class_docstr: Abstract base class for naive Bayes estimators
class_docstr_tok: ["Abstract","base","class","for","naive","Bayes","estimators"]
docstr: Return log-probability estimates for the test vector X.
  
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
  
  Returns
  -------
  C : array-like, shape = [n_samples, n_classes]
      Returns the log-probability of the samples for each class in
      the model. The columns correspond to the classes in sorted
      order, as they appear in the attribute `classes_`.
docstr_tok: ["Return","log-probability","estimates","for","the","test","vector","X.","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","[","n_samples",",","n_features","]","Returns","--","--","--","-","C",":","array-like",",","shape","=","[","n_samples",",","n_classes","]","Returns","the","log-probability","of","the","samples","for","each","class","in","the","model",".","The","columns","correspond","to","the","classes","in","sorted","order",",","as","they","appear","in","the","attribute","`","classes_","`","."]
code: def predict_log_proba(self, X):
      """
      Return log-probability estimates for the test vector X.
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
  
      Returns
      -------
      C : array-like, shape = [n_samples, n_classes]
          Returns the log-probability of the samples for each class in
          the model. The columns correspond to the classes in sorted
          order, as they appear in the attribute `classes_`.
      """
      jll = self._joint_log_likelihood(X)
      # normalize by P(x) = P(f_1, ..., f_n)
      log_prob_x = logsumexp(jll, axis=1)
      return jll - np.atleast_2d(log_prob_x).T
  
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import issparse
  from .base import BaseEstimator, ClassifierMixin
  from .preprocessing import binarize
  from .preprocessing import LabelBinarizer
  from .preprocessing import label_binarize
  from .utils import check_X_y, check_array
  from .utils.extmath import safe_sparse_dot, logsumexp
  from .utils.multiclass import _check_partial_fit_first_call
  from .utils.fixes import in1d
  from .utils.validation import check_is_fitted
  from .externals import six
label_desc: Use image node_id 3 for calling the BaseNB obj's underlying member method code with example usage: obj.predict_log_proba(X) and returns: unknown