1040 lines
41 KiB
Python
1040 lines
41 KiB
Python
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# Author: Wei Xue <xuewei4d@gmail.com>
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# Thierry Guillemot <thierry.guillemot.work@gmail.com>
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# License: BSD 3 clause
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import sys
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import copy
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import warnings
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import pytest
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import numpy as np
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from scipy import stats, linalg
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from sklearn.covariance import EmpiricalCovariance
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from sklearn.datasets import make_spd_matrix
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from io import StringIO
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from sklearn.metrics.cluster import adjusted_rand_score
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from sklearn.mixture import GaussianMixture
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from sklearn.mixture._gaussian_mixture import (
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_estimate_gaussian_covariances_full,
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_estimate_gaussian_covariances_tied,
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_estimate_gaussian_covariances_diag,
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_estimate_gaussian_covariances_spherical,
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_compute_precision_cholesky,
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_compute_log_det_cholesky,
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)
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from sklearn.exceptions import ConvergenceWarning, NotFittedError
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from sklearn.utils.extmath import fast_logdet
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from sklearn.utils._testing import assert_allclose
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from sklearn.utils._testing import assert_almost_equal
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from sklearn.utils._testing import assert_array_almost_equal
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from sklearn.utils._testing import assert_array_equal
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from sklearn.utils._testing import assert_raise_message
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from sklearn.utils._testing import assert_warns_message
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from sklearn.utils._testing import ignore_warnings
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COVARIANCE_TYPE = ['full', 'tied', 'diag', 'spherical']
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def generate_data(n_samples, n_features, weights, means, precisions,
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covariance_type):
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rng = np.random.RandomState(0)
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X = []
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if covariance_type == 'spherical':
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for _, (w, m, c) in enumerate(zip(weights, means,
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precisions['spherical'])):
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X.append(rng.multivariate_normal(m, c * np.eye(n_features),
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int(np.round(w * n_samples))))
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if covariance_type == 'diag':
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for _, (w, m, c) in enumerate(zip(weights, means,
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precisions['diag'])):
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X.append(rng.multivariate_normal(m, np.diag(c),
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int(np.round(w * n_samples))))
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if covariance_type == 'tied':
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for _, (w, m) in enumerate(zip(weights, means)):
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X.append(rng.multivariate_normal(m, precisions['tied'],
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int(np.round(w * n_samples))))
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if covariance_type == 'full':
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for _, (w, m, c) in enumerate(zip(weights, means,
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precisions['full'])):
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X.append(rng.multivariate_normal(m, c,
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int(np.round(w * n_samples))))
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X = np.vstack(X)
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return X
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class RandomData:
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def __init__(self, rng, n_samples=200, n_components=2, n_features=2,
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scale=50):
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self.n_samples = n_samples
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self.n_components = n_components
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self.n_features = n_features
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self.weights = rng.rand(n_components)
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self.weights = self.weights / self.weights.sum()
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self.means = rng.rand(n_components, n_features) * scale
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self.covariances = {
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'spherical': .5 + rng.rand(n_components),
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'diag': (.5 + rng.rand(n_components, n_features)) ** 2,
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'tied': make_spd_matrix(n_features, random_state=rng),
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'full': np.array([
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make_spd_matrix(n_features, random_state=rng) * .5
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for _ in range(n_components)])}
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self.precisions = {
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'spherical': 1. / self.covariances['spherical'],
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'diag': 1. / self.covariances['diag'],
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'tied': linalg.inv(self.covariances['tied']),
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'full': np.array([linalg.inv(covariance)
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for covariance in self.covariances['full']])}
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self.X = dict(zip(COVARIANCE_TYPE, [generate_data(
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n_samples, n_features, self.weights, self.means, self.covariances,
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covar_type) for covar_type in COVARIANCE_TYPE]))
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self.Y = np.hstack([np.full(int(np.round(w * n_samples)), k,
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dtype=int)
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for k, w in enumerate(self.weights)])
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def test_gaussian_mixture_attributes():
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# test bad parameters
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rng = np.random.RandomState(0)
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X = rng.rand(10, 2)
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n_components_bad = 0
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gmm = GaussianMixture(n_components=n_components_bad)
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assert_raise_message(ValueError,
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"Invalid value for 'n_components': %d "
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"Estimation requires at least one component"
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% n_components_bad, gmm.fit, X)
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# covariance_type should be in [spherical, diag, tied, full]
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covariance_type_bad = 'bad_covariance_type'
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gmm = GaussianMixture(covariance_type=covariance_type_bad)
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assert_raise_message(ValueError,
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"Invalid value for 'covariance_type': %s "
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"'covariance_type' should be in "
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"['spherical', 'tied', 'diag', 'full']"
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% covariance_type_bad,
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gmm.fit, X)
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tol_bad = -1
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gmm = GaussianMixture(tol=tol_bad)
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assert_raise_message(ValueError,
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"Invalid value for 'tol': %.5f "
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"Tolerance used by the EM must be non-negative"
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% tol_bad, gmm.fit, X)
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reg_covar_bad = -1
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gmm = GaussianMixture(reg_covar=reg_covar_bad)
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assert_raise_message(ValueError,
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"Invalid value for 'reg_covar': %.5f "
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"regularization on covariance must be "
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"non-negative" % reg_covar_bad, gmm.fit, X)
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max_iter_bad = 0
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gmm = GaussianMixture(max_iter=max_iter_bad)
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assert_raise_message(ValueError,
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"Invalid value for 'max_iter': %d "
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"Estimation requires at least one iteration"
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% max_iter_bad, gmm.fit, X)
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n_init_bad = 0
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gmm = GaussianMixture(n_init=n_init_bad)
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assert_raise_message(ValueError,
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"Invalid value for 'n_init': %d "
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"Estimation requires at least one run"
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% n_init_bad, gmm.fit, X)
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init_params_bad = 'bad_method'
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gmm = GaussianMixture(init_params=init_params_bad)
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assert_raise_message(ValueError,
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"Unimplemented initialization method '%s'"
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% init_params_bad,
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gmm.fit, X)
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# test good parameters
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n_components, tol, n_init, max_iter, reg_covar = 2, 1e-4, 3, 30, 1e-1
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covariance_type, init_params = 'full', 'random'
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gmm = GaussianMixture(n_components=n_components, tol=tol, n_init=n_init,
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max_iter=max_iter, reg_covar=reg_covar,
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covariance_type=covariance_type,
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init_params=init_params).fit(X)
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assert gmm.n_components == n_components
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assert gmm.covariance_type == covariance_type
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assert gmm.tol == tol
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assert gmm.reg_covar == reg_covar
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assert gmm.max_iter == max_iter
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assert gmm.n_init == n_init
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assert gmm.init_params == init_params
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def test_check_X():
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from sklearn.mixture._base import _check_X
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rng = np.random.RandomState(0)
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n_samples, n_components, n_features = 10, 2, 2
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X_bad_dim = rng.rand(n_components - 1, n_features)
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assert_raise_message(ValueError,
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'Expected n_samples >= n_components '
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'but got n_components = %d, n_samples = %d'
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% (n_components, X_bad_dim.shape[0]),
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_check_X, X_bad_dim, n_components)
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X_bad_dim = rng.rand(n_components, n_features + 1)
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assert_raise_message(ValueError,
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'Expected the input data X have %d features, '
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'but got %d features'
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% (n_features, X_bad_dim.shape[1]),
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_check_X, X_bad_dim, n_components, n_features)
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X = rng.rand(n_samples, n_features)
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assert_array_equal(X, _check_X(X, n_components, n_features))
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def test_check_weights():
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rng = np.random.RandomState(0)
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rand_data = RandomData(rng)
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n_components = rand_data.n_components
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X = rand_data.X['full']
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g = GaussianMixture(n_components=n_components)
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# Check bad shape
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weights_bad_shape = rng.rand(n_components, 1)
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g.weights_init = weights_bad_shape
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assert_raise_message(ValueError,
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"The parameter 'weights' should have the shape of "
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"(%d,), but got %s" %
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(n_components, str(weights_bad_shape.shape)),
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g.fit, X)
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# Check bad range
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weights_bad_range = rng.rand(n_components) + 1
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g.weights_init = weights_bad_range
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assert_raise_message(ValueError,
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"The parameter 'weights' should be in the range "
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"[0, 1], but got max value %.5f, min value %.5f"
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% (np.min(weights_bad_range),
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np.max(weights_bad_range)),
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g.fit, X)
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# Check bad normalization
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weights_bad_norm = rng.rand(n_components)
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weights_bad_norm = weights_bad_norm / (weights_bad_norm.sum() + 1)
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g.weights_init = weights_bad_norm
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assert_raise_message(ValueError,
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"The parameter 'weights' should be normalized, "
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"but got sum(weights) = %.5f"
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% np.sum(weights_bad_norm),
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g.fit, X)
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# Check good weights matrix
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weights = rand_data.weights
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g = GaussianMixture(weights_init=weights, n_components=n_components)
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g.fit(X)
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assert_array_equal(weights, g.weights_init)
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def test_check_means():
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rng = np.random.RandomState(0)
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rand_data = RandomData(rng)
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n_components, n_features = rand_data.n_components, rand_data.n_features
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X = rand_data.X['full']
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g = GaussianMixture(n_components=n_components)
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# Check means bad shape
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means_bad_shape = rng.rand(n_components + 1, n_features)
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g.means_init = means_bad_shape
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assert_raise_message(ValueError,
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"The parameter 'means' should have the shape of ",
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g.fit, X)
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# Check good means matrix
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means = rand_data.means
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g.means_init = means
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g.fit(X)
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assert_array_equal(means, g.means_init)
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def test_check_precisions():
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rng = np.random.RandomState(0)
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rand_data = RandomData(rng)
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n_components, n_features = rand_data.n_components, rand_data.n_features
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# Define the bad precisions for each covariance_type
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precisions_bad_shape = {
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'full': np.ones((n_components + 1, n_features, n_features)),
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'tied': np.ones((n_features + 1, n_features + 1)),
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'diag': np.ones((n_components + 1, n_features)),
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'spherical': np.ones((n_components + 1))}
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# Define not positive-definite precisions
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precisions_not_pos = np.ones((n_components, n_features, n_features))
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precisions_not_pos[0] = np.eye(n_features)
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precisions_not_pos[0, 0, 0] = -1.
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precisions_not_positive = {
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'full': precisions_not_pos,
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'tied': precisions_not_pos[0],
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'diag': np.full((n_components, n_features), -1.),
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'spherical': np.full(n_components, -1.)}
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not_positive_errors = {
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'full': 'symmetric, positive-definite',
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'tied': 'symmetric, positive-definite',
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'diag': 'positive',
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'spherical': 'positive'}
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for covar_type in COVARIANCE_TYPE:
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X = RandomData(rng).X[covar_type]
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g = GaussianMixture(n_components=n_components,
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covariance_type=covar_type,
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random_state=rng)
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# Check precisions with bad shapes
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g.precisions_init = precisions_bad_shape[covar_type]
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assert_raise_message(ValueError,
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"The parameter '%s precision' should have "
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"the shape of" % covar_type,
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g.fit, X)
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# Check not positive precisions
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g.precisions_init = precisions_not_positive[covar_type]
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assert_raise_message(ValueError,
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"'%s precision' should be %s"
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% (covar_type, not_positive_errors[covar_type]),
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g.fit, X)
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# Check the correct init of precisions_init
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g.precisions_init = rand_data.precisions[covar_type]
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g.fit(X)
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assert_array_equal(rand_data.precisions[covar_type], g.precisions_init)
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def test_suffstat_sk_full():
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# compare the precision matrix compute from the
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# EmpiricalCovariance.covariance fitted on X*sqrt(resp)
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# with _sufficient_sk_full, n_components=1
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rng = np.random.RandomState(0)
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n_samples, n_features = 500, 2
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# special case 1, assuming data is "centered"
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X = rng.rand(n_samples, n_features)
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resp = rng.rand(n_samples, 1)
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X_resp = np.sqrt(resp) * X
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nk = np.array([n_samples])
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xk = np.zeros((1, n_features))
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covars_pred = _estimate_gaussian_covariances_full(resp, X, nk, xk, 0)
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ecov = EmpiricalCovariance(assume_centered=True)
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ecov.fit(X_resp)
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assert_almost_equal(ecov.error_norm(covars_pred[0], norm='frobenius'), 0)
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assert_almost_equal(ecov.error_norm(covars_pred[0], norm='spectral'), 0)
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# check the precision computation
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|
precs_chol_pred = _compute_precision_cholesky(covars_pred, 'full')
|
||
|
|
precs_pred = np.array([np.dot(prec, prec.T) for prec in precs_chol_pred])
|
||
|
|
precs_est = np.array([linalg.inv(cov) for cov in covars_pred])
|
||
|
|
assert_array_almost_equal(precs_est, precs_pred)
|
||
|
|
|
||
|
|
# special case 2, assuming resp are all ones
|
||
|
|
resp = np.ones((n_samples, 1))
|
||
|
|
nk = np.array([n_samples])
|
||
|
|
xk = X.mean(axis=0).reshape((1, -1))
|
||
|
|
covars_pred = _estimate_gaussian_covariances_full(resp, X, nk, xk, 0)
|
||
|
|
ecov = EmpiricalCovariance(assume_centered=False)
|
||
|
|
ecov.fit(X)
|
||
|
|
assert_almost_equal(ecov.error_norm(covars_pred[0], norm='frobenius'), 0)
|
||
|
|
assert_almost_equal(ecov.error_norm(covars_pred[0], norm='spectral'), 0)
|
||
|
|
|
||
|
|
# check the precision computation
|
||
|
|
precs_chol_pred = _compute_precision_cholesky(covars_pred, 'full')
|
||
|
|
precs_pred = np.array([np.dot(prec, prec.T) for prec in precs_chol_pred])
|
||
|
|
precs_est = np.array([linalg.inv(cov) for cov in covars_pred])
|
||
|
|
assert_array_almost_equal(precs_est, precs_pred)
|
||
|
|
|
||
|
|
|
||
|
|
def test_suffstat_sk_tied():
|
||
|
|
# use equation Nk * Sk / N = S_tied
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
n_samples, n_features, n_components = 500, 2, 2
|
||
|
|
|
||
|
|
resp = rng.rand(n_samples, n_components)
|
||
|
|
resp = resp / resp.sum(axis=1)[:, np.newaxis]
|
||
|
|
X = rng.rand(n_samples, n_features)
|
||
|
|
nk = resp.sum(axis=0)
|
||
|
|
xk = np.dot(resp.T, X) / nk[:, np.newaxis]
|
||
|
|
|
||
|
|
covars_pred_full = _estimate_gaussian_covariances_full(resp, X, nk, xk, 0)
|
||
|
|
covars_pred_full = np.sum(nk[:, np.newaxis, np.newaxis] * covars_pred_full,
|
||
|
|
0) / n_samples
|
||
|
|
|
||
|
|
covars_pred_tied = _estimate_gaussian_covariances_tied(resp, X, nk, xk, 0)
|
||
|
|
|
||
|
|
ecov = EmpiricalCovariance()
|
||
|
|
ecov.covariance_ = covars_pred_full
|
||
|
|
assert_almost_equal(ecov.error_norm(covars_pred_tied, norm='frobenius'), 0)
|
||
|
|
assert_almost_equal(ecov.error_norm(covars_pred_tied, norm='spectral'), 0)
|
||
|
|
|
||
|
|
# check the precision computation
|
||
|
|
precs_chol_pred = _compute_precision_cholesky(covars_pred_tied, 'tied')
|
||
|
|
precs_pred = np.dot(precs_chol_pred, precs_chol_pred.T)
|
||
|
|
precs_est = linalg.inv(covars_pred_tied)
|
||
|
|
assert_array_almost_equal(precs_est, precs_pred)
|
||
|
|
|
||
|
|
|
||
|
|
def test_suffstat_sk_diag():
|
||
|
|
# test against 'full' case
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
n_samples, n_features, n_components = 500, 2, 2
|
||
|
|
|
||
|
|
resp = rng.rand(n_samples, n_components)
|
||
|
|
resp = resp / resp.sum(axis=1)[:, np.newaxis]
|
||
|
|
X = rng.rand(n_samples, n_features)
|
||
|
|
nk = resp.sum(axis=0)
|
||
|
|
xk = np.dot(resp.T, X) / nk[:, np.newaxis]
|
||
|
|
covars_pred_full = _estimate_gaussian_covariances_full(resp, X, nk, xk, 0)
|
||
|
|
covars_pred_diag = _estimate_gaussian_covariances_diag(resp, X, nk, xk, 0)
|
||
|
|
|
||
|
|
ecov = EmpiricalCovariance()
|
||
|
|
for (cov_full, cov_diag) in zip(covars_pred_full, covars_pred_diag):
|
||
|
|
ecov.covariance_ = np.diag(np.diag(cov_full))
|
||
|
|
cov_diag = np.diag(cov_diag)
|
||
|
|
assert_almost_equal(ecov.error_norm(cov_diag, norm='frobenius'), 0)
|
||
|
|
assert_almost_equal(ecov.error_norm(cov_diag, norm='spectral'), 0)
|
||
|
|
|
||
|
|
# check the precision computation
|
||
|
|
precs_chol_pred = _compute_precision_cholesky(covars_pred_diag, 'diag')
|
||
|
|
assert_almost_equal(covars_pred_diag, 1. / precs_chol_pred ** 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_suffstat_sk_spherical():
|
||
|
|
# computing spherical covariance equals to the variance of one-dimension
|
||
|
|
# data after flattening, n_components=1
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
n_samples, n_features = 500, 2
|
||
|
|
|
||
|
|
X = rng.rand(n_samples, n_features)
|
||
|
|
X = X - X.mean()
|
||
|
|
resp = np.ones((n_samples, 1))
|
||
|
|
nk = np.array([n_samples])
|
||
|
|
xk = X.mean()
|
||
|
|
covars_pred_spherical = _estimate_gaussian_covariances_spherical(resp, X,
|
||
|
|
nk, xk, 0)
|
||
|
|
covars_pred_spherical2 = (np.dot(X.flatten().T, X.flatten()) /
|
||
|
|
(n_features * n_samples))
|
||
|
|
assert_almost_equal(covars_pred_spherical, covars_pred_spherical2)
|
||
|
|
|
||
|
|
# check the precision computation
|
||
|
|
precs_chol_pred = _compute_precision_cholesky(covars_pred_spherical,
|
||
|
|
'spherical')
|
||
|
|
assert_almost_equal(covars_pred_spherical, 1. / precs_chol_pred ** 2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_compute_log_det_cholesky():
|
||
|
|
n_features = 2
|
||
|
|
rand_data = RandomData(np.random.RandomState(0))
|
||
|
|
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
covariance = rand_data.covariances[covar_type]
|
||
|
|
|
||
|
|
if covar_type == 'full':
|
||
|
|
predected_det = np.array([linalg.det(cov) for cov in covariance])
|
||
|
|
elif covar_type == 'tied':
|
||
|
|
predected_det = linalg.det(covariance)
|
||
|
|
elif covar_type == 'diag':
|
||
|
|
predected_det = np.array([np.prod(cov) for cov in covariance])
|
||
|
|
elif covar_type == 'spherical':
|
||
|
|
predected_det = covariance ** n_features
|
||
|
|
|
||
|
|
# We compute the cholesky decomposition of the covariance matrix
|
||
|
|
expected_det = _compute_log_det_cholesky(_compute_precision_cholesky(
|
||
|
|
covariance, covar_type), covar_type, n_features=n_features)
|
||
|
|
assert_array_almost_equal(expected_det, - .5 * np.log(predected_det))
|
||
|
|
|
||
|
|
|
||
|
|
def _naive_lmvnpdf_diag(X, means, covars):
|
||
|
|
resp = np.empty((len(X), len(means)))
|
||
|
|
stds = np.sqrt(covars)
|
||
|
|
for i, (mean, std) in enumerate(zip(means, stds)):
|
||
|
|
resp[:, i] = stats.norm.logpdf(X, mean, std).sum(axis=1)
|
||
|
|
return resp
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_log_probabilities():
|
||
|
|
from sklearn.mixture._gaussian_mixture import _estimate_log_gaussian_prob
|
||
|
|
|
||
|
|
# test against with _naive_lmvnpdf_diag
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng)
|
||
|
|
n_samples = 500
|
||
|
|
n_features = rand_data.n_features
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
|
||
|
|
means = rand_data.means
|
||
|
|
covars_diag = rng.rand(n_components, n_features)
|
||
|
|
X = rng.rand(n_samples, n_features)
|
||
|
|
log_prob_naive = _naive_lmvnpdf_diag(X, means, covars_diag)
|
||
|
|
|
||
|
|
# full covariances
|
||
|
|
precs_full = np.array([np.diag(1. / np.sqrt(x)) for x in covars_diag])
|
||
|
|
|
||
|
|
log_prob = _estimate_log_gaussian_prob(X, means, precs_full, 'full')
|
||
|
|
assert_array_almost_equal(log_prob, log_prob_naive)
|
||
|
|
|
||
|
|
# diag covariances
|
||
|
|
precs_chol_diag = 1. / np.sqrt(covars_diag)
|
||
|
|
log_prob = _estimate_log_gaussian_prob(X, means, precs_chol_diag, 'diag')
|
||
|
|
assert_array_almost_equal(log_prob, log_prob_naive)
|
||
|
|
|
||
|
|
# tied
|
||
|
|
covars_tied = np.array([x for x in covars_diag]).mean(axis=0)
|
||
|
|
precs_tied = np.diag(np.sqrt(1. / covars_tied))
|
||
|
|
|
||
|
|
log_prob_naive = _naive_lmvnpdf_diag(X, means,
|
||
|
|
[covars_tied] * n_components)
|
||
|
|
log_prob = _estimate_log_gaussian_prob(X, means, precs_tied, 'tied')
|
||
|
|
|
||
|
|
assert_array_almost_equal(log_prob, log_prob_naive)
|
||
|
|
|
||
|
|
# spherical
|
||
|
|
covars_spherical = covars_diag.mean(axis=1)
|
||
|
|
precs_spherical = 1. / np.sqrt(covars_diag.mean(axis=1))
|
||
|
|
log_prob_naive = _naive_lmvnpdf_diag(X, means,
|
||
|
|
[[k] * n_features for k in
|
||
|
|
covars_spherical])
|
||
|
|
log_prob = _estimate_log_gaussian_prob(X, means,
|
||
|
|
precs_spherical, 'spherical')
|
||
|
|
assert_array_almost_equal(log_prob, log_prob_naive)
|
||
|
|
|
||
|
|
# skip tests on weighted_log_probabilities, log_weights
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_estimate_log_prob_resp():
|
||
|
|
# test whether responsibilities are normalized
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng, scale=5)
|
||
|
|
n_samples = rand_data.n_samples
|
||
|
|
n_features = rand_data.n_features
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
|
||
|
|
X = rng.rand(n_samples, n_features)
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
weights = rand_data.weights
|
||
|
|
means = rand_data.means
|
||
|
|
precisions = rand_data.precisions[covar_type]
|
||
|
|
g = GaussianMixture(n_components=n_components, random_state=rng,
|
||
|
|
weights_init=weights, means_init=means,
|
||
|
|
precisions_init=precisions,
|
||
|
|
covariance_type=covar_type)
|
||
|
|
g.fit(X)
|
||
|
|
resp = g.predict_proba(X)
|
||
|
|
assert_array_almost_equal(resp.sum(axis=1), np.ones(n_samples))
|
||
|
|
assert_array_equal(g.weights_init, weights)
|
||
|
|
assert_array_equal(g.means_init, means)
|
||
|
|
assert_array_equal(g.precisions_init, precisions)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_predict_predict_proba():
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng)
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
X = rand_data.X[covar_type]
|
||
|
|
Y = rand_data.Y
|
||
|
|
g = GaussianMixture(n_components=rand_data.n_components,
|
||
|
|
random_state=rng, weights_init=rand_data.weights,
|
||
|
|
means_init=rand_data.means,
|
||
|
|
precisions_init=rand_data.precisions[covar_type],
|
||
|
|
covariance_type=covar_type)
|
||
|
|
|
||
|
|
# Check a warning message arrive if we don't do fit
|
||
|
|
assert_raise_message(NotFittedError,
|
||
|
|
"This GaussianMixture instance is not fitted "
|
||
|
|
"yet. Call 'fit' with appropriate arguments "
|
||
|
|
"before using this estimator.", g.predict, X)
|
||
|
|
|
||
|
|
g.fit(X)
|
||
|
|
Y_pred = g.predict(X)
|
||
|
|
Y_pred_proba = g.predict_proba(X).argmax(axis=1)
|
||
|
|
assert_array_equal(Y_pred, Y_pred_proba)
|
||
|
|
assert adjusted_rand_score(Y, Y_pred) > .95
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings("ignore:.*did not converge.*")
|
||
|
|
@pytest.mark.parametrize('seed, max_iter, tol', [
|
||
|
|
(0, 2, 1e-7), # strict non-convergence
|
||
|
|
(1, 2, 1e-1), # loose non-convergence
|
||
|
|
(3, 300, 1e-7), # strict convergence
|
||
|
|
(4, 300, 1e-1), # loose convergence
|
||
|
|
])
|
||
|
|
def test_gaussian_mixture_fit_predict(seed, max_iter, tol):
|
||
|
|
rng = np.random.RandomState(seed)
|
||
|
|
rand_data = RandomData(rng)
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
X = rand_data.X[covar_type]
|
||
|
|
Y = rand_data.Y
|
||
|
|
g = GaussianMixture(n_components=rand_data.n_components,
|
||
|
|
random_state=rng, weights_init=rand_data.weights,
|
||
|
|
means_init=rand_data.means,
|
||
|
|
precisions_init=rand_data.precisions[covar_type],
|
||
|
|
covariance_type=covar_type,
|
||
|
|
max_iter=max_iter, tol=tol)
|
||
|
|
|
||
|
|
# check if fit_predict(X) is equivalent to fit(X).predict(X)
|
||
|
|
f = copy.deepcopy(g)
|
||
|
|
Y_pred1 = f.fit(X).predict(X)
|
||
|
|
Y_pred2 = g.fit_predict(X)
|
||
|
|
assert_array_equal(Y_pred1, Y_pred2)
|
||
|
|
assert adjusted_rand_score(Y, Y_pred2) > .95
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_fit_predict_n_init():
|
||
|
|
# Check that fit_predict is equivalent to fit.predict, when n_init > 1
|
||
|
|
X = np.random.RandomState(0).randn(1000, 5)
|
||
|
|
gm = GaussianMixture(n_components=5, n_init=5, random_state=0)
|
||
|
|
y_pred1 = gm.fit_predict(X)
|
||
|
|
y_pred2 = gm.predict(X)
|
||
|
|
assert_array_equal(y_pred1, y_pred2)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_fit():
|
||
|
|
# recover the ground truth
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng)
|
||
|
|
n_features = rand_data.n_features
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
X = rand_data.X[covar_type]
|
||
|
|
g = GaussianMixture(n_components=n_components, n_init=20,
|
||
|
|
reg_covar=0, random_state=rng,
|
||
|
|
covariance_type=covar_type)
|
||
|
|
g.fit(X)
|
||
|
|
|
||
|
|
# needs more data to pass the test with rtol=1e-7
|
||
|
|
assert_allclose(np.sort(g.weights_), np.sort(rand_data.weights),
|
||
|
|
rtol=0.1, atol=1e-2)
|
||
|
|
|
||
|
|
arg_idx1 = g.means_[:, 0].argsort()
|
||
|
|
arg_idx2 = rand_data.means[:, 0].argsort()
|
||
|
|
assert_allclose(g.means_[arg_idx1], rand_data.means[arg_idx2],
|
||
|
|
rtol=0.1, atol=1e-2)
|
||
|
|
|
||
|
|
if covar_type == 'full':
|
||
|
|
prec_pred = g.precisions_
|
||
|
|
prec_test = rand_data.precisions['full']
|
||
|
|
elif covar_type == 'tied':
|
||
|
|
prec_pred = np.array([g.precisions_] * n_components)
|
||
|
|
prec_test = np.array([rand_data.precisions['tied']] * n_components)
|
||
|
|
elif covar_type == 'spherical':
|
||
|
|
prec_pred = np.array([np.eye(n_features) * c
|
||
|
|
for c in g.precisions_])
|
||
|
|
prec_test = np.array([np.eye(n_features) * c for c in
|
||
|
|
rand_data.precisions['spherical']])
|
||
|
|
elif covar_type == 'diag':
|
||
|
|
prec_pred = np.array([np.diag(d) for d in g.precisions_])
|
||
|
|
prec_test = np.array([np.diag(d) for d in
|
||
|
|
rand_data.precisions['diag']])
|
||
|
|
|
||
|
|
arg_idx1 = np.trace(prec_pred, axis1=1, axis2=2).argsort()
|
||
|
|
arg_idx2 = np.trace(prec_test, axis1=1, axis2=2).argsort()
|
||
|
|
for k, h in zip(arg_idx1, arg_idx2):
|
||
|
|
ecov = EmpiricalCovariance()
|
||
|
|
ecov.covariance_ = prec_test[h]
|
||
|
|
# the accuracy depends on the number of data and randomness, rng
|
||
|
|
assert_allclose(ecov.error_norm(prec_pred[k]), 0, atol=0.15)
|
||
|
|
|
||
|
|
|
||
|
|
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))
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_fit_convergence_warning():
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng, scale=1)
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
max_iter = 1
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
X = rand_data.X[covar_type]
|
||
|
|
g = GaussianMixture(n_components=n_components, n_init=1,
|
||
|
|
max_iter=max_iter, reg_covar=0, random_state=rng,
|
||
|
|
covariance_type=covar_type)
|
||
|
|
assert_warns_message(ConvergenceWarning,
|
||
|
|
'Initialization %d did not converge. '
|
||
|
|
'Try different init parameters, '
|
||
|
|
'or increase max_iter, tol '
|
||
|
|
'or check for degenerate data.'
|
||
|
|
% max_iter, g.fit, X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_multiple_init():
|
||
|
|
# Test that multiple inits does not much worse than a single one
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
n_samples, n_features, n_components = 50, 5, 2
|
||
|
|
X = rng.randn(n_samples, n_features)
|
||
|
|
for cv_type in COVARIANCE_TYPE:
|
||
|
|
train1 = GaussianMixture(n_components=n_components,
|
||
|
|
covariance_type=cv_type,
|
||
|
|
random_state=0).fit(X).score(X)
|
||
|
|
train2 = GaussianMixture(n_components=n_components,
|
||
|
|
covariance_type=cv_type,
|
||
|
|
random_state=0, n_init=5).fit(X).score(X)
|
||
|
|
assert train2 >= train1
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_n_parameters():
|
||
|
|
# Test that the right number of parameters is estimated
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
n_samples, n_features, n_components = 50, 5, 2
|
||
|
|
X = rng.randn(n_samples, n_features)
|
||
|
|
n_params = {'spherical': 13, 'diag': 21, 'tied': 26, 'full': 41}
|
||
|
|
for cv_type in COVARIANCE_TYPE:
|
||
|
|
g = GaussianMixture(
|
||
|
|
n_components=n_components, covariance_type=cv_type,
|
||
|
|
random_state=rng).fit(X)
|
||
|
|
assert g._n_parameters() == n_params[cv_type]
|
||
|
|
|
||
|
|
|
||
|
|
def test_bic_1d_1component():
|
||
|
|
# Test all of the covariance_types return the same BIC score for
|
||
|
|
# 1-dimensional, 1 component fits.
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
n_samples, n_dim, n_components = 100, 1, 1
|
||
|
|
X = rng.randn(n_samples, n_dim)
|
||
|
|
bic_full = GaussianMixture(n_components=n_components,
|
||
|
|
covariance_type='full',
|
||
|
|
random_state=rng).fit(X).bic(X)
|
||
|
|
for covariance_type in ['tied', 'diag', 'spherical']:
|
||
|
|
bic = GaussianMixture(n_components=n_components,
|
||
|
|
covariance_type=covariance_type,
|
||
|
|
random_state=rng).fit(X).bic(X)
|
||
|
|
assert_almost_equal(bic_full, bic)
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_aic_bic():
|
||
|
|
# Test the aic and bic criteria
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
n_samples, n_features, n_components = 50, 3, 2
|
||
|
|
X = rng.randn(n_samples, n_features)
|
||
|
|
# standard gaussian entropy
|
||
|
|
sgh = 0.5 * (fast_logdet(np.cov(X.T, bias=1)) +
|
||
|
|
n_features * (1 + np.log(2 * np.pi)))
|
||
|
|
for cv_type in COVARIANCE_TYPE:
|
||
|
|
g = GaussianMixture(
|
||
|
|
n_components=n_components, covariance_type=cv_type,
|
||
|
|
random_state=rng, max_iter=200)
|
||
|
|
g.fit(X)
|
||
|
|
aic = 2 * n_samples * sgh + 2 * g._n_parameters()
|
||
|
|
bic = (2 * n_samples * sgh +
|
||
|
|
np.log(n_samples) * g._n_parameters())
|
||
|
|
bound = n_features / np.sqrt(n_samples)
|
||
|
|
assert (g.aic(X) - aic) / n_samples < bound
|
||
|
|
assert (g.bic(X) - bic) / n_samples < bound
|
||
|
|
|
||
|
|
|
||
|
|
def test_gaussian_mixture_verbose():
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng)
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
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,
|
||
|
|
verbose=1)
|
||
|
|
h = GaussianMixture(n_components=n_components, n_init=1, reg_covar=0,
|
||
|
|
random_state=rng, covariance_type=covar_type,
|
||
|
|
verbose=2)
|
||
|
|
old_stdout = sys.stdout
|
||
|
|
sys.stdout = StringIO()
|
||
|
|
try:
|
||
|
|
g.fit(X)
|
||
|
|
h.fit(X)
|
||
|
|
finally:
|
||
|
|
sys.stdout = old_stdout
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings('ignore:.*did not converge.*')
|
||
|
|
@pytest.mark.parametrize("seed", (0, 1, 2))
|
||
|
|
def test_warm_start(seed):
|
||
|
|
random_state = seed
|
||
|
|
rng = np.random.RandomState(random_state)
|
||
|
|
n_samples, n_features, n_components = 500, 2, 2
|
||
|
|
X = rng.rand(n_samples, n_features)
|
||
|
|
|
||
|
|
# Assert the warm_start give the same result for the same number of iter
|
||
|
|
g = GaussianMixture(n_components=n_components, n_init=1, max_iter=2,
|
||
|
|
reg_covar=0, random_state=random_state,
|
||
|
|
warm_start=False)
|
||
|
|
h = GaussianMixture(n_components=n_components, n_init=1, max_iter=1,
|
||
|
|
reg_covar=0, random_state=random_state,
|
||
|
|
warm_start=True)
|
||
|
|
|
||
|
|
g.fit(X)
|
||
|
|
score1 = h.fit(X).score(X)
|
||
|
|
score2 = h.fit(X).score(X)
|
||
|
|
|
||
|
|
assert_almost_equal(g.weights_, h.weights_)
|
||
|
|
assert_almost_equal(g.means_, h.means_)
|
||
|
|
assert_almost_equal(g.precisions_, h.precisions_)
|
||
|
|
assert score2 > score1
|
||
|
|
|
||
|
|
# Assert that by using warm_start we can converge to a good solution
|
||
|
|
g = GaussianMixture(n_components=n_components, n_init=1,
|
||
|
|
max_iter=5, reg_covar=0, random_state=random_state,
|
||
|
|
warm_start=False, tol=1e-6)
|
||
|
|
h = GaussianMixture(n_components=n_components, n_init=1,
|
||
|
|
max_iter=5, reg_covar=0, random_state=random_state,
|
||
|
|
warm_start=True, tol=1e-6)
|
||
|
|
|
||
|
|
g.fit(X)
|
||
|
|
assert not g.converged_
|
||
|
|
|
||
|
|
h.fit(X)
|
||
|
|
# depending on the data there is large variability in the number of
|
||
|
|
# refit necessary to converge due to the complete randomness of the
|
||
|
|
# data
|
||
|
|
for _ in range(1000):
|
||
|
|
h.fit(X)
|
||
|
|
if h.converged_:
|
||
|
|
break
|
||
|
|
assert h.converged_
|
||
|
|
|
||
|
|
|
||
|
|
@ignore_warnings(category=ConvergenceWarning)
|
||
|
|
def test_convergence_detected_with_warm_start():
|
||
|
|
# We check that convergence is detected when warm_start=True
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng)
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
X = rand_data.X['full']
|
||
|
|
|
||
|
|
for max_iter in (1, 2, 50):
|
||
|
|
gmm = GaussianMixture(n_components=n_components, warm_start=True,
|
||
|
|
max_iter=max_iter, random_state=rng)
|
||
|
|
for _ in range(100):
|
||
|
|
gmm.fit(X)
|
||
|
|
if gmm.converged_:
|
||
|
|
break
|
||
|
|
assert gmm.converged_
|
||
|
|
assert max_iter >= gmm.n_iter_
|
||
|
|
|
||
|
|
|
||
|
|
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_score_samples():
|
||
|
|
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
|
||
|
|
gmm = GaussianMixture(n_components=n_components, n_init=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.", gmm.score_samples, X)
|
||
|
|
|
||
|
|
gmm_score_samples = gmm.fit(X).score_samples(X)
|
||
|
|
assert gmm_score_samples.shape[0] == rand_data.n_samples
|
||
|
|
|
||
|
|
|
||
|
|
def test_monotonic_likelihood():
|
||
|
|
# We check that each step of the EM without regularization improve
|
||
|
|
# monotonically the training set likelihood
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng, scale=7)
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
X = rand_data.X[covar_type]
|
||
|
|
gmm = GaussianMixture(n_components=n_components,
|
||
|
|
covariance_type=covar_type, reg_covar=0,
|
||
|
|
warm_start=True, max_iter=1, random_state=rng,
|
||
|
|
tol=1e-7)
|
||
|
|
current_log_likelihood = -np.infty
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("ignore", ConvergenceWarning)
|
||
|
|
# Do one training iteration at a time so we can make sure that the
|
||
|
|
# training log likelihood increases after each iteration.
|
||
|
|
for _ in range(600):
|
||
|
|
prev_log_likelihood = current_log_likelihood
|
||
|
|
current_log_likelihood = gmm.fit(X).score(X)
|
||
|
|
assert current_log_likelihood >= prev_log_likelihood
|
||
|
|
|
||
|
|
if gmm.converged_:
|
||
|
|
break
|
||
|
|
|
||
|
|
assert gmm.converged_
|
||
|
|
|
||
|
|
|
||
|
|
def test_regularisation():
|
||
|
|
# We train the GaussianMixture on degenerate data by defining two clusters
|
||
|
|
# of a 0 covariance.
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
n_samples, n_features = 10, 5
|
||
|
|
|
||
|
|
X = np.vstack((np.ones((n_samples // 2, n_features)),
|
||
|
|
np.zeros((n_samples // 2, n_features))))
|
||
|
|
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
gmm = GaussianMixture(n_components=n_samples, reg_covar=0,
|
||
|
|
covariance_type=covar_type, random_state=rng)
|
||
|
|
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("ignore", RuntimeWarning)
|
||
|
|
assert_raise_message(ValueError,
|
||
|
|
"Fitting the mixture model failed because "
|
||
|
|
"some components have ill-defined empirical "
|
||
|
|
"covariance (for instance caused by "
|
||
|
|
"singleton or collapsed samples). Try to "
|
||
|
|
"decrease the number of components, or "
|
||
|
|
"increase reg_covar.", gmm.fit, X)
|
||
|
|
|
||
|
|
gmm.set_params(reg_covar=1e-6).fit(X)
|
||
|
|
|
||
|
|
|
||
|
|
def test_property():
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng, scale=7)
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
X = rand_data.X[covar_type]
|
||
|
|
gmm = GaussianMixture(n_components=n_components,
|
||
|
|
covariance_type=covar_type, random_state=rng,
|
||
|
|
n_init=5)
|
||
|
|
gmm.fit(X)
|
||
|
|
if covar_type == 'full':
|
||
|
|
for prec, covar in zip(gmm.precisions_, gmm.covariances_):
|
||
|
|
|
||
|
|
assert_array_almost_equal(linalg.inv(prec), covar)
|
||
|
|
elif covar_type == 'tied':
|
||
|
|
assert_array_almost_equal(linalg.inv(gmm.precisions_),
|
||
|
|
gmm.covariances_)
|
||
|
|
else:
|
||
|
|
assert_array_almost_equal(gmm.precisions_, 1. / gmm.covariances_)
|
||
|
|
|
||
|
|
|
||
|
|
def test_sample():
|
||
|
|
rng = np.random.RandomState(0)
|
||
|
|
rand_data = RandomData(rng, scale=7, n_components=3)
|
||
|
|
n_features, n_components = rand_data.n_features, rand_data.n_components
|
||
|
|
|
||
|
|
for covar_type in COVARIANCE_TYPE:
|
||
|
|
X = rand_data.X[covar_type]
|
||
|
|
|
||
|
|
gmm = GaussianMixture(n_components=n_components,
|
||
|
|
covariance_type=covar_type, random_state=rng)
|
||
|
|
# To sample we need that GaussianMixture is fitted
|
||
|
|
assert_raise_message(NotFittedError, "This GaussianMixture instance "
|
||
|
|
"is not fitted", gmm.sample, 0)
|
||
|
|
gmm.fit(X)
|
||
|
|
|
||
|
|
assert_raise_message(ValueError, "Invalid value for 'n_samples",
|
||
|
|
gmm.sample, 0)
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||
|
|
|
||
|
|
# Just to make sure the class samples correctly
|
||
|
|
n_samples = 20000
|
||
|
|
X_s, y_s = gmm.sample(n_samples)
|
||
|
|
|
||
|
|
for k in range(n_components):
|
||
|
|
if covar_type == 'full':
|
||
|
|
assert_array_almost_equal(gmm.covariances_[k],
|
||
|
|
np.cov(X_s[y_s == k].T), decimal=1)
|
||
|
|
elif covar_type == 'tied':
|
||
|
|
assert_array_almost_equal(gmm.covariances_,
|
||
|
|
np.cov(X_s[y_s == k].T), decimal=1)
|
||
|
|
elif covar_type == 'diag':
|
||
|
|
assert_array_almost_equal(gmm.covariances_[k],
|
||
|
|
np.diag(np.cov(X_s[y_s == k].T)),
|
||
|
|
decimal=1)
|
||
|
|
else:
|
||
|
|
assert_array_almost_equal(
|
||
|
|
gmm.covariances_[k], np.var(X_s[y_s == k] - gmm.means_[k]),
|
||
|
|
decimal=1)
|
||
|
|
|
||
|
|
means_s = np.array([np.mean(X_s[y_s == k], 0)
|
||
|
|
for k in range(n_components)])
|
||
|
|
assert_array_almost_equal(gmm.means_, means_s, decimal=1)
|
||
|
|
|
||
|
|
# Check shapes of sampled data, see
|
||
|
|
# https://github.com/scikit-learn/scikit-learn/issues/7701
|
||
|
|
assert X_s.shape == (n_samples, n_features)
|
||
|
|
|
||
|
|
for sample_size in range(1, 100):
|
||
|
|
X_s, _ = gmm.sample(sample_size)
|
||
|
|
assert X_s.shape == (sample_size, n_features)
|
||
|
|
|
||
|
|
|
||
|
|
@ignore_warnings(category=ConvergenceWarning)
|
||
|
|
def test_init():
|
||
|
|
# We check that by increasing the n_init number we have a better solution
|
||
|
|
for random_state in range(15):
|
||
|
|
rand_data = RandomData(np.random.RandomState(random_state),
|
||
|
|
n_samples=50, scale=1)
|
||
|
|
n_components = rand_data.n_components
|
||
|
|
X = rand_data.X['full']
|
||
|
|
|
||
|
|
gmm1 = GaussianMixture(n_components=n_components, n_init=1,
|
||
|
|
max_iter=1, random_state=random_state).fit(X)
|
||
|
|
gmm2 = GaussianMixture(n_components=n_components, n_init=10,
|
||
|
|
max_iter=1, random_state=random_state).fit(X)
|
||
|
|
|
||
|
|
assert gmm2.lower_bound_ >= gmm1.lower_bound_
|