parameters: (self, X)
return_statement: return np.log(self.predict_proba(X))
docstring: Predict logarithm of probability estimates.
  
          The returned estimates for all classes are ordered by the
          label of classes.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Vector to be scored, where `n_samples` is the number of samples and
              `n_features` is the number of features.
  
          Returns
          -------
          T : array-like of shape (n_samples, n_classes)
              Returns the log-probability of the sample for each class in the
              model, where classes are ordered as they are in ``self.classes_``.
docstring_summary: Predict logarithm of probability estimates.
docstring_tokens: Predict logarithm of probability estimates .
function: def predict_log_proba(self, X):
          """
          Predict logarithm of probability estimates.
  
          The returned estimates for all classes are ordered by the
          label of classes.
  
          Parameters
          ----------
          X : array-like of shape (n_samples, n_features)
              Vector to be scored, where `n_samples` is the number of samples and
              `n_features` is the number of features.
  
          Returns
          -------
          T : array-like of shape (n_samples, n_classes)
              Returns the log-probability of the sample for each class in the
              model, where classes are ordered as they are in ``self.classes_``.
          """
          return np.log(self.predict_proba(X))
function_tokens: def predict_log_proba ( self , X ) : return np . log ( self . predict_proba ( X ) )