projektAI/venv/Lib/site-packages/sklearn/mixture/tests/test_bayesian_mixture.py
2021-06-06 22:13:05 +02:00

489 lines
20 KiB
Python

# Author: Wei Xue <xuewei4d@gmail.com>
# Thierry Guillemot <thierry.guillemot.work@gmail.com>
# License: BSD 3 clause
import copy
import numpy as np
from scipy.special import gammaln
import pytest
from sklearn.utils._testing import assert_raise_message
from sklearn.utils._testing import assert_almost_equal
from sklearn.utils._testing import assert_array_equal
from sklearn.metrics.cluster import adjusted_rand_score
from sklearn.mixture._bayesian_mixture import _log_dirichlet_norm
from sklearn.mixture._bayesian_mixture import _log_wishart_norm
from sklearn.mixture import BayesianGaussianMixture
from sklearn.mixture.tests.test_gaussian_mixture import RandomData
from sklearn.exceptions import ConvergenceWarning, NotFittedError
from sklearn.utils._testing import ignore_warnings
COVARIANCE_TYPE = ['full', 'tied', 'diag', 'spherical']
PRIOR_TYPE = ['dirichlet_process', 'dirichlet_distribution']
def test_log_dirichlet_norm():
rng = np.random.RandomState(0)
weight_concentration = rng.rand(2)
expected_norm = (gammaln(np.sum(weight_concentration)) -
np.sum(gammaln(weight_concentration)))
predected_norm = _log_dirichlet_norm(weight_concentration)
assert_almost_equal(expected_norm, predected_norm)
def test_log_wishart_norm():
rng = np.random.RandomState(0)
n_components, n_features = 5, 2
degrees_of_freedom = np.abs(rng.rand(n_components)) + 1.
log_det_precisions_chol = n_features * np.log(range(2, 2 + n_components))
expected_norm = np.empty(5)
for k, (degrees_of_freedom_k, log_det_k) in enumerate(
zip(degrees_of_freedom, log_det_precisions_chol)):
expected_norm[k] = -(
degrees_of_freedom_k * (log_det_k + .5 * n_features * np.log(2.)) +
np.sum(gammaln(.5 * (degrees_of_freedom_k -
np.arange(0, n_features)[:, np.newaxis])), 0))
predected_norm = _log_wishart_norm(degrees_of_freedom,
log_det_precisions_chol, n_features)
assert_almost_equal(expected_norm, predected_norm)
def test_bayesian_mixture_covariance_type():
rng = np.random.RandomState(0)
n_samples, n_features = 10, 2
X = rng.rand(n_samples, n_features)
covariance_type = 'bad_covariance_type'
bgmm = BayesianGaussianMixture(covariance_type=covariance_type,
random_state=rng)
assert_raise_message(ValueError,
"Invalid value for 'covariance_type': %s "
"'covariance_type' should be in "
"['spherical', 'tied', 'diag', 'full']"
% covariance_type, bgmm.fit, X)
def test_bayesian_mixture_weight_concentration_prior_type():
rng = np.random.RandomState(0)
n_samples, n_features = 10, 2
X = rng.rand(n_samples, n_features)
bad_prior_type = 'bad_prior_type'
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=bad_prior_type, random_state=rng)
assert_raise_message(ValueError,
"Invalid value for 'weight_concentration_prior_type':"
" %s 'weight_concentration_prior_type' should be in "
"['dirichlet_process', 'dirichlet_distribution']"
% bad_prior_type, bgmm.fit, X)
def test_bayesian_mixture_weights_prior_initialisation():
rng = np.random.RandomState(0)
n_samples, n_components, n_features = 10, 5, 2
X = rng.rand(n_samples, n_features)
# Check raise message for a bad value of weight_concentration_prior
bad_weight_concentration_prior_ = 0.
bgmm = BayesianGaussianMixture(
weight_concentration_prior=bad_weight_concentration_prior_,
random_state=0)
assert_raise_message(ValueError,
"The parameter 'weight_concentration_prior' "
"should be greater than 0., but got %.3f."
% bad_weight_concentration_prior_,
bgmm.fit, X)
# Check correct init for a given value of weight_concentration_prior
weight_concentration_prior = rng.rand()
bgmm = BayesianGaussianMixture(
weight_concentration_prior=weight_concentration_prior,
random_state=rng).fit(X)
assert_almost_equal(weight_concentration_prior,
bgmm.weight_concentration_prior_)
# Check correct init for the default value of weight_concentration_prior
bgmm = BayesianGaussianMixture(n_components=n_components,
random_state=rng).fit(X)
assert_almost_equal(1. / n_components, bgmm.weight_concentration_prior_)
def test_bayesian_mixture_mean_prior_initialisation():
rng = np.random.RandomState(0)
n_samples, n_components, n_features = 10, 3, 2
X = rng.rand(n_samples, n_features)
# Check raise message for a bad value of mean_precision_prior
bad_mean_precision_prior_ = 0.
bgmm = BayesianGaussianMixture(
mean_precision_prior=bad_mean_precision_prior_,
random_state=rng)
assert_raise_message(ValueError,
"The parameter 'mean_precision_prior' should be "
"greater than 0., but got %.3f."
% bad_mean_precision_prior_,
bgmm.fit, X)
# Check correct init for a given value of mean_precision_prior
mean_precision_prior = rng.rand()
bgmm = BayesianGaussianMixture(
mean_precision_prior=mean_precision_prior,
random_state=rng).fit(X)
assert_almost_equal(mean_precision_prior, bgmm.mean_precision_prior_)
# Check correct init for the default value of mean_precision_prior
bgmm = BayesianGaussianMixture(random_state=rng).fit(X)
assert_almost_equal(1., bgmm.mean_precision_prior_)
# Check raise message for a bad shape of mean_prior
mean_prior = rng.rand(n_features + 1)
bgmm = BayesianGaussianMixture(n_components=n_components,
mean_prior=mean_prior,
random_state=rng)
assert_raise_message(ValueError,
"The parameter 'means' should have the shape of ",
bgmm.fit, X)
# Check correct init for a given value of mean_prior
mean_prior = rng.rand(n_features)
bgmm = BayesianGaussianMixture(n_components=n_components,
mean_prior=mean_prior,
random_state=rng).fit(X)
assert_almost_equal(mean_prior, bgmm.mean_prior_)
# Check correct init for the default value of bemean_priorta
bgmm = BayesianGaussianMixture(n_components=n_components,
random_state=rng).fit(X)
assert_almost_equal(X.mean(axis=0), bgmm.mean_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.
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_check_is_fitted():
rng = np.random.RandomState(0)
n_samples, n_features = 10, 2
# Check raise message
bgmm = BayesianGaussianMixture(random_state=rng)
X = rng.rand(n_samples, n_features)
assert_raise_message(ValueError,
'This BayesianGaussianMixture instance is not '
'fitted yet.', bgmm.score, X)
def test_bayesian_mixture_weights():
rng = np.random.RandomState(0)
n_samples, n_features = 10, 2
X = rng.rand(n_samples, n_features)
# Case Dirichlet distribution for the weight concentration prior type
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type="dirichlet_distribution",
n_components=3, random_state=rng).fit(X)
expected_weights = (bgmm.weight_concentration_ /
np.sum(bgmm.weight_concentration_))
assert_almost_equal(expected_weights, bgmm.weights_)
assert_almost_equal(np.sum(bgmm.weights_), 1.0)
# Case Dirichlet process for the weight concentration prior type
dpgmm = BayesianGaussianMixture(
weight_concentration_prior_type="dirichlet_process",
n_components=3, random_state=rng).fit(X)
weight_dirichlet_sum = (dpgmm.weight_concentration_[0] +
dpgmm.weight_concentration_[1])
tmp = dpgmm.weight_concentration_[1] / weight_dirichlet_sum
expected_weights = (dpgmm.weight_concentration_[0] / weight_dirichlet_sum *
np.hstack((1, np.cumprod(tmp[:-1]))))
expected_weights /= np.sum(expected_weights)
assert_almost_equal(expected_weights, dpgmm.weights_)
assert_almost_equal(np.sum(dpgmm.weights_), 1.0)
@ignore_warnings(category=ConvergenceWarning)
def test_monotonic_likelihood():
# We check that each step of the each step of variational inference without
# regularization improve monotonically the training set of the bound
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=20)
n_components = rand_data.n_components
for prior_type in PRIOR_TYPE:
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type=covar_type,
warm_start=True, max_iter=1, random_state=rng, tol=1e-3)
current_lower_bound = -np.infty
# 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_lower_bound = current_lower_bound
current_lower_bound = bgmm.fit(X).lower_bound_
assert current_lower_bound >= prev_lower_bound
if bgmm.converged_:
break
assert(bgmm.converged_)
def test_compare_covar_type():
# We can compare the 'full' precision with the other cov_type if we apply
# 1 iter of the M-step (done during _initialize_parameters).
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7)
X = rand_data.X['full']
n_components = rand_data.n_components
for prior_type in PRIOR_TYPE:
# Computation of the full_covariance
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='full',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
full_covariances = (
bgmm.covariances_ *
bgmm.degrees_of_freedom_[:, np.newaxis, np.newaxis])
# Check tied_covariance = mean(full_covariances, 0)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='tied',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
tied_covariance = bgmm.covariances_ * bgmm.degrees_of_freedom_
assert_almost_equal(tied_covariance, np.mean(full_covariances, 0))
# Check diag_covariance = diag(full_covariances)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='diag',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
diag_covariances = (bgmm.covariances_ *
bgmm.degrees_of_freedom_[:, np.newaxis])
assert_almost_equal(diag_covariances,
np.array([np.diag(cov)
for cov in full_covariances]))
# Check spherical_covariance = np.mean(diag_covariances, 0)
bgmm = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=2 * n_components, covariance_type='spherical',
max_iter=1, random_state=0, tol=1e-7)
bgmm._check_initial_parameters(X)
bgmm._initialize_parameters(X, np.random.RandomState(0))
spherical_covariances = bgmm.covariances_ * bgmm.degrees_of_freedom_
assert_almost_equal(
spherical_covariances, np.mean(diag_covariances, 1))
@ignore_warnings(category=ConvergenceWarning)
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))
@ignore_warnings(category=ConvergenceWarning)
def test_invariant_translation():
# We check here that adding a constant in the data change correctly the
# parameters of the mixture
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=100)
n_components = 2 * rand_data.n_components
for prior_type in PRIOR_TYPE:
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
bgmm1 = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=n_components, max_iter=100, random_state=0,
tol=1e-3, reg_covar=0).fit(X)
bgmm2 = BayesianGaussianMixture(
weight_concentration_prior_type=prior_type,
n_components=n_components, max_iter=100, random_state=0,
tol=1e-3, reg_covar=0).fit(X + 100)
assert_almost_equal(bgmm1.means_, bgmm2.means_ - 100)
assert_almost_equal(bgmm1.weights_, bgmm2.weights_)
assert_almost_equal(bgmm1.covariances_, bgmm2.covariances_)
@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_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_fit_predict_n_init():
# Check that fit_predict is equivalent to fit.predict, when n_init > 1
X = np.random.RandomState(0).randn(50, 5)
gm = BayesianGaussianMixture(n_components=5, n_init=10, random_state=0)
y_pred1 = gm.fit_predict(X)
y_pred2 = gm.predict(X)
assert_array_equal(y_pred1, y_pred2)
def test_bayesian_mixture_predict_predict_proba():
# this is the same test as test_gaussian_mixture_predict_predict_proba()
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
for prior_type in PRIOR_TYPE:
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
Y = rand_data.Y
bgmm = BayesianGaussianMixture(
n_components=rand_data.n_components,
random_state=rng,
weight_concentration_prior_type=prior_type,
covariance_type=covar_type)
# Check a warning message arrive if we don't do fit
assert_raise_message(NotFittedError,
"This BayesianGaussianMixture instance"
" is not fitted yet. Call 'fit' with "
"appropriate arguments before using "
"this estimator.", bgmm.predict, X)
bgmm.fit(X)
Y_pred = bgmm.predict(X)
Y_pred_proba = bgmm.predict_proba(X).argmax(axis=1)
assert_array_equal(Y_pred, Y_pred_proba)
assert adjusted_rand_score(Y, Y_pred) >= .95