# Authors: The jax-sklearn developers
# SPDX-License-Identifier: BSD-3-Clause

import copy

import numpy as np
import pytest
from scipy.special import gammaln

from xlearn.exceptions import NotFittedError
from xlearn.metrics.cluster import adjusted_rand_score
from xlearn.mixture import BayesianGaussianMixture
from xlearn.mixture._bayesian_mixture import _log_dirichlet_norm, _log_wishart_norm
from xlearn.mixture.tests.test_gaussian_mixture import RandomData
from xlearn.utils._testing import (
    assert_almost_equal,
    assert_array_equal,
)

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)) + 1.0
    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 + 0.5 * n_features * np.log(2.0))
            + np.sum(
                gammaln(
                    0.5
                    * (degrees_of_freedom_k - np.arange(0, n_features)[:, np.newaxis])
                ),
                0,
            )
        ).item()
    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_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 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(1.0 / 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 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(1.0, bgmm.mean_precision_prior_)

    # 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 - 1.0
    bgmm = BayesianGaussianMixture(
        degrees_of_freedom_prior=bad_degrees_of_freedom_prior_, random_state=rng
    )
    msg = (
        "The parameter 'degrees_of_freedom_prior' should be greater than"
        f" {n_features - 1}, but got {bad_degrees_of_freedom_prior_:.3f}."
    )
    with pytest.raises(ValueError, match=msg):
        bgmm.fit(X)

    # Check correct init for a given value of degrees_of_freedom_prior
    degrees_of_freedom_prior = rng.rand() + n_features - 1.0
    bgmm = BayesianGaussianMixture(
        degrees_of_freedom_prior=degrees_of_freedom_prior, random_state=rng
    ).fit(X)
    assert_almost_equal(degrees_of_freedom_prior, bgmm.degrees_of_freedom_prior_)

    # Check correct init for the default value of degrees_of_freedom_prior
    degrees_of_freedom_prior_default = n_features
    bgmm = BayesianGaussianMixture(
        degrees_of_freedom_prior=degrees_of_freedom_prior_default, random_state=rng
    ).fit(X)
    assert_almost_equal(
        degrees_of_freedom_prior_default, bgmm.degrees_of_freedom_prior_
    )

    # Check correct init for a given value of covariance_prior
    covariance_prior = {
        "full": np.cov(X.T, bias=1) + 10,
        "tied": np.cov(X.T, bias=1) + 5,
        "diag": np.diag(np.atleast_2d(np.cov(X.T, bias=1))) + 3,
        "spherical": rng.rand(),
    }

    bgmm = BayesianGaussianMixture(random_state=rng)
    for cov_type in ["full", "tied", "diag", "spherical"]:
        bgmm.covariance_type = cov_type
        bgmm.covariance_prior = covariance_prior[cov_type]
        bgmm.fit(X)
        assert_almost_equal(covariance_prior[cov_type], bgmm.covariance_prior_)

    # Check 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)

    msg = "This BayesianGaussianMixture instance is not fitted yet."
    with pytest.raises(ValueError, match=msg):
        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)


@pytest.mark.filterwarnings("ignore::xlearn.exceptions.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.inf
            # 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_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_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_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_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))


@pytest.mark.filterwarnings("ignore::xlearn.exceptions.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)
            )


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
            msg = (
                "This BayesianGaussianMixture instance is not fitted yet. "
                "Call 'fit' with appropriate arguments before using this "
                "estimator."
            )
            with pytest.raises(NotFittedError, match=msg):
                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) >= 0.95
