def _create_test_input_config(self):
"""Generate test input_config_pb2.InputConfig proto."""
feature_group_1 = self._create_test_feature_group(
encoder_type=input_config_pb2.EncoderType.BOW)
feature_context_movie_genre = input_config_pb2.Feature(
feature_name='context_movie_genre',
feature_type=input_config_pb2.FeatureType.STRING,
vocab_name='movie_genre_vocab.txt',
vocab_size=19,
embedding_dim=3)
feature_group_2 = input_config_pb2.FeatureGroup(
features=[feature_context_movie_genre],
encoder_type=input_config_pb2.EncoderType.BOW)
feature_label = input_config_pb2.Feature(
feature_name='label_movie_id',
feature_type=input_config_pb2.FeatureType.INT,
vocab_size=3952,
embedding_dim=4)
input_config = input_config_pb2.InputConfig(
activity_feature_groups=[feature_group_1, feature_group_2],
label_feature=feature_label)
return input_config
def _create_test_input_config(self,
encoder_type: input_config_pb2.EncoderType):
"""Generate test input_config_pb2.InputConfig proto."""
feature_context_movie_id = input_config_pb2.Feature(
feature_name='context_movie_id',
feature_type=input_config_pb2.FeatureType.INT,
vocab_size=20,
embedding_dim=4)
feature_context_movie_rating = input_config_pb2.Feature(
feature_name='context_movie_rating',
feature_type=input_config_pb2.FeatureType.FLOAT)
feature_group_1 = input_config_pb2.FeatureGroup(
features=[feature_context_movie_id, feature_context_movie_rating],
encoder_type=encoder_type)
feature_label = input_config_pb2.Feature(
feature_name='label_movie_id',
feature_type=input_config_pb2.FeatureType.INT,
vocab_size=20,
embedding_dim=4)
input_config = input_config_pb2.InputConfig(
activity_feature_groups=[feature_group_1],
label_feature=feature_label)
return input_config
def load_input_config():
"""Load input config."""
assert FLAGS.input_config_file, 'input_config_file cannot be empty.'
with tf.io.gfile.GFile(FLAGS.input_config_file, 'rb') as reader:
return text_format.Parse(reader.read(), input_config_pb2.InputConfig())
feature_type: STRING
vocab_name: "movie_genre_vocab.txt"
vocab_size: 19
embedding_dim: 8
feature_length: 8
}
encoder_type: BOW
}
label_feature {
feature_name: "label_movie_id"
feature_type: INT
vocab_size: 3952
embedding_dim: 8
feature_length: 1
}
""", input_config_pb2.InputConfig())
EXAMPLE1 = text_format.Parse(
"""
features {
feature {
key: "context_movie_id"
value {
int64_list {
value: [1, 2, 0, 0, 0]
}
}
}
feature {
key: "context_movie_rating"
value {