| # Copyright 2018 The TensorFlow Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # ============================================================================== | |
| """Central location for NCF specific values.""" | |
| import sys | |
| import numpy as np | |
| from official.recommendation import movielens | |
| # ============================================================================== | |
| # == Main Thread Data Processing =============================================== | |
| # ============================================================================== | |
| # Keys for data shards | |
| TRAIN_USER_KEY = "train_{}".format(movielens.USER_COLUMN) | |
| TRAIN_ITEM_KEY = "train_{}".format(movielens.ITEM_COLUMN) | |
| TRAIN_LABEL_KEY = "train_labels" | |
| MASK_START_INDEX = "mask_start_index" | |
| VALID_POINT_MASK = "valid_point_mask" | |
| EVAL_USER_KEY = "eval_{}".format(movielens.USER_COLUMN) | |
| EVAL_ITEM_KEY = "eval_{}".format(movielens.ITEM_COLUMN) | |
| USER_MAP = "user_map" | |
| ITEM_MAP = "item_map" | |
| USER_DTYPE = np.int32 | |
| ITEM_DTYPE = np.int32 | |
| # In both datasets, each user has at least 20 ratings. | |
| MIN_NUM_RATINGS = 20 | |
| # The number of negative examples attached with a positive example | |
| # when performing evaluation. | |
| NUM_EVAL_NEGATIVES = 999 | |
| # keys for evaluation metrics | |
| TOP_K = 10 # Top-k list for evaluation | |
| HR_KEY = "HR" | |
| NDCG_KEY = "NDCG" | |
| DUPLICATE_MASK = "duplicate_mask" | |
| # Metric names | |
| HR_METRIC_NAME = "HR_METRIC" | |
| NDCG_METRIC_NAME = "NDCG_METRIC" | |
| # Trying to load a cache created in py2 when running in py3 will cause an | |
| # error due to differences in unicode handling. | |
| RAW_CACHE_FILE = "raw_data_cache_py{}.pickle".format(sys.version_info[0]) | |
| CACHE_INVALIDATION_SEC = 3600 * 24 | |
| # ============================================================================== | |
| # == Data Generation =========================================================== | |
| # ============================================================================== | |
| CYCLES_TO_BUFFER = 3 # The number of train cycles worth of data to "run ahead" | |
| # of the main training loop. | |
| # Number of batches to run per epoch when using synthetic data. At high batch | |
| # sizes, we run for more batches than with real data, which is good since | |
| # running more batches reduces noise when measuring the average batches/second. | |
| SYNTHETIC_BATCHES_PER_EPOCH = 2000 | |
| # Only used when StreamingFilesDataset is used. | |
| NUM_FILE_SHARDS = 16 | |
| TRAIN_FOLDER_TEMPLATE = "training_cycle_{}" | |
| EVAL_FOLDER = "eval_data" | |
| SHARD_TEMPLATE = "shard_{}.tfrecords" | |