How to use the cornac.exception.ScoreException function in cornac

-------
        res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items
        """
        if item_idx is None:
            if self.train_set.is_unk_user(user_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d)" % user_idx)

            known_item_scores = self.sess.run(self.prediction, feed_dict={
                self.user_id: np.ones(self.train_set.num_items) * user_idx,
                self.item_id: np.arange(self.train_set.num_items)
            })
            return known_item_scores.ravel()
        else:
            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx))

            user_pred = self.sess.run(self.prediction, feed_dict={
                self.user_id: [user_idx], self.item_id: [item_idx]
            })
            return user_pred.ravel()

The index of the user for whom to perform item raking.

        item_idx: int, required
            The index of the item to be rated by the user.

        clipping: bool, default: True
            Whether to clip the predicted rating value.

        Returns
        -------
        A scalar
            A rating score of the user for the item
        """
        try:
            rating_pred = self.score(user_idx, item_idx)
        except ScoreException:
            rating_pred = self.default_score()

        if clipping:
            rating_pred = clip(values=rating_pred,
                               lower_bound=self.train_set.min_rating,
                               upper_bound=self.train_set.max_rating)

        return rating_pred

res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items
        """
        if item_idx is None:
            if self.train_set.is_unk_user(user_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d)" % user_idx)

            known_item_scores = self.sess.run(self.prediction, feed_dict={
                self.gmf_user_id: [user_idx],
                self.mlp_user_id: np.ones(self.train_set.num_items) * user_idx,
                self.item_id: np.arange(self.train_set.num_items)
            })
            return known_item_scores.ravel()
        else:
            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx))

            user_pred = self.sess.run(self.prediction, feed_dict={
                self.gmf_user_id: [user_idx], self.mlp_user_id: [user_idx], self.item_id: [item_idx]
            })
            return user_pred.ravel()

The index of the user for whom to perform score prediction.

        item_id: int, optional, default: None
            The index of the item for that to perform score prediction.
            If None, scores for all known items will be returned.

        Returns
        -------
        res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items

        """

        if item_id is None:
            if self.train_set.is_unk_user(user_id):
                raise ScoreException("Can't make score prediction for (user_id=%d)" % user_id)

            known_item_scores = self.V.dot(self.U[user_id, :])
            return known_item_scores
        else:
            if self.train_set.is_unk_user(user_id) or self.train_set.is_unk_item(item_id):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_id, item_id))

            user_pred = self.V[item_id, :].dot(self.U[user_id, :])
            return user_pred

Returns
        -------
        res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items

        """
        if item_idx is None:
            if self.train_set.is_unk_user(user_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d)" % user_idx)

            known_item_scores = np.sum(np.abs(self.V - self.U[user_idx, :]) ** 2, axis=-1) ** (1. / 2)
            return known_item_scores
        else:
            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx))

            user_pred = np.sum(np.abs(self.V[item_idx, :] - self.U[user_idx, :]) ** 2, axis=-1) ** (1. / 2)
            return user_pred

----------
        user_idx: int, required
            The index of the user for whom to perform score prediction.

        item_idx: int, optional, default: None
            The index of the item for that to perform score prediction.
            If None, scores for all known items will be returned.

        Returns
        -------
        res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items
        """
        if item_idx is None:
            if self.train_set.is_unk_user(user_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d)" % user_idx)

            known_item_scores = self.V.dot(self.U[user_idx, :])
            return known_item_scores
        else:
            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx))
            user_pred = self.V[item_idx, :].dot(self.U[user_idx, :])
            return user_pred

Returns
        -------
        res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items

        """
        if item_idx is None:
            if self.train_set.is_unk_user(user_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d)" % user_idx)

            known_item_scores = self.V.dot(self.U[user_idx, :])
            return known_item_scores
        else:
            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx))

            user_pred = self.V[item_idx, :].dot(self.U[user_idx, :])

            user_pred = sigmoid(user_pred)
            if self.train_set.min_rating == self.train_set.max_rating:
                user_pred = scale(user_pred, 0., self.train_set.max_rating, 0., 1.)
            else:
                user_pred = scale(user_pred, self.train_set.min_rating, self.train_set.max_rating, 0., 1.)

            return user_pred

res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items

        """
        if item_idx is None:
            if self.train_set.is_unk_user(user_idx):
                u_representation = np.ones(self.k)
            else:
                u_representation = self.Theta[user_idx, :]

            known_item_scores = self.Beta.dot(u_representation)
            known_item_scores = np.array(known_item_scores, dtype='float64').flatten()
            return known_item_scores
        else:
            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx))

            user_pred = self.Beta[item_idx, :].dot(self.Theta[user_idx, :])
            user_pred = np.array(user_pred, dtype='float64').flatten()[0]

            return user_pred

user_idx: int, required
            The index of the user for whom to perform score prediction.

        item_idx: int, optional, default: None
            The index of the item for that to perform score prediction.
            If None, scores for all known items will be returned.

        Returns
        -------
        res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items

        """
        if item_idx is None:
            if self.train_set.is_unk_user(user_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d)" % user_idx)

            known_item_scores = self.V.dot(self.U[user_idx, :])
            return known_item_scores
        else:
            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx))

            user_pred = self.V[item_idx, :].dot(self.U[user_idx, :])

            return user_pred

----------
        user_idx: int, required
            The index of the user for whom to perform score prediction.

        item_idx: int, optional, default: None
            The index of the item for that to perform score prediction.
            If None, scores for all known items will be returned.

        Returns
        -------
        res : A scalar or a Numpy array
            Relative scores that the user gives to the item or to all known items
        """
        if item_idx is None:
            if self.train_set.is_unk_user(user_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d)" % user_idx)

            known_item_scores = self.V.dot(self.U[user_idx, :])
            return known_item_scores
        else:
            if self.train_set.is_unk_user(user_idx) or self.train_set.is_unk_item(item_idx):
                raise ScoreException("Can't make score prediction for (user_id=%d, item_id=%d)" % (user_idx, item_idx))
            user_pred = self.V[item_idx, :].dot(self.U[user_idx, :])
            return user_pred