Pushing the docs to dev/ for branch: master, commit 6c52a247d14ccf409d1bf2abeb309a0fe3893142

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -12,7 +12,7 @@
 
 To install the latest version (with pip)::
 
-    pip install --upgrade scikit-learn
+    pip install -U scikit-learn --upgrade
 
 or with conda::
 
@@ -172,43 +172,3 @@
     # recomputed.
     estimator.set_params(isomap__n_neighbors=5)
     estimator.fit(X)
-
-############################################################################
-# Stacking Classifier and Regressor
-# ---------------------------------
-# :class:`~ensemble.StackingClassifier` and
-# :class:`~ensemble.StackingRegressor`
-# allow you to have a stack of estimators with a final classifier or
-# a regressor.
-# Stacked generalization consists in stacking the output of individual
-# estimators and use a classifier to compute the final prediction. Stacking
-# allows to use the strength of each individual estimator by using their output
-# as input of a final estimator.
-# Base estimators are fitted on the full ``X`` while
-# the final estimator is trained using cross-validated predictions of the
-# base estimators using ``cross_val_predict``.
-#
-# Read more in the :ref:`User Guide <stacking>`.
-
-from sklearn.datasets import load_iris
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.svm import LinearSVC
-from sklearn.linear_model import LogisticRegression
-from sklearn.preprocessing import StandardScaler
-from sklearn.pipeline import make_pipeline
-from sklearn.ensemble import StackingClassifier
-from sklearn.model_selection import train_test_split
-
-X, y = load_iris(return_X_y=True)
-estimators = [
-    ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),
-    ('svr', make_pipeline(StandardScaler(),
-                          LinearSVC(random_state=42)))
-]
-clf = StackingClassifier(
-    estimators=estimators, final_estimator=LogisticRegression()
-)
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, stratify=y, random_state=42
-)
-clf.fit(X_train, y_train).score(X_test, y_test)

dev/_downloads/7ee55c12f8d3eb1dd8d2005d9dd7b6f1/plot_release_highlights_0_22_0.py

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n========================================\nRelease Highlights for scikit-learn 0.22\n========================================\n\n.. currentmodule:: sklearn\n\nWe are pleased to announce the release of scikit-learn 0.22, which comes\nwith many bug fixes and new features! We detail below a few of the major\nfeatures of this release. For an exhaustive list of all the changes, please\nrefer to the `release notes <changes_0_22>`.\n\nTo install the latest version (with pip)::\n\n    pip install --upgrade scikit-learn\n\nor with conda::\n\n    conda install scikit-learn\n"
+        "\n========================================\nRelease Highlights for scikit-learn 0.22\n========================================\n\n.. currentmodule:: sklearn\n\nWe are pleased to announce the release of scikit-learn 0.22, which comes\nwith many bug fixes and new features! We detail below a few of the major\nfeatures of this release. For an exhaustive list of all the changes, please\nrefer to the `release notes <changes_0_22>`.\n\nTo install the latest version (with pip)::\n\n    pip install -U scikit-learn --upgrade\n\nor with conda::\n\n    conda install scikit-learn\n"
       ]
     },
     {
@@ -143,24 +143,6 @@
       "source": [
         "from tempfile import TemporaryDirectory\nfrom sklearn.neighbors import KNeighborsTransformer\nfrom sklearn.manifold import Isomap\nfrom sklearn.pipeline import make_pipeline\n\nwith TemporaryDirectory(prefix=\"sklearn_cache_\") as tmpdir:\n    estimator = make_pipeline(\n        KNeighborsTransformer(n_neighbors=10, mode='distance'),\n        Isomap(n_neighbors=10, metric='precomputed'),\n        memory=tmpdir)\n    estimator.fit(X)\n\n    # We can decrease the number of neighbors and the graph will not be\n    # recomputed.\n    estimator.set_params(isomap__n_neighbors=5)\n    estimator.fit(X)"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Stacking Classifier and Regressor\n---------------------------------\n:class:`~ensemble.StackingClassifier` and\n:class:`~ensemble.StackingRegressor`\nallow you to have a stack of estimators with a final classifier or\na regressor.\nStacked generalization consists in stacking the output of individual\nestimators and use a classifier to compute the final prediction. Stacking\nallows to use the strength of each individual estimator by using their output\nas input of a final estimator.\nBase estimators are fitted on the full ``X`` while\nthe final estimator is trained using cross-validated predictions of the\nbase estimators using ``cross_val_predict``.\n\nRead more in the `User Guide <stacking>`.\n\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from sklearn.datasets import load_iris\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.svm import LinearSVC\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.ensemble import StackingClassifier\nfrom sklearn.model_selection import train_test_split\n\nX, y = load_iris(return_X_y=True)\nestimators = [\n    ('rf', RandomForestClassifier(n_estimators=10, random_state=42)),\n    ('svr', make_pipeline(StandardScaler(),\n                          LinearSVC(random_state=42)))\n]\nclf = StackingClassifier(\n    estimators=estimators, final_estimator=LogisticRegression()\n)\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, stratify=y, random_state=42\n)\nclf.fit(X_train, y_train).score(X_test, y_test)"
-      ]
     }
   ],
   "metadata": {