"""Base class for mixture models."""
# Author: Wei Xue <xuewei4d@gmail.com>
# Modified by Thierry Guillemot <thierry.guillemot.work@gmail.com>
# License: BSD 3 clause
import warnings
from abc import ABCMeta, abstractmethod
from time import time
import numpy as np
from scipy.special import logsumexp
from .. import cluster
from ..base import BaseEstimator
from ..base import DensityMixin
from ..exceptions import ConvergenceWarning
from ..utils import check_array, check_random_state
from ..utils.validation import check_is_fitted
def _check_shape(param, param_shape, name):
"""Validate the shape of the input parameter 'param'.
Parameters
----------
param : array
param_shape : tuple
name : string
"""
param = np.array(param)
if param.shape != param_shape:
raise ValueError("The parameter '%s' should have the shape of %s, "
"but got %s" % (name, param_shape, param.shape))
def _check_X(X, n_components=None, n_features=None, ensure_min_samples=1):
"""Check the input data X.
Parameters
----------
X : array-like, shape (n_samples, n_features)
n_components : int
Returns
-------
X : array, shape (n_samples, n_features)
"""
X = check_array(X, dtype=[np.float64, np.float32],
ensure_min_samples=ensure_min_samples)
if n_components is not None and X.shape[0] < n_components:
raise ValueError('Expected n_samples >= n_components '
'but got n_components = %d, n_samples = %d'
% (n_components, X.shape[0]))
if n_features is not None and X.shape[1] != n_features:
raise ValueError("Expected the input data X have %d features, "
"but got %d features"
% (n_features, X.shape[1]))
return X
class BaseMixture(DensityMixin, BaseEstimator, metaclass=ABCMeta):
"""Base class for mixture models.
This abstract class specifies an interface for all mixture classes and
provides basic common methods for mixture models.
"""
def __init__(self, n_components, tol, reg_covar,
max_iter, n_init, init_params, random_state, warm_start,
verbose, verbose_interval):
self.n_components = n_components
self.tol = tol
self.reg_covar = reg_covar
self.max_iter = max_iter
self.n_init = n_init
self.init_params = init_params
self.random_state = random_state
self.warm_start = warm_start
self.verbose = verbose
self.verbose_interval = verbose_interval
def _check_initial_parameters(self, X):
"""Check values of the basic parameters.
Parameters
----------
X : array-like, shape (n_samples, n_features)
"""
if self.n_components < 1:
raise ValueError("Invalid value for 'n_components': %d "
"Estimation requires at least one component"
% self.n_components)
if self.tol < 0.:
raise ValueError("Invalid value for 'tol': %.5f "
"Tolerance used by the EM must be non-negative"
% self.tol)
if self.n_init < 1:
raise ValueError("Invalid value for 'n_init': %d "
"Estimation requires at least one run"
% self.n_init)
if self.max_iter < 1:
raise ValueError("Invalid value for 'max_iter': %d "
"Estimation requires at least one iteration"
% self.max_iter)
if self.reg_covar < 0.:
raise ValueError("Invalid value for 'reg_covar': %.5f "
"regularization on covariance must be "
"non-negative"
% self.reg_covar)
# Check all the parameters values of the derived class
self._check_parameters(X)
@abstractmethod
def _check_parameters(self, X):
"""Check initial parameters of the derived class.
Parameters
----------
X : array-like, shape (n_samples, n_features)
"""
pass
def _initialize_parameters(self, X, random_state):
"""Initialize the model parameters.
Parameters
----------
X : array-like, shape (n_samples, n_features)
random_state : RandomState
A random number generator instance that controls the random seed
used for the method chosen to initialize the parameters.
"""
n_samples, _ = X.shape
if self.init_params == 'kmeans':
resp = np.zeros((n_samples, self.n_components))
label = cluster.KMeans(n_clusters=self.n_components, n_init=1,
random_state=random_state).fit(X).labels_
resp[np.arange(n_samples), label] = 1
elif self.init_params == 'random':
resp = random_state.rand(n_samples, self.n_components)
resp /= resp.sum(axis=1)[:, np.newaxis]
else:
raise ValueError("Unimplemented initialization method '%s'"
% self.init_params)
self._initialize(X, resp)
@abstractmethod
def _initialize(self, X, resp):
"""Initialize the model parameters of the derived class.
Parameters
----------
X : array-like, shape (n_samples, n_features)
resp : array-like, shape (n_samples, n_components)
"""
pass
def fit(self, X, y=None):
"""Estimate model parameters with the EM algorithm.
The method fits the model ``n_init`` times and sets the parameters with
which the model has the largest likelihood or lower bound. Within each
trial, the method iterates between E-step and M-step for ``max_iter``
times until the change of likelihood or lower bound is less than
``tol``, otherwise, a ``ConvergenceWarning`` is raised.
If ``warm_start`` is ``True``, then ``n_init`` is ignored and a single
initialization is performed upon the first call. Upon consecutive
calls, training starts where it left off.
Parameters
----------
X : array-like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
self
"""
self.fit_predict(X, y)
return self
def fit_predict(self, X, y=None):
"""Estimate model parameters using X and predict the labels for X.
The method fits the model n_init times and sets the parameters with
which the model has the largest likelihood or lower bound. Within each
trial, the method iterates between E-step and M-step for `max_iter`
times until the change of likelihood or lower bound is less than
`tol`, otherwise, a :class:`~sklearn.exceptions.ConvergenceWarning` is
raised. After fitting, it predicts the most probable label for the
input data points.
.. versionadded:: 0.20
Parameters
----------
X : array-like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
labels : array, shape (n_samples,)
Component labels.
"""
X = _check_X(X, self.n_components, ensure_min_samples=2)
self._check_n_features(X, reset=True)
self._check_initial_parameters(X)
# if we enable warm_start, we will have a unique initialisation
do_init = not(self.warm_start and hasattr(self, 'converged_'))
n_init = self.n_init if do_init else 1
max_lower_bound = -np.infty
self.converged_ = False
random_state = check_random_state(self.random_state)
n_samples, _ = X.shape
for init in range(n_init):
self._print_verbose_msg_init_beg(init)
if do_init:
self._initialize_parameters(X, random_state)
lower_bound = (-np.infty if do_init else self.lower_bound_)
for n_iter in range(1, self.max_iter + 1):
prev_lower_bound = lower_bound
log_prob_norm, log_resp = self._e_step(X)
self._m_step(X, log_resp)
lower_bound = self._compute_lower_bound(
log_resp, log_prob_norm)
change = lower_bound - prev_lower_bound
self._print_verbose_msg_iter_end(n_iter, change)
if abs(change) < self.tol:
self.converged_ = True
break
self._print_verbose_msg_init_end(lower_bound)
if lower_bound > max_lower_bound:
max_lower_bound = lower_bound
best_params = self._get_parameters()
best_n_iter = n_iter
if not self.converged_:
warnings.warn('Initialization %d did not converge. '
'Try different init parameters, '
'or increase max_iter, tol '
'or check for degenerate data.'
% (init + 1), ConvergenceWarning)
self._set_parameters(best_params)
self.n_iter_ = best_n_iter
self.lower_bound_ = max_lower_bound
# Always do a final e-step to guarantee that the labels returned by
# fit_predict(X) are always consistent with fit(X).predict(X)
# for any value of max_iter and tol (and any random_state).
_, log_resp = self._e_step(X)
return log_resp.argmax(axis=1)
def _e_step(self, X):
"""E step.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Returns
-------
log_prob_norm : float
Mean of the logarithms of the probabilities of each sample in X
log_responsibility : array, shape (n_samples, n_components)
Logarithm of the posterior probabilities (or responsibilities) of
the point of each sample in X.
"""
log_prob_norm, log_resp = self._estimate_log_prob_resp(X)
return np.mean(log_prob_norm), log_resp
@abstractmethod
def _m_step(self, X, log_resp):
"""M step.
Parameters
----------
X : array-like, shape (n_samples, n_features)
log_resp : array-like, shape (n_samples, n_components)
Logarithm of the posterior probabilities (or responsibilities) of
the point of each sample in X.
"""
pass
@abstractmethod
def _get_parameters(self):
pass
@abstractmethod
def _set_parameters(self, params):
pass
def score_samples(self, X):
"""Compute the weighted log probabilities for each sample.
Parameters
----------
X : array-like, shape (n_samples, n_features)
List of n_features-dimensional data points. Each row
corresponds to a single data point.
Returns
-------
log_prob : array, shape (n_samples,)
Log probabilities of each data point in X.
"""
check_is_fitted(self)
X = _check_X(X, None, self.means_.shape[1])
return logsumexp(self._estimate_weighted_log_prob(X), axis=1)
def score(self, X, y=None):
"""Compute the per-sample average log-likelihood of the given data X.
Parameters
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