# 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 assert_greater_equal, 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_means_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-4)
            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_greater_equal(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, 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_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 method.", 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_greater_equal(adjusted_rand_score(Y, Y_pred), .95)