def test_decomposed_ratio_identity():
components = [Normal(mu=0.0), Normal(mu=0.25), Normal(mu=0.5)]
p = Mixture(components=components, weights=[0.45, 0.1, 0.45])
ratio = DecomposedRatio(
ClassifierRatio(CalibratedClassifierCV(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 test_decomposed_ratio():
components = [Normal(mu=0.0), Normal(mu=0.25), Normal(mu=0.5)]
p0 = Mixture(components=components, weights=[0.45, 0.1, 0.45])
p1 = Mixture(components=[components[0]] + [components[2]])
ratio = DecomposedRatio(
ClassifierRatio(CalibratedClassifierCV(base_estimator=ElasticNetCV())))
ratio.fit(numerator=p0, denominator=p1, n_samples=10000)
reals = np.linspace(-0.5, 1.0, 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
def test_decomposed_ratio_identity():
components = [Normal(mu=0.0), Normal(mu=0.25), Normal(mu=0.5)]
p = Mixture(components=components, weights=[0.45, 0.1, 0.45])
ratio = DecomposedRatio(
ClassifierRatio(CalibratedClassifierCV(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 test_decomposed_ratio():
components = [Normal(mu=0.0), Normal(mu=0.25), Normal(mu=0.5)]
p0 = Mixture(components=components, weights=[0.45, 0.1, 0.45])
p1 = Mixture(components=[components[0]] + [components[2]])
ratio = DecomposedRatio(
ClassifierRatio(CalibratedClassifierCV(base_estimator=ElasticNetCV())))
ratio.fit(numerator=p0, denominator=p1, n_samples=10000)
reals = np.linspace(-0.5, 1.0, 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
