def __call__(self, y_true, y_pred):
        """Compute all metrics and store into a dict.

        Parameters
        ----------
        y_true : np.ndarray
            Target matrix or vector
        y_pred : np.ndarray
            Score matrix or vector

        Returns
        -------
        dict
            Dict of metrics ({metric_name: metric_value}).

        """
        logs = {}
        for metric in self.metrics:
            if isinstance(y_pred, list):
                res = np.mean(
                    [metric(y_true[:, i], y_pred[i]) for i in range(len(y_pred))]
                )
            else:
                res = metric(y_true, y_pred)
            logs[self.prefix + metric._name] = res
        return logs
Compute all metrics and store into a dict.

        Parameters
        ----------
        y_true : np.ndarray
            Target matrix or vector
        y_pred : np.ndarray
            Score matrix or vector

        Returns
        -------
        dict
            Dict of metrics ({metric_name: metric_value}).

        
pytorch_tabnet/metrics.py