def fit_predict_score(self, X, y, scoring='roc_auc_score'):
        """
        Fit the detector with labels, predict on samples, and evaluate the model by predefined metrics.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.
        y : numpy array of shape (n_samples,)
            The labels or target values corresponding to X.
        scoring : str, optional (default='roc_auc_score')
            Evaluation metric:
            - 'roc_auc_score': ROC score
            - 'prc_n_score': Precision @ rank n score

        Returns
        -------
        score : float
        """

        # Fit the model with both X and y
        self.fit(X, y)

        # Prediction and scoring
        if scoring == 'roc_auc_score':
            from sklearn.metrics import roc_auc_score
            score = roc_auc_score(y, self.decision_scores_)
        elif scoring == 'prc_n_score':
            from sklearn.metrics import precision_recall_curve
            precision, _, _ = precision_recall_curve(y, self.decision_scores_)
            score = precision[
                1]  # Assuming this is how you'd compute Precision @ rank n
        else:
            raise NotImplementedError('PyOD built-in scoring only supports '
                                      'ROC and Precision @ rank n')

        print("{metric}: {score}".format(metric=scoring, score=score))

        return score

        Fit the detector with labels, predict on samples, and evaluate the model by predefined metrics.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.
        y : numpy array of shape (n_samples,)
            The labels or target values corresponding to X.
        scoring : str, optional (default='roc_auc_score')
            Evaluation metric:
            - 'roc_auc_score': ROC score
            - 'prc_n_score': Precision @ rank n score

        Returns
        -------
        score : float
        
pyod/models/devnet.py