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"""
Testing Recursive feature elimination
"""
import numpy as np
from numpy.testing import assert_array_almost_equal
from nose.tools import assert_true
from sklearn.feature_selection.rfe import RFE, RFECV
from sklearn.datasets import load_iris
from sklearn.metrics import zero_one
from sklearn.svm import SVC
from sklearn.utils import check_random_state
def test_rfe():
generator = check_random_state(0)
iris = load_iris()
X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
y = iris.target
clf = SVC(kernel="linear")
rfe = RFE(estimator=clf, n_features_to_select=4, step=0.1)
rfe.fit(X, y)
X_r = rfe.transform(X)
assert_true(X_r.shape == iris.data.shape)
assert_array_almost_equal(X_r[:10], iris.data[:10])
assert_array_almost_equal(rfe.predict(X), clf.predict(iris.data))
assert_true(rfe.score(X, y) == clf.score(iris.data, iris.target))
def test_rfecv():
generator = check_random_state(0)
iris = load_iris()
X = np.c_[iris.data, generator.normal(size=(len(iris.data), 6))]
y = iris.target
# Test using the score function
rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, cv=3)
rfecv.fit(X, y)
X_r = rfecv.transform(X)
assert_true(X_r.shape == iris.data.shape)
assert_array_almost_equal(X_r[:10], iris.data[:10])
# Test using a customized loss function
rfecv = RFECV(estimator=SVC(kernel="linear"), step=1, cv=3,
loss_func=zero_one)
rfecv.fit(X, y)
X_r = rfecv.transform(X)
assert_true(X_r.shape == iris.data.shape)
assert_array_almost_equal(X_r[:10], iris.data[:10])
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