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"""
==================
Pipeline Anova SVM
==================
Simple usage of Pipeline that runs successively a univariate
feature selection with anova and then a C-SVM of the selected features.
"""
print __doc__
from sklearn import svm
from sklearn.datasets import samples_generator
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.pipeline import Pipeline
# import some data to play with
X, y = samples_generator.make_classification(
n_features=20, n_informative=3, n_redundant=0,
n_classes=4, n_clusters_per_class=2)
# ANOVA SVM-C
# 1) anova filter, take 3 best ranked features
anova_filter = SelectKBest(f_regression, k=3)
# 2) svm
clf = svm.SVC(kernel='linear')
anova_svm = Pipeline([('anova', anova_filter), ('svm', clf)])
anova_svm.fit(X, y)
anova_svm.predict(X)
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