMGVGWIRQPSGKGLEWLAHIWWDGDESYNPSLKSRLTISKDTSKNQVSLKITSVTAADTAVYFCARNRYDPPWFVDWGQGTLVTVSS,DIQMTQSTSSLSASVGDRVTITCRASQDISNYLSWYQQKPGKAVKLLIYYTSKLHSGVPSRFSGSGSGTDYTLTISSLQQEDFATYFCLQGKMLPWTFGQGTKLEIK,2.7159457,82.919205,243.79504,0.23327304,5.140201
+gimsilumab,2,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRHWMHWLRQVPGKGPVWVSRINGAGTSITYADSVRGRFTISRDNANNTLFLQMNSLRADDTALYFCARANSVWFRGLFDYWGQGTPVTVSS,EIVLTQSPVTLSVSPGERVTLSCRASQSVSTNLAWYQQKLGQGPRLLIYGASTRATDIPARFSGSGSETEFTLTISSLQSEDFAVYYCQQYDKWPDTFGQGTKLEIK,2.757462,82.427216,219.89606,0.1271687,6.5476484
+girentuximab,3,DVKLVESGGGLVKLGGSLKLSCAASGFTFSNYYMSWVRQTPEKRLELVAAINSDGGITYYLDTVKGRFTISRDNAKNTLYLQMSSLKSEDTALFYCARHRSGYFSMDYWGQGTSVTVSS,DIVMTQSQRFMSTTVGDRVSITCKASQNVVSAVAWYQQKPGQSPKLLIYSASNRYTGVPDRFTGSGSGTDFTLTISNMQSEDLADFFCQQYSNYPWTFGGGTKLEIK,2.7484806,84.426346,191.206,0.073024735,1.6270533
+glembatumumab,1,QVQLQESGPGLVKPSQTLSLTCTVSGGSISSFNYYWSWIRHHPGKGLEWIGYIYYSGSTYSNPSLKSRVTISVDTSKNQFSLTLSSVTAADTAVYYCARGYNWNYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVDNNLVWYQQKPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQYNNWPPWTFGQGTKVEIK,3.0430362,82.37615,218.55515,0.30717155,8.141554
+golimumab,2,QVQLVESGGGVVQPGRSLRLSCAASGFIFSSYAMHWVRQAPGNGLEWVAFMSYDGSNKKYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDRGIAAGGNYYYYGMDVWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVYSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPFTFGPGTKVDIK,2.9906616,81.98731,222.90128,0.16606742,9.641692
+guselkumab,4,EVQLVQSGAEVKKPGESLKISCKGSGYSFSNYWIGWVRQMPGKGLEWMGIIDPSNSYTRYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARWYYKPFDVWGQGTLVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSSSNIGSGYDVHWYQQLPGTAPKLLIYGNSKRPSGVPDRFSGSKSGTSASLAITGLQSEDEADYYCASWTDGLSLVVFGGGTKLTVL,2.8155906,81.785866,266.97275,0.3195578,11.90643
+ianalumab,3,QVQLQQSGPGLVKPSQTLSLTCAISGDSVSSNSAAWGWIRQSPGRGLEWLGRIYYRSKWYNSYAVSVKSRITINPDTSKNQFSLQLNSVTPEDTAVYYCARYQWVPKIGVFDSWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQFILPEYLSWYQQKPGQAPRLLIYGSSSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQFYSSPLTFGQGTKVEIK,2.914835,81.333824,194.65506,0.29033625,7.61465
+ibalizumab,4,QVQLQQSGPEVVKPGASVKMSCKASGYTFTSYVIHWVRQKPGQGLDWIGYINPYNDGTDYDEKFKGKATLTSDTSTSTAYMELSSLRSEDTAVYYCAREKDNYATGAWFAYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERVTMNCKSSQSLLYSTNQKNYLAWYQQKPGQSPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSVQAEDVAVYYCQQYYSYRTFGGGTKLEIK,2.8810503,82.47787,188.52853,0.18792301,1.1439028
+icrucumab,2,QAQVVESGGGVVQSGRSLRLSCAASGFAFSSYGMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVRGRFTISRDNSENTLYLQMNSLRAEDTAVYYCARDHYGSGVHHYFYYGLDVWGQGTTVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPLTFGGGTKVEIK,2.9582272,81.476685,244.95442,0.18276061,10.158821
+imgatuzumab,1,QVQLVQSGAEVKKPGSSVKVSCKASGFTFTDYKIHWVRQAPGQGLEWMGYFNPNSGYSTYAQKFQGRVTITADKSTSTAYMELSSLRSEDTAVYYCARLSPGGYYVMDAWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGINNYLNWYQQKPGKAPKRLIYNTNNLQTGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQHNSFPTFGQGTKLEIK,2.6757073,81.46669,274.08975,0.19630596,6.8747206
+inclacumab,3,EVQLVESGGGLVRPGGSLRLSCAASGFTFSNYDMHWVRQATGKGLEWVSAITAAGDIYYPGSVKGRFTISRENAKNSLYLQMNSLRAGDTAVYYCARGRYSGSGSYYNDWFDPWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPLTFGGGTKVEIK,2.8951945,82.454384,245.01338,0.13783623,9.17209
+inebilizumab,3,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSSWMNWVRQAPGKGLEWVGRIYPGDGDTNYNVKFKGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCARSGFITTVRDFDYWGQGTLVTVSS,EIVLTQSPDFQSVTPKEKVTITCRASESVDTFGISFMNWFQQKPDQSPKLLIHEASNQGSGVPSRFSGSGSGTDFTLTINSLEAEDAATYYCQQSKEVPFTFGGGTKVEIK,2.567872,83.63109,164.92361,0.04437712,-0.052948236
+infliximab,1,EVKLEESGGGLVQPGGSMKLSCVASGFIFSNHWMNWVRQSPEKGLEWVAEIRSKSINSATHYAESVKGRFTISRDDSKSAVYLQMTDLRTEDTGVYYCSRNYYGSTYDYWGQGTTLTVSS,DILLTQSPAILSVSPGERVSFSCRASQFVGSSIHWYQQRTNGSPRLLIKYASESMSGIPSRFSGSGSGTDFTLSINTVESEDIADYYCQQSHSWPFTFGSGTNLEVK,2.9125648,79.10674,-111.17227,0.30942214,-6.505471
+inotuzumab,0,EVQLVQSGAEVKKPGASVKVSCKASGYRFTNYWIHWVRQAPGQGLEWIGGINPGNNYATYRRKFQGRVTMTADTSTSTVYMELSSLRSEDTAVYYCTREGYGNYGAWFAYWGQGTLVTVSS,DVQVTQSPSSLSASVGDRVTITCRSSQSLANSYGNTFLSWYLHKPGKAPQLLIYGISNRFSGVPDRFSGSGSGTDFTLTISSLQPEDFATYYCLQGTHQPYTFGQGTKVEIK,2.8090262,82.07662,205.8005,0.21438652,3.6456575
+intetumumab,2,QVQLVESGGGVVQPGRSRRLSCAASGFTFSRYTMHWVRQAPGKGLEWVAVISFDGSNKYYVDSVKGRFTISRDNSENTLYLQVNILRAEDTAVYYCAREARGSYAFDIWGQGTMVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPFTFGPGTKVDIK,2.8410187,81.652016,240.01872,0.1287487,8.067575
+ipilimumab,2,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYTMHWVRQAPGKGLEWVTFISYDGNNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAIYYCARTGWLGPFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVGSSYLAWYQQKPGQAPRLLIYGAFSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPWTFGQGTKVEIK,2.8507555,81.723305,253.66145,0.15610199,9.991716
+isatuximab,2,QVQLVQSGAEVAKPGTSVKLSCKASGYTFTDYWMQWVKQRPGQGLEWIGTIYPGDGDTGYAQKFQGKATLTADKSSKTVYMHLSSLASEDSAVYYCARGDYYGSNSLDYWGQGTSVTVSS,DIVMTQSHLSMSTSLGDPVSITCKASQDVSTVVAWYQQKPGQSPRRLIYSASYRYIGVPDRFTGSGAGTDFTFTISSVQAEDLAVYYCQQHYSPPYTFGGGTKLEIK,2.8169453,82.911995,210.19086,0.17046662,5.345314
+iscalimab,1,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVISYEESNRYHADSVKGRFTISRDNSKITLYLQMNSLRTEDTAVYYCARDGGIAAPGPDYWGQGTLVTVSS,DIVMTQSPLSLTVTPGEPASISCRSSQSLLYSNGYNYLDWYLQKPGQSPQVLISLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQARQTPFTFGPGTKVDIR,2.8768163,82.54471,248.93848,0.09379546,7.4006243
+itolizumab,3,EVQLVESGGGLVKPGGSLKLSCAASGFKFSRYAMSWVRQAPGKRLEWVATISSGGSYIYYPDSVKGRFTISRDNVKNTLYLQMSSLRSEDTAMYYCARRDYDLDYFDSWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASRDIRSYLTWYQQKPGKAPKTLIYYATSLADGVPSRFSGSGSGQDYSLTISSLESDDTATYYCLQHGESPFTLGSGTKLEIK,2.665071,83.57204,257.28363,0.094166994,7.826782
+ixekizumab,4,QVQLVQSGAEVKKPGSSVKVSCKASGYSFTDYHIHWVRQAPGQGLEWMGVINPMYGTTDYNQRFKGRVTITADESTSTAYMELSSLRSEDTAVYYCARYDYFTGTGVYWGQGTLVTVSS,DIVMTQTPLSLSVTPGQPASISCRSSRSLVHSRGNTYLHWYLQKPGQSPQLLIYKVSNRFIGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHLPFTFGQGTKLEIK,2.8979464,82.34777,162.53488,0.21297611,0.8876953
+ladiratuzumab,4,QVQLVQSGAEVKKPGASVKVSCKASGLTIEDYYMHWVRQAPGQGLEWMGWIDPENGDTEYGPKFQGRVTMTRDTSINTAYMELSRLRSDDTAVYYCAVHNAHYGTWFAYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLLHSSGNTYLEWYQQRPGQSPRPLIYKISTRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPYTFGGGTKVEIK,2.7716835,81.735535,178.49886,0.21766818,2.1255198
+lampalizumab,1,EVQLVQSGPELKKPGASVKVSCKASGYTFTNYGMNWVRQAPGQGLEWMGWINTYTGETTYADDFKGRFVFSLDTSVSTAYLQISSLKAEDTAVYYCEREGGVNNWGQGTLVTVSS,DIQVTQSPSSLSASVGDRVTITCITSTDIDDDMNWYQQKPGKVPKLLISGGNTLRPGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCLQSDSLPYTFGQGTKVEIK,2.7950757,81.54979,244.28683,0.14719322,2.3073196
+lanadelumab,0,EVQLLESGGGLVQPGGSLRLSCAASGFTFSHYIMMWVRQAPGKGLEWVSGIYSSGGITVYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAYRRIGVPRRDEFDIWGQGTMVTVSS,DIQMTQSPSTLSASVGDRVTITCRASQSISSWLAWYQQKPGKAPKLLIYKASTLESGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQYNTYWTFGQGTKVEIK,2.7413676,81.90844,272.11478,0.082285,5.5554523
+landogrozumab,2,EVQLVESGGGLVQPGGSLRLSCAASGLTFSRYPMSWVRQAPGKGLVWVSAITSSGGSTYYSDTVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCARLPDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASSSVSSSYLHWYQQKPGQAPRLLIYSTSNLVAGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQHHSGYHFTFGGGTKVEIK,2.7208314,80.97046,259.89822,0.1670812,10.256226
+lebrikizumab,2,QVTLRESGPALVKPTQTLTLTCTVSGFSLSAYSVNWIRQPPGKALEWLAMIWGDGKIVYNSALKSRLTISKDTSKNQVVLTMTNMDPVDTATYYCAGDGYYPYAMDNWGQGSLVTVSS,DIVMTQSPDSLSVSLGERATINCRASKSVDSYGNSFMHWYQQKPGQPPKLLIYLASNLESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQNNEDPRTFGGGTKVEIK,2.9280148,82.9657,219.89478,0.18594995,-1.277987
+lenzilumab,3,QVQLVQSGAEVKKPGASVKVSCKASGYSFTNYYIHWVRQAPGQRLEWMGWINAGNGNTKYSQKFQGRVTITRDTSASTAYMELSSLRSEDTAVYYCVRRQRFPYYFDYWGQGTLVTVSS,EIVLTQSPATLSVSPGERATLSCRASQSVGTNVAWYQQKPGQAPRVLIYSTSSRATGITDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQFNKSPLTFGGGTKVEIK,2.7616615,81.765976,248.5958,0.25059378,8.733372
+lexatumumab,4,EVQLVQSGGGVERPGGSLRLSCAASGFTFDDYGMSWVRQAPGKGLEWVSGINWNGGSTGYADSVKGRVTISRDNAKNSLYLQMNSLRAEDTAVYYCAKILGAGRGWYFDLWGKGTTVTVSS,SSELTQDPAVSVALGQTVRITCQGDSLRSYYASWYQQKPGQAPVLVIYGKNNRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCNSRDSSGNHVVFGGGTKLTVL,2.799014,81.94319,313.27524,0.14394806,9.318833
+ligelizumab,3,QVQLVQSGAEVMKPGSSVKVSCKASGYTFSWYWLEWVRQAPGHGLEWMGEIDPGTFTTNYNEKFKARVTFTADTSTSTAYMELSSLRSEDTAVYYCARFSHFSGSNYDYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSIGTNIHWYQQKPGQAPRLLIYYASESISGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQSWSWPTTFGGGTKVEIK,2.9885917,81.516335,183.99866,0.20606089,6.3097506
+lintuzumab,1,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDYNMHWVRQAPGQGLEWIGYIYPYNGGTGYNQKFKSKATITADESTNTAYMELSSLRSEDTAVYYCARGRPAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASESVDNYGISFMNWFQQKPGKAPKLLIYAASNQGSGVPSRFSGSGSGTDFTLTISSLQPDDFATYYCQQSKEVPWTFGQGTKVEIK,2.7175663,81.5222,249.60213,0.17300895,4.9635057
+lirilumab,3,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSFYAISWVRQAPGQGLEWMGGFIPIFGAANYAQKFQGRVTITADESTSTAYMELSSLRSDDTAVYYCARIPSGSYYYDYDMDVWGQGTTVTVSS,EIVLTQSPVTLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWMYTFGQGTKLEIK,2.950921,81.683945,227.70259,0.22369912,5.377082
+loncastuximab,2,QVQLVQPGAEVVKPGASVKLSCKTSGYTFTSNWMHWVKQAPGQGLEWIGEIDPSDSYTNYNQNFQGKAKLTVDKSTSTAYMEVSSLRSDDTAVYYCARGSNPYYYAMDYWGQGTSVTVSS,EIVLTQSPAIMSASPGERVTMTCSASSGVNYMHWYQQKPGTSPRRWIYDTSKLASGVPARFSGSGSGTSYSLTISSMEPEDAATYYCHQRGSYTFGGGTKLEIK,2.9621005,82.33404,233.9291,0.19480193,7.2235684
+lorvotuzumab,1,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSFGMHWVRQAPGKGLEWVAYISSGSFTIYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARMRKGYAMDYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQIIIHSDGNTYLEWFQQRPGQSPRRLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPHTFGQGTKVEIK,2.654986,82.29668,243.96054,0.17233321,9.382553
+lucatumumab,1,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVISYEESNRYHADSVKGRFTISRDNSKITLYLQMNSLRTEDTAVYYCARDGGIAAPGPDYWGQGTLVTVSS,DIVMTQSPLSLTVTPGEPASISCRSSQSLLYSNGYNYLDWYLQKPGQSPQVLISLGSNRASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQARQTPFTFGPGTKVDIR,2.8662512,82.31068,251.30252,0.12134157,7.972784
+lumiliximab,0,EVQLVESGGGLAKPGGSLRLSCAASGFRFTFNNYYMDWVRQAPGQGLEWVSRISSSGDPTWYADSVKGRFTISRENANNTLFLQMNSLRAEDTAVYYCASLTTGSDSWGQGVLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDIRYYLNWYQQKPGKAPKLLIYVASSLQSGVPSRFSGSGSGTEFTLTVSSLQPEDFATYYCLQVYSTPRTFGQGTKVEIK,2.7484787,81.96861,243.54167,0.13054226,4.617902
+lumretuzumab,4,QVQLVQSGAEVKKPGASVKVSCKASGYTFRSSYISWVRQAPGQGLEWMGWIYAGTGSPSYNQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARHRDYYSNSLTYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSVLNSGNQKNYLTWYQQKPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQSDYSYPYTFGQGTKLEIK,2.7810676,82.136635,238.00165,0.22984596,5.2522383
+margetuximab,2,QVQLQQSGPELVKPGASLKLSCTASGFNIKDTYIHWVKQRPEQGLEWIGRIYPTNGYTRYDPKFQDKATITADTSSNTAYLQVSRLTSEDTAVYYCSRWGGDGFYAMDYWGQGASVTVSS,DIVMTQSHKFMSTSVGDRVSITCKASQDVNTAVAWYQQKPGHSPKLLIYSASFRYTGVPDRFTGSRSGTDFTFTISSVQAEDLAVYYCQQHYTTPPTFGGGTKVEIK,2.7373672,83.10972,203.66988,0.15472072,1.0795159
+matuzumab,1,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSHWMHWVRQAPGQGLEWIGEFNPSNGRTNYNEKFKSKATMTVDTSTNTAYMELSSLRSEDTAVYYCASRDYDYDGRYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVTYMYWYQQKPGKAPKLLIYDTSNLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSHIFTFGQGTKVEIK,2.8076305,81.29401,247.88882,0.20759298,4.5121264
+mavrilimumab,2,QVQLVQSGAEVKKPGASVKVSCKVSGYTLTELSIHWVRQAPGKGLEWMGGFDPEENEIVYAQRFQGRVTMTEDTSTDTAYMELSSLRSEDTAVYYCAIVGSFSPLTLGLWGQGTMVTVSS,QSVLTQPPSVSGAPGQRVTISCTGSGSNIGAPYDVSWYQQLPGTAPKLLIYHNNKRPSGVPDRFSGSKSGTSASLAITGLQAEDEADYYCATVEAGLSGSVFGGGTKLTVL,3.0439065,79.216446,332.20425,0.23034568,7.329054
+mepolizumab,3,QVTLRESGPALVKPTQTLTLTCTVSGFSLTSYSVHWVRQPPGKGLEWLGVIWASGGTDYNSALMSRLSISKDTSRNQVVLTMTNMDPVDTATYYCARDPPSSLLRLDYWGRGTPVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSLLNSGNQKNYLAWYQQKPGQPPKLLIYGASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQNVHSFPFTFGGGTKLEIK,2.9156008,81.8395,224.41287,0.25189096,6.596465
+milatuzumab,4,QVQLQQSGSELKKPGASVKVSCKASGYTFTNYGVNWIKQAPGQGLQWMGWINPNTGEPTFDDDFKGRFAFSLDTSVSTAYLQISSLKADDTAVYFCSRSRGKNEAWFAYWGQGTLVTVSS,DIQLTQSPLSLPVTLGQPASISCRSSQSLVHRNGNTYLHWFQQRPGQSPRLLIYTVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYFCSQSSHVPPTFGAGTRLEIK,2.808508,82.34951,147.7225,0.26211232,3.205565
+mirikizumab,0,QVQLVQSGAEVKKPGSSVKVSCKASGYKFTRYVMHWVRQAPGQGLEWMGYINPYNDGTNYNEKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARNWDTGLWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCKASDHILKFLTWYQQKPGKAPKLLIYGATSLETGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQMYWSTPFTFGGGTKVEIK,2.755672,82.53875,222.34589,0.22518556,5.2157927
+mirvetuximab,4,QVQLVQSGAEVVKPGASVKISCKASGYTFTGYFMNWVKQSPGQSLEWIGRIHPYDGDTFYNQKFQGKATLTVDKSSNTAHMELLSLTSEDFAVYYCTRYDGSRAMDYWGQGTTVTVSS,DIVLTQSPLSLAVSLGQPAIISCKASQSVSFAGTSLMHWYHQKPGQQPRLLIYRASNLEAGVPDRFSGSGSKTDFTLTISPVEAEDAATYYCQQSREYPYTFGGGTKLEIK,2.8420374,82.21968,157.79726,0.2301769,2.2281141
+mitazalimab,4,EVQLLESGGGLVQPGGSLRLSCAASGFTFSTYGMHWVRQAPGKGLEWLSYISGGSSYIFYADSVRGRFTISRDNSENALYLQMNSLRAEDTAVYYCARILRGGSGMDLWGQGTLVTVSS,QSVLTQPPSASGTPGQRVTISCTGSSSNIGAGYNVYWYQQLPGTAPKLLIYGNINRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCAAWDKSISGLVFGGGTKLTVL,2.7847874,81.972855,341.03146,0.14168635,10.773299
+mogamulizumab,1,EVQLVESGGDLVQPGRSLRLSCAASGFIFSNYGMSWVRQAPGKGLEWVATISSASTYSYYPDSVKGRFTISRDNAKNSLYLQMNSLRVEDTALYYCGRHSDGNFAFGYWGQGTLVTVSS,DVLMTQSPLSLPVTPGEPASISCRSSRNIVHINGDTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSLLPWTFGQGTKVEIK,2.7218637,82.848915,180.10812,0.14396456,6.8957243
+monalizumab,0,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYWMNWVRQAPGQGLEWMGRIDPYDSETHYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARGGYDFDVGTLYWFFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASENIYSYLAWYQQKPGKAPKLLIYNAKTLAEGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHHYGTPRTFGGGTKVEIK,2.8805943,82.24907,210.07983,0.18249448,4.06326
+mosunetuzumab,4,EVQLVQSGAEVKKPGASVKVSCKASGYTFTNYYIHWVRQAPGQGLEWIGWIYPGDGNTKYNEKFKGRATLTADTSTSTAYLELSSLRSEDTAVYYCARDSYSNYYFDYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSQSLLNSRTRKNYLAWYQQKPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCTQSFILRTFGQGTKVEIK,2.7879326,82.49405,183.30377,0.18548268,2.3066573
+motavizumab,3,QVTLRESGPALVKPTQTLTLTCTFSGFSLSTAGMSVGWIRQPPGKALEWLADIWWDDKKHYNPSLKDRLTISKDTSKNQVVLKVTNMDPADTATYYCARDMIFNFYFDVWGQGTTVTVSS,DIQMTQSPSTLSASVGDRVTITCSASSRVGYMHWYQQKPGKAPKLLIYDTSKLASGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCFQGSGYPFTFGGGTKVEIK,2.9099138,81.779465,220.3361,0.22441354,6.507618
+muromonab,4,QVQLQQSGAELARPGASVKMSCKASGYTFTRYTMHWVKQRPGQGLEWIGYINPSRGYTNYNQKFKDKATLTTDKSSSTAYMQLSSLTSEDSAVYYCARYYDDHYCLDYWGQGTTLTVSS,QIVLTQSPAIMSASPGEKVTMTCSASSSVSYMNWYQQKSGTSPKRWIYDTSKLASGVPAHFRGSGSGTSYSLTISGMEAEDAATYYCQQWSSNPFTFGSGTKLEIK,2.7144873,82.371086,222.37325,0.24117967,9.481355
+natalizumab,0,QVQLVQSGAEVKKPGASVKVSCKASGFNIKDTYIHWVRQAPGQRLEWMGRIDPANGYTKYDPKFQGRVTITADTSASTAYMELSSLRSEDTAVYYCAREGYYGNYGVYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKTSQDINKYMAWYQQTPGKAPRLLIHYTSALQPGIPSRFSGSGSGRDYTFTISSLQPEDIATYYCLQYDNLWTFGQGTKVEIK,2.8109834,82.60207,189.21143,0.19229572,1.6841125
+necitumumab,3,QVQLQESGPGLVKPSQTLSLTCTVSGGSISSGDYYWSWIRQPPGKGLEWIGYIYYSGSTDYNPSLKSRVTMSVDTSKNQFSLKVNSVTAADTAVYYCARVSIFGVGTFDYWGQGTLVTVSS,EIVMTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCHQYGSTPLTFGGGTKAEIK,3.1052117,81.85388,209.36623,0.23290068,7.5789404
+nesvacumab,2,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYDIHWVRQATGKGLEWVSAIGPAGDTYYPGSVKGRFTISRENAKNSLYLQMNSLRAGDTAVYYCARGLITFGGLIAPFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSTYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQHYDNSQTFGQGTKVEIK,2.9024463,82.07873,208.5629,0.13653146,6.283535
+nimotuzumab,1,QVQLQQSGAEVKKPGSSVKVSCKASGYTFTNYYIYWVRQAPGQGLEWIGGINPTSGGSNFNEKFKTRVTITADESSTTAYMELSSLRSEDTAFYFCTRQGLWFDSDGRGFDFWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQNIVHSNGNTYLDWYQQTPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCFQYSHVPWTFGQGTKLQIT,2.8240876,80.656746,259.79663,0.24075958,3.736742
+nivolumab,3,QVQLVESGGGVVQPGRSLRLDCKASGITFSNSGMHWVRQAPGKGLEWVAVIWYDGSKRYYADSVKGRFTISRDNSKNTLFLQMNSLRAEDTAVYYCATNDDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQSSNWPRTFGQGTKVEIK,2.8118675,82.470505,252.77312,0.14857286,9.49428
+obexelimab,4,EVQLVESGGGLVKPGGSLKLSCAASGYTFTSYVMHWVRQAPGKGLEWIGYINPYNDGTKYNEKFQGRVTISSDKSISTAYMELSSLRSEDTAMYYCARGTYYYGTRVFDYWGQGTLVTVSS,DIVMTQSPATLSLSPGERATLSCRSSKSLQNVNGNTYLYWFQQKPGQSPQLLIYRMSNLNSGVPDRFSGSGSGTEFTLTISSLEPEDFAVYYCMQHLEYPITFGAGTKLEIK,2.9052982,82.21544,146.10823,0.1709504,3.258202
+obiltoxaximab,1,QVQLQQSGPELKKPGASVKVSCKDSGYAFSSSWMNWVRQAPGQGLEWIGRIYPGDGDTNYNGKFQGRVTITADKSSSTAYMELSSLRSEDTAVYFCARSGLLRYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDIRNYLNWYQQKPGKAVKLLIYYTSRLLPGVPSRFSGSGSGTDYSLTISSQEQEDIGTYFCQQGNTLPWTFGQGTKVEIR,2.6555147,81.19706,215.7485,0.23913702,7.384162
+obinutuzumab,4,QVQLVQSGAEVKKPGSSVKVSCKASGYAFSYSWINWVRQAPGQGLEWMGRIFPGDGDTDYNGKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCARNVFDGYWLVYWGQGTLVTVSS,DIVMTQTPLSLPVTPGEPASISCRSSKSLLHSNGITYLYWYLQKPGQSPQLLIYQMSNLVSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCAQNLELPYTFGGGTKVEIK,2.7991629,82.33889,170.48376,0.2386061,5.0060844
+ocrelizumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYTFTSYNMHWVRQAPGKGLEWVGAIYPGNGDTSYNQKFKGRFTISVDKSKNTLYLQMNSLRAEDTAVYYCARVVYYSNSYWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASSSVSYMHWYQQKPGKAPKPLIYAPSNLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQWSFNPPTFGQGTKVEIK,2.8150194,82.14286,240.05502,0.13458264,5.582757
+ofatumumab,2,EVQLVESGGGLVQPGRSLRLSCAASGFTFNDYAMHWVRQAPGKGLEWVSTISWNSGSIGYADSVKGRFTISRDNAKKSLYLQMNSLRAEDTALYYCAKDIQYGNYYYGMDVWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPITFGQGTRLEIK,2.898296,82.08743,195.98053,0.14392407,8.270056
+olaratumab,3,QLQLQESGPGLVKPSETLSLTCTVSGGSINSSSYYWGWLRQSPGKGLEWIGSFFYTGSTYYNPSLRSRLTISVDTSKNQFSLMLSSVTAADTAVYYCARQSTYYYGSGNYYGWFDRWDQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPAFGQGTKVEIK,3.2098005,81.81041,236.11845,0.24459155,8.208352
+oleclumab,4,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSYAYSWVRQAPGKGLEWVSAISGSGGRTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARLGYGRVDEWGRGTLVTVSS,QSVLTQPPSASGTPGQRVTISCSGSLSNIGRNPVNWYQQLPGTAPKLLIYLDNLRLSGVPDRFSGSKSGTSASLAISGLQSEDEADYYCATWDDSHPGWTFGGGTKLTVL,2.6845934,81.83332,334.85196,0.14688957,14.055673
+olokizumab,1,EVQLVESGGGLVQPGGSLRLSCAASGFNFNDYFMNWVRQAPGKGLEWVAQMRNKNYQYGTYYAESLEGRFTISRDDSKNSLYLQMNSLKTEDTAVYYCARESYYGFTSYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDIGISLSWYQQKPGKAPKLLIYNANNLADGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCLQHNSAPYTFGQGTKLEIK,2.7759714,82.367455,209.8053,0.07064463,3.8574705
+omalizumab,0,EVQLVESGGGLVQPGGSLRLSCAVSGYSITSGYSWNWIRQAPGKGLEWVASITYDGSTNYNPSLKGRITISRDDSKNTFYLQMNSLRAEDTAVYYCARGSHYFGHWHFAVWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCRASQSVDYDGDSYMNWYQQKPGKAPKLLIYAASYLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSHEDPYTFGQGTKVEIK,2.84906,82.10592,242.35345,0.10106245,4.0797405
+omburtamab,2,QVQLQQSGAELVKPGASVKLSCKASGYTFTNYDINWVRQRPEQGLEWIGWIFPGDGSTQYNEKFKGKATLTTDTSSSTAYMQLSRLTSEDSAVYFCARQTTATWFAYWGQGTLVTVSA,DIVMTQSPATLSVTPGDRVSLSCRASQSISDYLHWYQQKSHESPRLLIKYASQSISGIPSRFSGSGSGSDFTLSINSVEPEDVGVYYCQNGHSFPLTFGAGTKLELK,2.721797,81.79152,205.65453,0.21653408,3.1444924
+onartuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYTFTSYWLHWVRQAPGKGLEWVGMIDPSNSDTRFNPNFKDRFTISADTSKNTAYLQMNSLRAEDTAVYYCATYRSYVTPLDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSSQSLLYTSSQKNYLAWYQQKPGKAPKLLIYWASTRESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYAYPWTFGQGTKVEIK,2.7905567,81.64395,245.74075,0.15121907,4.9728537
+opicinumab,2,EVQLLESGGGLVQPGGSLRLSCAASGFTFSAYEMKWVRQAPGKGLEWVSVIGPSGGFTFYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCATEGDNDAFDIWGQGTTVTVSS,DIQMTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPMYTFGQGTKLEIK,2.8138196,81.54585,217.97475,0.08786006,6.640968
+orticumab,3,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNAWMSWVRQAPGKGLEWVSSISVGGHRTYYADSVKGRSTISRDNSKNTLYLQMNSLRAEDTAVYYCARIRVGPSGGAFDYWGQGTLVTVSS,QSVLTQPPSASGTPGQRVTISCSGSNTNIGKNYVSWYQQLPGTAPKLLIYANSNRPSGVPDRFSGSKSGTSASLAISGLRSEDEADYYCASWDASLNGWVFGGGTKLTVL,2.8609288,82.384315,335.57397,0.15765895,12.000846
+osocimab,1,EVQLLESGGGLVQPGGSLRLSCAASGFTFSQYGMDWVRQAPGKGLEWVSGIGPSGGSTVYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCTRGGPYYYYGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQADSFPVTFGGGTKVEIK,2.7297795,83.456955,272.1829,0.021038413,5.785137
+otelixizumab,1,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSFPMAWVRQAPGKGLEWVSTISTSGGRTYYRDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKFRQYSGGFDYWGQGTLVTVSS,DIQLTQPNSVSTSLGSTVKLSCTLSSGNIENNYVHWYQLYEGRSPTTMIYDDDKRPDGVPDRFSGSIDRSSNSAFLTIHNVAIEDEAIYFCHSYVSSFNVFGGGTKLTVL,2.7229495,83.365234,239.66095,0.13482103,10.409654
+otlertuzumab,3,EVQLVQSGAEVKKPGESLKISCKGSGYSFTGYNMNWVRQMPGKGLEWMGNIDPYYGGTTYNRKFKGQVTISADKSISTAYLQWSSLKASDTAMYYCARSVGPFDSWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASENVYSYLAWYQQKPGQAPRLLIYFAKTLAEGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQHHSDNPWTFGQGTKVEIK,2.7971694,82.079346,191.07126,0.21396972,6.938141
+ozanezumab,4,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYWMHWVRQAPGQGLEWIGNINPSNGGTNYNEKFKSKATMTRDTSTSTAYMELSSLRSEDTAVYYCELMQGYWGQGTLVTVSS,DIVMTQSPLSNPVTLGQPVSISCRSSKSLLYKDGKTYLNWFLQRPGQSPQLLIYLMSTRASGVPDRFSGGGSGTDFTLKISRVEAEDVGVYYCQQLVEYPLTFGQGTKLEIK,2.7846463,82.39821,218.78662,0.19452262,3.0839543
+palivizumab,3,QVTLRESGPALVKPTQTLTLTCTFSGFSLSTSGMSVGWIRQPPGKALEWLADIWWDDKKDYNPSLKSRLTISKDTSKNQVVLKVTNMDPADTATYYCARSMITNWYFDVWGAGTTVTVSS,DIQMTQSPSTLSASVGDRVTITCKCQLSVGYMHWYQQKPGKAPKLLIYDTSKLASGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCFQGSGYPFTFGGGTKLEIK,2.948156,81.58638,225.90224,0.24040802,6.9408445
+pamrevlumab,1,EGQLVQSGGGLVHPGGSLRLSCAGSGFTFSSYGMHWVRQAPGKGLEWVSGIGTGGGTYSTDSVKGRFTISRDNAKNSLYLQMNSLRAEDMAVYYCARGDYYGSGSFFDCWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNSYPPTFGQGTKLEIK,2.71876,83.14099,260.61102,0.16376278,8.831088
+panitumumab,1,QVQLQESGPGLVKPSETLSLTCTVSGGSVSSGDYYWTWIRQSPGKGLEWIGHIYYSGNTNYNPSLKSRLTISIDTSKTQFSLKLSSVTAADTAIYYCVRDRVTGAFDIWGQGTMVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASNLETGVPSRFSGSGSGTDFTFTISSLQPEDIATYFCQHFDHLPLAFGGGTKVEIK,2.7772164,83.481384,227.51486,0.21083048,2.5293274
+panobacumab,1,EEQVVESGGGFVQPGGSLRLSCAASGFTFSPYWMHWVRQAPGKGLVWVSRINSDGSTYYADSVKGRFTISRDNARNTLYLQMNSLRAEDTAVYYCARDRYYGPEMWGQGTMVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLVYSDGNTYLNWFQQRPGQSPRRLIYKVSNRDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQGTHWPLTFGGGTKVEIK,2.790153,82.23086,259.49734,0.1345953,9.132188
+parsatuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYTFIDYYMNWVRQAPGKGLEWVGDINLDNSGTHYNQKFKGRFTISRDKSKNTAYLQMNSLRAEDTAVYYCAREGVYHDYDDYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRTSQSLVHINAITYLHWYQQKPGKAPKLLIYRVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCGQSTHVPLTFGQGTKVEIK,2.7408931,81.09859,197.86182,0.14856665,2.9794388
+patritumab,3,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVETSKNQFSLKLSSVTAADTAVYYCARDKWTWYFDLWGRGTLVTVSS,DIEMTQSPDSLAVSLGERATINCRSSQSVLYSSSNRNYLAWYQQNPGQPPKLLIYWASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQYYSTPRTFGQGTKVEIK,2.911863,81.90061,176.32124,0.26392636,7.177244
+pembrolizumab,3,QVQLVQSGVEVKKPGASVKVSCKASGYTFTNYYMYWVRQAPGQGLEWMGGINPSNGGTNFNEKFKNRVTLTTDSSTTTAYMELKSLQFDDTAVYYCARRDYRFDMGFDYWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCRASKGVSTSGYSYLHWYQQKPGQAPRLLIYLASYLESGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQHSRDLPLTFGGGTKVEIK,2.5337825,82.49075,-29.24247,0.3397153,-5.6637354
+pertuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFTFTDYTMDWVRQAPGKGLEWVADVNPNSGGSIYNQRFKGRFTLSVDRSKNTLYLQMNSLRAEDTAVYYCARNLGPSFYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVSIGVAWYQQKPGKAPKLLIYSASYRYTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYIYPYTFGQGTKVEIK,2.7203498,82.71892,244.60577,0.10443851,4.098691
+pidilizumab,1,QVQLVQSGSELKKPGASVKISCKASGYTFTNYGMNWVRQAPGQGLQWMGWINTDSGESTYAEEFKGRFVFSLDTSVNTAYLQITSLTAEDTGMYFCVRVGYDALDYWGQGTLVTVSS,EIVLTQSPSSLSASVGDRVTITCSARSSVSYMHWFQQKPGKAPKLWIYRTSNLASGVPSRFSGSGSGTSYCLTINSLQPEDFATYYCQQRSSFPLTFGGGTKLEIK,2.9138544,81.193794,248.63977,0.23398873,7.618718
+pinatuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYEFSRSWMNWVRQAPGKGLEWVGRIYPGDGDTNYSGKFKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCARDGSSWDWYFDVWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSIVHSVGNTFLEWYQQKPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCFQGSQFPYTFGQGTKVEIK,2.69723,82.09589,225.62271,0.14659818,5.60373
+plozalizumab,4,EVQLVESGGGLVKPGGSLRLSCAASGFTFSAYAMNWVRQAPGKGLEWVGRIRTKNNNYATYYADSVKDRFTISRDDSKNTLYLQMNSLKTEDTAVYYCTTFYGNGVWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCKSSQSLLDSDGKTFLNWFQQRPGQSPRRLIYLVSKLDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCWQGTHFPYTFGQGTRLEIK,2.701438,83.2996,205.32439,0.096123636,5.3291254
+polatuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYTFSSYWIEWVRQAPGKGLEWIGEILPGGGDTNYNEIFKGRATFSADTSKNTAYLQMNSLRAEDTAVYYCTRRVPIRLDYWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCKASQSVDYEGDSFLNWYQQKPGKAPKLLIYAASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSNEDPLTFGQGTKVEIK,2.6679807,82.517456,223.26282,0.12158479,3.6546164
+ponezumab,4,QVQLVQSGAEVKKPGASVKVSCKASGYYTEAYYIHWVRQAPGQGLEWMGRIDPATGNTKYAPRLQDRVTMTRDTSTSTVYMELSSLRSEDTAVYYCASLYSLPVYWGQGTTVTVSS,DVVMTQSPLSLPVTLGQPASISCKSSQSLLYSDAKTYLNWFQQRPGQSPRRLIYQISRLDPGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQGTHYPVLFGQGTRLEIK,2.808843,81.79986,186.60886,0.24143001,7.8042965
+prasinezumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYGMSWVRQAPGKGLEWVASISSGGGSTYYPDNVKGRFTISRDDAKNSLYLQMNSLRAEDTAVYYCARGGAGIDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSIQTLLYSSNQKNYLAWFQQKPGKAPKLLIYWASIRKSGVPSRFSGSGSGTDFTLTISSLQPEDLATYYCQQYYSYPLTFGGGTKLEIK,2.7195194,82.21864,298.097,0.07804036,8.564603
+prezalumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYWMSWVRQAPGKGLEWVAYIKQDGNEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGILWFGDLPTFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISNWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYDSYPRTFGQGTKVEIK,2.7584932,81.7301,261.39166,0.07597914,5.0904036
+prolgolimab,4,QVQLVQSGGGLVQPGGSLRLSCAASGFTFSSYWMYWVRQVPGKGLEWVSAIDTGGGRTYYADSVKGRFAISRVNAKNTMYLQMNSLRAEDTAVYYCARDEGGGTGWGVLKDWPYGLDAWGQGTLVTVSS,QPVLTQPLSVSVALGQTARITCGGNNIGSKNVHWYQQKPGQAPVLVIYRDSNRPSGIPERFSGSNSGNTATLTISRAQAGDEADYYCQVWDSSTAVFGTGTKLTVL,2.8566556,81.38191,287.28665,0.13288502,8.895459
+quilizumab,2,EVQLVESGGGLVQPGGSLRLSCAASGFTFSDYGIAWVRQAPGKGLEWVAFISDLAYTIYYADTVTGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDNWDAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRSSQSLVHNNANTYLHWYQQKPGKAPKLLIYKVSNRFSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCSQNTLVPWTFGQGTKVEIK,2.7191093,82.20717,234.93275,0.0775139,5.1391625
+racotumomab,1,QVQLQQSGAELVKPGASVKLSCKASGYTFTSYDINWVRQRPEQGLEWIGWIFPGDGSTKYNEKFKGKATLTTDKSSSTAYMQLSRLTSEDSAVYFCAREDYYDNSYYFDYWGQGTTLTVSS,DIQMTQTTSSLSASLGDRVTISCRASQDISNYLNWYQQKPDGTVKLLIYYTSRLHSGVPSRFSGSGSGTDYSLTISNLEQEDIATYFCQQGNTLPWTFGGGTKLEIK,2.801315,81.5533,206.09651,0.21421307,6.973669
+radretumab,2,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSFSMSWVRQAPGKGLEWVSSISGSSGTTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCAKPFPYFDYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSFLAWYQQKPGQAPRLLIYYASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQTGRIPPTFGQGTKVEIK,2.8168952,81.659424,226.03734,0.14556752,10.946334
+ramucirumab,0,EVQLVQSGGGLVKPGGSLRLSCAASGFTFSSYSMNWVRQAPGKGLEWVSSISSSSSYIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARVTDAFDIWGQGTMVTVSS,DIQMTQSPSSVSASIGDRVTITCRASQGIDNWLGWYQQKPGKAPKLLIYDASNLDTGVPSRFSGSGSGTYFTLTISSLQAEDFAVYFCQQAKAFPPTFGGGTKVDIK,2.7563558,82.311775,316.25348,0.08326374,8.135921
+ranibizumab,0,EVQLVESGGGLVQPGGSLRLSCAASGYDFTHYGMNWVRQAPGKGLEWVGWINTYTGEPTYAADFKRRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCAKYPYYYGTSHWYFDVWGQGTLVTVSS,DIQLTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKVLIYFTSSLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSTVPWTFGQGTKVEIK,2.8729043,81.692726,215.20364,0.13205692,4.562689
+relatlimab,2,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSDYYWNWIRQPPGKGLEWIGEINHRGSTNSNPSLKSRVTLSLDTSKNQFSLKLRSVTAADTAVYYCAFGYSDYEYNWFDPWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSISSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPLTFGQGTNLEIK,2.9751842,81.958984,173.03134,0.30794823,6.641935
+reslizumab,1,EVQLVESGGGLVQPGGSLRLSCAVSGLSLTSNSVNWIRQAPGKGLEWVGLIWSNGDTDYNSAIKSRFTISRDTSKSTVYLQMNSLRAEDTAVYYCAREYYGYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCLASEGISSYLAWYQQKPGKAPKLLIYGANSLQTGVPSRFSGSGSATDYTLTISSLQPEDFATYYCQQSYKFPNTFGQGTKVEVK,2.825058,83.04397,268.6819,0.118982345,5.5315742
+rilotumumab,1,QVQLQESGPGLVKPSETLSLTCTVSGGSISIYYWSWIRQPPGKGLEWIGYVYYSGSTNYNPSLKSRVTISVDTSKNQFSLKLNSVTAADTAVYYCARGGYDFWSGYFDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVDSNLAWYRQKPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQYINWPPITFGQGTRLEIK,3.0435998,82.32663,211.53934,0.31763688,7.4115095
+rinucumab,3,QLQLQESGPGLVKPSETLSLTCTVSGGSITSSSYYWGWIRQPPGKGLEWIGSIYYRGSTNYNPSLKSRVTISVDSSKNQFYLKVSSVTAVDTAVYYCARQNGAARPSWFDPWGQGTLVTVSS,EIVLTQSPDTISLSPGERATLSCRASQSISSIYLAWYQQKPGQAPRLLIYGASSRVTGIPDRFSVSGSGTDFTLTISRLEPEDFAVYYCQHYGISPFTFGPGTKVDIR,2.9918878,81.76463,184.02979,0.2149894,7.41109
+risankizumab,1,QVQLVQSGAEVKKPGSSVKVSCKASGYTFTDQTIHWMRQAPGQGLEWIGYIYPRDDSPKYNENFKGKVTITADKSTSTAYMELSSLRSEDTAVYYCAIPDRSGYAWFIYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASRDVAIAVAWYQQKPGKVPKLLIYWASTRHTGVPSRFSGSGSRTDFTLTISSLQPEDVADYFCHQYSSYPFTFGSGTKLEIK,2.8236074,81.553604,221.60715,0.2188062,4.5479717
+rituximab,2,QVQLQQPGAELVKPGASVKMSCKASGYTFTSYNMHWVKQTPGRGLEWIGAIYPGNGDTSYNQKFKGKATLTADKSSSTAYMQLSSLTSEDSAVYYCARSTYYGGDWYFNVWGAGTTVTVSA,QIVLSQSPAILSASPGEKVTMTCRASSSVSYIHWFQQKPGSSPKPWIYATSNLASGVPVRFSGSGSGTSYSLTISRVEAEDAATYYCQQWTSNPPTFGGGTKLEIK,2.9179204,81.52937,270.23566,0.2582897,9.959574
+robatumumab,2,EVQLVQSGGGLVKPGGSLRLSCAASGFTFSSFAMHWVRQAPGKGLEWISVIDTRGATYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARLGNFYYGMDVWGQGTTVTVSS,EIVLTQSPGTLSVSPGERATLSCRASQSIGSSLHWYQQKPGQAPRLLIKYASQSLSGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCHQSSRLPHTFGQGTKVEIK,2.7279186,81.09694,201.4277,0.15137246,7.8230705
+romosozumab,0,EVQLVQSGAEVKKPGASVKVSCKASGYTFTDYNMHWVRQAPGQGLEWMGEINPNSGGAGYNQKFKGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARLGYDDIYDDWYFDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISNYLNWYQQKPGKAPKLLIYYTSRLLSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQGDTLPYTFGGGTKVEIK,2.905832,81.99469,206.69955,0.17423376,1.7318411
+rontalizumab,0,EVQLVESGGGLVQPGGSLRLSCATSGYTFTEYIIHWVRQAPGKGLEWVASINPDYDITNYNQRFKGRFTISLDKSKRTAYLQMNSLRAEDTAVYYCASWISDFFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSVSTSSYSYMHWYQQKPGKAPKVLISYASNLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHSWGIPRTFGQGTKVEIK,2.7393777,81.64831,228.19751,0.15873222,6.1980734
+rovalpituzumab,3,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYGMNWVRQAPGQGLEWMGWINTYTGEPTYADDFKGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARIGDSSPSDYWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCKASQSVSNDVVWYQQKPGQAPRLLIYYASNRYTGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQDYTSPWTFGQGTKLEIK,2.9237125,81.38546,240.15471,0.20813754,4.398694
+rozanolixizumab,1,EVPLVESGGGLVQPGGSLRLSCAVSGFTFSNYGMVWVRQAPGKGLEWVAYIDSDGDNTYYRDSVKGRFTISRDNAKSSLYLQMNSLRAEDTAVYYCTTGIVRPFLYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKSSQSLVGASGKTYLYWLFQKPGKAPKRLIYLVSTLDSGIPSRFSGSGSGTEFTLTISSLQPEDFATYYCLQGTHFPHTFGQGTKLEIK,2.7283201,82.59348,280.42456,0.084577024,8.311658
+sarilumab,0,EVQLVESGGGLVQPGRSLRLSCAASRFTFDDYAMHWVRQAPGKGLEWVSGISWNSGRIGYADSVKGRFTISRDNAENSLFLQMNGLRAEDTALYYCAKGRDSFDIWGQGTMVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYGASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFASYYCQQANSFPYTFGQGTKLEIK,2.6505418,81.860565,256.22638,0.11109108,7.5615664
+satralizumab,1,QVQLQESGPGLVKPSETLSLTCAVSGHSISHDHAWSWVRQPPGEGLEWIGFISYSGITNYNPSLQGRVTISRDNSKNTLYLQMNSLRAEDTAVYYCARSLARTTAMDYWGEGTLVTVSS,DIQMTQSPSSLSASVGDSVTITCQASTDISSHLNWYQQKPGKAPELLIYYGSHLLSGVPSRFSGSGSGTDFTFTISSLEAEDAATYYCGQGNRLPYTFGQGTKVEIE,2.7738223,82.288734,195.24707,0.12503919,2.652556
+secukinumab,2,EVQLVESGGGLVQPGGSLRLSCAASGFTFSNYWMNWVRQAPGKGLEWVAAINQDGSEKYYVGSVKGRFTISRDNAKNSLYLQMNSLRVEDTAVYYCVRDYYDILTDYYIHYWYFDLWGRGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPCTFGQGTRLEIK,2.9517584,81.5381,192.32849,0.20903035,7.5774813
+selicrelumab,1,QVQLVQSGAEVKKPGASVKVSCKASGYTFTGYYMHWVRQAPGQGLEWMGWINPDSGGTNYAQKFQGRVTMTRDTSISTAYMELNRLRSDDTAVYYCARDQPLGYCTNGVCSYFDYWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGIYSWLAWYQQKPGKAPNLLIYTASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANIFPLTFGGGTKVEIK,2.8596544,80.55041,251.55148,0.22221,5.582351
+seribantumab,3,EVQLLESGGGLVQPGGSLRLSCAASGFTFSHYVMAWVRQAPGKGLEWVSSISSSGGWTLYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCTRGLKMATIFDYWGQGTLVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGSYNVVSWYQQHPGKAPKLIIYEVSQRPSGVSNRFSGSKSGNTASLTISGLQTEDEADYYCCSYAGSSIFVIFGGGTKVTVL,2.9388356,82.36366,304.46796,0.12469493,11.002164
+setrusumab,4,QVQLVESGGGLVQPGGSLRLSCAASGFTFRSHWLSWVRQAPGKGLEWVSNINYDGSSTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDTYLHFDYWGQGTLVTVSS,DIALTQPASVSGSPGQSITISCTGTSSDVGDINDVSWYQQHPGKAPKLMIYDVNNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCQSYAGSYLSEVFGGGTKLTVL,2.6701975,82.61224,339.20322,0.07535085,8.9027605
+sifalimumab,3,QVQLVQSGAEVKKPGASVKVSCKASGYTFTSYSISWVRQAPGQGLEWMGWISVYNGNTNYAQKFQGRVTMTTDTSTSTAYLELRSLRSDDTAVYYCARDPIAAGYWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSTYLAWYQQKPGQAPRLLIYGASSRATGIPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQYGSSPRTFGQGTKVEIK,2.881883,81.096664,266.69897,0.2572819,10.411262
+siltuximab,3,EVQLVESGGKLLKPGGSLKLSCAASGFTFSSFAMSWFRQSPEKRLEWVAEISSGGSYTYYPDTVTGRFTISRDNAKNTLYLEMSSLRSEDTAMYYCARGLWGYYALDYWGQGTSVTVSS,QIVLIQSPAIMSASPGEKVTMTCSASSSVSYMYWYQQKPGSSPRLLIYDTSNLASGVPVRFSGSGSGTSYSLTISRMEAEDAATYYCQQWSGYPYTFGGGTKLEIK,2.9141188,82.82573,203.39674,0.13461243,7.053852
+simtuzumab,4,QVQLVQSGAEVKKPGASVKVSCKASGYAFTYYLIEWVRQAPGQGLEWIGVINPGSGGTNYNEKFKGRATITADKSTSTAYMELSSLRSEDTAVYFCARNWMNFDYWGQGTTVTVSS,DIVMTQTPLSLSVTPGQPASISCRSSKSLLHSNGNTYLYWFLQKPGQSPQFLIYRMSNLASGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQHLEYPYTFGGGTKVEIK,2.795718,82.26013,202.18964,0.19921479,3.5328398
+sintilimab,0,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSYAISWVRQAPGQGLEWMGLIIPMFDTAGYAQKFQGRVAITVDESTSTAYMELSSLRSEDTAVYYCARAEHSSTGTFDYWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLISAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANHLPFTFGGGTKVEIK,2.8658314,82.42244,239.39394,0.24853382,4.740943
+sirukumab,2,EVQLVESGGGLVQPGGSLRLSCAASGFTFSPFAMSWVRQAPGKGLEWVAKISPGGSWTYYSDTVTGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARQLWGYYALDIWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCSASISVSYMYWYQQKPGQAPRLLIYDMSNLASGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCMQWSGYPYTFGGGTKVEIK,2.9171019,81.50329,259.11734,0.099734396,8.792252
+solanezumab,1,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRYSMSWVRQAPGKGLELVAQINSVGNSTYYPDTVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCASGDYWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLIYSDGNAYLHWFLQKPGQSPRLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCSQSTHVPWTFGQGTKVEIK,2.7697735,83.02067,247.7112,0.13932335,8.920283
+spartalizumab,3,EVQLVQSGAEVKKPGESLRISCKGSGYTFTTYWMHWVRQATGQGLEWMGNIYPGTGGSNFDEKFKNRVTITADKSTSTAYMELSSLRSEDTAVYYCTRWTTGTGAYWGQGTTVTVSS,EIVLTQSPATLSLSPGERATLSCKSSQSLLDSGNQKNFLTWYQQKPGQAPRLLIYWASTRESGVPSRFSGSGSGTDFTFTISSLEAEDAATYYCQNDYSYPYTFGQGTKVEIK,2.8138664,81.40266,249.03557,0.22354577,6.960238
+sutimlimab,3,EVQLVESGGGLVKPGGSLRLSCAASGFTFSNYAMSWVRQAPGKGLEWVATISSGGSHTYYLDSVKGRFTISRDNSKNTLYLQMNSLRAEDTALYYCARLFTGYAMDYWGQGTLVTVSS,QIVLTQSPATLSLSPGERATMSCTASSSVSSSYLHWYQQKPGKAPKLWIYSTSNLASGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCHQYYRLPPITFGQGTKLEIK,2.7942111,82.25334,288.67825,0.1087366,9.73449
+tabalumab,2,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARGYYDILTGYYYYFDYWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSRYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDSTLTISSLEPEDFAVYYCQQRSNWPRTFGQGTKVEIK,2.9903612,82.49063,173.88216,0.33246917,6.6527824
+tanezumab,1,QVQLQESGPGLVKPSETLSLTCTVSGFSLIGYDLNWIRQPPGKGLEWIGIIWGDGTTDYNSAVKSRVTISKDTSKNQFSLKLSSVTAADTAVYYCARGGYWYATSYYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSISNNLNWYQQKPGKAPKLLIYYTSRFHSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQEHTLPYTFGQGTKLEIK,2.8250437,82.34405,219.3092,0.20731935,3.6776638
+tarextumab,4,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSSGMSWVRQAPGKGLEWVSVIASSGSNTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSIFYTTWGQGTLVTVSS,DIVLTQSPATLSLSPGERATLSCRASQSVRSNYLAWYQQKPGQAPRLLIYGASSRATGVPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQYSNFPITFGQGTKVEIK,2.8028984,82.66898,266.65964,0.078398645,9.309123
+tavolimab,3,QVQLQESGPGLVKPSQTLSLTCAVYGGSFSSGYWNWIRKHPGKGLEYIGYISYNGITYHNPSLKSRITINRDTSKNQYSLQLNSVTPEDTAVYYCARYKYDYDGGHAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISNYLNWYQQKPGKAPKLLIYYTSKLHSGVPSRFSGSGSGTDYTLTISSLQPEDFATYYCQQGSALPWTFGQGTKVEIK,2.9812617,81.83907,238.63509,0.24376404,5.7247534
+telisotuzumab,4,QVQLVQSGAEVKKPGASVKVSCKASGYIFTAYTMHWVRQAPGQGLEWMGWIKPNNGLANYAQKFQGRVTMTRDTSISTAYMELSRLRSDDTAVYYCARSEITTEFDYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCKSSESVDSYANSFLHWYQQKPGQPPKLLIYRASTRESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQQSKEDPLTFGGGTKVEIK,2.711162,82.0844,177.11305,0.18264538,2.369433
+teplizumab,1,QVQLVQSGGGVVQPGRSLRLSCKASGYTFTRYTMHWVRQAPGKGLEWIGYINPSRGYTNYNQKVKDRFTISRDNSKNTAFLQMDSLRPEDTGVYFCARYYDDHYCLDYWGQGTPVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVSYMNWYQQTPGKAPKRWIYDTSKLASGVPSRFSGSGSGTDYTFTISSLQPEDIATYYCQQWSSNPFTFGQGTKLQIT,2.7003794,81.49202,272.2421,0.1520298,9.913527
+teprotumumab,2,QVELVESGGGVVQPGRSQRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAIIWFDGSSTYYADSVRGRFTISRDNSKNTLYLQMNSLRAEDTAVYFCARELGRRYFDLWGRGTLVSVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASKRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSKWPPWTFGQGTKVESK,2.8269074,81.55993,245.77356,0.14512622,9.150595
+tezepelumab,3,QMQLVESGGGVVQPGRSLRLSCAASGFTFRTYGMHWVRQAPGKGLEWVAVIWYDGSNKHYADSVKGRFTITRDNSKNTLNLQMNSLRAEDTAVYYCARAPQWELVHEAFDIWGQGTMVTVSS,SYVLTQPPSVSVAPGQTARITCGGNNLGSKSVHWYQQKPGQAPVLVVYDDSDRPSWIPERFSGSNSGNTATLTISRGEAGDEADYYCQVWDSSSDHVVFGGGTKLTVL,2.877334,81.9328,266.6267,0.12998286,8.417394
+tigatuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYVMSWVRQAPGKGLEWVATISSGGSYTYYPDSVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCARRGDSMITTDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCKASQDVGTAVAWYQQKPGKAPKLLIYWASTRHTGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSSYRTFGQGTKVEIK,2.7528691,82.377396,294.0775,0.044686407,7.63418
+tildrakizumab,1,QVQLVQSGAEVKKPGASVKVSCKASGYIFITYWMTWVRQAPGQGLEWMGQIFPASGSADYNEKFEGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARGGGGFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRTSENIYSYLAWYQQKPGKAPKLLIYNAKTLAEGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQHHYGIPFTFGQGTKVEIK,2.7554796,81.430626,240.92365,0.22540446,4.2423697
+tislelizumab,2,QVQLQESGPGLVKPSETLSLTCTVSGFSLTSYGVHWIRQPPGKGLEWIGVIYADGSTNYNPSLKSRVTISKDTSKNQVSLKLSSVTAADTAVYYCARAYGNYWYIDVWGQGTTVTVSS,DIVMTQSPDSLAVSLGERATINCKSSESVSNDVAWYQQKPGQPPKLLINYAFHRFTGVPDRFSGSGYGTDFTLTISSLQAEDVAVYYCHQAYSSPYTFGQGTKLEIK,2.863388,83.13903,229.46782,0.20871451,2.1964383
+tisotumab,0,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNYAMSWVRQAPGKGLEWVSSISGSGDYTYYTDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARSPWGYYLDSWGQGTLVTVSS,DIQMTQSPPSLSASAGDRVTITCRASQGISSRLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNSYPYTFGQGTKLEIK,2.7564232,81.654755,282.14758,0.06829056,8.805938
+tocilizumab,3,QVQLQESGPGLVRPSQTLSLTCTVSGYSITSDHAWSWVRQPPGRGLEWIGYISYSGITTYNPSLKSRVTMLRDTSKNQFSLRLSSVTAADTAVYYCARSLARTTAMDYWGQGSLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISSYLNWYQQKPGKAPKLLIYYTSRLHSGVPSRFSGSGSGTDFTFTISSLQPEDIATYYCQQGNTLPYTFGQGTKVEIK,2.7667658,81.65114,256.39255,0.26122928,9.16087
+toripalimab,4,QGQLVQSGAEVKKPGASVKVSCKASGYTFTDYEMHWVRQAPIHGLEWIGVIESETGGTAYNQKFKGRVTITADKSTSTAYMELSSLRSEDTAVYYCAREGITTVATTYYWYFDVWGQGTTVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSIVHSNGNTYLEWYLQKPGQSPQLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCFQGSHVPLTFGQGTKLEIK,2.9025002,81.939415,161.95384,0.22439489,1.0131311
+tovetumab,2,QVQLVESGGGLVKPGGSLRLSCAASGFTFSDYYMNWIRQAPGKGLEWVSYISSSGSIIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGRIAARGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVSITCRPSQSFSRYINWYQQKPGKAPKLLIHAASSLVGGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQTYSNPPITFGQGTRLEMK,2.729237,82.07568,237.24898,0.080825,8.149289
+tralokinumab,2,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYGLSWVRQAPGQGLEWMGWISANNGDTNYGQEFQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARDSSSSWARWFFDLWGRGTLVTVSS,SYVLTQPPSVSVAPGKTARITCGGNIIGSKLVHWYQQKPGQAPVLVIYDDGDRPSGIPERFSGSNSGNTATLTISRVEAGDEADYYCQVWDTGSDPVVFGGGTKLTVL,2.9539034,80.97977,292.45197,0.16795322,6.487011
+trastuzumab,0,EVQLVESGGGLVQPGGSLRLSCAASGFNIKDTYIHWVRQAPGKGLEWVARIYPTNGYTRYADSVKGRFTISADTSKNTAYLQMNSLRAEDTAVYYCSRWGGDGFYAMDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDVNTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSRSGTDFTLTISSLQPEDFATYYCQQHYTTPPTFGQGTKVEIK,2.770232,81.90074,240.14511,0.10915831,3.101202
+tregalizumab,2,EEQLVESGGGLVKPGGSLRLSCAASGFSFSDCRMYWLRQAPGKGLEWIGVISVKSENYGANYAESVRGRFTISRDDSKNTVYLQMNSLKTEDTAVYYCSASYYRYDVGAWFAYWGQGTLVTVSS,DIVMTQSPDSLAVSLGERATINCRASKSVSTSGYSYIYWYQQKPGQPPKLLIYLASILESGVPDRFSGSGSGTDFTLTISSLQAEDVAVYYCQHSRELPWTFGQGTKVEIK,2.8272061,82.49804,188.09032,0.12694693,4.812239
+tremelimumab,2,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYGMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDPRGATLYYYYYGMDVWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQSINSYLDWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYYSTPFTFGPGTKVEIK,2.8257968,81.98144,273.0562,0.104588464,8.848438
+ublituximab,2,QAYLQQSGAELVRPGASVKMSCKASGYTFTSYNMHWVKQTPRQGLEWIGGIYPGNGDTSYNQKFKGKATLTVGKSSSTAYMQLSSLTSEDSAVYFCARYDYNYAMDYWGQGTSVTVSS,QIVLSQSPAILSASPGEKVTMTCRASSSVSYMHWYQQKPGSSPKPWIYATSNLASGVPARFSGSGSGTSYSFTISRVEAEDAATYYCQQWTFNPPTFGGGTRLEIK,2.9030552,81.44813,250.43336,0.26113385,11.260117
+urelumab,2,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQSPEKGLEWIGEINHGGYVTYNPSLESRVTISVDTSKNQFSLKLSSVTAADTAVYYCARDYGPGNYDWYFDLWGRGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASQSVSSYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQQRSNWPPALTFGGGTKVEIK,3.054281,81.610825,196.1637,0.29011434,6.436971
+ustekinumab,3,EVQLVQSGAEVKKPGESLKISCKGSGYSFTTYWLGWVRQMPGKGLDWIGIMSPVDSDIRYSPSFQGQVTMSVDKSITTAYLQWNSLKASDTAMYYCARRRPGQGYFDFWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISSWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNIYPYTFGQGTKLEIK,2.795904,82.44706,235.58064,0.2223768,5.4941077
+utomilumab,3,EVQLVQSGAEVKKPGESLRISCKGSGYSFSTYWISWVRQMPGKGLEWMGKIYPGDSYTNYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARGYGIFDYWGQGTLVTVSS,SYELTQPPSVSVSPGQTASITCSGDNIGDQYAHWYQQKPGQSPVLVIYQDKNRPSGIPERFSGSNSGNTATLTISGTQAMDEADYYCATYTGFGSLAVFGGGTKLTVL,3.0305266,81.95864,270.56738,0.20141897,7.0019503
+vadastuximab,1,QVQLVQSGAEVKKPGASVKVSCKASGYTFTNYDINWVRQAPGQGLEWIGWIYPGDGSTKYNEKFKAKATLTADTSTSTAYMELRSLRSDDTAVYYCASGYEDAMDYWGQGTTVTVSS,DIQMTQSPSSLSASVGDRVTINCKASQDINSYLSWFQQKPGKAPKTLIYRANRLVDGVPSRFSGSGSGQDYTLTISSLQPEDFATYYCLQYDEFPLTFGGGTKVEIK,2.7337418,81.95375,226.22015,0.18126838,3.446052
+varlilumab,0,QVQLVESGGGVVQPGRSLRLSCAASGFTFSSYDMHWVRQAPGKGLEWVAVIWYDGSNKYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARGSGNWGFFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQGISRWLAWYQQKPEKAPKSLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYNTYPRTFGQGTKVEIK,2.7671757,81.6061,259.1632,0.10592173,6.524865
+vatelizumab,3,QVQLQESGPGLVKPSETLSLTCTVSGFSLTNYGIHWIRQPPGKGLEWLGVIWARGFTNYNSALMSRLTISKDNSKNQVSLKLSSVTAADTAVYYCARANDGVYYAMDYWGQGTLVTVSS,DFVMTQSPAFLSVTPGEKVTITCSAQSSVNYIHWYQQKPDQAPKKLIYDTSKLASGVPSRFSGSGSGTDYTFTISSLEAEDAATYYCQQWTTNPLTFGQGTKVEIK,2.8666525,83.04231,211.23274,0.14477661,4.8043356
+vedolizumab,4,QVQLVQSGAEVKKPGASVKVSCKGSGYTFTSYWMHWVRQAPGQRLEWIGEIDPSESNTNYNQKFKGRVTLTVDISASTAYMELSSLRSEDTAVYYCARGGYDGWDYAIDYWGQGTLVTVSS,DVVMTQSPLSLPVTPGEPASISCRSSQSLAKSYGNTYLSWYLQKPGQSPQLLIYGISNRFSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCLQGTHQPYTFGQGTKVEIK,2.8207228,81.90372,184.74347,0.21010841,2.5984478
+veltuzumab,1,QVQLQQSGAEVKKPGSSVKVSCKASGYTFTSYNMHWVKQAPGQGLEWIGAIYPGMGDTSYNQKFKGKATLTADESTNTAYMELSSLRSEDTAFYYCARSTYYGGDWYFDVWGQGTTVTVSS,DIQLTQSPSSLSASVGDRVTMTCRASSSVSYIHWFQQKPGKAPKPWIYATSNLASGVPVRFSGSGSGTDYTFTISSLQPEDIATYYCQQWTSNPPTFGGGTKLEIK,2.8137727,81.604774,264.80786,0.23941846,7.5695877
+visilizumab,0,QVQLVQSGAEVKKPGASVKVSCKASGYTFISYTMHWVRQAPGQGLEWMGYINPRSGYTHYNQKLKDKATLTADKSASTAYMELSSLRSEDTAVYYCARSAYYDYDGFAYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCSASSSVSYMNWYQQKPGKAPKRLIYDTSKLASGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQWSSNPPTFGGGTKVEIK,2.805769,82.12828,251.71074,0.2327068,7.444211
+xentuzumab,4,QVELVESGGGLVQPGGSLRLSCAASGFTFTSYWMSWVRQAPGKGLELVSSITSYGSFTYYADSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARNMYTHFDSWGQGTLVTVSS,DIVLTQPPSVSGAPGQRVTISCSGSSSNIGSNSVSWYQQLPGTAPKLLIYDNSKRPSGVPDRFSGSKSGTSASLAITGLQSEDEADYYCQSRDTYGYYWVFGGGTKLTVL,2.7360418,81.79363,328.49274,0.11131379,10.77944
+zalutumumab,0,QVQLVESGGGVVQPGRSLRLSCAASGFTFSTYGMHWVRQAPGKGLEWVAVIWDDGSYKYYGDSVKGRFTISRDNSKNTLYLQMNSLRAEDTAVYYCARDGITMVRGVMKDYFDYWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQDISSALVWYQQKPGKAPKLLIYDASSLESGVPSRFSGSESGTDFTLTISSLQPEDFATYYCQQFNSYPLTFGGGTKVEIK,2.8072002,81.994194,243.21014,0.10807425,5.774623
+zanolimumab,3,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWSWIRQPPGKGLEWIGEINHSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARVINWFDPWGQGTLVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQDISSWLAWYQHKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQANSFPYTFGQGTKLEIK,2.9128609,82.108185,242.9966,0.2375413,6.7335324
+zolbetuximab,4,QVQLQQPGAELVRPGASVKLSCKASGYTFTSYWINWVKQRPGQGLEWIGNIYPSDSYTNYNQKFKDKATLTVDKSSSTAYMQLSSPTSEDSAVYYCTRSWRGNSFDYWGQGTTLTVSS,DIVMTQSPSSLTVTAGEKVTMSCKSSQSLLNSGNQKNYLTWYQQKPGQPPKLLIYWASTRESGVPDRFTGSGSGTDFTLTISSVQAEDLAVYYCQNDYSYPFTFGSGTKLEIK,2.7385066,82.47089,218.55511,0.20259538,5.232442
diff --git a/outputs/predictions/heldout_test/saprot_vh_vl/predictions.csv b/outputs/predictions/heldout_test/saprot_vh_vl/predictions.csv
new file mode 100644
index 0000000..98a80fb
--- /dev/null
+++ b/outputs/predictions/heldout_test/saprot_vh_vl/predictions.csv
@@ -0,0 +1,81 @@
+antibody_name,vh_protein_sequence,vl_protein_sequence,HIC,Tm2,Titer,PR_CHO,AC-SINS_pH7.4
+P907-A14-arid-pond-618a6,QVRLVESGGGVAQPGKSLRLSCAASGFTFSNFGMHWIRQAPGKGLEWVAVISFDGSNKFERDSLKGRFTISRDNSKNTLYLHMNSLRPEDTAVYYCAKEFGGAISGKDAFDAWGQGTMVAVSS,DIQLIQSPSTLSASVGDRVTIACRASQSMGPWLAWYQQKPGKGPKLLIYRTSRLEVGVPSRFSGSGSGTEFTLTISGLQPDDVATYYCQQYDSHLLTFGGGTKLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-asphalt-resin-c0dd5,EVQLVESGGGLVKPGGSLTLSCAASGFTFSTYTMNWVRQAPGKGLEWVSSISSSSDDIFYADSLKGRFTVSRDNARNSLYLQMNSLRAEDTAVYYCARDQLTSTIVARALRYYPYMDVWGRGTTVTVSS,SSEVTQDPAVSVALGQAVRITCQGDSLRGYFTNWYQQKPGQAPVLVIFAGNNRPSGIPDRFSGSSSGNTAFLTITGAQAEDEADFYCSSRDRSGNRYVFGPGTKVTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-associative-shrike-139f0,QVQLVASGGGVVQPGRSLRLSCAASGFTFSNYAMHWVRQAPGKGLEWVAFISYDGSNKYYADSVKGRFTISRDNSENTLYLQMNSLIPEDTAVYFCARSGVASTLDYWGQGTLVTVSS,DIQLTQSPSFLSASVGDRVTITCRASQGISSYLAWYQQKPGKAPKLLIYAASTLQSGVPSRFSGSGSGTEFTLTISSLQPEDFATYYCQQLNSYPRTFGQGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-average-limiter-a125e,QVQLVESGGDLVKPGGSLRLSCAGSGFSFSDYYMNWIRQPPGKGPEWLSYISGSSNLTAYAASVKGRFTISRDNAKNSLYLQMDSLRVDDTAIYYCGTPGIPWGQGTLVTVSS,EILLTQSPGTLSLSPGERATLSCRASQSVTNRFLAWYQQRPGQSPRLLLYRASNRDTGVPDRFSGSGSGSDFTLTISRLEPEDFAVYYCQQYGSLPITFGPGTRLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-broad-vertex-224ba,QVHLQESGPGLVKPSETLSLICSVSGFSISSGYYWGWIRQPPGRALEWVGSMYHDGDAYYSPSLMSRVAMSADTSKNQVSLKLRSVTAADTAVYYCARGLVGGASDYWGQGILVSVSS,EILLTQFPATLSMSPGERATLSCRASQSLSRSVAWYQQKPGQAPRLLIYGASTRATGVPARFSGSGSGTDFTLTITSLQSEDFAVYYCQSHSNRPPWTFGQGTRVDI,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-celeste-render-c7237,EVQLVESGGGLVQPGGSLRLSCAASGFTFSSYEMNWVRQAPGKGLEWVSYISSSGSTIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARGGIEGEILWWRDAGSDDAFDIWGQGTMVTVSS,SSELTQDPAVSVALGQTVRITCQGDSLRSYYASWYQQKPGQAPVLVIYGKNNRPSGIPDRFSGSSSGNTASLTITGAQAEDEADYYCNSRDSSGNHLGVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-citric-margarine-1e21c,EVHLVESGGGLVKPGGSLRLSCGASGFTFADYTMRWFRQAPGKGLEWVSFIRSEAYGGTTEYAASVKGRFTILRDDSKSTAYLQMNSLKTDDTAVYYCTRDRSEYSSPSLYWGQGTLVTVSS,DVQMTQSPSTLSASVGDRVTITCRASQSIGYRLTWYQQKPGKAPKLLIYDASSFERGVPSRFSGSGSGTEFTLTINNLQPDDFATYYCQQYHSFPTTFGQGTRLDIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-clever-sub-84f5e,QVQLVQSGTEVKKTGASVRVSCKASGFSFSKYGVSWVRQAPGQGLEWMGYISANDGYREFTQTLQGRVTMTTDTSTNTAYMDVTSLRSDDSAVYYCARDVGHFGDLLNFSAERSSLMDVWGQGTTVTVSS,QAVVTQEPSLTVSPGGTVTLTCGSNTGAVTSGHYPYWFQQKPGQAPRTLIYDTSNKHSWTPARFSGSLLGGKAALTLSGAQAEDEAEYYCLLSYSTTREFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-compact-ginger-fdb61,EVQLVESGGGLVQPGRSLRLSCTASGFTFGDYAMSWVRQAPGKGLEWVGFIRSKAYGGTTEYAASVKGRFTISRDDSKSIAYLQMNSLKTEDTAVYYCTRGVEDIVVVVAATPYYYYYMDVWGKGTTVTVSS,SYELTQPPSVSVSPGQTARITCSGDALPKQYAYWYQQKPGQAPVLVIYKDSERPSGIPERFSGSSSGTTVTLTISGVQAEDEADYYCQSADSSGTYQVVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-concave-pinot-66fc0,EVQLVESGGGLIQPGGSLRLSCAASGFSVSNSYMSWVRQAPGKGLEWVSFIYSGGSTYYADSVKGRFTISRDNSKNTLFIQMNSLRVEDTAVYYCARVSEYDFWTGQRYYFYYMDVWGKGTTVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGGYNYVSWYQQHPGKAPKLMIYDVTNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCSSYTSSSPLGVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-coped-modulation-43c25,EVQLLESGGRLVQPGGSLRLSCATSGFTFTSFAMTWVRRAPGKGLEWVSSIGGSGVRTYYADSVKGRFTISRDNSKNTVYLQMNSLRAEDTAIFYCAKFAPFWTTTDYDYYMDVWGKGTTVTVSS,SYVLTQPPSVSMAPGKTAKITCGGNNIGTKTVHWYQQKPGQAPLLVMYYDSDRPSGIPERFSGSNSGNTATLTISRVEAGDEADYFCQVWDNTSGQPVFGGGTTLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-crazy-bifocal-7c4f1,QVQLVQSGAEVKKPGASVNISCKASGYTFSDYFIHWVRQAPGQGLEWMGWINPDTGITKYAEKFQGRVSMTRDTSISTAHMALSRLRSDDTAVYYCAKDRVYSLDLWGQGTLVTVSS,NFMLTQPHSVSESPGKTVTIACTRSSGRIASNYVQWYQQRPGSAPTIVIYEDNQRPSGVPDRFSGSIDSSSNSASLTISGLKTEDEADYYCQSYDSSWVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-dark-nucleus-4b90e,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSFAMSWVRQAPGKGLEWVSGISGSGGSTYYADSVKGRFTISRDNSKNTLHLHMNNLRAEDTAVYYCASNWNFQHWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLVHSDGNAYLNWFQQRPGQSPRRLIYMVSNRDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMHGTQLTFGGGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-decidable-application-a0de6,QLVESGGGVVRPGSSLRLSCASSGFTLSSYGMHWVRQAPGKGLDWVAVIWYDRSNVYYGDSVKGRFTISRDNSKNTVFLQMNSVRAEDTAVYYCARVKEQWLSRGGNSFDLWGQGTLVTVS,QSVLTQPPSVSGAPGQRVTISCTGSSSNIGAGYDVHWYQQFPGTAPKLLIYNNRNRPSGVPDRFSGSKSGSSASLAITGLQAEDAADYYCQSSDSSLSGSKVVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-descriptive-cap-ed267,QLQLQESGPGRWKPSETLALRCSVFGPAIRNPSVYWGWIRQPPGKGLEWIGSIHYTGTTRYNPSLKTRVTISIDTSKNQFSLNLTSVTAADTAVYFCARHSQAVNAFDIWGHGTLV,QSVVTQTPSASGTPGQRVTISCSGSSSNIGDNFVSWYQHVPGTAPRLLVYMNNQRPSGVPDRFSGSKSGTSASLAISGLRAEDEAEYYCETWDDILDVVVFGGGSKVTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-desert-purlin-53451,QVQLQQSGPGLVKPSQTLSLTCVISGDSVSSNSAAWNWIRQSPSRGLEWLGRTYDRSKWYNDYAVSVKGRITINPDTSKNQFSLQLTSVTPEDTAVYYCARDRGLAGHYYDSNVYYGAFDIWGQGTMVTVSS,DIQLTQSPSSLSASVGDRVTITCRASQGISSYLAWYQQKPGKAPQLLIYAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQLNSYPPITFGQGTRLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-dynamic-home-0575d,EVQVVESGGGLVQPGGSLRLSCAVSGLSFSNYWMNWVRQAPGKGLEWVANIKPDGSQKYYVDSVKGRFTISRDDAKYSVYLQMNNLRAEDTAVYYCMTGGGYWGQGTLVTVSS,QAVVTQEPSLTVSPGGTVTLTCGSGTGGVTSGHYPYWFQQMPGQVPRTLIYDTGNKHSWTPARFSGSLLGGKAALTLSGAQPEDEAEYYCLLVYSGVVVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-ecru-combat-e2619,EVQLVESGGGLVQPGGSLRLSCAASGFTFSTYWMQWVRQDPGKGLVWVSRINSDGSRTNYADSVKGRFTISRDNAKNTLYLQMNSLRAEDTAVYYCARGGYFDYWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCQASQDISNYLNWYQQKPGKAPKLLIYDASYLERGVPARFSGSGSGTDFTFTVSGLQPEDIATYYCQQYDNLPPTFGQGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-ecru-deque-ba8c9,QITLKESGPALVKPTQTLTLTCTLSGFSVSTSGVGVGWIRQPPGKALEWLALIYWDDDKRYSPSLKNRLTITKDPSKNQVVLTMTNMDPVDTGTYYCAHRVYYSDRSSYPRRPRIPSAFDYWGQGTLVTVSS,DIQMTQSPSTLSASVGDRVTITCRASQGISEWLAWYQQRPGKAPNLLIYRASTLQSGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQYYVYPYTFGQGTKLDIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-faint-click-67ee5,QVQLMESGGGLVKPGGSLRLSCAVSGFTFSDYYMSWIRQAPGKGLEWVSYISIGSADTNYADSVRGRFTISRDDAKNSLYLQMNSLRADDAAVYYCRLYSNYDYYFDYWGQGTLVTVSS,DIQMTQSPSTLSASVGDRVTITCRASQSISSRLAWYQQKPGKAPKLLIYEASSLESGVPSRFSGSGSGTEFTLTITSLQPDDFATYYCQQYNSYSTWTFGQGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-fast-poset-8ec15,HVQLVESGGGLVKPGGSLRLSCAVSGFTVSDSYMFWIRQSPGKGLEWVAHISSADWVIYYSDSVKGRFFISKDHANNSLFLQMNSLRDDDTAVYYCARGSGLAGPLDVWGKGTTVTVSS,DVQMTQSPSSLSASVGDRVTITCRASQSISTYLHWYQHRPGKAPKLLIDTASSLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSTHITFGQGTRLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-fermented-cathedral-185ee,EVQLVESGGGLVQPGGPLRLSCVASEFTFSEYWMTWIRQAPGEGLEWVANIKGDGSVKYYVDSVEGRFTISRDNADSSLFLRMNSLRAEDTAMYYCVRDRNYHDRRTYYDVLDVWGPGTMVTVSS,AIQMTQSPSSLSASVGDRVTITCRASQGIRSDLGWYQQKAGKAPKLLIYSASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFGTYYCLQDYNYPRTFGQGTKVEVK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-finite-pixel-1f5d2,QVQLHQWGAGLLKPSETLSLTCAVSGGSLSEDFWSWTRQSPGKGLEWLGEINHSGSTNYNASLRSRVTISVDASKNQFSLRLTSVTAADTAVYFCARRLTLVAHSFHNYMDVWGKGTPVTVSS,QSVLTQPPSVSGAPGQRVLISCTGSGSNIGAGYDVHWYQQVPGTAPKLLIYGNNYRPSGVPDRFSGSKSGPSASLAITGLRDEDEADYFCQSYDNKLSGVLFGGGTKLTV,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-frayed-particle-5e881,QVQLVQSGTEVKKPGASVIVSCKASGYNFRSFGISWVRQAPGQGLEWMGWISGYSGDTNYIQKLHDRVIMTTDTSTDTAYMELRSLTSDDTAVYFCARDDVGDGRNSDIHLRGDFEYWGQGTLVIVSS,DIQMTQSPSSLSASVGDRVTISCQASHDISDYINWYQQKPGKAPKLLIFDASNLETGVPSRFSGSGSGTDFTLTISSLQPEDIATYYCQQYDNLPLTFGGGTKVDMK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-generous-property-c943a,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWNWIRQHPGKGLEWIGEINQSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYFCARTLRATFVTGGQVHVWGQGTTVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSYLAWYQQKPGQAPKLLIYATSSRATGIPDRFSGGGSGTDFTLTISRLEPEDFAVYYCHQYSGSPPDTFGQGTKLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-gentle-valid-12932,QVQLVQSGAEVKKPGSSVKVSCKASGGTFSSFAISWVRQAPGQGLEWMGGIIPIYGTANYAQKFQGRVTITADESTSTAYMELSSLRSEDTAVFYCAIASMIADWYYGMDVWGQGTTVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGIASWLAWYQQNPGKAPKLLIYTASNLQGGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQTNSFPLTFGGGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-humongous-throw-52925,QVHLMQSGPELKKPGASVKVSCKASGYTFTDYGLTWVRQAPGQGLEWMGWISTYKGDTNYEQKFHHRVTLTTDTSTSTAYMELRSLRVDDTAVYYCARGLFGVLISKIRFSYYYMDVWGQGTTVTVSS,QSVVTQPPSASGSPGQRVTISCSGSSSNIGRNAVNWYQQLPGTAPKLLIYNNDQRPSGVPDRFSGSKSDTSASLAISGLQSEDEADYYCAAWDDGLNGRYVFGTGTKVAVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-icy-butter-8afa4,EVQVVESGGGLVKPGGSLRLSCAASGFTFTNAWVSWVRQAPGKGLEWVGRLRGKTGGGTRDYAAPVKGRFTISSDDSKVTLYLQMNSLKTDDTAVYYCITKNVPDWGQGTLVTVSS,QAVVTQEPSLTVSPGGTVTLTCDSSTGAVTSGHYPYWFQQKPGQAPKTLIYDTSNKHSWTPARFSGSLLGGKAALTLSGAQPEDEAEYYCLLSYSDARVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-immense-cone-377a5,QVQLVESGGGVIQPGRSLRLSCAASGFTFSSHVMQWVRQAPGKGLEWVTVISSDGSKKEYVDSVKGRFTISRDNSKNTVYLQMNSLRAEDTAVYYCARVDILIGFTDYWGQGTQVTVSP,DIQMTQSPSTLSASVGDRVTITCRASQSISHWLAWYQHKAGKAPTLLVYEASYLEGGVPSRFSGSGSGTEFTLTITSLQPDDFATYYCQHYNRHSWTFGQGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-inscribed-type-583c0,QVLLEEAGPGLVKPSETLSLTCTVSRGSVSSSSYNWGWIRQTPGKGLEWIGSIYHSGTTYYNPSLRSRVTISADTSNERFSLNLSYVTAADTAVYYCARGTITIFGPFIDRGYYFDYWGQGILVTVPS,QSALTQPASVSGSPGQSITISCTGTSSDVGAYNYVSWYQLHPGKAPKLIIYDVTNRPSGISYRFSGSKSDNTASLTISGLQTEDEADYYCSSYTTGSTLLFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-iron-time-7d5d8,EVQLLESGGGLVQPGGSLRLSCAVSGFTFSNYAMTWVRQAPGKGLEWVSSITNSGGGTYYADSVKGRFTVSRDNSKSTLYLQLDSLRAEDTAVYYCASNLVHWGQGTLVTVSS,DIQMTQSPSSLSASVGDSVTITCRASQGISNFLAWFQQKPGKAPKSLIYAASTLQSGVPSKFSGSGSGTDFTLTISSLQPEDFATYYCQQYSSYPQTFGQGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-khaki-cadet-37ddd,QVRLEQSGAEVKKPGASVKVSCEVFGYTLTKLSIHWVRQSPGKGLEWMGGFDPEDDETVYAQKFQGRVTMTKDTSTDTAYMELSSLRSEDTAVYYCATLTIFHMDVWGKGTTVTVSS,AIQMTQSPSSLSASVGDRVTITCRASQDIRNDLGWYQQKPGKAPKLLIYAASNVQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCLQDYSYPWTFGQGTRVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-khaki-goal-7976c,QLQLQQSGSRLLKPSQTLSVTCTVSGDSVDSGAHSWTWIRQPPGKGLEWIGYVSHSGVTYYVPSLRSRVTMSVDRSKKQVFLKVGSVTAADTAVYYCARGGWDYEDYVPNLDYWGQGTLLTVSS,QSVLTQPPSASGTPGQRVTISCSGSSSNIGNNAVNWYQHLPGTAPKLLIYTNNQRPSGVPDRFSGSKSDTSASLAISGLQSEDEADYYCATWDDSLYVLLFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-late-coffee-b8f3d,EVQLVQSGAEVKKPGESLRISCKGSGYSFTSYWISWVRQMPGKGLEWMGRIDPSDSSANYSPSFQGHVTISADKSISTAYLQWSSLKASDTAVYFCMIFGAWGQGTLVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSIYLAWYQQKPGQAPRLLIYGASSRATGTPDRFSGSGSGTDFTLTISRLEPEDFAVYYCQQHGSSPYTFGPGTKLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-linear-hadron-ab9f7,QVQLQQSGPGLVKPSQTLSITCDISGDSVSSNSAAWNWIRQSPSRGLEWLGRTYYKSKWYDDYAVSVKSRITINPDTSMNRFSLHLKSVTPEDAAVYYCARERVSMVRGIIMTYYGMDVWGQGTTVSVSS,QSELTQPPSASGTPGQRVTISCSGSSSNIGSYTVNWYQQLPGTAPKLLIHNDDRRPSGVPDRFSGSKSGTSASLAISGLQSDDEADYYCATWDDSLNGRVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-massive-bocaccio-47a3c,EVQLVQSGAEVKKPGESLRISCQGSGYTFTSYWIGWVRQMPGKGLEWMGITNPDDSDSRYSPSFQGLVTLSVDKSINAAYLQWSRLKASDTAIYYCARAQWDIVSPGNAFDIWGQGTRVTVSS,DIQMTQSPSSVSASVGDRVTITCRASQGISSWLAWYQQKPGKAPKLLIYIVSTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQTNTFPWTFGQGTKVEI,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-meek-buck-dae99,QVQLQQWGAGLLKASETLSLSCAVYGGSFSGYSWSWIRQPPGKGLEWIGEINDSETTTYDPSLKGRVTMSRDTSTNLFSLKLTSVTAADTAVYYCAITYFWGQGTVVTVSS,NFIMTQPHSVSESPEKTVTISCTRSSGSIASNYVQWYQQRPGSAPTTVIYEDKQRPSGVPDRFSGSIDSSSNSASLTISGLKTEDEADYYCQSYGRSGWVFGGGTRLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-messy-right-8c58c,EVQLVESGGGLVQPGGSLRLSCATSGFTFNRYWMSWVRQAPGKGLEWVANIDEHGSEKNYADYVKGRFTISRDNATTSLFLQMNSLRVEDSALYYCTLGDYWGQGTLVTVSS,SYELTQPSSVSVSPGQTATITCSGDVLAKRYARWFQQKPGQAPVLVIYKDSERPSGIPERFSGSSSGTTVTLTISGAQVEDEADYYCYSVPDNRWVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-metallic-liquidation-6b34d,EVQLVQSGAEVKKPGESLKISCKGSGYNFATYWIAWVRQMPGKGLEWMGIIYPADSDTTYSPSFQGQVTISADKSISTAYLQWSSLKASDTAMYYCARRADYYDSSGDYLDAFDIWGQGTMVTVSS,QSALTQPASVSGSPGQSITISCTGTSSDVGGYNYVSWYQQHPGKAPKLMIYDVSNRPSGVSNRFSGSKSGNTASLTISGLQAEDEADYYCSSHTSSSSLGVFGTGTKVAVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-metallic-pole-64d63,EVQLLESGGGLVQPGGSLRLSCEASGFTLSDYAMSWVRQTPGTGLVWVSTTGRVGDTYYADSVKGRFSISRDNSKNTMFLQMNGLRAEDTAVYYCARNIVPARSKWFDPWGQGTLVTVSS,ETVLTQSPATLSLSPGERATLSCRASRSVGNYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISSLEPEDFAVYYCQHRDNLWTFGQGTNVE,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-mild-chick-b8732,EVQLVESGGGLVKPGGSLRLSCAASGFTFSSYSMNWVRQAPGKGLEWVSSISSSSSYIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCAREGGSYYDILTGYYNGPPNWFDPWGQGTLVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVSSNLAWYQQKPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQYNNWPPPITFGQGTRLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-mild-cider-93b9a,QVQLQESGPGLVKPSQTLSLTCTVSGGSISSGTYHWSWIRQPAGKGLEWIGRIYTSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCASSGFYFDFWGQGTLITVSS,DIVLTQSPGTLSLSPGERATLSCRASQYVISSYLTWYQQKPGQAPRLLIYGASNRATGIPDRFSGSGSATDFTLTITRLEPEDFAVYYCQHYGSSAMYTFGQGTKLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-mild-territory-6277f,EVQLVQSGAEVKEPGESLRISCKGSGYSFTKSYITWVRQLPGKGLEWMGEIDPRDSYVNYSPSFQGHVTLSADNFITTAYLQWSSLRASDTAMYYCARRSDDYGDYYGRYFDYWGQGTLVTVSS,QSALTQPASVSGSPGQSITISCIGTSSDVGGYNYVSWYQQHPGKAPKLMIYAVTDRPSGVSDRFSGSRSGDTAHLTISGLQAEDEADYYCSSYVDNSALPLVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-miniature-hill-eff2e,QVELHQWGAELLKPSETLSLTCAVYGGPLSGYYWSWIRQSPGKGLEWIGEIHHGGSANYNPSLKSRVAISTVTYKNQFSLKLTSVTAADTGVYYCAVGFNFHYYYMDVWGKGTTVIVSS,QSVLTQPPSVSAAPGQKVTISCSGGDSTIGNNYVSWFQLVPGTAPKLLIFDNNKRPSGIPDRFSGSRSGTSATLDITGVQTGDEADYHCGTWDSSLNSFVFGTGTFVTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-minty-mercury-75369,QGQLVQSASEVKKPGTSVTVSCKASGYSFTSHGVSWVRQAPGQGLEWIGWVSPKNQNTKYSQRFQGRVSMTTDTSATTAYMELRSLTSDDTGVYYCARDSPPMGLGMGEYFGAYYYGMDVWGQGTTVSVSS,LTQSPGTLSLSPGESATLSCRASQRMSSTYLAWYQQRPGQAPRLLMYGSSKRATGVPDRFSGFGSGTDFVLTINNLEPEDSAFYYCQQYGFSPFTHTFGQGTKLEI,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-national-dataset-d6941,EVQLLESGGDLVHPGGSLRLSCVVSGFTFDTQAFAWIRQAPGKGLEWVSGITGDTGATYYADAVRGRFTLSRDNSKKTLYLQMNDLRVEDTALYYCARYDYIGRLKASFGLWGRGALVTVSS,SYELTQAPSVSVSPGQTANITCSEDTLGDKYVSWYQQKSGQSPILVIFQDSKRPSGISERFSGSNSGNTATLTISGAQAMDEADYSCQVWDNGLVRVVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-navy-channel-87ace,EVQLVESGGGLVQPGRSLRLSCEASGFSFNDYTMHWVRQAPGKGLEWVSGISWNSGSIGYADSVKGRFIISRDNAKNSLYLHINSLKVEDTAFYYCAKDIPLDSWGQGTLVTVSS,EIVLTQSPATLSLSPGERATLSCRASRSVNDYLAWYQQKPGQAPRLLIYDASNRATGIPARFSGSGSGTDFTLTISNLEPEDFAVYYCQQRSDWFKFTFGPGTKVDIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-nearest-interference-9055c,EVQLVQSGAEVKKPGESLRISCKGSGYSFTNYWITWVRQMPGKGLEWMGRIDPSDSYTNYSPTFQGHVTFSADKSNSTAYLQWSSLEASDTAMYYCARQGLDVWGQGTTVTVSS,QSALTQPPSASGSPGQSVTISCTGTSSDVGRSNYVSWYQQHPGKAPKLMIYEVSKRPSGVPDRFSGSKSGNTASLTVSGLQADDEADYYCSSYAGSNKVFETGTKVTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-obvious-character-128dd,EVQLLESGGGLVQPGGSLRLSCAGSGFTFSSYAMNWVRQAPGKGLEWVSAIDNIGSSIYYADSVKGRFTISRDNSKNTLHLQMTSLRAEDTAIYYCGRGGGVGATMVEYWGQGSQVTVSS,SYVLTQPPSVAVAPGKTATITCGGNNIESKSVHWYQQKPGQAPVLVISFDADRPSGIPERFSGSNSGNTATLTISRVEAGDEADYYCQVWDSNSDHRVFGGGTKLTV,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-plain-trombone-db73c,EVQLMESGGALVQPGGSLRLSCVVSGFTVSTNYMSWVRQARGKGLEWVSAIDVGGSTFYADSVKGRFTISRDNSKNTLYLQMNSLRVEDTAVYYCARDRIAIFSWGKGTTVTVSS,QSVLTQPRSASGTPGQRVTISCSGSSSNIGSNTVHWYQQLPGTAPKLLIYNNDQRPSGVPGRFSGSKSGTSASLAISGLQSEDEADYYCASWDDSLNGLFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-pointed-bud-b2840,EVQLLESGGGLVQPGGSLRLSCAASGFTFSSHAMSWVRQAPGKGLEWVSIISIRGDITYYADSVKGRFTISRDNSKNTLILQMNSLRADDTGVYYCAKPRGENIGRLGVDYWGQGTLVTVSS,AIQLTQSPSSLSASVGDRVTITCRASQGISSALAWYQQKPGKAPKLLIYDASSLESGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQFNTYPITFGQGTRLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-random-borzoi-de6fb,EVQVVESGGGLIQPGGSLRLSCAASGFTVSSSYMGWVRQAPGKGLAWVSLFYSDTTGGATYYADLVKGRFTISRDRSKNTLDLQMNNLRAEDTALYYCAGHSRVYGSPAFDFWGQGTMVTVSS,DIQMTQSPSSLSASVGDRLTITCRASQSIRSYLNWYQQKPGKAPKLLISAASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSYSIPLTFGGGTKVVIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-rectilinear-layout-04495,EVQLVESGGGLVQPGGSLRLSCAGSGFIFSSYEVNWVRQAPGKGLEWISYITESGNVIFYADSVKGRFTISRDNAKSSVYLQMNSLRVDDTAVYFCARMGEWNLRLGDPYYHAMDVWGQGTRVTVSS,DVQMTQSPSSLSASVGDRVTITCRASQSISPYVNWYQQKPGKAPKLLIYAASSLQSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQSFNKRGFGGGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-reduced-withdrawal-f4e86,VQLVESGGGLVEPGGSLRLSCAASGFTFSNSDMNWVRQAPGKGLEWVSGVSWNGSRTHYADSVKGRFIISRDNSRNFLYQQMNSLRPEDMAVYYCVKVVTTEWGQGTLVTVSS,SYMLTQPPSVSVAPGKTARITCGGNNIGSKSVHWYQQKPGQAPVLVIYYDSDRPSGIPERFSGSNSGNTATLTISRVEAGDEADYYCQVWDSNSDHPVFGTGTKVTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-regional-tray-5f973,QVQLVESGGGLVKPGGSLRLSCTASGFIFGDHYMSWIRQAPGKGLEWVSYITSSSSYTNYADSVKGRFTISRDNAKKSLFLQMNSLRAEDTAVYYCARVESVVVPIAGNWFDSWGQGTLVTVSS,SYELTQPPSVSVSPGQTASITCSGDKLGNKYVSWYQQRPGQSPVLVIYQDDKRPSGIPERFSGSNSGNTATLTISGTQAMDEADYYCQAWDSSPVVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-rigid-product-5d155,QLQLQESGPGLVKPSETLSLTCSVSGGSISTSPYYWGWIRQPPGKGMEWIGTINYRGTTFHNPSLTSRVTLSVDPSKNQFSLRLNSVTAADTAVFYCARLAEDYRSSSGFPRGYYFDYWGQGILVTVSS,EIVMTQSPATLSVSPGERATLSCRASQSVSSNLAWHQQKPGQAPRLLIYGASTRATGIPARFSGSGSGTEFTLTISSLQSEDFAVYYCQQYNTWPPVYTFGQGTKLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-rowdy-restaurant-852e1,QVQLQESGPGLVKPSETLSLTCTVSGGSVSSGSYYWSWIRQPPGKGLEWIGYIYYSGSTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAVYYCARDNSFDPSDAVRDYYYYYMDVWGKGTTVTVSS,QSVLTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKLLIYDNNKRPSGIPDRFSGSKSGTSATLGITGLQTGDEADYYCGTWDSSLSAGWVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-rubbery-disk-fc338,QVQVVESGGGVVQPGGSLRLSCVASGFTFSTSGMHWVRQAPGKGLEWVAFIHYHGGEIDYVDSVKGRFTISRDNSMNTLYLQMDSLNVEDTAIYSCARDVRGLPFDIWGQGTRVTVSS,DLQMTQSPSSLSASVGDRVSITCRASQGIRNFLAWYQQKPGKVPQLLIYGASTLQSGVPSRFSGSGSGTDFTLTISSLQPEDVATYYCQKYDTAIRTFGQGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-salty-developer-1a97c,LQLQESGPGLVRPSETLSLTCSVSGGSISSSDHLWGWIRQPPGKGLQWVGSIYYTGRTYDNPSLRGRLTISVDTSRNQFSLRLNSVTAADTAVYYCARHDEARPYFYRSSGYFDMWGQGTVLTVSS,QSVLTQPPSVSGAPGQRVTISCAGTSSDIGAGYDVHWYQHLPGTAPKLLIYGDNNRPSGVPDRFSGSKSGASASLAITGLQVEDEAEYYCQSYDSSLSGSWVFGGGTKLTV,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-salty-flunky-7a230,QVQMVESRGGVVQPGRSLRLSCAASGFTFSSYAIHWVRQAPGKALDWVALISYNGNDKYYADSVKGRFTVSRDNSKNTLYLQMNSLRGEDTAVYYCARGGIQITVTTSLRPAYFYYMDVWGKGTTVTVSS,YELTQPPSVSVSPGQTARITCSGDALSKQYAYWYQQKPGQAPVMVIYKDTERPSGIPERFSGSSSGTTVTLSISGVQAEDEADYYCQSTDSSTIYVLFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-salty-sledder-a9417,QLQLQESGPGLVKASETLSLSCTVSGGSINTASYYWGWIRQPPGKGLEWIGSIFYNGYTQYNPSLKSRVTMSIATSKNQFSLKLTSVTAADTALYYCARSTWYFDSWGQGTLVPVSS,QSVLTQPPSVSAAPGQKVTISCFGGSSNLGKNYVSWYQQFPGMAPKLLMYENNKRPSGIPDRFSGSKSGTSATLGITGLQTGDEADYYCGTWDSSLNNFVFGTGSKVTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-selfish-bungalow-dd3f4,QVQLVESGGGLVKPGGSLRLSCAASGFTFSDYYMSWIRQAPGKGLEWVSYISSSSSYTNYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARDFTDVWGKGTTVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQSLVYSDGNTYLNWFQQRPGQSPRRLIYKVSNRDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQGTHWPYTFGQGTKLEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-sharp-quanta-4ca51,HLAQSGGGVVQPGRSLTLSCAASGFTFASSDMHWIRQGPGKGLEWVAVISFTGETKNYADSVRGRFTISRDNSRSTLHLQMYGLRADDTAVYYCASGPHHSGSGTYYPSYLYGWGPGAPVTVSS,EIEMTQSPATLSVSPGETATLSCRASQSIRSNLAWYQQKPGQAPRLLIYGASTRATGIPGRFSGSGSGTEFTLTISSLQSEDFVVYYCQQYDEWPPLTFGGGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-shrewd-data-9aba8,QVQLQESGPGLVKPSETLSLTCSVSGGSISRSYWSWIRQPPGQGLEWIGYIYDSGSTNYNPSLKSRVTISVDTSKNQFSLKLNSVTAADTAVYYCARRIREAFDIWGQGTMVTVSS,DIQMTQSPSSLSASVGDRVSITCRASQSIRSYLNWYQQKPGKAPKLLMYVASSLQSGVPSRFSGSGSGTDFTLTIRSLQPEDFATYYCQQSYSTPYTFGQGTKLQIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-skinny-tab-c6558,QAQLVQSGAEVKKPGASVKVSCKASGFIFTMFGISWVRQAPGQGLEWMGWISAYNGDTKYAQKFQGRITVTTDKSTSTAYLHLKSLTRDDTAVYYCARAYDSLDVWGPGTTVTVSS,EIVLTQSPATLSVSPGERVTLSCWASQTVSTNFAWYQQKPGQAPRLLIYGASTRATGIPERFTGSGSGTDFTLTITSLQSEDFAVYYCQQYNDWPALTFGGGTKVEI,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-speedy-bandwidth-545ec,EVQLVQSGAEVKKPGESLRISCKGSGYSFTSYWISWVRQMPGKGLEWMGKIDPSDSYTNYSPSFQGHVTISADKSISTAYLQWSSLKASDTAMYYCARHSRGGSSYWFDPWGQGTLVTVSS,QTVVTQEPSFSVSPGGTVTLTCGLRSGSVSTSYYPSWYQQTPGQAPRTLIYNTNTRSSGVPDRFSGSILGNKAALTITGVQADDESDYYCVLYMGSGISVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-stone-latency-99dcd,QVQLVESGGGLVKPGGSLRLSCAASGFTFSDYYMSWIRQAPGKGLEWVSYISSTSSYTKYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTSVFYCARLLEPTYYDILTGSNGYYFDYWGQGTLVTVSS,QSVLTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKLLIYENNKRPSGIPDRFSGSKSGTSATLGITGLQTGDEADYYCGTWESSLSAVVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-straight-omelette-3de59,EVQLLESGGGLVQPGGSLRLSCAASGFTFSNSAMSWVRQAPGKGLEWVSGIGGSGRTTYYAESVKGRFTISRDNSNNTLYLQMNSLRAEDTAFYYCAKDGRGLSIFGVLTPYYFDYWGQGALVTVSS,QSALTQPASVAGSPGQSITISCTGTSSDVGGYNYVSWYQHHPGKAPKLIIYDVIYRPSGVSDRFSASKSGNTASLTISGLQTEDEADYYCSSYTSNSTPFVFGTGTKVTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-succulent-raisin-910a8,EVHLVESGGGLVKPGGSLRLSCAASGFTFRNYSMNWVRQAPGKGLEWVSSISNSSSYIYYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARTSDTWGQGTLVTVSS,DVVMTQSPLSLPVTLGQPASISCRSSQNLVHRDGNTYLTWLQQRPGQSPRRLIYKVSKRDSGVPDRFSGSGSGTDFTLKISRVEAEDVGVYYCMQGTQWPPAFGQGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-swarm-arpeggio-73992,QVQLQQWGAGLLKPSETLSLTCAVYGGSFSGYYWIWIRQPPGKGLEWIGEINHSGGTNYNPSLKSRVTISVDTSKNQFSLKLSSVTAADTAMYYCARLKSRSSWAAFDIWGQGTMVSVSS,QAGLTQPPSVSKGLRQTATLTCTGNTNNVGNQGAAWLQQHQGHPPKLLSYRNNNRPSGISERVSASRSGNTASLTITGLQPEDEADYYCSAWDSSLSAWVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-syrupy-pentatonic-39802,QVQLVDSGGGVVQPGRSLRLSCAASGFTFSDYAFHWVRQAPGKGLQWVTFMSYDGSKKFYADSVKGRFTISRDNSKNTVYLQMNSLRPEDTAVYYCARDNLQMGILTAIGVVDHWGQGTLVTVSS,SYVLTQPPSVSVAPGQTARITCGGNNIRSKSVHWYQQKPGQAPVLVVSDDIARPSGIPDRFSASNSGNTAALTISRVDGGDEADYFCQVWDSNTEHVVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-taxonomic-peninsula-32674,EVQLVESGGGLVQPGRSLRLSCAASGFTFDDSAMHWVRQAPGKGLEWVSGISWNSGSIGYADSVKGRFTISRDNAKNSLYLQMNSLRAEDTALYYCAKDTDFSGYYYYMDVWGKGTTVTVSS,QSALTQPPSVSGSPGQSVTISCTGTSSDVGSYNRVSWYQQPPGTAPKLMIYEVNNRPSGVPDRFSGSKSGNTASLTISGLQAEDEADYYCSSYTSSSTFYVFGTGTKVTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-timid-gang-841a7,QEQLVESGGGVVQPGRSLRLSCAASGFTFRTFAIHWVRQVPGKGLEWVAVISYDGSNEYYSDSVKGRFTISRDNSKNTLYLQMNSLRAEDTALYYCARDWGGYSHFAYYSYMDVWGKGTTVTVSS,QSVLTQPPSVSAAPGQKVTISCSGSSSNIGNNYVSWYQQLPGTAPKLLIYVNDKRPSGIPDRFSGSRSGTSATLAITGLQTGDEADYYCGTWDSSLSTGVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-tiny-cover-b07ec,QVRLVESGGGAVQPGESLRLSCAASGFTFRNYPMQWVRHAPGRGLEWVGYIRSDGSNAYYGDSVKGRFTISRDNSNNIVFLQMDSLRLEDTAIYFCAKEDWGLPRDWGQGTRVTVSS,AIQMTQSPSSLSASVGDRVTITCRASQGVRNTLGWFQQKPGTAPKLLVYEASSLPSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCLQNYNYPYTFGQGTKLEI,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-trite-leaf-1fefb,EVKLVESGGGFVQPGGSLRLSCAASGFTFSGYWMTWVRQAPGKGLEWVANIKYDGSEKYYVDSVKGRFTISRDNAENSLYLQVDNLRAEDTAVYSCARGRDIDFWGQGTLVSVSS,SYELTQPSSVSVSPGQTARITCTGDVVAKKFTRWFQQKPGQAPIVLIYRDSERPSGIPDRFSGSSSGTTVTLTISEAQVEDEADYYCYSAAENNIWVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-unary-estuary-9ae8d,EVQLVESGGGLVQPGGSLRLSCAASGFTFSRYWMSWVRQAPGKGLEWVANIKQDGSEKYYVDSVKGRFTISRDNAKNSLYLQMNSLRAEDTAVYYCARASYDFWSGYYEPKKSATYAFDYWGQGTLVTVSS,DIQLTQSPSTLSASVGDRVTITCRAARSINGWVAWYQQKPGKAPKPLIFKASSLGSGVPSRFSGSGSGTEFTLTISSLQPDDFATYYCQQYQSLSVRTFGQGTKVDIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-undirected-hull-8daff,QMQLVQSGAEVRKPGASVKVSCKASGYTFTGHYIHWVRQAPGRGPEWMGWINPNSGGTNSSQSFQGRVTMTRDTSISTAYMELSRLTSDDTAVYSCARARYGDYYYFDSWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQDISSYLAWYQQKPEKAPKSLIYAASSLQGGVPSRFSGSGSGTHFTLTISSLQPEDFATYYCQQYYSYPVTFGPGTKVDIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-vain-bucket-0f231,QVQLQQWGAGLLKPSETLSLTCAVYNGSSSAHYWSWVRQPPGKGLEWIGEISHGGSTTYNPSLKGRVSISVDTPKNQFSLNLSSVTAADTAVYYCATRAIHFRNRNFYSFYVEVWGKGTTVTVSS,EIVLTQSPGTLSLSPGERATLSCRASQSVSSSKLVWYQQRPGQAPRPLIYGASSRATGIPDRFSGSGSETDFTLTISWLEPEDFAVYYCHQYGSSPRTFGQGTKVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-wintry-couple-24188,QVQLQQWGAGLLKPSETLSVTCAVYGGSFIGSSWIWIRQPPEKGLEWIGEINHGGSTTYNPSLKSRVTISLDMSKNQFSLNLTSVTAADTAVYYCATDRGSLAAVDWGQGTLVTVSS,DIQMTQSPSSLSASVGDRVTITCRASQAISSYLAWYQQKPGKVPKLLIYAASTLQSGVASRFTGSGSGTDFTLTISSLQPEDVATYYCQKYNSAPRTFGQGTRVEIK,2.776159,81.619965,-161.75743,0.31505337,-4.6602244
+P907-A14-witty-fugue-86932,EVQLVESGGGLVQPGRSLRLSCTASGFTFGDYAMNWVRQAPGKGLEWLGFIESKGYGGTTEYAASVKGRFIISRDDSKSIAYLQMNSLKTEDTAVYYCTPGDYWGQGTLVTVSS,SYELTQPPSVSVSPGQTARITCSGDALPKKYAYWYQQKSGQAPVQVIYEDSGRPSGIPERFSGSSSGTMATLTISGAQVEDEADYYCYSIDSSGNHRVFGGGTKLTVL,2.776159,81.619965,-161.75743,0.31505337,-4.6602244