@@ -19,11 +19,6 @@ You can either download the datasets from the link or prepare the datasets from
1919
2020# # 2. Configurations
2121
22- # ## Data Config
23-
24- - ** ` datasets.yaml` :** Provide base prompts, metrics, and target columns for respective datasets.
25- - ** ` data.yaml` :** Modify ` datasets_dir` to the base directory of all prepared datasets.
26-
2722# ## LLM Config
2823
2924` ` ` yaml
3429 api_key: sk-xxx
3530 temperature: 0.5
3631` ` `
37-
38-
39- # # 3. SELA
40-
41- # ## Run SELA
42-
43- # ### Setup
32+ # ## Setup
4433
4534` ` ` bash
4635pip install -e .
@@ -50,33 +39,43 @@ cd metagpt/ext/sela
5039pip install -r requirements.txt
5140` ` `
5241
53- # ### Quick Start
42+ # # 3. Quick Start
5443
55- - ** Example : Running SELA on the House Price Prediction Task**
56- - To run the project, simply execute the following command:
57- ` ` ` bash
58- python run_sela.py
59- ` ` `
60- - Explanation of ` run_sela.py` :
61- ` ` ` bash
62- requirement = (" Optimize dataset using MCTS with 10 rollouts. "
63- " This is a 05_house-prices-advanced-regression-techniques dataset."
64- " Your goal is to predict the target column ` SalePrice` ."
65- " Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target."
66- " Report rmse on the eval data. Do not plot or make any visualizations." )
67- data_dir = " Path/to/dataset"
68-
69- sela = SELA ()
70- await sela.run(requirement, data_dir)
71- ` ` `
72-
73- # ### Running Experiments
44+ # ## Example : Running SELA on the House Price Prediction Task
7445
75- - ** Examples: **
46+ - ** To run the project, simply execute the following command **
7647 ` ` ` bash
48+ python run_sela.py
49+ ` ` `
50+
51+ - ** Explanation of ` run_sela.py` **
52+ ` ` ` bash
53+ requirement = (' ' '
54+ Optimize dataset using MCTS with 10 rollouts.
55+ This is a 05_house-prices-advanced-regression-techniques dataset.
56+ Your goal is to predict the target column `SalePrice`.
57+ Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target.
58+ Report rmse on the eval data. Do not plot or make any visualizations.' ' ' )
59+ data_dir = " Path/to/dataset"
60+
61+ sela = SELA ()
62+ await sela.run(requirement, data_dir)
63+ ` ` `
64+
65+ # # 4. SELA Reproduction Details
66+
67+ # ## Data Config
68+ - ** ` datasets.yaml` :** Provide base prompts, metrics, and target columns for respective datasets.
69+ - ** ` data.yaml` :** Modify ` datasets_dir` to the base directory of all prepared datasets.
70+
71+ # ## Run SELA
72+
73+ # ### Examples
74+
75+ ` ` ` bash
7776 python run_experiment.py --exp_mode mcts --task titanic --rollouts 10
7877 python run_experiment.py --exp_mode mcts --task house-prices --rollouts 10 --low_is_better
79- ` ` `
78+ ` ` `
8079
8180# ### Parameters
8281
@@ -98,7 +97,7 @@ pip install -r requirements.txt
9897
9998# ## Ablation Study
10099
101- ** RandomSearch**
100+ # ### RandomSearch
102101
103102- ** Use a single insight:**
104103 ` ` ` bash
@@ -110,7 +109,7 @@ pip install -r requirements.txt
110109 python run_experiment.py --exp_mode rs --task titanic --rs_mode set
111110 ` ` `
112111
113- # # 4 . Citation
112+ # # 5 . Citation
114113Please cite our paper if you use SELA or find it cool or useful!
115114
116115` ` ` bibtex
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