content: """
  =================================================
  Concatenating multiple feature extraction methods
  =================================================
  
  In many real-world examples, there are many ways to extract features from a
  dataset. Often it is beneficial to combine several methods to obtain good
  performance. This example shows how to use ``FeatureUnion`` to combine
  features obtained by PCA and univariate selection.
  
  Combining features using this transformer has the benefit that it allows
  cross validation and grid searches over the whole process.
  
  The combination used in this example is not particularly helpful on this
  dataset and is only used to illustrate the usage of FeatureUnion.
  """
  
  # Author: Andreas Mueller <amueller@ais.uni-bonn.de>
  #
  # License: BSD 3 clause
  
  from sklearn.pipeline import Pipeline, FeatureUnion
  from sklearn.grid_search import GridSearchCV
  from sklearn.svm import SVC
  from sklearn.datasets import load_iris
  from sklearn.decomposition import PCA
  from sklearn.feature_selection import SelectKBest
  
  iris = load_iris()
  
  X, y = iris.data, iris.target
  
  # This dataset is way to high-dimensional. Better do PCA:
  pca = PCA(n_components=2)
  
  # Maybe some original features where good, too?
  selection = SelectKBest(k=1)
  
  # Build estimator from PCA and Univariate selection:
  
  combined_features = FeatureUnion([("pca", pca), ("univ_select", selection)])
  
  # Use combined features to transform dataset:
  X_features = combined_features.fit(X, y).transform(X)
  
  svm = SVC(kernel="linear")
  
  # Do grid search over k, n_components and C:
  
  pipeline = Pipeline([("features", combined_features), ("svm", svm)])
  
  param_grid = dict(features__pca__n_components=[1, 2, 3],
                    features__univ_select__k=[1, 2],
                    svm__C=[0.1, 1, 10])
  
  grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=10)
  grid_search.fit(X, y)
  print(grid_search.best_estimator_)
  