def test_score(): covar_type = 'full' rng = np.random.RandomState(0) rand_data = RandomData(rng, scale=7) n_components = rand_data.n_components X = rand_data.X[covar_type] # Check the error message if we don't call fit gmm1 = GaussianMixture(n_components=n_components, n_init=1, max_iter=1, reg_covar=0, random_state=rng, covariance_type=covar_type) assert_raise_message( NotFittedError, "This GaussianMixture instance is not fitted " "yet. Call 'fit' with appropriate arguments " "before using this estimator.", gmm1.score, X) # Check score value with warnings.catch_warnings(): warnings.simplefilter("ignore", ConvergenceWarning) gmm1.fit(X) gmm_score = gmm1.score(X) gmm_score_proba = gmm1.score_samples(X).mean() assert_almost_equal(gmm_score, gmm_score_proba) # Check if the score increase gmm2 = GaussianMixture(n_components=n_components, n_init=1, reg_covar=0, random_state=rng, covariance_type=covar_type).fit(X) assert gmm2.score(X) > gmm1.score(X)
def test_gaussian_mixture_fit_best_params(): rng = np.random.RandomState(0) rand_data = RandomData(rng) n_components = rand_data.n_components n_init = 10 for covar_type in COVARIANCE_TYPE: X = rand_data.X[covar_type] g = GaussianMixture(n_components=n_components, n_init=1, reg_covar=0, random_state=rng, covariance_type=covar_type) ll = [] for _ in range(n_init): g.fit(X) ll.append(g.score(X)) ll = np.array(ll) g_best = GaussianMixture(n_components=n_components, n_init=n_init, reg_covar=0, random_state=rng, covariance_type=covar_type) g_best.fit(X) assert_almost_equal(ll.min(), g_best.score(X))