feature_stacker.txt

.. _sphx_glr_auto_examples_feature_stacker.py:


=================================================
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.


.. code-block:: python


    # Author: Andreas Mueller <amueller@ais.uni-bonn.de>
    #
    # 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)

    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_)

**Total running time of the script:**
(0 minutes 0.000 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`feature_stacker.py <feature_stacker.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`feature_stacker.ipynb <feature_stacker.ipynb>`