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 0x2b60c14cbea0>))],
transformer...,
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False))])
# Author: Andreas Mueller <[email protected]>
#
# 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 0.518 seconds)