old_contents: """
  ==================
  Pipeline Anova SVM
  ==================
  
  Simple usage of Pipeline that runs successively a univariate
  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 make_classification
  from sklearn.feature_selection import SelectKBest, f_regression
  from sklearn.pipeline import make_pipeline
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import classification_report
  
  print(__doc__)
  
  # import some data to play with
  X, y = make_classification(
      n_features=20, n_informative=3, n_redundant=0, n_classes=4,
      n_clusters_per_class=2)
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
  
  # ANOVA SVM-C
  # 1) anova filter, take 3 best ranked features
  anova_filter = SelectKBest(f_regression, k=3)
  # 2) svm
  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)
  
new_contents: """
  ==================
  Pipeline Anova SVM
  ==================
  
  Simple usage of Pipeline that runs successively a univariate
  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 make_classification
  from sklearn.feature_selection import SelectKBest, f_classif
  from sklearn.pipeline import make_pipeline
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import classification_report
  
  print(__doc__)
  
  # import some data to play with
  X, y = make_classification(
      n_features=20, n_informative=3, n_redundant=0, n_classes=4,
      n_clusters_per_class=2)
  
  X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
  
  # ANOVA SVM-C
  # 1) anova filter, take 3 best ranked features
  anova_filter = SelectKBest(f_classif, k=3)
  # 2) svm
  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)
  
subject: FIX Uses f_classif instead f_regress in classification example (#17537)