parameters: (self, X)
return_statement: return np.log(proba)
docstring: Predict class log-probabilities for X.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              The input samples. Internally, it will be converted to
              ``dtype=np.float32`` and if a sparse matrix is provided
              to a sparse ``csr_matrix``.
  
          Raises
          ------
          AttributeError
              If the ``loss`` does not support probabilities.
  
          Returns
          -------
          p : array, shape (n_samples, n_classes)
              The class log-probabilities of the input samples. The order of the
              classes corresponds to that in the attribute :term:`classes_`.
docstring_summary: Predict class log-probabilities for X.
docstring_tokens: Predict class log - probabilities for X .
function: def predict_log_proba(self, X):
          """Predict class log-probabilities for X.
  
          Parameters
          ----------
          X : {array-like, sparse matrix}, shape (n_samples, n_features)
              The input samples. Internally, it will be converted to
              ``dtype=np.float32`` and if a sparse matrix is provided
              to a sparse ``csr_matrix``.
  
          Raises
          ------
          AttributeError
              If the ``loss`` does not support probabilities.
  
          Returns
          -------
          p : array, shape (n_samples, n_classes)
              The class log-probabilities of the input samples. The order of the
              classes corresponds to that in the attribute :term:`classes_`.
          """
          proba = self.predict_proba(X)
          return np.log(proba)
function_tokens: def predict_log_proba ( self , X ) : proba = self . predict_proba ( X ) return np . log ( proba )