503 lines
16 KiB
Python
503 lines
16 KiB
Python
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"""Base class for mixture models."""
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# Author: Wei Xue <xuewei4d@gmail.com>
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# Modified by Thierry Guillemot <thierry.guillemot.work@gmail.com>
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# License: BSD 3 clause
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from __future__ import print_function
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import warnings
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from abc import ABCMeta, abstractmethod
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from time import time
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import numpy as np
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from .. import cluster
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from ..base import BaseEstimator
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from ..base import DensityMixin
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from ..externals import six
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from ..exceptions import ConvergenceWarning
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from ..utils import check_array, check_random_state
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from ..utils.fixes import logsumexp
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def _check_shape(param, param_shape, name):
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"""Validate the shape of the input parameter 'param'.
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Parameters
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----------
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param : array
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param_shape : tuple
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name : string
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"""
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param = np.array(param)
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if param.shape != param_shape:
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raise ValueError("The parameter '%s' should have the shape of %s, "
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"but got %s" % (name, param_shape, param.shape))
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def _check_X(X, n_components=None, n_features=None):
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"""Check the input data X.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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n_components : int
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Returns
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-------
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X : array, shape (n_samples, n_features)
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"""
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X = check_array(X, dtype=[np.float64, np.float32])
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if n_components is not None and X.shape[0] < n_components:
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raise ValueError('Expected n_samples >= n_components '
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'but got n_components = %d, n_samples = %d'
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% (n_components, X.shape[0]))
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if n_features is not None and X.shape[1] != n_features:
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raise ValueError("Expected the input data X have %d features, "
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"but got %d features"
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% (n_features, X.shape[1]))
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return X
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class BaseMixture(six.with_metaclass(ABCMeta, DensityMixin, BaseEstimator)):
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"""Base class for mixture models.
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This abstract class specifies an interface for all mixture classes and
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provides basic common methods for mixture models.
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"""
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def __init__(self, n_components, tol, reg_covar,
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max_iter, n_init, init_params, random_state, warm_start,
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verbose, verbose_interval):
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self.n_components = n_components
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self.tol = tol
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self.reg_covar = reg_covar
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self.max_iter = max_iter
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self.n_init = n_init
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self.init_params = init_params
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self.random_state = random_state
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self.warm_start = warm_start
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self.verbose = verbose
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self.verbose_interval = verbose_interval
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def _check_initial_parameters(self, X):
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"""Check values of the basic parameters.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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"""
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if self.n_components < 1:
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raise ValueError("Invalid value for 'n_components': %d "
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"Estimation requires at least one component"
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% self.n_components)
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if self.tol < 0.:
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raise ValueError("Invalid value for 'tol': %.5f "
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"Tolerance used by the EM must be non-negative"
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% self.tol)
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if self.n_init < 1:
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raise ValueError("Invalid value for 'n_init': %d "
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"Estimation requires at least one run"
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% self.n_init)
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if self.max_iter < 1:
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raise ValueError("Invalid value for 'max_iter': %d "
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"Estimation requires at least one iteration"
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% self.max_iter)
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if self.reg_covar < 0.:
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raise ValueError("Invalid value for 'reg_covar': %.5f "
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"regularization on covariance must be "
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"non-negative"
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% self.reg_covar)
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# Check all the parameters values of the derived class
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self._check_parameters(X)
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@abstractmethod
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def _check_parameters(self, X):
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"""Check initial parameters of the derived class.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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"""
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pass
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def _initialize_parameters(self, X, random_state):
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"""Initialize the model parameters.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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random_state : RandomState
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A random number generator instance.
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"""
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n_samples, _ = X.shape
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if self.init_params == 'kmeans':
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resp = np.zeros((n_samples, self.n_components))
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label = cluster.KMeans(n_clusters=self.n_components, n_init=1,
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random_state=random_state).fit(X).labels_
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resp[np.arange(n_samples), label] = 1
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elif self.init_params == 'random':
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resp = random_state.rand(n_samples, self.n_components)
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resp /= resp.sum(axis=1)[:, np.newaxis]
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else:
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raise ValueError("Unimplemented initialization method '%s'"
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% self.init_params)
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self._initialize(X, resp)
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@abstractmethod
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def _initialize(self, X, resp):
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"""Initialize the model parameters of the derived class.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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resp : array-like, shape (n_samples, n_components)
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"""
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pass
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def fit(self, X, y=None):
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"""Estimate model parameters with the EM algorithm.
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The method fit the model `n_init` times and set the parameters with
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which the model has the largest likelihood or lower bound. Within each
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trial, the method iterates between E-step and M-step for `max_iter`
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times until the change of likelihood or lower bound is less than
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`tol`, otherwise, a `ConvergenceWarning` is raised.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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List of n_features-dimensional data points. Each row
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corresponds to a single data point.
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Returns
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-------
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self
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"""
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X = _check_X(X, self.n_components)
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self._check_initial_parameters(X)
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# if we enable warm_start, we will have a unique initialisation
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do_init = not(self.warm_start and hasattr(self, 'converged_'))
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n_init = self.n_init if do_init else 1
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max_lower_bound = -np.infty
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self.converged_ = False
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random_state = check_random_state(self.random_state)
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n_samples, _ = X.shape
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for init in range(n_init):
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self._print_verbose_msg_init_beg(init)
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if do_init:
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self._initialize_parameters(X, random_state)
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self.lower_bound_ = -np.infty
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for n_iter in range(self.max_iter):
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prev_lower_bound = self.lower_bound_
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log_prob_norm, log_resp = self._e_step(X)
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self._m_step(X, log_resp)
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self.lower_bound_ = self._compute_lower_bound(
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log_resp, log_prob_norm)
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change = self.lower_bound_ - prev_lower_bound
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self._print_verbose_msg_iter_end(n_iter, change)
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if abs(change) < self.tol:
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self.converged_ = True
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break
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self._print_verbose_msg_init_end(self.lower_bound_)
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if self.lower_bound_ > max_lower_bound:
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max_lower_bound = self.lower_bound_
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best_params = self._get_parameters()
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best_n_iter = n_iter
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if not self.converged_:
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warnings.warn('Initialization %d did not converge. '
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'Try different init parameters, '
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'or increase max_iter, tol '
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'or check for degenerate data.'
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% (init + 1), ConvergenceWarning)
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self._set_parameters(best_params)
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self.n_iter_ = best_n_iter
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return self
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def _e_step(self, X):
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"""E step.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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Returns
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-------
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log_prob_norm : float
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Mean of the logarithms of the probabilities of each sample in X
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log_responsibility : array, shape (n_samples, n_components)
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Logarithm of the posterior probabilities (or responsibilities) of
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the point of each sample in X.
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"""
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log_prob_norm, log_resp = self._estimate_log_prob_resp(X)
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return np.mean(log_prob_norm), log_resp
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@abstractmethod
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def _m_step(self, X, log_resp):
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"""M step.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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log_resp : array-like, shape (n_samples, n_components)
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Logarithm of the posterior probabilities (or responsibilities) of
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the point of each sample in X.
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"""
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pass
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@abstractmethod
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def _check_is_fitted(self):
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pass
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@abstractmethod
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def _get_parameters(self):
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pass
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@abstractmethod
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def _set_parameters(self, params):
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pass
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def score_samples(self, X):
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"""Compute the weighted log probabilities for each sample.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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List of n_features-dimensional data points. Each row
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corresponds to a single data point.
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Returns
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-------
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log_prob : array, shape (n_samples,)
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Log probabilities of each data point in X.
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"""
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self._check_is_fitted()
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X = _check_X(X, None, self.means_.shape[1])
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return logsumexp(self._estimate_weighted_log_prob(X), axis=1)
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def score(self, X, y=None):
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"""Compute the per-sample average log-likelihood of the given data X.
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Parameters
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----------
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X : array-like, shape (n_samples, n_dimensions)
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List of n_features-dimensional data points. Each row
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corresponds to a single data point.
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Returns
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-------
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log_likelihood : float
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Log likelihood of the Gaussian mixture given X.
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"""
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return self.score_samples(X).mean()
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def predict(self, X):
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"""Predict the labels for the data samples in X using trained model.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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List of n_features-dimensional data points. Each row
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corresponds to a single data point.
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Returns
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-------
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labels : array, shape (n_samples,)
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Component labels.
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"""
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self._check_is_fitted()
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X = _check_X(X, None, self.means_.shape[1])
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return self._estimate_weighted_log_prob(X).argmax(axis=1)
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def predict_proba(self, X):
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"""Predict posterior probability of each component given the data.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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List of n_features-dimensional data points. Each row
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corresponds to a single data point.
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Returns
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-------
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resp : array, shape (n_samples, n_components)
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Returns the probability each Gaussian (state) in
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the model given each sample.
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"""
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self._check_is_fitted()
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X = _check_X(X, None, self.means_.shape[1])
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_, log_resp = self._estimate_log_prob_resp(X)
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return np.exp(log_resp)
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def sample(self, n_samples=1):
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"""Generate random samples from the fitted Gaussian distribution.
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Parameters
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----------
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n_samples : int, optional
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Number of samples to generate. Defaults to 1.
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Returns
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-------
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X : array, shape (n_samples, n_features)
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Randomly generated sample
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y : array, shape (nsamples,)
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Component labels
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"""
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self._check_is_fitted()
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if n_samples < 1:
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raise ValueError(
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"Invalid value for 'n_samples': %d . The sampling requires at "
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"least one sample." % (self.n_components))
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_, n_features = self.means_.shape
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rng = check_random_state(self.random_state)
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n_samples_comp = rng.multinomial(n_samples, self.weights_)
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if self.covariance_type == 'full':
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X = np.vstack([
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rng.multivariate_normal(mean, covariance, int(sample))
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for (mean, covariance, sample) in zip(
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self.means_, self.covariances_, n_samples_comp)])
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elif self.covariance_type == "tied":
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X = np.vstack([
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|
rng.multivariate_normal(mean, self.covariances_, int(sample))
|
||
|
|
for (mean, sample) in zip(
|
||
|
|
self.means_, n_samples_comp)])
|
||
|
|
else:
|
||
|
|
X = np.vstack([
|
||
|
|
mean + rng.randn(sample, n_features) * np.sqrt(covariance)
|
||
|
|
for (mean, covariance, sample) in zip(
|
||
|
|
self.means_, self.covariances_, n_samples_comp)])
|
||
|
|
|
||
|
|
y = np.concatenate([j * np.ones(sample, dtype=int)
|
||
|
|
for j, sample in enumerate(n_samples_comp)])
|
||
|
|
|
||
|
|
return (X, y)
|
||
|
|
|
||
|
|
def _estimate_weighted_log_prob(self, X):
|
||
|
|
"""Estimate the weighted log-probabilities, log P(X | Z) + log weights.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like, shape (n_samples, n_features)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
weighted_log_prob : array, shape (n_samples, n_component)
|
||
|
|
"""
|
||
|
|
return self._estimate_log_prob(X) + self._estimate_log_weights()
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def _estimate_log_weights(self):
|
||
|
|
"""Estimate log-weights in EM algorithm, E[ log pi ] in VB algorithm.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
log_weight : array, shape (n_components, )
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def _estimate_log_prob(self, X):
|
||
|
|
"""Estimate the log-probabilities log P(X | Z).
|
||
|
|
|
||
|
|
Compute the log-probabilities per each component for each sample.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like, shape (n_samples, n_features)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
log_prob : array, shape (n_samples, n_component)
|
||
|
|
"""
|
||
|
|
pass
|
||
|
|
|
||
|
|
def _estimate_log_prob_resp(self, X):
|
||
|
|
"""Estimate log probabilities and responsibilities for each sample.
|
||
|
|
|
||
|
|
Compute the log probabilities, weighted log probabilities per
|
||
|
|
component and responsibilities for each sample in X with respect to
|
||
|
|
the current state of the model.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like, shape (n_samples, n_features)
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
log_prob_norm : array, shape (n_samples,)
|
||
|
|
log p(X)
|
||
|
|
|
||
|
|
log_responsibilities : array, shape (n_samples, n_components)
|
||
|
|
logarithm of the responsibilities
|
||
|
|
"""
|
||
|
|
weighted_log_prob = self._estimate_weighted_log_prob(X)
|
||
|
|
log_prob_norm = logsumexp(weighted_log_prob, axis=1)
|
||
|
|
with np.errstate(under='ignore'):
|
||
|
|
# ignore underflow
|
||
|
|
log_resp = weighted_log_prob - log_prob_norm[:, np.newaxis]
|
||
|
|
return log_prob_norm, log_resp
|
||
|
|
|
||
|
|
def _print_verbose_msg_init_beg(self, n_init):
|
||
|
|
"""Print verbose message on initialization."""
|
||
|
|
if self.verbose == 1:
|
||
|
|
print("Initialization %d" % n_init)
|
||
|
|
elif self.verbose >= 2:
|
||
|
|
print("Initialization %d" % n_init)
|
||
|
|
self._init_prev_time = time()
|
||
|
|
self._iter_prev_time = self._init_prev_time
|
||
|
|
|
||
|
|
def _print_verbose_msg_iter_end(self, n_iter, diff_ll):
|
||
|
|
"""Print verbose message on initialization."""
|
||
|
|
if n_iter % self.verbose_interval == 0:
|
||
|
|
if self.verbose == 1:
|
||
|
|
print(" Iteration %d" % n_iter)
|
||
|
|
elif self.verbose >= 2:
|
||
|
|
cur_time = time()
|
||
|
|
print(" Iteration %d\t time lapse %.5fs\t ll change %.5f" % (
|
||
|
|
n_iter, cur_time - self._iter_prev_time, diff_ll))
|
||
|
|
self._iter_prev_time = cur_time
|
||
|
|
|
||
|
|
def _print_verbose_msg_init_end(self, ll):
|
||
|
|
"""Print verbose message on the end of iteration."""
|
||
|
|
if self.verbose == 1:
|
||
|
|
print("Initialization converged: %s" % self.converged_)
|
||
|
|
elif self.verbose >= 2:
|
||
|
|
print("Initialization converged: %s\t time lapse %.5fs\t ll %.5f" %
|
||
|
|
(self.converged_, time() - self._init_prev_time, ll))
|