mdr/mdr_ensemble.py
def score(self, features, classes, scoring_function=None, **scoring_function_kwargs):
        """Estimates the accuracy of the predictions from the MDR ensemble

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix to predict from
        classes: array-like {n_samples}
            List of true class labels

        Returns
        -------
        accuracy_score: float
            The estimated accuracy based on the constructed feature

        """
        new_feature = self.ensemble.predict(features)

        if scoring_function is None:
            return accuracy_score(classes, new_feature)
        else:
            return scoring_function(classes, new_feature, **scoring_function_kwargs)
def score(self, features, classes, scoring_function=None, **scoring_function_kwargs):
        """Estimates the accuracy of the predictions from the MDR ensemble

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix to predict from
        classes: array-like {n_samples}
            List of true class labels

        Returns
        -------
        accuracy_score: float
            The estimated accuracy based on the constructed feature

        """
        new_feature = self.ensemble.predict(features)

        if scoring_function is None:
            return accuracy_score(classes, new_feature)
        else:
            return scoring_function(classes, new_feature, **scoring_function_kwargs)
def score ( self , features , classes , scoring_function = None , * * scoring_function_kwargs ) : new_feature = self . ensemble . predict ( features ) if scoring_function is None : return accuracy_score ( classes , new_feature ) else : return scoring_function ( classes , new_feature , * * scoring_function_kwargs )
Estimates the accuracy of the predictions from the MDR ensemble

        Parameters
        ----------
        features: array-like {n_samples, n_features}
            Feature matrix to predict from
        classes: array-like {n_samples}
            List of true class labels

        Returns
        -------
        accuracy_score: float
            The estimated accuracy based on the constructed feature
Estimates the accuracy of the predictions from the MDR ensemble