Pushing the docs to dev/ for branch: master, commit 22071218b221e4a48f78f834864302b6d4b69b91

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Pipeline Anova SVM\n\n\nSimple usage of Pipeline that runs successively a univariate\nfeature selection with anova and then a C-SVM of the selected features.\n\n"
+        "\n# Pipeline Anova SVM\n\n\nSimple usage of Pipeline that runs successively a univariate\nfeature selection with anova and then a SVM of the selected features.\n\nUsing a sub-pipeline, the fitted coefficients can be mapped back into\nthe original feature space.\n\n"
       ]
     },
     {
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn import svm\nfrom sklearn.datasets import samples_generator\nfrom sklearn.feature_selection import SelectKBest, f_regression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\n\nprint(__doc__)\n\n# import some data to play with\nX, y = samples_generator.make_classification(\n    n_features=20, n_informative=3, n_redundant=0, n_classes=4,\n    n_clusters_per_class=2)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n\n# ANOVA SVM-C\n# 1) anova filter, take 3 best ranked features\nanova_filter = SelectKBest(f_regression, k=3)\n# 2) svm\nclf = svm.SVC(kernel='linear')\n\nanova_svm = make_pipeline(anova_filter, clf)\nanova_svm.fit(X_train, y_train)\ny_pred = anova_svm.predict(X_test)\nprint(classification_report(y_test, y_pred))"
+        "from sklearn import svm\nfrom sklearn.datasets import samples_generator\nfrom sklearn.feature_selection import SelectKBest, f_regression\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import classification_report\n\nprint(__doc__)\n\n# import some data to play with\nX, y = samples_generator.make_classification(\n    n_features=20, n_informative=3, n_redundant=0, n_classes=4,\n    n_clusters_per_class=2)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n\n# ANOVA SVM-C\n# 1) anova filter, take 3 best ranked features\nanova_filter = SelectKBest(f_regression, k=3)\n# 2) svm\nclf = svm.LinearSVC()\n\nanova_svm = make_pipeline(anova_filter, clf)\nanova_svm.fit(X_train, y_train)\ny_pred = anova_svm.predict(X_test)\nprint(classification_report(y_test, y_pred))\n\ncoef = anova_svm[:-1].inverse_transform(anova_svm['linearsvc'].coef_)\nprint(coef)"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -4,7 +4,10 @@
 ==================
 
 Simple usage of Pipeline that runs successively a univariate
-feature selection with anova and then a C-SVM of the selected features.
+feature selection with anova and then a SVM of the selected features.
+
+Using a sub-pipeline, the fitted coefficients can be mapped back into
+the original feature space.
 """
 from sklearn import svm
 from sklearn.datasets import samples_generator
@@ -26,9 +29,12 @@
 # 1) anova filter, take 3 best ranked features
 anova_filter = SelectKBest(f_regression, k=3)
 # 2) svm
-clf = svm.SVC(kernel='linear')
+clf = svm.LinearSVC()
 
 anova_svm = make_pipeline(anova_filter, clf)
 anova_svm.fit(X_train, y_train)
 y_pred = anova_svm.predict(X_test)
 print(classification_report(y_test, y_pred))
+
+coef = anova_svm[:-1].inverse_transform(anova_svm['linearsvc'].coef_)
+print(coef)