Francisco Rivera Valverde: Remove the __main__ restriction from examples (#1053)

Author: Github Actions

development/_downloads/16fb4037a0b549292dbf3df70af70372/example_classification.ipynb

@@ -26,7 +26,79 @@
       },
       "outputs": [],
       "source": [
-        "import sklearn.datasets\nimport sklearn.metrics\n\nimport autosklearn.classification\n\n\nif __name__ == \"__main__\":\n    ############################################################################\n    # Data Loading\n    # ============\n\n    X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n    X_train, X_test, y_train, y_test = \\\n        sklearn.model_selection.train_test_split(X, y, random_state=1)\n\n    ############################################################################\n    # Build and fit a regressor\n    # =========================\n\n    automl = autosklearn.classification.AutoSklearnClassifier(\n        time_left_for_this_task=120,\n        per_run_time_limit=30,\n        tmp_folder='/tmp/autosklearn_classification_example_tmp',\n        output_folder='/tmp/autosklearn_classification_example_out',\n    )\n    automl.fit(X_train, y_train, dataset_name='breast_cancer')\n\n    ############################################################################\n    # Print the final ensemble constructed by auto-sklearn\n    # ====================================================\n\n    print(automl.show_models())\n\n    ###########################################################################\n    # Get the Score of the final ensemble\n    # ===================================\n\n    predictions = automl.predict(X_test)\n    print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_test, predictions))"
+        "import sklearn.datasets\nimport sklearn.metrics\n\nimport autosklearn.classification"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Data Loading\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\nX_train, X_test, y_train, y_test = \\\n    sklearn.model_selection.train_test_split(X, y, random_state=1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Build and fit a regressor\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "automl = autosklearn.classification.AutoSklearnClassifier(\n    time_left_for_this_task=120,\n    per_run_time_limit=30,\n    tmp_folder='/tmp/autosklearn_classification_example_tmp',\n    output_folder='/tmp/autosklearn_classification_example_out',\n)\nautoml.fit(X_train, y_train, dataset_name='breast_cancer')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Print the final ensemble constructed by auto-sklearn\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "print(automl.show_models())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Get the Score of the final ensemble\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "predictions = automl.predict(X_test)\nprint(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_test, predictions))"
       ]
     }
   ],

development/_downloads/1a053e45a20a2c15032411b9fee890a3/example_sequential.py

@@ -16,46 +16,45 @@
 import autosklearn.classification
 
 
-if __name__ == "__main__":
-    ############################################################################
-    # Data Loading
-    # ======================================
-
-    X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
-    X_train, X_test, y_train, y_test = \
-        sklearn.model_selection.train_test_split(X, y, random_state=1)
-
-    ############################################################################
-    # Build and fit the classifier
-    # ======================================
-
-    automl = autosklearn.classification.AutoSklearnClassifier(
-        time_left_for_this_task=120,
-        per_run_time_limit=30,
-        tmp_folder='/tmp/autosklearn_sequential_example_tmp',
-        output_folder='/tmp/autosklearn_sequential_example_out',
-        # Do not construct ensembles in parallel to avoid using more than one
-        # core at a time. The ensemble will be constructed after auto-sklearn
-        # finished fitting all machine learning models.
-        ensemble_size=0,
-        delete_tmp_folder_after_terminate=False,
-    )
-    automl.fit(X_train, y_train, dataset_name='breast_cancer')
-
-    # This call to fit_ensemble uses all models trained in the previous call
-    # to fit to build an ensemble which can be used with automl.predict()
-    automl.fit_ensemble(y_train, ensemble_size=50)
-
-    ############################################################################
-    # Print the final ensemble constructed by auto-sklearn
-    # ====================================================
-
-    print(automl.show_models())
-
-    ############################################################################
-    # Get the Score of the final ensemble
-    # ===================================
-
-    predictions = automl.predict(X_test)
-    print(automl.sprint_statistics())
-    print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))
+############################################################################
+# Data Loading
+# ======================================
+
+X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)
+X_train, X_test, y_train, y_test = \
+    sklearn.model_selection.train_test_split(X, y, random_state=1)
+
+############################################################################
+# Build and fit the classifier
+# ======================================
+
+automl = autosklearn.classification.AutoSklearnClassifier(
+    time_left_for_this_task=120,
+    per_run_time_limit=30,
+    tmp_folder='/tmp/autosklearn_sequential_example_tmp',
+    output_folder='/tmp/autosklearn_sequential_example_out',
+    # Do not construct ensembles in parallel to avoid using more than one
+    # core at a time. The ensemble will be constructed after auto-sklearn
+    # finished fitting all machine learning models.
+    ensemble_size=0,
+    delete_tmp_folder_after_terminate=False,
+)
+automl.fit(X_train, y_train, dataset_name='breast_cancer')
+
+# This call to fit_ensemble uses all models trained in the previous call
+# to fit to build an ensemble which can be used with automl.predict()
+automl.fit_ensemble(y_train, ensemble_size=50)
+
+############################################################################
+# Print the final ensemble constructed by auto-sklearn
+# ====================================================
+
+print(automl.show_models())
+
+############################################################################
+# Get the Score of the final ensemble
+# ===================================
+
+predictions = automl.predict(X_test)
+print(automl.sprint_statistics())
+print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))