def score(self, X):
        """Score of the dataset.

        Parameters
        ----------
        X : pandas.DataFrame (n_samples, n_features)
            Training vector, where n_samples is the number of samples.

        Returns
        -------
        score: array of shape (n_samples)
            The score of the input samples.
        """
        X_t = self._transform(
            X=X, metric="indices", metric_special="empirical",
            metric_missing="empirical")

        score_ = np.zeros(X_t.shape[0])
        selected_variables = self.binning_process_.get_support(names=True)

        for variable in selected_variables:
            mask = self._df_scorecard.Variable == variable
            points = self._df_scorecard[mask].Points.values
            score_ += points[X_t[variable]]

        return score_ + self.intercept_
Score of the dataset.

        Parameters
        ----------
        X : pandas.DataFrame (n_samples, n_features)
            Training vector, where n_samples is the number of samples.

        Returns
        -------
        score: array of shape (n_samples)
            The score of the input samples.
        
optbinning/scorecard/scorecard.py