def score(self, X, y=None, **kwargs):
        """
        Generates the Scikit-Learn classification report.

        Parameters
        ----------
        X : ndarray or DataFrame of shape n x m
            A matrix of n instances with m features

        y : ndarray or Series of length n
            An array or series of target or class values

        Returns
        -------

        score_ : float
            Global accuracy score
        """
        y_pred = self.predict(X)

        scores = precision_recall_fscore_support(y, y_pred)

        # Calculate the percentage for the support metric
        # and store the percent in place of raw support counts
        self.support_score_ = scores[-1]

        scores = list(scores)
        scores[-1] = scores[-1] / scores[-1].sum()

        # Create a mapping composed of precision, recall, F1, and support
        # to their respective values
        scores = map(lambda s: dict(zip(self.classes_, s)), scores)
        self.scores_ = dict(zip(SCORES_KEYS, scores))

        # Remove support scores if not required
        if not self.support:
            self.scores_.pop('support')

        self.draw()

        # Retrieve and store the score attribute from the sklearn classifier
        self.score_ = self.estimator.score(X, y)

        return self.score_
def score ( self , X , y = None , ** kwargs ) : y_pred = self . predict ( X ) scores = precision_recall_fscore_support ( y , y_pred ) self . support_score_ = scores [ - 1 ] scores = list ( scores ) scores [ - 1 ] = scores [ - 1 ] / scores [ - 1 ] . sum ( ) scores = map ( lambda s : dict ( zip ( self . classes_ , s ) ) , scores ) self . scores_ = dict ( zip ( SCORES_KEYS , scores ) ) if not self . support : self . scores_ . pop ( 'support' ) self . draw ( ) self . score_ = self . estimator . score ( X , y ) return self . score_
Generates the Scikit-Learn classification report.

        Parameters
        ----------
        X : ndarray or DataFrame of shape n x m
            A matrix of n instances with m features

        y : ndarray or Series of length n
            An array or series of target or class values

        Returns
        -------

        score_ : float
            Global accuracy score