parameters: (estimators, estimators_features, X, n_classes)
return_statement: return log_proba
docstring: Private function used to compute log probabilities within a job.
docstring_summary: Private function used to compute log probabilities within a job.
docstring_tokens: Private function used to compute log probabilities within a job .
function: def _parallel_predict_log_proba(estimators, estimators_features, X, n_classes):
      """Private function used to compute log probabilities within a job."""
      n_samples = X.shape[0]
      log_proba = np.empty((n_samples, n_classes))
      log_proba.fill(-np.inf)
      all_classes = np.arange(n_classes, dtype=np.int)
  
      for estimator, features in zip(estimators, estimators_features):
          log_proba_estimator = estimator.predict_log_proba(X[:, features])
  
          if n_classes == len(estimator.classes_):
              log_proba = np.logaddexp(log_proba, log_proba_estimator)
  
          else:
              log_proba[:, estimator.classes_] = np.logaddexp(
                  log_proba[:, estimator.classes_],
                  log_proba_estimator[:, range(len(estimator.classes_))])
  
              missing = np.setdiff1d(all_classes, estimator.classes_)
              log_proba[:, missing] = np.logaddexp(log_proba[:, missing],
                                                   -np.inf)
  
      return log_proba
function_tokens: def _parallel_predict_log_proba ( estimators , estimators_features , X , n_classes ) : n_samples = X . shape [ 0 ] log_proba = np . empty ( ( n_samples , n_classes ) ) log_proba . fill ( - np . inf ) all_classes = np . arange ( n_classes , dtype = np . int ) for estimator , features in zip ( estimators , estimators_features ) : log_proba_estimator = estimator . predict_log_proba ( X [ : , features ] ) if n_classes == len ( estimator . classes_ ) : log_proba = np . logaddexp ( log_proba , log_proba_estimator ) else : log_proba [ : , estimator . classes_ ] = np . logaddexp ( log_proba [ : , estimator . classes_ ] , log_proba_estimator [ : , range ( len ( estimator . classes_ ) ) ] ) missing = np . setdiff1d ( all_classes , estimator . classes_ ) log_proba [ : , missing ] = np . logaddexp ( log_proba [ : , missing ] , - np . inf ) return log_proba