def test_likelihood_free_mixture():
p1 = Normal(random_state=1)
p2 = Normal(mu=2.0, random_state=1)
h1 = Histogram(bins=50).fit(p1.rvs(10000))
h2 = Histogram(bins=50).fit(p2.rvs(10000))
m1 = Mixture(components=[p1, p2])
m2 = Mixture(components=[h1, h2])
# Check whether pdf, nnlf and cdf have been overriden
assert isinstance(m1.pdf, theano.compile.function_module.Function)
assert isinstance(m1.nnlf, theano.compile.function_module.Function)
assert isinstance(m1.cdf, theano.compile.function_module.Function)
assert isinstance(m2.pdf, types.MethodType)
assert isinstance(m2.nnlf, types.MethodType)
assert isinstance(m2.cdf, types.MethodType)
# Compare pdfs
rng = check_random_state(1)
X = rng.rand(100, 1) * 10 - 5
assert np.mean(np.abs(m1.pdf(X) - m2.pdf(X))) < 0.05
# Test sampling
X = m2.rvs(10)
assert X.shape == (10, 1)
# Check errors
assert_raises(NotImplementedError, m2.fit, X)
def test_likelihood_free_mixture():
p1 = Normal(random_state=1)
p2 = Normal(mu=2.0, random_state=1)
h1 = Histogram(bins=50).fit(p1.rvs(10000))
h2 = Histogram(bins=50).fit(p2.rvs(10000))
m1 = Mixture(components=[p1, p2])
m2 = Mixture(components=[h1, h2])
# Check whether pdf, nnlf and cdf have been overriden
assert isinstance(m1.pdf, theano.compile.function_module.Function)
assert isinstance(m1.nnlf, theano.compile.function_module.Function)
assert isinstance(m1.cdf, theano.compile.function_module.Function)
assert isinstance(m2.pdf, types.MethodType)
assert isinstance(m2.nnlf, types.MethodType)
assert isinstance(m2.cdf, types.MethodType)
# Compare pdfs
rng = check_random_state(1)
X = rng.rand(100, 1) * 10 - 5
assert np.mean(np.abs(m1.pdf(X) - m2.pdf(X))) < 0.05
# Test sampling
X = m2.rvs(10)
assert X.shape == (10, 1)
# Check errors
assert_raises(NotImplementedError, m2.fit, X)
def test_known_density():
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 = KnownDensityRatio(numerator=p0, denominator=p1)
reals = np.linspace(-0.5, 1.0, num=100).reshape(-1, 1)
assert ratio.score(reals, p0.pdf(reals) / p1.pdf(reals)) > -0.01
assert np.mean(np.abs(np.log(ratio.predict(reals)) -
ratio.predict(reals, log=True))) < 0.01
assert ratio.nllr(reals) == -ratio.predict(reals, log=True).sum()
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
def check_mixture_pdf(w0, w1, mu1, sigma1, mu2, sigma2):
rng = check_random_state(1)
p1 = Normal(mu=mu1, sigma=sigma1)
p2 = Normal(mu=mu2, sigma=sigma2)
m = Mixture(components=[p1, p2], weights=[w0, w1])
q1 = st.norm(loc=mu1, scale=sigma1)
q2 = st.norm(loc=mu2, scale=sigma2)
X = rng.rand(50, 1)
assert_array_almost_equal(m.pdf(X).ravel(),
w0 * q1.pdf(X).ravel() +
(w1 if w1 is not None
else (1 - w0)) * q2.pdf(X).ravel())
def check_mixture_pdf(w0, w1, mu1, sigma1, mu2, sigma2):
rng = check_random_state(1)
p1 = Normal(mu=mu1, sigma=sigma1)
p2 = Normal(mu=mu2, sigma=sigma2)
m = Mixture(components=[p1, p2], weights=[w0, w1])
q1 = st.norm(loc=mu1, scale=sigma1)
q2 = st.norm(loc=mu2, scale=sigma2)
X = rng.rand(50, 1)
assert_array_almost_equal(
m.pdf(X).ravel(),
w0 * q1.pdf(X).ravel() + (w1 if w1 is not None else
(1 - w0)) * q2.pdf(X).ravel())
