"""Gaussian Mixture Model.""" # Author: Wei Xue # Modified by Thierry Guillemot # License: BSD 3 clause import numpy as np from scipy import linalg from ..utils import check_array from ..utils._param_validation import StrOptions from ..utils.extmath import row_norms from ._base import BaseMixture, _check_shape ############################################################################### # Gaussian mixture shape checkers used by the GaussianMixture class def _check_weights(weights, n_components): """Check the user provided 'weights'. Parameters ---------- weights : array-like of shape (n_components,) The proportions of components of each mixture. n_components : int Number of components. Returns ------- weights : array, shape (n_components,) """ weights = check_array(weights, dtype=[np.float64, np.float32], ensure_2d=False) _check_shape(weights, (n_components,), "weights") # check range if any(np.less(weights, 0.0)) or any(np.greater(weights, 1.0)): raise ValueError( "The parameter 'weights' should be in the range " "[0, 1], but got max value %.5f, min value %.5f" % (np.min(weights), np.max(weights)) ) # check normalization if not np.allclose(np.abs(1.0 - np.sum(weights)), 0.0): raise ValueError( "The parameter 'weights' should be normalized, but got sum(weights) = %.5f" % np.sum(weights) ) return weights def _check_means(means, n_components, n_features): """Validate the provided 'means'. Parameters ---------- means : array-like of shape (n_components, n_features) The centers of the current components. n_components : int Number of components. n_features : int Number of features. Returns ------- means : array, (n_components, n_features) """ means = check_array(means, dtype=[np.float64, np.float32], ensure_2d=False) _check_shape(means, (n_components, n_features), "means") return means def _check_precision_positivity(precision, covariance_type): """Check a precision vector is positive-definite.""" if np.any(np.less_equal(precision, 0.0)): raise ValueError("'%s precision' should be positive" % covariance_type) def _check_precision_matrix(precision, covariance_type): """Check a precision matrix is symmetric and positive-definite.""" if not ( np.allclose(precision, precision.T) and np.all(linalg.eigvalsh(precision) > 0.0) ): raise ValueError( "'%s precision' should be symmetric, positive-definite" % covariance_type ) def _check_precisions_full(precisions, covariance_type): """Check the precision matrices are symmetric and positive-definite.""" for prec in precisions: _check_precision_matrix(prec, covariance_type) def _check_precisions(precisions, covariance_type, n_components, n_features): """Validate user provided precisions. Parameters ---------- precisions : array-like 'full' : shape of (n_components, n_features, n_features) 'tied' : shape of (n_features, n_features) 'diag' : shape of (n_components, n_features) 'spherical' : shape of (n_components,) covariance_type : str n_components : int Number of components. n_features : int Number of features. Returns ------- precisions : array """ precisions = check_array( precisions, dtype=[np.float64, np.float32], ensure_2d=False, allow_nd=covariance_type == "full", ) precisions_shape = { "full": (n_components, n_features, n_features), "tied": (n_features, n_features), "diag": (n_components, n_features), "spherical": (n_components,), } _check_shape( precisions, precisions_shape[covariance_type], "%s precision" % covariance_type ) _check_precisions = { "full": _check_precisions_full, "tied": _check_precision_matrix, "diag": _check_precision_positivity, "spherical": _check_precision_positivity, } _check_precisions[covariance_type](precisions, covariance_type) return precisions ############################################################################### # Gaussian mixture parameters estimators (used by the M-Step) def _estimate_gaussian_covariances_full(resp, X, nk, means, reg_covar): """Estimate the full covariance matrices. Parameters ---------- resp : array-like of shape (n_samples, n_components) X : array-like of shape (n_samples, n_features) nk : array-like of shape (n_components,) means : array-like of shape (n_components, n_features) reg_covar : float Returns ------- covariances : array, shape (n_components, n_features, n_features) The covariance matrix of the current components. """ n_components, n_features = means.shape covariances = np.empty((n_components, n_features, n_features)) for k in range(n_components): diff = X - means[k] covariances[k] = np.dot(resp[:, k] * diff.T, diff) / nk[k] covariances[k].flat[:: n_features + 1] += reg_covar return covariances def _estimate_gaussian_covariances_tied(resp, X, nk, means, reg_covar): """Estimate the tied covariance matrix. Parameters ---------- resp : array-like of shape (n_samples, n_components) X : array-like of shape (n_samples, n_features) nk : array-like of shape (n_components,) means : array-like of shape (n_components, n_features) reg_covar : float Returns ------- covariance : array, shape (n_features, n_features) The tied covariance matrix of the components. """ avg_X2 = np.dot(X.T, X) avg_means2 = np.dot(nk * means.T, means) covariance = avg_X2 - avg_means2 covariance /= nk.sum() covariance.flat[:: len(covariance) + 1] += reg_covar return covariance def _estimate_gaussian_covariances_diag(resp, X, nk, means, reg_covar): """Estimate the diagonal covariance vectors. Parameters ---------- responsibilities : array-like of shape (n_samples, n_components) X : array-like of shape (n_samples, n_features) nk : array-like of shape (n_components,) means : array-like of shape (n_components, n_features) reg_covar : float Returns ------- covariances : array, shape (n_components, n_features) The covariance vector of the current components. """ avg_X2 = np.dot(resp.T, X * X) / nk[:, np.newaxis] avg_means2 = means**2 avg_X_means = means * np.dot(resp.T, X) / nk[:, np.newaxis] return avg_X2 - 2 * avg_X_means + avg_means2 + reg_covar def _estimate_gaussian_covariances_spherical(resp, X, nk, means, reg_covar): """Estimate the spherical variance values. Parameters ---------- responsibilities : array-like of shape (n_samples, n_components) X : array-like of shape (n_samples, n_features) nk : array-like of shape (n_components,) means : array-like of shape (n_components, n_features) reg_covar : float Returns ------- variances : array, shape (n_components,) The variance values of each components. """ return _estimate_gaussian_covariances_diag(resp, X, nk, means, reg_covar).mean(1) def _estimate_gaussian_parameters(X, resp, reg_covar, covariance_type): """Estimate the Gaussian distribution parameters. Parameters ---------- X : array-like of shape (n_samples, n_features) The input data array. resp : array-like of shape (n_samples, n_components) The responsibilities for each data sample in X. reg_covar : float The regularization added to the diagonal of the covariance matrices. covariance_type : {'full', 'tied', 'diag', 'spherical'} The type of precision matrices. Returns ------- nk : array-like of shape (n_components,) The numbers of data samples in the current components. means : array-like of shape (n_components, n_features) The centers of the current components. covariances : array-like The covariance matrix of the current components. The shape depends of the covariance_type. """ nk = resp.sum(axis=0) + 10 * np.finfo(resp.dtype).eps means = np.dot(resp.T, X) / nk[:, np.newaxis] covariances = { "full": _estimate_gaussian_covariances_full, "tied": _estimate_gaussian_covariances_tied, "diag": _estimate_gaussian_covariances_diag, "spherical": _estimate_gaussian_covariances_spherical, }[covariance_type](resp, X, nk, means, reg_covar) return nk, means, covariances def _compute_precision_cholesky(covariances, covariance_type): """Compute the Cholesky decomposition of the precisions. Parameters ---------- covariances : array-like The covariance matrix of the current components. The shape depends of the covariance_type. covariance_type : {'full', 'tied', 'diag', 'spherical'} The type of precision matrices. Returns ------- precisions_cholesky : array-like The cholesky decomposition of sample precisions of the current components. The shape depends of the covariance_type. """ estimate_precision_error_message = ( "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." ) if covariance_type == "full": n_components, n_features, _ = covariances.shape precisions_chol = np.empty((n_components, n_features, n_features)) for k, covariance in enumerate(covariances): try: cov_chol = linalg.cholesky(covariance, lower=True) except linalg.LinAlgError: raise ValueError(estimate_precision_error_message) precisions_chol[k] = linalg.solve_triangular( cov_chol, np.eye(n_features), lower=True ).T elif covariance_type == "tied": _, n_features = covariances.shape try: cov_chol = linalg.cholesky(covariances, lower=True) except linalg.LinAlgError: raise ValueError(estimate_precision_error_message) precisions_chol = linalg.solve_triangular( cov_chol, np.eye(n_features), lower=True ).T else: if np.any(np.less_equal(covariances, 0.0)): raise ValueError(estimate_precision_error_message) precisions_chol = 1.0 / np.sqrt(covariances) return precisions_chol def _flipudlr(array): """Reverse the rows and columns of an array.""" return np.flipud(np.fliplr(array)) def _compute_precision_cholesky_from_precisions(precisions, covariance_type): r"""Compute the Cholesky decomposition of precisions using precisions themselves. As implemented in :func:`_compute_precision_cholesky`, the `precisions_cholesky_` is an upper-triangular matrix for each Gaussian component, which can be expressed as the $UU^T$ factorization of the precision matrix for each Gaussian component, where $U$ is an upper-triangular matrix. In order to use the Cholesky decomposition to get $UU^T$, the precision matrix $\Lambda$ needs to be permutated such that its rows and columns are reversed, which can be done by applying a similarity transformation with an exchange matrix $J$, where the 1 elements reside on the anti-diagonal and all other elements are 0. In particular, the Cholesky decomposition of the transformed precision matrix is $J\Lambda J=LL^T$, where $L$ is a lower-triangular matrix. Because $\Lambda=UU^T$ and $J=J^{-1}=J^T$, the `precisions_cholesky_` for each Gaussian component can be expressed as $JLJ$. Refer to #26415 for details. Parameters ---------- precisions : array-like The precision matrix of the current components. The shape depends on the covariance_type. covariance_type : {'full', 'tied', 'diag', 'spherical'} The type of precision matrices. Returns ------- precisions_cholesky : array-like The cholesky decomposition of sample precisions of the current components. The shape depends on the covariance_type. """ if covariance_type == "full": precisions_cholesky = np.array( [ _flipudlr(linalg.cholesky(_flipudlr(precision), lower=True)) for precision in precisions ] ) elif covariance_type == "tied": precisions_cholesky = _flipudlr( linalg.cholesky(_flipudlr(precisions), lower=True) ) else: precisions_cholesky = np.sqrt(precisions) return precisions_cholesky ############################################################################### # Gaussian mixture probability estimators def _compute_log_det_cholesky(matrix_chol, covariance_type, n_features): """Compute the log-det of the cholesky decomposition of matrices. Parameters ---------- matrix_chol : array-like Cholesky decompositions of the matrices. 'full' : shape of (n_components, n_features, n_features) 'tied' : shape of (n_features, n_features) 'diag' : shape of (n_components, n_features) 'spherical' : shape of (n_components,) covariance_type : {'full', 'tied', 'diag', 'spherical'} n_features : int Number of features. Returns ------- log_det_precision_chol : array-like of shape (n_components,) The determinant of the precision matrix for each component. """ if covariance_type == "full": n_components, _, _ = matrix_chol.shape log_det_chol = np.sum( np.log(matrix_chol.reshape(n_components, -1)[:, :: n_features + 1]), 1 ) elif covariance_type == "tied": log_det_chol = np.sum(np.log(np.diag(matrix_chol))) elif covariance_type == "diag": log_det_chol = np.sum(np.log(matrix_chol), axis=1) else: log_det_chol = n_features * (np.log(matrix_chol)) return log_det_chol def _estimate_log_gaussian_prob(X, means, precisions_chol, covariance_type): """Estimate the log Gaussian probability. Parameters ---------- X : array-like of shape (n_samples, n_features) means : array-like of shape (n_components, n_features) precisions_chol : array-like Cholesky decompositions of the precision matrices. 'full' : shape of (n_components, n_features, n_features) 'tied' : shape of (n_features, n_features) 'diag' : shape of (n_components, n_features) 'spherical' : shape of (n_components,) covariance_type : {'full', 'tied', 'diag', 'spherical'} Returns ------- log_prob : array, shape (n_samples, n_components) """ n_samples, n_features = X.shape n_components, _ = means.shape # The determinant of the precision matrix from the Cholesky decomposition # corresponds to the negative half of the determinant of the full precision # matrix. # In short: det(precision_chol) = - det(precision) / 2 log_det = _compute_log_det_cholesky(precisions_chol, covariance_type, n_features) if covariance_type == "full": log_prob = np.empty((n_samples, n_components)) for k, (mu, prec_chol) in enumerate(zip(means, precisions_chol)): y = np.dot(X, prec_chol) - np.dot(mu, prec_chol) log_prob[:, k] = np.sum(np.square(y), axis=1) elif covariance_type == "tied": log_prob = np.empty((n_samples, n_components)) for k, mu in enumerate(means): y = np.dot(X, precisions_chol) - np.dot(mu, precisions_chol) log_prob[:, k] = np.sum(np.square(y), axis=1) elif covariance_type == "diag": precisions = precisions_chol**2 log_prob = ( np.sum((means**2 * precisions), 1) - 2.0 * np.dot(X, (means * precisions).T) + np.dot(X**2, precisions.T) ) elif covariance_type == "spherical": precisions = precisions_chol**2 log_prob = ( np.sum(means**2, 1) * precisions - 2 * np.dot(X, means.T * precisions) + np.outer(row_norms(X, squared=True), precisions) ) # Since we are using the precision of the Cholesky decomposition, # `- 0.5 * log_det_precision` becomes `+ log_det_precision_chol` return -0.5 * (n_features * np.log(2 * np.pi) + log_prob) + log_det class GaussianMixture(BaseMixture): """Gaussian Mixture. Representation of a Gaussian mixture model probability distribution. This class allows to estimate the parameters of a Gaussian mixture distribution. Read more in the :ref:`User Guide `. .. versionadded:: 0.18 Parameters ---------- n_components : int, default=1 The number of mixture components. covariance_type : {'full', 'tied', 'diag', 'spherical'}, default='full' String describing the type of covariance parameters to use. Must be one of: - 'full': each component has its own general covariance matrix. - 'tied': all components share the same general covariance matrix. - 'diag': each component has its own diagonal covariance matrix. - 'spherical': each component has its own single variance. tol : float, default=1e-3 The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold. reg_covar : float, default=1e-6 Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive. max_iter : int, default=100 The number of EM iterations to perform. n_init : int, default=1 The number of initializations to perform. The best results are kept. init_params : {'kmeans', 'k-means++', 'random', 'random_from_data'}, \ default='kmeans' The method used to initialize the weights, the means and the precisions. String must be one of: - 'kmeans' : responsibilities are initialized using kmeans. - 'k-means++' : use the k-means++ method to initialize. - 'random' : responsibilities are initialized randomly. - 'random_from_data' : initial means are randomly selected data points. .. versionchanged:: v1.1 `init_params` now accepts 'random_from_data' and 'k-means++' as initialization methods. weights_init : array-like of shape (n_components, ), default=None The user-provided initial weights. If it is None, weights are initialized using the `init_params` method. means_init : array-like of shape (n_components, n_features), default=None The user-provided initial means, If it is None, means are initialized using the `init_params` method. precisions_init : array-like, default=None The user-provided initial precisions (inverse of the covariance matrices). If it is None, precisions are initialized using the 'init_params' method. The shape depends on 'covariance_type':: (n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full' random_state : int, RandomState instance or None, default=None Controls the random seed given to the method chosen to initialize the parameters (see `init_params`). In addition, it controls the generation of random samples from the fitted distribution (see the method `sample`). Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. warm_start : bool, default=False If 'warm_start' is True, the solution of the last fitting is used as initialization for the next call of fit(). This can speed up convergence when fit is called several times on similar problems. In that case, 'n_init' is ignored and only a single initialization occurs upon the first call. See :term:`the Glossary `. verbose : int, default=0 Enable verbose output. If 1 then it prints the current initialization and each iteration step. If greater than 1 then it prints also the log probability and the time needed for each step. verbose_interval : int, default=10 Number of iteration done before the next print. Attributes ---------- weights_ : array-like of shape (n_components,) The weights of each mixture components. means_ : array-like of shape (n_components, n_features) The mean of each mixture component. covariances_ : array-like The covariance of each mixture component. The shape depends on `covariance_type`:: (n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full' precisions_ : array-like The precision matrices for each component in the mixture. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the log-likelihood of new samples at test time. The shape depends on `covariance_type`:: (n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full' precisions_cholesky_ : array-like The cholesky decomposition of the precision matrices of each mixture component. A precision matrix is the inverse of a covariance matrix. A covariance matrix is symmetric positive definite so the mixture of Gaussian can be equivalently parameterized by the precision matrices. Storing the precision matrices instead of the covariance matrices makes it more efficient to compute the log-likelihood of new samples at test time. The shape depends on `covariance_type`:: (n_components,) if 'spherical', (n_features, n_features) if 'tied', (n_components, n_features) if 'diag', (n_components, n_features, n_features) if 'full' converged_ : bool True when convergence was reached in fit(), False otherwise. n_iter_ : int Number of step used by the best fit of EM to reach the convergence. lower_bound_ : float Lower bound value on the log-likelihood (of the training data with respect to the model) of the best fit of EM. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- BayesianGaussianMixture : Gaussian mixture model fit with a variational inference. Examples -------- >>> import numpy as np >>> from sklearn.mixture import GaussianMixture >>> X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]) >>> gm = GaussianMixture(n_components=2, random_state=0).fit(X) >>> gm.means_ array([[10., 2.], [ 1., 2.]]) >>> gm.predict([[0, 0], [12, 3]]) array([1, 0]) """ _parameter_constraints: dict = { **BaseMixture._parameter_constraints, "covariance_type": [StrOptions({"full", "tied", "diag", "spherical"})], "weights_init": ["array-like", None], "means_init": ["array-like", None], "precisions_init": ["array-like", None], } def __init__( self, n_components=1, *, covariance_type="full", tol=1e-3, reg_covar=1e-6, max_iter=100, n_init=1, init_params="kmeans", weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10, ): super().__init__( n_components=n_components, tol=tol, reg_covar=reg_covar, max_iter=max_iter, n_init=n_init, init_params=init_params, random_state=random_state, warm_start=warm_start, verbose=verbose, verbose_interval=verbose_interval, ) self.covariance_type = covariance_type self.weights_init = weights_init self.means_init = means_init self.precisions_init = precisions_init def _check_parameters(self, X): """Check the Gaussian mixture parameters are well defined.""" _, n_features = X.shape if self.weights_init is not None: self.weights_init = _check_weights(self.weights_init, self.n_components) if self.means_init is not None: self.means_init = _check_means( self.means_init, self.n_components, n_features ) if self.precisions_init is not None: self.precisions_init = _check_precisions( self.precisions_init, self.covariance_type, self.n_components, n_features, ) def _initialize_parameters(self, X, random_state): # If all the initial parameters are all provided, then there is no need to run # the initialization. compute_resp = ( self.weights_init is None or self.means_init is None or self.precisions_init is None ) if compute_resp: super()._initialize_parameters(X, random_state) else: self._initialize(X, None) def _initialize(self, X, resp): """Initialization of the Gaussian mixture parameters. Parameters ---------- X : array-like of shape (n_samples, n_features) resp : array-like of shape (n_samples, n_components) """ n_samples, _ = X.shape weights, means, covariances = None, None, None if resp is not None: weights, means, covariances = _estimate_gaussian_parameters( X, resp, self.reg_covar, self.covariance_type ) if self.weights_init is None: weights /= n_samples self.weights_ = weights if self.weights_init is None else self.weights_init self.means_ = means if self.means_init is None else self.means_init if self.precisions_init is None: self.covariances_ = covariances self.precisions_cholesky_ = _compute_precision_cholesky( covariances, self.covariance_type ) else: self.precisions_cholesky_ = _compute_precision_cholesky_from_precisions( self.precisions_init, self.covariance_type ) def _m_step(self, X, log_resp): """M step. Parameters ---------- X : array-like of shape (n_samples, n_features) log_resp : array-like of shape (n_samples, n_components) Logarithm of the posterior probabilities (or responsibilities) of the point of each sample in X. """ self.weights_, self.means_, self.covariances_ = _estimate_gaussian_parameters( X, np.exp(log_resp), self.reg_covar, self.covariance_type ) self.weights_ /= self.weights_.sum() self.precisions_cholesky_ = _compute_precision_cholesky( self.covariances_, self.covariance_type ) def _estimate_log_prob(self, X): return _estimate_log_gaussian_prob( X, self.means_, self.precisions_cholesky_, self.covariance_type ) def _estimate_log_weights(self): return np.log(self.weights_) def _compute_lower_bound(self, _, log_prob_norm): return log_prob_norm def _get_parameters(self): return ( self.weights_, self.means_, self.covariances_, self.precisions_cholesky_, ) def _set_parameters(self, params): ( self.weights_, self.means_, self.covariances_, self.precisions_cholesky_, ) = params # Attributes computation _, n_features = self.means_.shape if self.covariance_type == "full": self.precisions_ = np.empty(self.precisions_cholesky_.shape) for k, prec_chol in enumerate(self.precisions_cholesky_): self.precisions_[k] = np.dot(prec_chol, prec_chol.T) elif self.covariance_type == "tied": self.precisions_ = np.dot( self.precisions_cholesky_, self.precisions_cholesky_.T ) else: self.precisions_ = self.precisions_cholesky_**2 def _n_parameters(self): """Return the number of free parameters in the model.""" _, n_features = self.means_.shape if self.covariance_type == "full": cov_params = self.n_components * n_features * (n_features + 1) / 2.0 elif self.covariance_type == "diag": cov_params = self.n_components * n_features elif self.covariance_type == "tied": cov_params = n_features * (n_features + 1) / 2.0 elif self.covariance_type == "spherical": cov_params = self.n_components mean_params = n_features * self.n_components return int(cov_params + mean_params + self.n_components - 1) def bic(self, X): """Bayesian information criterion for the current model on the input X. You can refer to this :ref:`mathematical section ` for more details regarding the formulation of the BIC used. Parameters ---------- X : array of shape (n_samples, n_dimensions) The input samples. Returns ------- bic : float The lower the better. """ return -2 * self.score(X) * X.shape[0] + self._n_parameters() * np.log( X.shape[0] ) def aic(self, X): """Akaike information criterion for the current model on the input X. You can refer to this :ref:`mathematical section ` for more details regarding the formulation of the AIC used. Parameters ---------- X : array of shape (n_samples, n_dimensions) The input samples. Returns ------- aic : float The lower the better. """ return -2 * self.score(X) * X.shape[0] + 2 * self._n_parameters()