def test_check_covariance_precision():
# We check that the dot product of the covariance and the precision
# matrices is identity.
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7)
n_components, n_features = 2 * rand_data.n_components, 2
# Computation of the full_covariance
bgmm = BayesianGaussianMixture(n_components=n_components,
max_iter=100, random_state=rng, tol=1e-3,
reg_covar=0)
for covar_type in COVARIANCE_TYPE:
bgmm.covariance_type = covar_type
bgmm.fit(rand_data.X[covar_type])
if covar_type == 'full':
for covar, precision in zip(bgmm.covariances_, bgmm.precisions_):
assert_almost_equal(np.dot(covar, precision),
np.eye(n_features))
elif covar_type == 'tied':
assert_almost_equal(np.dot(bgmm.covariances_, bgmm.precisions_),
np.eye(n_features))
elif covar_type == 'diag':
assert_almost_equal(bgmm.covariances_ * bgmm.precisions_,
np.ones((n_components, n_features)))
else:
assert_almost_equal(bgmm.covariances_ * bgmm.precisions_,
np.ones(n_components))
def test_check_covariance_precision():
# We check that the dot product of the covariance and the precision
# matrices is identity.
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7)
n_components, n_features = 2 * rand_data.n_components, 2
# Computation of the full_covariance
bgmm = BayesianGaussianMixture(n_components=n_components,
max_iter=100,
random_state=rng,
tol=1e-3,
reg_covar=0)
for covar_type in COVARIANCE_TYPE:
bgmm.covariance_type = covar_type
bgmm.fit(rand_data.X[covar_type])
if covar_type == 'full':
for covar, precision in zip(bgmm.covariances_, bgmm.precisions_):
assert_almost_equal(np.dot(covar, precision),
np.eye(n_features))
elif covar_type == 'tied':
assert_almost_equal(np.dot(bgmm.covariances_, bgmm.precisions_),
np.eye(n_features))
elif covar_type == 'diag':
assert_almost_equal(bgmm.covariances_ * bgmm.precisions_,
np.ones((n_components, n_features)))
else:
assert_almost_equal(bgmm.covariances_ * bgmm.precisions_,
np.ones(n_components))
def test_bayesian_mixture_fit_predict(seed, max_iter, tol):
rng = np.random.RandomState(seed)
rand_data = RandomData(rng, scale=7)
n_components = 2 * rand_data.n_components
for covar_type in COVARIANCE_TYPE:
bgmm1 = BayesianGaussianMixture(n_components=n_components,
max_iter=max_iter, random_state=rng,
tol=tol, reg_covar=0)
bgmm1.covariance_type = covar_type
bgmm2 = copy.deepcopy(bgmm1)
X = rand_data.X[covar_type]
Y_pred1 = bgmm1.fit(X).predict(X)
Y_pred2 = bgmm2.fit_predict(X)
assert_array_equal(Y_pred1, Y_pred2)
def test_bayesian_mixture_fit_predict(seed, max_iter, tol):
rng = np.random.RandomState(seed)
rand_data = RandomData(rng, n_samples=50, scale=7)
n_components = 2 * rand_data.n_components
for covar_type in COVARIANCE_TYPE:
bgmm1 = BayesianGaussianMixture(n_components=n_components,
max_iter=max_iter, random_state=rng,
tol=tol, reg_covar=0)
bgmm1.covariance_type = covar_type
bgmm2 = copy.deepcopy(bgmm1)
X = rand_data.X[covar_type]
Y_pred1 = bgmm1.fit(X).predict(X)
Y_pred2 = bgmm2.fit_predict(X)
assert_array_equal(Y_pred1, Y_pred2)
def test_bayesian_mixture_precisions_prior_initialisation():
rng = np.random.RandomState(0)
n_samples, n_features = 10, 2
X = rng.rand(n_samples, n_features)
# Check raise message for a bad value of degrees_of_freedom_prior
bad_degrees_of_freedom_prior_ = n_features - 1.
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=bad_degrees_of_freedom_prior_,
random_state=rng)
assert_raise_message(
ValueError, "The parameter 'degrees_of_freedom_prior' should be "
"greater than %d, but got %.3f." %
(n_features - 1, bad_degrees_of_freedom_prior_), bgmm.fit, X)
# Check correct init for a given value of degrees_of_freedom_prior
degrees_of_freedom_prior = rng.rand() + n_features - 1.
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=degrees_of_freedom_prior,
random_state=rng).fit(X)
assert_almost_equal(degrees_of_freedom_prior,
bgmm.degrees_of_freedom_prior_)
# Check correct init for the default value of degrees_of_freedom_prior
degrees_of_freedom_prior_default = n_features
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=degrees_of_freedom_prior_default,
random_state=rng).fit(X)
assert_almost_equal(degrees_of_freedom_prior_default,
bgmm.degrees_of_freedom_prior_)
# Check correct init for a given value of covariance_prior
covariance_prior = {
'full': np.cov(X.T, bias=1) + 10,
'tied': np.cov(X.T, bias=1) + 5,
'diag': np.diag(np.atleast_2d(np.cov(X.T, bias=1))) + 3,
'spherical': rng.rand()
}
bgmm = BayesianGaussianMixture(random_state=rng)
for cov_type in ['full', 'tied', 'diag', 'spherical']:
bgmm.covariance_type = cov_type
bgmm.covariance_prior = covariance_prior[cov_type]
bgmm.fit(X)
assert_almost_equal(covariance_prior[cov_type], bgmm.covariance_prior_)
# Check raise message for a bad spherical value of covariance_prior
bad_covariance_prior_ = -1.
bgmm = BayesianGaussianMixture(covariance_type='spherical',
covariance_prior=bad_covariance_prior_,
random_state=rng)
assert_raise_message(
ValueError, "The parameter 'spherical covariance_prior' "
"should be greater than 0., but got %.3f." % bad_covariance_prior_,
bgmm.fit, X)
# Check correct init for the default value of covariance_prior
covariance_prior_default = {
'full': np.atleast_2d(np.cov(X.T)),
'tied': np.atleast_2d(np.cov(X.T)),
'diag': np.var(X, axis=0, ddof=1),
'spherical': np.var(X, axis=0, ddof=1).mean()
}
bgmm = BayesianGaussianMixture(random_state=0)
for cov_type in ['full', 'tied', 'diag', 'spherical']:
bgmm.covariance_type = cov_type
bgmm.fit(X)
assert_almost_equal(covariance_prior_default[cov_type],
bgmm.covariance_prior_)
def test_bayesian_mixture_precisions_prior_initialisation():
rng = np.random.RandomState(0)
n_samples, n_features = 10, 2
X = rng.rand(n_samples, n_features)
# Check raise message for a bad value of degrees_of_freedom_prior
bad_degrees_of_freedom_prior_ = n_features - 1.0
bgmm = BayesianGaussianMixture(degrees_of_freedom_prior=bad_degrees_of_freedom_prior_, random_state=rng)
assert_raise_message(
ValueError,
"The parameter 'degrees_of_freedom_prior' should be "
"greater than %d, but got %.3f." % (n_features - 1, bad_degrees_of_freedom_prior_),
bgmm.fit,
X,
)
# Check correct init for a given value of degrees_of_freedom_prior
degrees_of_freedom_prior = rng.rand() + n_features - 1.0
bgmm = BayesianGaussianMixture(degrees_of_freedom_prior=degrees_of_freedom_prior, random_state=rng).fit(X)
assert_almost_equal(degrees_of_freedom_prior, bgmm.degrees_of_freedom_prior_)
# Check correct init for the default value of degrees_of_freedom_prior
degrees_of_freedom_prior_default = n_features
bgmm = BayesianGaussianMixture(degrees_of_freedom_prior=degrees_of_freedom_prior_default, random_state=rng).fit(X)
assert_almost_equal(degrees_of_freedom_prior_default, bgmm.degrees_of_freedom_prior_)
# Check correct init for a given value of covariance_prior
covariance_prior = {
"full": np.cov(X.T, bias=1) + 10,
"tied": np.cov(X.T, bias=1) + 5,
"diag": np.diag(np.atleast_2d(np.cov(X.T, bias=1))) + 3,
"spherical": rng.rand(),
}
bgmm = BayesianGaussianMixture(random_state=rng)
for cov_type in ["full", "tied", "diag", "spherical"]:
bgmm.covariance_type = cov_type
bgmm.covariance_prior = covariance_prior[cov_type]
bgmm.fit(X)
assert_almost_equal(covariance_prior[cov_type], bgmm.covariance_prior_)
# Check raise message for a bad spherical value of covariance_prior
bad_covariance_prior_ = -1.0
bgmm = BayesianGaussianMixture(
covariance_type="spherical", covariance_prior=bad_covariance_prior_, random_state=rng
)
assert_raise_message(
ValueError,
"The parameter 'spherical covariance_prior' "
"should be greater than 0., but got %.3f." % bad_covariance_prior_,
bgmm.fit,
X,
)
# Check correct init for the default value of covariance_prior
covariance_prior_default = {
"full": np.atleast_2d(np.cov(X.T)),
"tied": np.atleast_2d(np.cov(X.T)),
"diag": np.var(X, axis=0, ddof=1),
"spherical": np.var(X, axis=0, ddof=1).mean(),
}
bgmm = BayesianGaussianMixture(random_state=0)
for cov_type in ["full", "tied", "diag", "spherical"]:
bgmm.covariance_type = cov_type
bgmm.fit(X)
assert_almost_equal(covariance_prior_default[cov_type], bgmm.covariance_prior_)
def test_bayesian_mixture_precisions_prior_initialisation():
rng = np.random.RandomState(0)
n_samples, n_features = 10, 2
X = rng.rand(n_samples, n_features)
# Check raise message for a bad value of degrees_of_freedom_prior
bad_degrees_of_freedom_prior_ = n_features - 1.0
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=bad_degrees_of_freedom_prior_,
random_state=rng)
msg = ("The parameter 'degrees_of_freedom_prior' should be greater than"
f" {n_features -1}, but got {bad_degrees_of_freedom_prior_:.3f}.")
with pytest.raises(ValueError, match=msg):
bgmm.fit(X)
# Check correct init for a given value of degrees_of_freedom_prior
degrees_of_freedom_prior = rng.rand() + n_features - 1.0
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=degrees_of_freedom_prior,
random_state=rng).fit(X)
assert_almost_equal(degrees_of_freedom_prior,
bgmm.degrees_of_freedom_prior_)
# Check correct init for the default value of degrees_of_freedom_prior
degrees_of_freedom_prior_default = n_features
bgmm = BayesianGaussianMixture(
degrees_of_freedom_prior=degrees_of_freedom_prior_default,
random_state=rng).fit(X)
assert_almost_equal(degrees_of_freedom_prior_default,
bgmm.degrees_of_freedom_prior_)
# Check correct init for a given value of covariance_prior
covariance_prior = {
"full": np.cov(X.T, bias=1) + 10,
"tied": np.cov(X.T, bias=1) + 5,
"diag": np.diag(np.atleast_2d(np.cov(X.T, bias=1))) + 3,
"spherical": rng.rand(),
}
bgmm = BayesianGaussianMixture(random_state=rng)
for cov_type in ["full", "tied", "diag", "spherical"]:
bgmm.covariance_type = cov_type
bgmm.covariance_prior = covariance_prior[cov_type]
bgmm.fit(X)
assert_almost_equal(covariance_prior[cov_type], bgmm.covariance_prior_)
# Check raise message for a bad spherical value of covariance_prior
bad_covariance_prior_ = -1.0
bgmm = BayesianGaussianMixture(
covariance_type="spherical",
covariance_prior=bad_covariance_prior_,
random_state=rng,
)
msg = ("The parameter 'spherical covariance_prior' "
f"should be greater than 0., but got {bad_covariance_prior_:.3f}.")
with pytest.raises(ValueError, match=msg):
bgmm.fit(X)
# Check correct init for the default value of covariance_prior
covariance_prior_default = {
"full": np.atleast_2d(np.cov(X.T)),
"tied": np.atleast_2d(np.cov(X.T)),
"diag": np.var(X, axis=0, ddof=1),
"spherical": np.var(X, axis=0, ddof=1).mean(),
}
bgmm = BayesianGaussianMixture(random_state=0)
for cov_type in ["full", "tied", "diag", "spherical"]:
bgmm.covariance_type = cov_type
bgmm.fit(X)
assert_almost_equal(covariance_prior_default[cov_type],
bgmm.covariance_prior_)
