Pushing the docs to dev/ for branch: master, commit f7a987ab530f5993d715b36f3af132dfad946db4

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -51,8 +51,9 @@
 # - ``random_cat`` is a low cardinality categorical variable (3 possible
 #   values).
 X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)
-X['random_cat'] = np.random.randint(3, size=X.shape[0])
-X['random_num'] = np.random.randn(X.shape[0])
+rng = np.random.RandomState(seed=42)
+X['random_cat'] = rng.randint(3, size=X.shape[0])
+X['random_num'] = rng.randn(X.shape[0])
 
 categorical_columns = ['pclass', 'sex', 'embarked', 'random_cat']
 numerical_columns = ['age', 'sibsp', 'parch', 'fare', 'random_num']

dev/_downloads/9481da9ed1cbf016109715bdf7e79a6f/plot_permutation_importance.py

@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "X, y = fetch_openml(\"titanic\", version=1, as_frame=True, return_X_y=True)\nX['random_cat'] = np.random.randint(3, size=X.shape[0])\nX['random_num'] = np.random.randn(X.shape[0])\n\ncategorical_columns = ['pclass', 'sex', 'embarked', 'random_cat']\nnumerical_columns = ['age', 'sibsp', 'parch', 'fare', 'random_num']\n\nX = X[categorical_columns + numerical_columns]\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, stratify=y, random_state=42)\n\ncategorical_pipe = Pipeline([\n    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n])\nnumerical_pipe = Pipeline([\n    ('imputer', SimpleImputer(strategy='mean'))\n])\n\npreprocessing = ColumnTransformer(\n    [('cat', categorical_pipe, categorical_columns),\n     ('num', numerical_pipe, numerical_columns)])\n\nrf = Pipeline([\n    ('preprocess', preprocessing),\n    ('classifier', RandomForestClassifier(random_state=42))\n])\nrf.fit(X_train, y_train)"
+        "X, y = fetch_openml(\"titanic\", version=1, as_frame=True, return_X_y=True)\nrng = np.random.RandomState(seed=42)\nX['random_cat'] = rng.randint(3, size=X.shape[0])\nX['random_num'] = rng.randn(X.shape[0])\n\ncategorical_columns = ['pclass', 'sex', 'embarked', 'random_cat']\nnumerical_columns = ['age', 'sibsp', 'parch', 'fare', 'random_num']\n\nX = X[categorical_columns + numerical_columns]\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, stratify=y, random_state=42)\n\ncategorical_pipe = Pipeline([\n    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n])\nnumerical_pipe = Pipeline([\n    ('imputer', SimpleImputer(strategy='mean'))\n])\n\npreprocessing = ColumnTransformer(\n    [('cat', categorical_pipe, categorical_columns),\n     ('num', numerical_pipe, numerical_columns)])\n\nrf = Pipeline([\n    ('preprocess', preprocessing),\n    ('classifier', RandomForestClassifier(random_state=42))\n])\nrf.fit(X_train, y_train)"
       ]
     },
     {