Concatenating multiple feature extraction methodsΒΆ

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.

Out:

Fitting 3 folds for each of 18 candidates, totalling 54 fits
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1, score=0.9019607843137255, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=1, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=10, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=10, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=1, features__univ_select__k=1, svm__C=10, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=1, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=1, score=1.0, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=10, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=10, score=0.9019607843137255, total=   0.0s
[CV] features__pca__n_components=1, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=1, features__univ_select__k=2, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1, score=0.9019607843137255, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=1, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=10, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=10, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=2, features__univ_select__k=1, svm__C=10, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=1, score=1.0, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=10, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=10, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=2, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=2, features__univ_select__k=2, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=1, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=1
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=10, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=1, svm__C=10
[CV]  features__pca__n_components=3, features__univ_select__k=1, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1, score=0.9803921568627451, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1, score=0.9411764705882353, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=0.1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=1, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=1, score=0.9607843137254902, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=1
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=1, score=0.9791666666666666, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=10, score=1.0, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=10, score=0.9215686274509803, total=   0.0s
[CV] features__pca__n_components=3, features__univ_select__k=2, svm__C=10
[CV]  features__pca__n_components=3, features__univ_select__k=2, svm__C=10, score=1.0, total=   0.0s
Pipeline(memory=None,
     steps=[('features', FeatureUnion(n_jobs=1,
       transformer_list=[('pca', PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,
  svd_solver='auto', tol=0.0, whiten=False)), ('univ_select', SelectKBest(k=2, score_func=<function f_classif at 0x2ad3b4bc5488>))],
       transformer...,
  max_iter=-1, probability=False, random_state=None, shrinking=True,
  tol=0.001, verbose=False))])

# 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 0.466 seconds)

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