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_)