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# EXAMPLE SEE http://scikit-learn.org/stable/auto_examples/feature_stacker.html#sphx-glr-auto-examples-feature-stacker-py
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# 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_)
Fitting 3 folds for each of 18 candidates, totalling 54 fits
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=1, score=0.960784 - 0.0s
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=1, score=0.901961 - 0.0s
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=1, score=0.979167 - 0.0s
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=1, score=0.941176 - 0.0s
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=1, score=0.921569 - 0.0s
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=1, score=0.979167 - 0.0s
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=1, score=0.960784 - 0.0s
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=1, score=0.921569 - 0.0s
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=1, score=0.979167 - 0.0s
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=2, score=0.960784 - 0.0s
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=2, score=0.921569 - 0.0s
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=0.1, features__univ_select__k=2, score=0.979167 - 0.0s
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=2, score=0.960784 - 0.0s
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=2, score=0.921569 - 0.0s
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=1, features__univ_select__k=2, score=1.000000 - 0.0s
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=2, score=0.980392 - 0.0s
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=2, score=0.901961 - 0.0s
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=1, svm__C=10, features__univ_select__k=2, score=1.000000 - 0.0s
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=1, score=0.960784 - 0.0s
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=1, score=0.901961 - 0.0s
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=1, score=0.979167 - 0.0s
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=1, score=0.980392 - 0.0s
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=1, score=0.941176 - 0.0s
[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s
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[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=1, score=0.979167 - 0.0s
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=1, score=0.980392 - 0.0s
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=1, score=0.941176 - 0.0s
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=1, score=0.979167 - 0.0s
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=2, score=0.980392 - 0.0s
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=2, score=0.941176 - 0.0s
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=0.1, features__univ_select__k=2, score=0.979167 - 0.0s
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=2, score=1.000000 - 0.0s
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=2, score=0.960784 - 0.0s
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=1, features__univ_select__k=2, score=0.979167 - 0.0s
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=2, score=0.980392 - 0.0s
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=2, score=0.921569 - 0.0s
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=2, svm__C=10, features__univ_select__k=2, score=1.000000 - 0.0s
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=1, score=0.980392 - 0.0s
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=1, score=0.941176 - 0.0s
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=1, score=0.979167 - 0.0s
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=1, score=1.000000 - 0.0s
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=1, score=0.941176 - 0.0s
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=1, score=0.979167 - 0.0s
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=1, score=1.000000 - 0.0s
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=1, score=0.921569 - 0.0s
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=1
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=1, score=1.000000 - 0.0s
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=2, score=0.980392 - 0.0s
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=2, score=0.941176 - 0.0s
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=0.1, features__univ_select__k=2, score=0.979167 - 0.0s
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=2, score=1.000000 - 0.0s
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=2, score=0.960784 - 0.0s
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=1, features__univ_select__k=2, score=0.979167 - 0.0s
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=2, score=1.000000 - 0.0s
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=2, score=0.921569 - 0.0s
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=2
[CV] features__pca__n_components=3, svm__C=10, features__univ_select__k=2, score=1.000000 - 0.0s
Pipeline(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 0x7f5b02dc8048>))],
transformer...,
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False))])
[Parallel(n_jobs=1)]: Done 54 out of 54 | elapsed: 0.7s finished
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Content source: Make-O-Matic/MOM-Platform
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