Pushing the docs to dev/ for branch: master, commit 01d16b1a2e46879c852d9e2277ecf1d28646e1de

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Andreas Mueller <[email protected]>\n#\n# License: BSD 3 clause\n\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import load_iris\nfrom sklearn.decomposition import PCA\nfrom sklearn.feature_selection import SelectKBest\n\niris = load_iris()\n\nX, y = iris.data, iris.target\n\n# This dataset is way too high-dimensional. Better do PCA:\npca = PCA(n_components=2)\n\n# Maybe some original features where good, too?\nselection = SelectKBest(k=1)\n\n# Build estimator from PCA and Univariate selection:\n\ncombined_features = FeatureUnion([(\"pca\", pca), (\"univ_select\", selection)])\n\n# Use combined features to transform dataset:\nX_features = combined_features.fit(X, y).transform(X)\n\nsvm = SVC(kernel=\"linear\")\n\n# Do grid search over k, n_components and C:\n\npipeline = Pipeline([(\"features\", combined_features), (\"svm\", svm)])\n\nparam_grid = dict(features__pca__n_components=[1, 2, 3],\n                  features__univ_select__k=[1, 2],\n                  svm__C=[0.1, 1, 10])\n\ngrid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)\ngrid_search.fit(X, y)\nprint(grid_search.best_estimator_)"
+        "# Author: Andreas Mueller <[email protected]>\n#\n# License: BSD 3 clause\n\nfrom __future__ import print_function\nfrom sklearn.pipeline import Pipeline, FeatureUnion\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.svm import SVC\nfrom sklearn.datasets import load_iris\nfrom sklearn.decomposition import PCA\nfrom sklearn.feature_selection import SelectKBest\n\niris = load_iris()\n\nX, y = iris.data, iris.target\n\n# This dataset is way too high-dimensional. Better do PCA:\npca = PCA(n_components=2)\n\n# Maybe some original features where good, too?\nselection = SelectKBest(k=1)\n\n# Build estimator from PCA and Univariate selection:\n\ncombined_features = FeatureUnion([(\"pca\", pca), (\"univ_select\", selection)])\n\n# Use combined features to transform dataset:\nX_features = combined_features.fit(X, y).transform(X)\nprint(\"Combined space has\", X_features.shape[1], \"features\")\n\nsvm = SVC(kernel=\"linear\")\n\n# Do grid search over k, n_components and C:\n\npipeline = Pipeline([(\"features\", combined_features), (\"svm\", svm)])\n\nparam_grid = dict(features__pca__n_components=[1, 2, 3],\n                  features__univ_select__k=[1, 2],\n                  svm__C=[0.1, 1, 10])\n\ngrid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)\ngrid_search.fit(X, y)\nprint(grid_search.best_estimator_)"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -19,6 +19,7 @@
 #
 # License: BSD 3 clause
 
+from __future__ import print_function
 from sklearn.pipeline import Pipeline, FeatureUnion
 from sklearn.model_selection import GridSearchCV
 from sklearn.svm import SVC
@@ -42,6 +43,7 @@
 
 # Use combined features to transform dataset:
 X_features = combined_features.fit(X, y).transform(X)
+print("Combined space has", X_features.shape[1], "features")
 
 svm = SVC(kernel="linear")