Python sklearn.mixture.BayesianGaussianMixture() Examples

def _fit_continuous(self, column, data):
        gm = BayesianGaussianMixture(
            self.n_clusters,
            weight_concentration_prior_type='dirichlet_process',
            weight_concentration_prior=0.001,
            n_init=1
        )
        gm.fit(data)
        components = gm.weights_ > self.epsilon
        num_components = components.sum()

        return {
            'name': column,
            'model': gm,
            'components': components,
            'output_info': [(1, 'tanh'), (num_components, 'softmax')],
            'output_dimensions': 1 + num_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_) 

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(epoch, prior):
    ans = np.zeros((50, 10))
    for data, lab in test_loader:
        C = prior.predict(data.numpy().reshape(data.numpy().shape[0],-1))
        for i in xrange(len(lab)):
            ans[C[i],lab[i]]+=1
    print(ans)
    s = np.sum(ans)
    v = 0
    for i in xrange(ans.shape[0]):
        for j in xrange(ans.shape[1]):
            if ans[i,j]>0:
                v += ans[i,j]/s*np.log(ans[i,j]/s/(np.sum(ans[i,:])/s)/(np.sum(ans[:,j])/s))
    print("Mutual information: "+str(v))

#prior = BayesianGaussianMixture(n_components=100, covariance_type='diag') 

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_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_) 

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 transform_rays_model_sets_mean_cdf_mixture(list_rays, nb_components=5, slic_size=15):
    """ compute the mixture model and transform it into cumulative distribution

    :param list(list(int)) list_rays: list ray features (distances)
    :param int nb_components: number components in mixture model
    :param int slic_size: superpixel size
    :return tuple(any,list(list(int))):  mixture model, list of stat/param of models

    >>> np.random.seed(0)
    >>> list_rays = [[9, 4, 9], [4, 9, 7], [9, 7, 11], [10, 8, 10],
    ...              [9, 11, 8], [4, 8, 5], [8, 10, 6], [9, 7, 11]]
    >>> mm, mean_cdf = transform_rays_model_sets_mean_cdf_mixture(list_rays, 2)
    >>> len(mean_cdf)
    2
    """
    rays = np.array(list_rays)
    # mm = mixture.GaussianMixture(n_components=nb_components,
    #                                      covariance_type='diag')
    mm = mixture.BayesianGaussianMixture(n_components=nb_components,
                                         covariance_type='diag')
    mm.fit(rays)
    logging.debug('Mixture model found % components with weights: %r',
                  len(mm.weights_), mm.weights_)

    list_mean_cdf = []
    # stds = mm.covariances_[:, np.eye(mm.means_.shape[1], dtype=bool)]
    # stds = mm.covariances_  # for covariance_type='diag'
    # diff_means = np.max(mm.means_, axis=0) - np.min(mm.means_, axis=0)
    for mean, covar in zip(mm.means_, mm.covariances_):
        std = np.sqrt(covar + 1) * 2 + slic_size
        mean = ndimage.gaussian_filter1d(mean, 1)
        std = ndimage.gaussian_filter1d(std, 1)
        max_dist = np.max(mean + 2 * std)
        cdist = compute_cumulative_distrib(np.array([mean]), np.array([std]),
                                           np.array([1]), max_dist)
        list_mean_cdf.append((mean.tolist(), cdist))

    return mm, list_mean_cdf 

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_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_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_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_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_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_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_objectmapper(self):
        df = pdml.ModelFrame([])
        self.assertIs(df.mixture.GaussianMixture, mixture.GaussianMixture)
        self.assertIs(df.mixture.BayesianGaussianMixture,
                      mixture.BayesianGaussianMixture) 

def fit_bgmm(X, n1, n2, gamma=1e-3, max_iter=100, n_init=1, **kwargs):
    """
    Compute Bayesian Gaussian mixture

    :param X: data frame (log transformed Ks values)
    :param n1: minimum number of components
    :param n2: maximum number of components
    :param gamma: inverse of regularization strength
    :param max_iter: maximum number of iterations
    :param n_init: number of k-means initializations
    :param kwargs: other keyword args for `GaussianMixture`
    :return: models
    """
    # fit models with 1 to n components
    N = np.arange(n1, n2 + 1)
    models = [None for i in range(len(N))]

    for i in range(len(N)):
        logging.info("Fitting BGMM with {} components".format(N[i]))
        models[i] = mixture.BayesianGaussianMixture(
                weight_concentration_prior=gamma, n_init=n_init,
                n_components=N[i], covariance_type='full', max_iter=max_iter,
                **kwargs
        ).fit(X)
        log_components(models[i])

    return models 

def fit(self, X: np.array, y: np.array) -> "BayesianGMMClassifier":
        """
        Fit the model using X, y as training data.

        :param X: array-like, shape=(n_columns, n_samples, ) training data.
        :param y: array-like, shape=(n_samples, ) training data.
        :return: Returns an instance of self.
        """
        X, y = check_X_y(X, y, estimator=self, dtype=FLOAT_DTYPES)
        if X.ndim == 1:
            X = np.expand_dims(X, 1)

        self.gmms_ = {}
        self.classes_ = unique_labels(y)
        for c in self.classes_:
            subset_x, subset_y = X[y == c], y[y == c]
            mixture = BayesianGaussianMixture(
                n_components=self.n_components,
                covariance_type=self.covariance_type,
                tol=self.tol,
                reg_covar=self.reg_covar,
                max_iter=self.max_iter,
                n_init=self.n_init,
                init_params=self.init_params,
                weight_concentration_prior_type=self.weight_concentration_prior_type,
                weight_concentration_prior=self.weight_concentration_prior,
                mean_precision_prior=self.mean_precision_prior,
                mean_prior=self.mean_prior,
                degrees_of_freedom_prior=self.degrees_of_freedom_prior,
                covariance_prior=self.covariance_prior,
                random_state=self.random_state,
                warm_start=self.warm_start,
                verbose=self.verbose,
                verbose_interval=self.verbose_interval,
            )
            self.gmms_[c] = mixture.fit(subset_x, subset_y)
        return self 

def transform_rays_model_cdf_mixture(list_rays, coef_components=1):
    """ compute the mixture model and transform it into cumulative distribution

    :param list(list(int)) list_rays: list ray features (distances)
    :param int coef_components: multiplication for number of components
    :return any, list(list(int)): mixture model, cumulative distribution

    >>> np.random.seed(0)
    >>> list_rays = [[9, 4, 9], [4, 9, 7], [9, 7, 11], [10, 8, 10],
    ...              [9, 11, 8], [4, 8, 5], [8, 10, 6], [9, 7, 11]]
    >>> mm, cdist = transform_rays_model_cdf_mixture(list_rays)
    >>> # the rounding variate a bit according GMM estimated model
    >>> np.round(np.array(cdist) * 4) / 4.  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    array([[ 1. , 1. , 1. , 1. , 1. , 1. , 0.75, 0.75, 0.5 , 0.25, 0. ],
           [ 1. , 1. , 1. , 1. , 1. , 1. , 1.  , 0.75, 0.5 , 0.25, 0. ],
           [ 1. , 1. , 1. , 1. , 1. , 1. , ...,  0.75, 0.5 , 0.25, 0. ]])
    """
    rays = np.array(list_rays)
    ms = cluster.MeanShift()
    ms.fit(rays)
    logging.debug('MeanShift found: %r', np.bincount(ms.labels_))

    nb_components = int(len(np.unique(ms.labels_)) * coef_components)
    mm = mixture.BayesianGaussianMixture(n_components=nb_components)
    # gmm.fit(np.array(list_rays))
    mm.fit(rays, ms.labels_)
    logging.debug('Mixture model found % components with weights: %r',
                  len(mm.weights_), mm.weights_)

    # compute the fairest mean + sigma over all components and ray angles
    max_dist = np.max([[m[i] + np.sqrt(c[i, i]) for i in range(len(m))]
                       for m, c in zip(mm.means_, mm.covariances_)])
    # max_dist = np.max(rays)

    # fixing, AttributeError: 'BayesianGaussianMixture' object has no attribute 'covariances'
    covs = mm.covariances if hasattr(mm, 'covariances') else mm.covariances_
    stds = np.sqrt(abs(covs))[:, np.eye(mm.means_.shape[1], dtype=bool)]
    # stds = np.sum(mm.covariances_, axis=-1)
    cdist = compute_cumulative_distrib(mm.means_, stds, mm.weights_, max_dist)
    return mm, cdist.tolist() 

def fit(self, data, categorical_columns=tuple(), ordinal_columns=tuple()):
        self.meta = self.get_metadata(data, categorical_columns, ordinal_columns)
        model = []

        self.output_info = []
        self.output_dim = 0
        self.components = []
        for id_, info in enumerate(self.meta):
            if info['type'] == CONTINUOUS:
                gm = BayesianGaussianMixture(
                    self.n_clusters,
                    weight_concentration_prior_type='dirichlet_process',
                    weight_concentration_prior=0.001,
                    n_init=1)
                gm.fit(data[:, id_].reshape([-1, 1]))
                model.append(gm)
                comp = gm.weights_ > self.eps
                self.components.append(comp)

                self.output_info += [(1, 'tanh'), (np.sum(comp), 'softmax')]
                self.output_dim += 1 + np.sum(comp)
            else:
                model.append(None)
                self.components.append(None)
                self.output_info += [(info['size'], 'softmax')]
                self.output_dim += info['size']

        self.model = model 

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 create_model(samples_x, samples_y_aggregation, percentage_goodbatch=0.34):
    '''
    Create the Gaussian Mixture Model
    '''
    samples = [samples_x[i] + [samples_y_aggregation[i]]
               for i in range(0, len(samples_x))]

    # Sorts so that we can get the top samples
    samples = sorted(samples, key=itemgetter(-1))
    samples_goodbatch_size = int(len(samples) * percentage_goodbatch)
    samples_goodbatch = samples[0:samples_goodbatch_size]
    samples_badbatch = samples[samples_goodbatch_size:]

    samples_x_goodbatch = [sample_goodbatch[0:-1]
                           for sample_goodbatch in samples_goodbatch]
    #samples_y_goodbatch = [sample_goodbatch[-1] for sample_goodbatch in samples_goodbatch]
    samples_x_badbatch = [sample_badbatch[0:-1]
                          for sample_badbatch in samples_badbatch]

    # === Trains GMM clustering models === #
    #sys.stderr.write("[%s] Train GMM's GMM model\n" % (os.path.basename(__file__)))
    bgmm_goodbatch = mm.BayesianGaussianMixture(
        n_components=max(1, samples_goodbatch_size - 1))
    bad_n_components = max(1, len(samples_x) - samples_goodbatch_size - 1)
    bgmm_badbatch = mm.BayesianGaussianMixture(n_components=bad_n_components)
    bgmm_goodbatch.fit(samples_x_goodbatch)
    bgmm_badbatch.fit(samples_x_badbatch)

    model = {}
    model['clusteringmodel_good'] = bgmm_goodbatch
    model['clusteringmodel_bad'] = bgmm_badbatch
    return model 

def train(epoch, prior):
    prior = BayesianGaussianMixture(n_components=50, covariance_type='diag', n_init=5, max_iter=1000)
    tmp = []
    for (data,_) in train_loader:
        #print(data.numpy().shape)
        tmp.append(data.numpy().reshape(data.numpy().shape[0],-1))
    prior.fit(np.vstack(tmp))
    return prior 

def train(epoch, prior):
    model.train()
    train_loss = 0
    #prior = BayesianGaussianMixture(n_components=1, covariance_type='diag')
    tmp = []
    for (data,_) in train_loader:
        data = Variable(data)
        if args.cuda:
            data = data.cuda()
        recon_batch, mu, logvar, z = model(data)
        tmp.append(z.cpu().data.numpy())
    print('Update Prior')
    prior.fit(np.vstack(tmp))
    print('prior: '+str(prior.weights_))
    for batch_idx, (data, _) in enumerate(train_loader):
        data = Variable(data)
        if args.cuda:
            data = data.cuda()
        optimizer.zero_grad()
        recon_batch, mu, logvar, z = model(data)
        loss = loss_function(recon_batch, data, mu, logvar, prior, z)
        loss.backward()
        train_loss += loss.data[0]
        optimizer.step()
        #if batch_idx % args.log_interval == 0:
        #    print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
        #        epoch, batch_idx * len(data), len(train_loader.dataset),
        #        100. * batch_idx / len(train_loader),
        #        loss.data[0] / len(data)))

    print('====> Epoch: {} Average loss: {:.4f}'.format(
          epoch, train_loss / len(train_loader.dataset)))
    return prior 

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) 

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_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_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)) 

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_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_)