# 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. # ============================================================================== """NCF framework to train and evaluate the NeuMF model. The NeuMF model assembles both MF and MLP models under the NCF framework. Check `neumf_model.py` for more details about the models. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import contextlib import gc import heapq import math import multiprocessing import os import signal import typing # pylint: disable=g-bad-import-order import numpy as np from absl import app as absl_app from absl import flags import tensorflow as tf # pylint: enable=g-bad-import-order from official.datasets import movielens from official.recommendation import constants as rconst from official.recommendation import data_preprocessing from official.recommendation import neumf_model from official.utils.flags import core as flags_core from official.utils.logs import hooks_helper from official.utils.logs import logger from official.utils.misc import distribution_utils from official.utils.misc import model_helpers _TOP_K = 10 # Top-k list for evaluation # keys for evaluation metrics _HR_KEY = "HR" _NDCG_KEY = "NDCG" def get_hit_rate_and_ndcg(predicted_scores_by_user, items_by_user, top_k=_TOP_K, match_mlperf=False): """Returns the hit rate and the normalized DCG for evaluation. `predicted_scores_by_user` and `items_by_user` are parallel NumPy arrays with shape (num_users, num_items) such that `predicted_scores_by_user[i, j]` is the predicted score that user `i` would rate item `items_by_user[i][j]`. `items_by_user[i, 0]` is the item that user `i` interacted with, while `items_by_user[i, 1:] are items that user `i` did not interact with. The goal of the NCF model to give a high score for `predicted_scores_by_user[i, 0]` compared to `predicted_scores_by_user[i, 1:]`, and the returned HR and NDCG will be higher the more successful the model is at this goal. If `match_mlperf` is True, then the HR and NDCG computations are done in a slightly unusual way to match the MLPerf reference implementation. Specifically, if `items_by_user[i, :]` contains duplicate items, it will be treated as if the item only appeared once. Effectively, for duplicate items in a row, the predicted score for all but one of the items will be set to -infinity For example, suppose we have that following inputs: predicted_scores_by_user: [[ 2, 3, 3], [ 5, 4, 4]] items_by_user: [[10, 20, 20], [30, 40, 40]] top_k: 2 Then with match_mlperf=True, the HR would be 2/2 = 1.0. With match_mlperf=False, the HR would be 1/2 = 0.5. This is because each user has predicted scores for only 2 unique items: 10 and 20 for the first user, and 30 and 40 for the second. Therefore, with match_mlperf=True, it's guarenteed the first item's score is in the top 2. With match_mlperf=False, this function would compute the first user's first item is not in the top 2, because item 20 has a higher score, and item 20 occurs twice. Args: predicted_scores_by_user: 2D Numpy array of the predicted scores. `predicted_scores_by_user[i, j]` is the predicted score that user `i` would rate item `items_by_user[i][j]`. items_by_user: 2d numpy array of the item IDs. For user `i`, `items_by_user[i][0]` is the itme that user `i` interacted with, while `predicted_scores_by_user[i, 1:] are items that user `i` did not interact with. top_k: Only consider the highest rated `top_k` items per user. The HR and NDCG for that user will only be nonzero if the predicted score for that user's first item is in the `top_k` top scores. match_mlperf: If True, compute HR and NDCG slightly differently to match the MLPerf reference implementation. Returns: (hr, ndcg) tuple of floats, averaged across all users. """ num_users = predicted_scores_by_user.shape[0] zero_indices = np.zeros((num_users, 1), dtype=np.int32) if match_mlperf: predicted_scores_by_user = predicted_scores_by_user.copy() items_by_user = items_by_user.copy() # For each user, sort the items and predictions by increasing item number. # We use mergesort since it's the only stable sort, which we need to be # equivalent to the MLPerf reference implementation. sorted_items_indices = items_by_user.argsort(kind="mergesort") sorted_items = items_by_user[ np.arange(num_users)[:, np.newaxis], sorted_items_indices] sorted_predictions = predicted_scores_by_user[ np.arange(num_users)[:, np.newaxis], sorted_items_indices] # For items that occur more than once in a user's row, set the predicted # score of the subsequent occurrences to -infinity, which effectively # removes them from the array. diffs = sorted_items[:, :-1] - sorted_items[:, 1:] diffs = np.concatenate( [np.ones((diffs.shape[0], 1), dtype=diffs.dtype), diffs], axis=1) predicted_scores_by_user = np.where(diffs, sorted_predictions, -np.inf) # After this block, `zero_indices` will be a (num_users, 1) shaped array # indicating, for each user, the index of item of value 0 in # `sorted_items_indices`. This item is the one we want to check if it is in # the top_k items. zero_indices = np.array(np.where(sorted_items_indices == 0)) assert np.array_equal(zero_indices[0, :], np.arange(num_users)) zero_indices = zero_indices[1, :, np.newaxis] # NumPy has an np.argparition() method, however log(1000) is so small that # sorting the whole array is simpler and fast enough. top_indicies = np.argsort(predicted_scores_by_user, axis=1)[:, -top_k:] top_indicies = np.flip(top_indicies, axis=1) # Both HR and NDCG vectorized computation takes advantage of the fact that if # the positive example for a user is not in the top k, that index does not # appear. That is to say: hit_ind.shape[0] <= num_users hit_ind = np.argwhere(np.equal(top_indicies, zero_indices)) hr = hit_ind.shape[0] / num_users ndcg = np.sum(np.log(2) / np.log(hit_ind[:, 1] + 2)) / num_users return hr, ndcg def evaluate_model(estimator, ncf_dataset, pred_input_fn): # type: (tf.estimator.Estimator, prepare.NCFDataset, typing.Callable) -> dict """Model evaluation with HR and NDCG metrics. The evaluation protocol is to rank the test interacted item (truth items) among the randomly chosen 999 items that are not interacted by the user. The performance of the ranked list is judged by Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). For evaluation, the ranked list is truncated at 10 for both metrics. As such, the HR intuitively measures whether the test item is present on the top-10 list, and the NDCG accounts for the position of the hit by assigning higher scores to hits at top ranks. Both metrics are calculated for each test user, and the average scores are reported. Args: estimator: The Estimator. ncf_dataset: An NCFDataSet object, which contains the information about test/eval dataset, such as: num_users: How many unique users are in the eval set. test_data: The points which are used for consistent evaluation. These are already included in the pred_input_fn. pred_input_fn: The input function for the test data. Returns: eval_results: A dict of evaluation results for benchmark logging. eval_results = { _HR_KEY: hr, _NDCG_KEY: ndcg, tf.GraphKeys.GLOBAL_STEP: global_step } where hr is an integer indicating the average HR scores across all users, ndcg is an integer representing the average NDCG scores across all users, and global_step is the global step """ tf.logging.info("Computing predictions for eval set...") # Get predictions predictions = estimator.predict(input_fn=pred_input_fn, yield_single_examples=False) predictions = list(predictions) prediction_batches = [p[movielens.RATING_COLUMN] for p in predictions] item_batches = [p[movielens.ITEM_COLUMN] for p in predictions] # Reshape the predicted scores and items. Each user takes one row. prediction_with_padding = np.concatenate(prediction_batches, axis=0) predicted_scores_by_user = prediction_with_padding[ :ncf_dataset.num_users * (1 + rconst.NUM_EVAL_NEGATIVES)]\ .reshape(ncf_dataset.num_users, -1) item_with_padding = np.concatenate(item_batches, axis=0) items_by_user = item_with_padding[ :ncf_dataset.num_users * (1 + rconst.NUM_EVAL_NEGATIVES)]\ .reshape(ncf_dataset.num_users, -1) tf.logging.info("Computing metrics...") hr, ndcg = get_hit_rate_and_ndcg(predicted_scores_by_user, items_by_user, match_mlperf=FLAGS.ml_perf) global_step = estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP) eval_results = { _HR_KEY: hr, _NDCG_KEY: ndcg, tf.GraphKeys.GLOBAL_STEP: global_step } return eval_results def construct_estimator(num_gpus, model_dir, params, batch_size, eval_batch_size): """Construct either an Estimator or TPUEstimator for NCF. Args: num_gpus: The number of gpus (Used to select distribution strategy) model_dir: The model directory for the estimator params: The params dict for the estimator batch_size: The mini-batch size for training. eval_batch_size: The batch size used during evaluation. Returns: An Estimator or TPUEstimator. """ if params["use_tpu"]: tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( tpu=params["tpu"], zone=params["tpu_zone"], project=params["tpu_gcp_project"], ) tf.logging.info("Issuing reset command to TPU to ensure a clean state.") tf.Session.reset(tpu_cluster_resolver.get_master()) tpu_config = tf.contrib.tpu.TPUConfig( iterations_per_loop=100, num_shards=8) run_config = tf.contrib.tpu.RunConfig( cluster=tpu_cluster_resolver, model_dir=model_dir, session_config=tf.ConfigProto( allow_soft_placement=True, log_device_placement=False), tpu_config=tpu_config) tpu_params = {k: v for k, v in params.items() if k != "batch_size"} train_estimator = tf.contrib.tpu.TPUEstimator( model_fn=neumf_model.neumf_model_fn, use_tpu=True, train_batch_size=batch_size, params=tpu_params, config=run_config) eval_estimator = tf.contrib.tpu.TPUEstimator( model_fn=neumf_model.neumf_model_fn, use_tpu=False, train_batch_size=1, predict_batch_size=eval_batch_size, params=tpu_params, config=run_config) return train_estimator, eval_estimator distribution = distribution_utils.get_distribution_strategy(num_gpus=num_gpus) run_config = tf.estimator.RunConfig(train_distribute=distribution) params["eval_batch_size"] = eval_batch_size estimator = tf.estimator.Estimator(model_fn=neumf_model.neumf_model_fn, model_dir=model_dir, config=run_config, params=params) return estimator, estimator def main(_): with logger.benchmark_context(FLAGS): run_ncf(FLAGS) def run_ncf(_): """Run NCF training and eval loop.""" if FLAGS.download_if_missing: movielens.download(FLAGS.dataset, FLAGS.data_dir) if FLAGS.seed is not None: np.random.seed(FLAGS.seed) num_gpus = flags_core.get_num_gpus(FLAGS) batch_size = distribution_utils.per_device_batch_size( int(FLAGS.batch_size), num_gpus) eval_batch_size = int(FLAGS.eval_batch_size or FLAGS.batch_size) ncf_dataset, cleanup_fn = data_preprocessing.instantiate_pipeline( dataset=FLAGS.dataset, data_dir=FLAGS.data_dir, batch_size=batch_size, eval_batch_size=eval_batch_size, num_neg=FLAGS.num_neg, epochs_per_cycle=FLAGS.epochs_between_evals, match_mlperf=FLAGS.ml_perf, deterministic=FLAGS.seed is not None) model_helpers.apply_clean(flags.FLAGS) train_estimator, eval_estimator = construct_estimator( num_gpus=num_gpus, model_dir=FLAGS.model_dir, params={ "use_seed": FLAGS.seed is not None, "hash_pipeline": FLAGS.hash_pipeline, "batch_size": batch_size, "learning_rate": FLAGS.learning_rate, "num_users": ncf_dataset.num_users, "num_items": ncf_dataset.num_items, "mf_dim": FLAGS.num_factors, "model_layers": [int(layer) for layer in FLAGS.layers], "mf_regularization": FLAGS.mf_regularization, "mlp_reg_layers": [float(reg) for reg in FLAGS.mlp_regularization], "use_tpu": FLAGS.tpu is not None, "tpu": FLAGS.tpu, "tpu_zone": FLAGS.tpu_zone, "tpu_gcp_project": FLAGS.tpu_gcp_project, }, batch_size=flags.FLAGS.batch_size, eval_batch_size=eval_batch_size) # Create hooks that log information about the training and metric values train_hooks = hooks_helper.get_train_hooks( FLAGS.hooks, model_dir=FLAGS.model_dir, batch_size=FLAGS.batch_size # for ExamplesPerSecondHook ) run_params = { "batch_size": FLAGS.batch_size, "eval_batch_size": eval_batch_size, "number_factors": FLAGS.num_factors, "hr_threshold": FLAGS.hr_threshold, "train_epochs": FLAGS.train_epochs, } benchmark_logger = logger.get_benchmark_logger() benchmark_logger.log_run_info( model_name="recommendation", dataset_name=FLAGS.dataset, run_params=run_params, test_id=FLAGS.benchmark_test_id) approx_train_steps = int(ncf_dataset.num_train_positives * (1 + FLAGS.num_neg) // FLAGS.batch_size) pred_input_fn = data_preprocessing.make_pred_input_fn(ncf_dataset=ncf_dataset) total_training_cycle = FLAGS.train_epochs // FLAGS.epochs_between_evals for cycle_index in range(total_training_cycle): tf.logging.info("Starting a training cycle: {}/{}".format( cycle_index + 1, total_training_cycle)) # Train the model train_input_fn, train_record_dir, batch_count = \ data_preprocessing.make_train_input_fn(ncf_dataset=ncf_dataset) if np.abs(approx_train_steps - batch_count) > 1: tf.logging.warning( "Estimated ({}) and reported ({}) number of batches differ by more " "than one".format(approx_train_steps, batch_count)) train_estimator.train(input_fn=train_input_fn, hooks=train_hooks, steps=batch_count) tf.gfile.DeleteRecursively(train_record_dir) # Evaluate the model eval_results = evaluate_model( eval_estimator, ncf_dataset, pred_input_fn) # Benchmark the evaluation results benchmark_logger.log_evaluation_result(eval_results) # Log the HR and NDCG results. hr = eval_results[_HR_KEY] ndcg = eval_results[_NDCG_KEY] tf.logging.info( "Iteration {}: HR = {:.4f}, NDCG = {:.4f}".format( cycle_index + 1, hr, ndcg)) # Some of the NumPy vector math can be quite large and likes to stay in # memory for a while. gc.collect() # If some evaluation threshold is met if model_helpers.past_stop_threshold(FLAGS.hr_threshold, hr): break cleanup_fn() # Cleanup data construction artifacts and subprocess. # Clear the session explicitly to avoid session delete error tf.keras.backend.clear_session() def define_ncf_flags(): """Add flags for running ncf_main.""" # Add common flags flags_core.define_base(export_dir=False) flags_core.define_performance( num_parallel_calls=False, inter_op=False, intra_op=False, synthetic_data=False, max_train_steps=False, dtype=False, all_reduce_alg=False ) flags_core.define_device(tpu=True) flags_core.define_benchmark() flags.adopt_module_key_flags(flags_core) flags_core.set_defaults( model_dir="/tmp/ncf/", data_dir="/tmp/movielens-data/", train_epochs=2, batch_size=256, hooks="ProfilerHook", tpu=None ) # Add ncf-specific flags flags.DEFINE_enum( name="dataset", default="ml-1m", enum_values=["ml-1m", "ml-20m"], case_sensitive=False, help=flags_core.help_wrap( "Dataset to be trained and evaluated.")) flags.DEFINE_boolean( name="download_if_missing", default=True, help=flags_core.help_wrap( "Download data to data_dir if it is not already present.")) flags.DEFINE_string( name="eval_batch_size", default=None, help=flags_core.help_wrap( "The batch size used for evaluation. This should generally be larger" "than the training batch size as the lack of back propagation during" "evaluation can allow for larger batch sizes to fit in memory. If not" "specified, the training batch size (--batch_size) will be used.")) flags.DEFINE_integer( name="num_factors", default=8, help=flags_core.help_wrap("The Embedding size of MF model.")) # Set the default as a list of strings to be consistent with input arguments flags.DEFINE_list( name="layers", default=["64", "32", "16", "8"], help=flags_core.help_wrap( "The sizes of hidden layers for MLP. Example " "to specify different sizes of MLP layers: --layers=32,16,8,4")) flags.DEFINE_float( name="mf_regularization", default=0., help=flags_core.help_wrap( "The regularization factor for MF embeddings. The factor is used by " "regularizer which allows to apply penalties on layer parameters or " "layer activity during optimization.")) flags.DEFINE_list( name="mlp_regularization", default=["0.", "0.", "0.", "0."], help=flags_core.help_wrap( "The regularization factor for each MLP layer. See mf_regularization " "help for more info about regularization factor.")) flags.DEFINE_integer( name="num_neg", default=4, help=flags_core.help_wrap( "The Number of negative instances to pair with a positive instance.")) flags.DEFINE_float( name="learning_rate", default=0.001, help=flags_core.help_wrap("The learning rate.")) flags.DEFINE_float( name="hr_threshold", default=None, help=flags_core.help_wrap( "If passed, training will stop when the evaluation metric HR is " "greater than or equal to hr_threshold. For dataset ml-1m, the " "desired hr_threshold is 0.68 which is the result from the paper; " "For dataset ml-20m, the threshold can be set as 0.95 which is " "achieved by MLPerf implementation.")) flags.DEFINE_bool( name="ml_perf", default=None, help=flags_core.help_wrap( "If set, changes the behavior of the model slightly to match the " "MLPerf reference implementations here: \n" "https://github.com/mlperf/reference/tree/master/recommendation/" "pytorch\n" "The two changes are:\n" "1. When computing the HR and NDCG during evaluation, remove " "duplicate user-item pairs before the computation. This results in " "better HRs and NDCGs.\n" "2. Use a different soring algorithm when sorting the input data, " "which performs better due to the fact the sorting algorithms are " "not stable.")) flags.DEFINE_integer( name="seed", default=None, help=flags_core.help_wrap( "This value will be used to seed both NumPy and TensorFlow.")) flags.DEFINE_bool( name="hash_pipeline", default=False, help=flags_core.help_wrap( "This flag will perform a separate run of the pipeline and hash " "batches as they are produced. \nNOTE: this will significantly slow " "training. However it is useful to confirm that a random seed is " "does indeed make the data pipeline deterministic.")) if __name__ == "__main__": tf.logging.set_verbosity(tf.logging.INFO) define_ncf_flags() FLAGS = flags.FLAGS absl_app.run(main)