29 lines
748 B
Python
29 lines
748 B
Python
"""
|
|
==================
|
|
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 scikits.learn import svm
|
|
from scikits.learn.datasets import samples_generator
|
|
from scikits.learn.feature_selection import SelectKBest, f_regression
|
|
from scikits.learn.pipeline import Pipeline
|
|
|
|
# import some data to play with
|
|
X, y = samples_generator.test_dataset_classif(k=5)
|
|
|
|
# ANOVA SVM-C
|
|
# 1) anova filter, take 5 best ranked features
|
|
anova_filter = SelectKBest(f_regression, k=5)
|
|
# 2) svm
|
|
clf = svm.SVC(kernel='linear')
|
|
|
|
anova_svm = Pipeline([('anova', anova_filter), ('svm', clf)])
|
|
anova_svm.fit(X, y)
|
|
anova_svm.predict(X)
|
|
|