Pushing the docs to dev/ for branch: main, commit 2fbded2d11e4802ab9028f476560b6987b07f365

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -98,9 +98,14 @@ def plot_digits(X, title):
 # uses a radial basis function (RBF) kernel.
 from sklearn.decomposition import PCA, KernelPCA
 
-pca = PCA(n_components=32)
+pca = PCA(n_components=32, random_state=42)
 kernel_pca = KernelPCA(
-    n_components=400, kernel="rbf", gamma=1e-3, fit_inverse_transform=True, alpha=5e-3
+    n_components=400,
+    kernel="rbf",
+    gamma=1e-3,
+    fit_inverse_transform=True,
+    alpha=5e-3,
+    random_state=42,
 )
 
 pca.fit(X_train_noisy)

dev/_downloads/32173eb704d697c23dffbbf3fd74942a/plot_digits_denoising.py

@@ -297,7 +297,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.model_selection import cross_validate\n\n\ncategorical_columns = [\n    \"weather\",\n    \"season\",\n    \"holiday\",\n    \"workingday\",\n]\ncategories = [\n    [\"clear\", \"misty\", \"rain\"],\n    [\"spring\", \"summer\", \"fall\", \"winter\"],\n    [\"False\", \"True\"],\n    [\"False\", \"True\"],\n]\nordinal_encoder = OrdinalEncoder(categories=categories)\n\n\ngbrt_pipeline = make_pipeline(\n    ColumnTransformer(\n        transformers=[\n            (\"categorical\", ordinal_encoder, categorical_columns),\n        ],\n        remainder=\"passthrough\",\n        # Use short feature names to make it easier to specify the categorical\n        # variables in the HistGradientBoostingRegressor in the next\n        # step of the pipeline.\n        verbose_feature_names_out=False,\n    ),\n    HistGradientBoostingRegressor(\n        categorical_features=categorical_columns,\n    ),\n).set_output(transform=\"pandas\")"
+        "from sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.model_selection import cross_validate\n\n\ncategorical_columns = [\n    \"weather\",\n    \"season\",\n    \"holiday\",\n    \"workingday\",\n]\ncategories = [\n    [\"clear\", \"misty\", \"rain\"],\n    [\"spring\", \"summer\", \"fall\", \"winter\"],\n    [\"False\", \"True\"],\n    [\"False\", \"True\"],\n]\nordinal_encoder = OrdinalEncoder(categories=categories)\n\n\ngbrt_pipeline = make_pipeline(\n    ColumnTransformer(\n        transformers=[\n            (\"categorical\", ordinal_encoder, categorical_columns),\n        ],\n        remainder=\"passthrough\",\n        # Use short feature names to make it easier to specify the categorical\n        # variables in the HistGradientBoostingRegressor in the next\n        # step of the pipeline.\n        verbose_feature_names_out=False,\n    ),\n    HistGradientBoostingRegressor(\n        categorical_features=categorical_columns,\n        random_state=42,\n    ),\n).set_output(transform=\"pandas\")"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -216,6 +216,7 @@
     ),
     HistGradientBoostingRegressor(
         categorical_features=categorical_columns,
+        random_state=42,
     ),
 ).set_output(transform="pandas")
 

dev/_downloads/7012baed63b9a27f121bae611b8285c2/plot_cyclical_feature_engineering.ipynb

@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.decomposition import PCA, KernelPCA\n\npca = PCA(n_components=32)\nkernel_pca = KernelPCA(\n    n_components=400, kernel=\"rbf\", gamma=1e-3, fit_inverse_transform=True, alpha=5e-3\n)\n\npca.fit(X_train_noisy)\n_ = kernel_pca.fit(X_train_noisy)"
+        "from sklearn.decomposition import PCA, KernelPCA\n\npca = PCA(n_components=32, random_state=42)\nkernel_pca = KernelPCA(\n    n_components=400,\n    kernel=\"rbf\",\n    gamma=1e-3,\n    fit_inverse_transform=True,\n    alpha=5e-3,\n    random_state=42,\n)\n\npca.fit(X_train_noisy)\n_ = kernel_pca.fit(X_train_noisy)"
       ]
     },
     {