def predict(self, X):
        """Predict if a particular sample is an outlier or not.
        Calling xgboost `predict` function.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.

        Returns
        -------
        outlier_labels : numpy array of shape (n_samples,)
            For each observation, tells whether or not
            it should be considered as an outlier according to the
            fitted model. 0 stands for inliers and 1 for outliers.
        """

        check_is_fitted(self, ['clf_', 'decision_scores_',
                               'labels_', '_scalar'])

        X = check_array(X)

        # construct the new feature space
        X_add = self._generate_new_features(X)
        X_new = np.concatenate((X, X_add), axis=1)

        pred_scores = self.clf_.predict(X_new)
        return pred_scores.ravel()
Predict if a particular sample is an outlier or not.
        Calling xgboost `predict` function.

        Parameters
        ----------
        X : numpy array of shape (n_samples, n_features)
            The input samples.

        Returns
        -------
        outlier_labels : numpy array of shape (n_samples,)
            For each observation, tells whether or not
            it should be considered as an outlier according to the
            fitted model. 0 stands for inliers and 1 for outliers.
        
pyod/models/xgbod.py