chico: [FIX] Update Readme

Author: Github Actions

development_update_bibtext/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: da1291ead51b7998a2311fe24055da4c
+config: fe944f1b1d3cb4e7269e41b4f8b98b0d
 tags: 645f666f9bcd5a90fca523b33c5a78b7

development_update_bibtext/_downloads/2f0f54a9831653ac5c12ee0e06324a62/basics_tabular_jupyter.zip

@@ -43,11 +43,6 @@
     # Build and fit a classifier with default resampling strategy
     # ===========================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_03',
-        output_directory='./tmp/autoPyTorch_example_out_03',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         # 'HoldoutValTypes.holdout_validation' with 'val_share': 0.33
         # is the default argument setting for TabularClassificationTask.
         # It is explicitly specified in this example for demonstrational
@@ -85,11 +80,6 @@
     # Build and fit a classifier with Cross validation resampling strategy
     # ====================================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_04',
-        output_directory='./tmp/autoPyTorch_example_out_04',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         resampling_strategy=CrossValTypes.k_fold_cross_validation,
         resampling_strategy_args={'num_splits': 3}
     )
@@ -123,11 +113,6 @@
     # Build and fit a classifier with Stratified resampling strategy
     # ==============================================================
     api = TabularClassificationTask(
-        temporary_directory='./tmp/autoPyTorch_example_tmp_05',
-        output_directory='./tmp/autoPyTorch_example_out_05',
-        # To maintain logs of the run, set the next two as False
-        delete_tmp_folder_after_terminate=True,
-        delete_output_folder_after_terminate=True,
         # For demonstration purposes, we use
         # Stratified hold out validation. However,
         # one can also use CrossValTypes.stratified_k_fold_cross_validation.

development_update_bibtext/_downloads/62e4ad0741dcbe663bc5f60c078c950f/example_resampling_strategy.py renamed to development_update_bibtext/_downloads/307f532dbef0476f85afc6b64b65f087/example_resampling_strategy.py

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "import os\nimport tempfile as tmp\nimport warnings\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nfrom autoPyTorch.api.tabular_regression import TabularRegressionTask\n\n\nif __name__ == '__main__':\n\n    ############################################################################\n    # Data Loading\n    # ============\n    X, y = sklearn.datasets.fetch_openml(name='boston', return_X_y=True, as_frame=True)\n    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n        X,\n        y,\n        random_state=1,\n    )\n\n    # Scale the regression targets to have zero mean and unit variance.\n    # This is important for Neural Networks since predicting large target values would require very large weights.\n    # One can later rescale the network predictions like this: y_pred = y_pred_scaled * y_train_std + y_train_mean\n    y_train_mean = y_train.mean()\n    y_train_std = y_train.std()\n\n    y_train_scaled = (y_train - y_train_mean) / y_train_std\n    y_test_scaled = (y_test - y_train_mean) / y_train_std\n\n    ############################################################################\n    # Build and fit a regressor\n    # ==========================\n    api = TabularRegressionTask(\n        temporary_directory='./tmp/autoPyTorch_example_tmp_02',\n        output_directory='./tmp/autoPyTorch_example_out_02',\n        # To maintain logs of the run, set the next two as False\n        delete_tmp_folder_after_terminate=True,\n        delete_output_folder_after_terminate=True\n    )\n\n    ############################################################################\n    # Search for an ensemble of machine learning algorithms\n    # =====================================================\n    api.search(\n        X_train=X_train,\n        y_train=y_train_scaled,\n        X_test=X_test.copy(),\n        y_test=y_test_scaled.copy(),\n        optimize_metric='r2',\n        total_walltime_limit=300,\n        func_eval_time_limit_secs=50,\n        enable_traditional_pipeline=False,\n    )\n\n    ############################################################################\n    # Print the final ensemble performance\n    # ====================================\n    print(api.run_history, api.trajectory)\n    y_pred_scaled = api.predict(X_test)\n\n    # Rescale the Neural Network predictions into the original target range\n    y_pred = y_pred_scaled * y_train_std + y_train_mean\n    score = api.score(y_pred, y_test)\n\n    print(score)\n    # Print the final ensemble built by AutoPyTorch\n    print(api.show_models())"
+        "import os\nimport tempfile as tmp\nimport warnings\n\nimport sklearn.datasets\nimport sklearn.model_selection\n\nos.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()\nos.environ['OMP_NUM_THREADS'] = '1'\nos.environ['OPENBLAS_NUM_THREADS'] = '1'\nos.environ['MKL_NUM_THREADS'] = '1'\n\nwarnings.simplefilter(action='ignore', category=UserWarning)\nwarnings.simplefilter(action='ignore', category=FutureWarning)\n\nfrom autoPyTorch.api.tabular_regression import TabularRegressionTask\n\n\nif __name__ == '__main__':\n\n    ############################################################################\n    # Data Loading\n    # ============\n    X, y = sklearn.datasets.fetch_openml(name='boston', return_X_y=True, as_frame=True)\n    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n        X,\n        y,\n        random_state=1,\n    )\n\n    # Scale the regression targets to have zero mean and unit variance.\n    # This is important for Neural Networks since predicting large target values would require very large weights.\n    # One can later rescale the network predictions like this: y_pred = y_pred_scaled * y_train_std + y_train_mean\n    y_train_mean = y_train.mean()\n    y_train_std = y_train.std()\n\n    y_train_scaled = (y_train - y_train_mean) / y_train_std\n    y_test_scaled = (y_test - y_train_mean) / y_train_std\n\n    ############################################################################\n    # Build and fit a regressor\n    # ==========================\n    api = TabularRegressionTask()\n\n    ############################################################################\n    # Search for an ensemble of machine learning algorithms\n    # =====================================================\n    api.search(\n        X_train=X_train,\n        y_train=y_train_scaled,\n        X_test=X_test.copy(),\n        y_test=y_test_scaled.copy(),\n        optimize_metric='r2',\n        total_walltime_limit=300,\n        func_eval_time_limit_secs=50,\n        enable_traditional_pipeline=False,\n    )\n\n    ############################################################################\n    # Print the final ensemble performance\n    # ====================================\n    print(api.run_history, api.trajectory)\n    y_pred_scaled = api.predict(X_test)\n\n    # Rescale the Neural Network predictions into the original target range\n    y_pred = y_pred_scaled * y_train_std + y_train_mean\n    score = api.score(y_pred, y_test)\n\n    print(score)\n    # Print the final ensemble built by AutoPyTorch\n    print(api.show_models())"
       ]
     }
   ],