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"""
GMM Bayes
---------
This implements generative classification based on mixtures of gaussians
to model the probability density of each class.
"""
import warnings
import numpy as np
try:
from sklearn.naive_bayes import _BaseNB
except ImportError:
# work around for sklearn < 0.22
from sklearn.naive_bayes import BaseNB
class _BaseNB(BaseNB):
pass
from sklearn.mixture import GaussianMixture
from sklearn.utils import check_array
class GMMBayes(_BaseNB):
"""GaussianMixture Bayes Classifier
This is a generalization to the Naive Bayes classifier: rather than
modeling the distribution of each class with axis-aligned gaussians,
GMMBayes models the distribution of each class with mixtures of
gaussians. This can lead to better classification in some cases.
Parameters
----------
n_components : int or list
number of components to use in the GaussianMixture. If specified as
a list, it must match the number of class labels. Default is 1.
**kwargs : dict, optional
other keywords are passed directly to GaussianMixture
"""
def __init__(self, n_components=1, **kwargs):
self.n_components = np.atleast_1d(n_components)
self.kwargs = kwargs
def fit(self, X, y):
X = self._check_X(X)
y = np.asarray(y)
n_samples, n_features = X.shape
if n_samples != y.shape[0]:
raise ValueError("X and y have incompatible shapes")
self.classes_ = np.unique(y)
self.classes_.sort()
unique_y = self.classes_
n_classes = unique_y.shape[0]
if self.n_components.size not in (1, len(unique_y)):
raise ValueError("n_components must be compatible with "
"the number of classes")
self.gmms_ = [None for i in range(n_classes)]
self.class_prior_ = np.zeros(n_classes)
n_comp = np.zeros(len(self.classes_), dtype=int) + self.n_components
for i, y_i in enumerate(unique_y):
if n_comp[i] > X[y == y_i].shape[0]:
warnstr = ("Expected n_samples >= n_components but got "
"n_samples={0}, n_components={1}, "
"n_components set to {0}.")
warnings.warn(warnstr.format(X[y == y_i].shape[0], n_comp[i]))
n_comp[i] = X[y == y_i].shape[0]
self.gmms_[i] = GaussianMixture(n_comp[i], **self.kwargs).fit(X[y == y_i])
self.class_prior_[i] = float(np.sum(y == y_i)) / n_samples
return self
def _joint_log_likelihood(self, X):
X = np.asarray(np.atleast_2d(X))
logprobs = np.array([g.score_samples(X) for g in self.gmms_]).T
return logprobs + np.log(self.class_prior_)
def _check_X(self, X):
return check_array(X)
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