def test_calibrated_classifier_ratio_identity():
p = Normal(mu=0.0)
ratio = CalibratedClassifierRatio(base_estimator=ElasticNetCV())
ratio.fit(numerator=p, denominator=p, n_samples=10000)
reals = np.linspace(-0.5, 1.0, num=100).reshape(-1, 1)
assert ratio.score(reals, p.pdf(reals) / p.pdf(reals)) == 0.0
assert_array_almost_equal(ratio.predict(reals), np.ones(len(reals)))
assert_array_almost_equal(ratio.predict(reals, log=True),
np.zeros(len(reals)))
def check_calibrated_classifier_ratio(clf, calibration, cv):
# Passing distributions directly
p0 = Normal(mu=0.0)
p1 = Normal(mu=0.1)
ratio = CalibratedClassifierRatio(base_estimator=clf,
calibration=calibration, cv=cv)
ratio.fit(numerator=p0, denominator=p1, n_samples=10000)
reals = np.linspace(-1, 1, num=100).reshape(-1, 1)
assert ratio.score(reals, p0.pdf(reals) / p1.pdf(reals)) > -0.1
assert np.mean(np.abs(np.log(ratio.predict(reals)) -
ratio.predict(reals, log=True))) < 0.01
# Passing X, y only
X = np.vstack((p0.rvs(5000), p1.rvs(5000)))
y = np.zeros(10000, dtype=np.int)
y[5000:] = 1
ratio = CalibratedClassifierRatio(base_estimator=clf,
calibration=calibration, cv=cv)
ratio.fit(X=X, y=y)
reals = np.linspace(-1, 1, num=100).reshape(-1, 1)
assert ratio.score(reals, p0.pdf(reals) / p1.pdf(reals)) > -0.1
assert np.mean(np.abs(np.log(ratio.predict(reals)) -
ratio.predict(reals, log=True))) < 0.01
