formatting

Author: kashif

ConvTSMixer-elec.ipynb

@@ -247,7 +247,7 @@
     "predictor = estimator.train(\n",
     "    training_data=dataset_train,\n",
     "    cache_data=True,\n",
-    "#     shuffle_buffer_length=1024,\n",
+    "    #     shuffle_buffer_length=1024,\n",
     ")"
    ]
   },
@@ -273,7 +273,8 @@
    "outputs": [],
    "source": [
     "evaluator = MultivariateEvaluator(\n",
-    "    quantiles=(np.arange(20) / 20.0)[1:], target_agg_funcs={\"sum\": np.sum},\n",
+    "    quantiles=(np.arange(20) / 20.0)[1:],\n",
+    "    target_agg_funcs={\"sum\": np.sum},\n",
     ")\n",
     "evaluator.aggregation_strategy = aggregate_valid"
    ]

Transformer-MV-Solar.ipynb

@@ -141,7 +141,7 @@
     "    lags_seq=[1],\n",
     "    d_model=128,\n",
     "    prediction_length=dataset.metadata.prediction_length,\n",
-    "    context_length=dataset.metadata.prediction_length*3,\n",
+    "    context_length=dataset.metadata.prediction_length * 3,\n",
     "    freq=dataset.metadata.freq,\n",
     "    scaling=\"std\",\n",
     "    trainer_kwargs=dict(max_epochs=100, accelerator=\"gpu\", devices=\"1\"),\n",

Transformer-exchange.ipynb

@@ -129,7 +129,7 @@
     "    input_size=int(dataset.metadata.feat_static_cat[0].cardinality),\n",
     "    d_model=32,\n",
     "    prediction_length=dataset.metadata.prediction_length,\n",
-    "    context_length=dataset.metadata.prediction_length*2,\n",
+    "    context_length=dataset.metadata.prediction_length * 2,\n",
     "    freq=dataset.metadata.freq,\n",
     "    scaling=\"std\",\n",
     "    batch_size=64,\n",

TsT-elec.ipynb

@@ -80,11 +80,13 @@
     "    depth=1,\n",
     "    dim=64,\n",
     "    norm_first=True,\n",
-    "    \n",
     "    patch_size=(3, 3),\n",
     "    batch_size=64,\n",
     "    num_batches_per_epoch=100,\n",
-    "    trainer_kwargs=dict(accelerator=\"cuda\", max_epochs=100,),\n",
+    "    trainer_kwargs=dict(\n",
+    "        accelerator=\"cuda\",\n",
+    "        max_epochs=100,\n",
+    "    ),\n",
     ")"
    ]
   },
@@ -167,7 +169,7 @@
     "predictor = estimator.train(\n",
     "    training_data=dataset_train,\n",
     "    cache_data=True,\n",
-    "    #shuffle_buffer_length=1024,\n",
+    "    # shuffle_buffer_length=1024,\n",
     ")"
    ]
   },

TsT-exchange.ipynb

@@ -70,7 +70,7 @@
     "    depth=3,\n",
     "    dim=32,\n",
     "    patch_size=(4, 4),\n",
-    "    patch_reverse_mapping_layer = \"mlp\",\n",
+    "    patch_reverse_mapping_layer=\"mlp\",\n",
     "    batch_size=64,\n",
     "    num_batches_per_epoch=100,\n",
     "    trainer_kwargs=dict(accelerator=\"cuda\", max_epochs=100),\n",

TsT-solar.ipynb

@@ -82,7 +82,10 @@
     "    # patch_size=(5, 5),\n",
     "    batch_size=128,\n",
     "    num_batches_per_epoch=100,\n",
-    "    trainer_kwargs=dict(accelerator=\"cuda\", max_epochs=100,),\n",
+    "    trainer_kwargs=dict(\n",
+    "        accelerator=\"cuda\",\n",
+    "        max_epochs=100,\n",
+    "    ),\n",
     ")"
    ]
   },

deepVAR-exchange.ipynb

@@ -113,7 +113,7 @@
     "    num_layers=3,\n",
     "    dropout_rate=0.1,\n",
     "    prediction_length=dataset.metadata.prediction_length,\n",
-    "    context_length=dataset.metadata.prediction_length*2,\n",
+    "    context_length=dataset.metadata.prediction_length * 2,\n",
     "    freq=dataset.metadata.freq,\n",
     "    scaling=\"std\",\n",
     "    batch_size=64,\n",