data.py 文件源码

def to_coo(self, tensor_mode=False):
        userid, itemid, feedback = self.fields
        user_item_data = self.training[[userid, itemid]].values

        if tensor_mode:
            # TODO this recomputes feedback data every new functon call,
            # but if data has not changed - no need for this, make a property
            new_feedback, feedback_transform = self.reindex(self.training, feedback, inplace=False)
            self.index = self.index._replace(feedback=feedback_transform)

            idx = np.hstack((user_item_data, new_feedback[:, np.newaxis]))
            idx = np.ascontiguousarray(idx)
            val = np.ones(self.training.shape[0],)
        else:
            idx = user_item_data
            val = self.training[feedback].values

        shp = tuple(idx.max(axis=0) + 1)
        idx = idx.astype(np.intp)
        val = np.ascontiguousarray(val)
        return idx, val, shp