""" ================================================= 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 # # License: BSD 3 clause from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.model_selection 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 too 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) print("Combined space has", X_features.shape[1], "features") 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_)