Compute the accuracy of the ported classifier.

        Parameters
        ----------
        :param X : ndarray, shape (n_samples, n_features)
            Input data.

        :param method : string, default: 'predict'
            The method which should be tested.

        :param normalize : bool, default: True
            If ``False``, return the number of correctly classified samples.
            Otherwise, return the fraction of correctly classified samples.

        :param num_format : lambda x, default: lambda x: str(x)
            The representation of the floating-point values.

        Returns
        -------
        score : float
            If ``normalize == True``, return the correctly classified samples
            (float), else it returns the number of correctly classified samples
            (int).
            The best performance is 1 with ``normalize == True`` and the number
            of samples with ``normalize == False``.

def integrity_score(self, X, method='predict', normalize=True,
                        num_format=lambda x: str(x)):
        """
        Compute the accuracy of the ported classifier.

        Parameters
        ----------
        :param X : ndarray, shape (n_samples, n_features)
            Input data.

        :param method : string, default: 'predict'
            The method which should be tested.

        :param normalize : bool, default: True
            If ``False``, return the number of correctly classified samples.
            Otherwise, return the fraction of correctly classified samples.

        :param num_format : lambda x, default: lambda x: str(x)
            The representation of the floating-point values.

        Returns
        -------
        score : float
            If ``normalize == True``, return the correctly classified samples
            (float), else it returns the number of correctly classified samples
            (int).
            The best performance is 1 with ``normalize == True`` and the number
            of samples with ``normalize == False``.
        """
        X = np.array(X)
        if not X.ndim > 1:
            X = np.array([X])

        method = str(method).strip().lower()
        if method not in ['predict', 'predict_proba']:
            error = "The given method '{}' isn't supported.".format(method)
            raise AttributeError(error)

        if method == 'predict':
            y_true = self.estimator.predict(X)
            y_pred = self.predict(X, tnp_dir='tmp_integrity_score',
                                  keep_tmp_dir=True, num_format=num_format)
            return accuracy_score(y_true, y_pred, normalize=normalize)

        return False