def _get_feature(cls, user_ids: List, item_ids: List, with_user_embedding,
graph: GraphConvolutionalMatrixCompletionGraph,
dataset: GcmcGraphDataset, session: tf.Session,
feature: str) -> np.ndarray:
if graph is None:
RuntimeError('Please call fit first.')
rating_adjacency_matrix = dataset.train_rating_adjacency_matrix()
user_indices, item_indices = dataset.to_indices(user_ids, item_ids)
if not with_user_embedding:
user_indices = np.array(
[0] * len(user_indices)) # TODO use default user index.
user_feature_indices, item_feature_indices = dataset.to_feature_indices(
user_ids, item_ids)
input_data = dict(user=user_indices,
item=item_indices,
user_feature_indices=user_feature_indices,
item_feature_indices=item_feature_indices)
feed_dict = cls._feed_dict(input_data,
graph,
dataset,
rating_adjacency_matrix,
is_train=False)
encoder_map = dict(user=graph.user_encoder, item=graph.item_encoder)
with session.as_default():
feature = session.run(encoder_map[feature], feed_dict=feed_dict)
return feature
def _predict(cls, user_ids: List, item_ids: List, with_user_embedding,
graph: GraphConvolutionalMatrixCompletionGraph,
dataset: GcmcGraphDataset, session: tf.Session) -> np.ndarray:
if graph is None:
RuntimeError('Please call fit first.')
rating_adjacency_matrix = dataset.train_rating_adjacency_matrix()
user_indices, item_indices = dataset.to_indices(user_ids, item_ids)
if not with_user_embedding:
user_indices = np.array(
[0] * len(user_indices)) # TODO use default user index.
user_feature_indices, item_feature_indices = dataset.to_feature_indices(
user_ids, item_ids)
input_data = dict(user=user_indices,
item=item_indices,
user_feature_indices=user_feature_indices,
item_feature_indices=item_feature_indices)
feed_dict = cls._feed_dict(input_data,
graph,
dataset,
rating_adjacency_matrix,
is_train=False)
with session.as_default():
predictions = session.run(graph.expectation, feed_dict=feed_dict)
predictions = predictions.flatten()
predictions = np.clip(predictions,
dataset.rating()[0],
dataset.rating()[-1])
return predictions