# Copyright 2023 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. """Asynchronous data producer for the NCF pipeline.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import atexit import functools import os import sys import tempfile import threading import time import timeit import traceback import typing from absl import logging import numpy as np from six.moves import queue import tensorflow as tf, tf_keras from tensorflow.python.tpu.datasets import StreamingFilesDataset from official.recommendation import constants as rconst from official.recommendation import movielens from official.recommendation import popen_helper from official.recommendation import stat_utils SUMMARY_TEMPLATE = """General: {spacer}Num users: {num_users} {spacer}Num items: {num_items} Training: {spacer}Positive count: {train_pos_ct} {spacer}Batch size: {train_batch_size} {multiplier} {spacer}Batch count per epoch: {train_batch_ct} Eval: {spacer}Positive count: {eval_pos_ct} {spacer}Batch size: {eval_batch_size} {multiplier} {spacer}Batch count per epoch: {eval_batch_ct}""" class DatasetManager(object): """Helper class for handling TensorFlow specific data tasks. This class takes the (relatively) framework agnostic work done by the data constructor classes and handles the TensorFlow specific portions (TFRecord management, tf.Dataset creation, etc.). """ def __init__(self, is_training, stream_files, batches_per_epoch, shard_root=None, deterministic=False, num_train_epochs=None): # type: (bool, bool, int, typing.Optional[str], bool, int) -> None """Constructs a `DatasetManager` instance. Args: is_training: Boolean of whether the data provided is training or evaluation data. This determines whether to reuse the data (if is_training=False) and the exact structure to use when storing and yielding data. stream_files: Boolean indicating whether data should be serialized and written to file shards. batches_per_epoch: The number of batches in a single epoch. shard_root: The base directory to be used when stream_files=True. deterministic: Forgo non-deterministic speedups. (i.e. sloppy=True) num_train_epochs: Number of epochs to generate. If None, then each call to `get_dataset()` increments the number of epochs requested. """ self._is_training = is_training self._deterministic = deterministic self._stream_files = stream_files self._writers = [] self._write_locks = [ threading.RLock() for _ in range(rconst.NUM_FILE_SHARDS) ] if stream_files else [] self._batches_per_epoch = batches_per_epoch self._epochs_completed = 0 self._epochs_requested = num_train_epochs if num_train_epochs else 0 self._shard_root = shard_root self._result_queue = queue.Queue() self._result_reuse = [] @property def current_data_root(self): subdir = ( rconst.TRAIN_FOLDER_TEMPLATE.format(self._epochs_completed) if self._is_training else rconst.EVAL_FOLDER) return os.path.join(self._shard_root, subdir) def buffer_reached(self): # Only applicable for training. return (self._epochs_completed - self._epochs_requested >= rconst.CYCLES_TO_BUFFER and self._is_training) @staticmethod def serialize(data): """Convert NumPy arrays into a TFRecords entry.""" def create_int_feature(values): values = np.squeeze(values) return tf.train.Feature(int64_list=tf.train.Int64List(value=list(values))) feature_dict = { k: create_int_feature(v.astype(np.int64)) for k, v in data.items() } return tf.train.Example(features=tf.train.Features( feature=feature_dict)).SerializeToString() @staticmethod def deserialize(serialized_data, batch_size=None, is_training=True): """Convert serialized TFRecords into tensors. Args: serialized_data: A tensor containing serialized records. batch_size: The data arrives pre-batched, so batch size is needed to deserialize the data. is_training: Boolean, whether data to deserialize to training data or evaluation data. """ def _get_feature_map(batch_size, is_training=True): """Returns data format of the serialized tf record file.""" if is_training: return { movielens.USER_COLUMN: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), movielens.ITEM_COLUMN: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), rconst.VALID_POINT_MASK: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), "labels": tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64) } else: return { movielens.USER_COLUMN: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), movielens.ITEM_COLUMN: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64), rconst.DUPLICATE_MASK: tf.io.FixedLenFeature([batch_size, 1], dtype=tf.int64) } features = tf.io.parse_single_example( serialized_data, _get_feature_map(batch_size, is_training=is_training)) users = tf.cast(features[movielens.USER_COLUMN], rconst.USER_DTYPE) items = tf.cast(features[movielens.ITEM_COLUMN], rconst.ITEM_DTYPE) if is_training: valid_point_mask = tf.cast(features[rconst.VALID_POINT_MASK], tf.bool) fake_dup_mask = tf.zeros_like(users) return { movielens.USER_COLUMN: users, movielens.ITEM_COLUMN: items, rconst.VALID_POINT_MASK: valid_point_mask, rconst.TRAIN_LABEL_KEY: tf.reshape(tf.cast(features["labels"], tf.bool), (batch_size, 1)), rconst.DUPLICATE_MASK: fake_dup_mask } else: labels = tf.cast(tf.zeros_like(users), tf.bool) fake_valid_pt_mask = tf.cast(tf.zeros_like(users), tf.bool) return { movielens.USER_COLUMN: users, movielens.ITEM_COLUMN: items, rconst.DUPLICATE_MASK: tf.cast(features[rconst.DUPLICATE_MASK], tf.bool), rconst.VALID_POINT_MASK: fake_valid_pt_mask, rconst.TRAIN_LABEL_KEY: labels } def put(self, index, data): # type: (int, dict) -> None """Store data for later consumption. Because there are several paths for storing and yielding data (queues, lists, files) the data producer simply provides the data in a standard format at which point the dataset manager handles storing it in the correct form. Args: index: Used to select shards when writing to files. data: A dict of the data to be stored. This method mutates data, and therefore expects to be the only consumer. """ if self._is_training: mask_start_index = data.pop(rconst.MASK_START_INDEX) batch_size = data[movielens.ITEM_COLUMN].shape[0] data[rconst.VALID_POINT_MASK] = np.expand_dims( np.less(np.arange(batch_size), mask_start_index), -1) if self._stream_files: example_bytes = self.serialize(data) with self._write_locks[index % rconst.NUM_FILE_SHARDS]: self._writers[index % rconst.NUM_FILE_SHARDS].write(example_bytes) else: self._result_queue.put(( data, data.pop("labels")) if self._is_training else data) def start_construction(self): if self._stream_files: tf.io.gfile.makedirs(self.current_data_root) template = os.path.join(self.current_data_root, rconst.SHARD_TEMPLATE) self._writers = [ tf.io.TFRecordWriter(template.format(i)) for i in range(rconst.NUM_FILE_SHARDS) ] def end_construction(self): if self._stream_files: [writer.close() for writer in self._writers] self._writers = [] self._result_queue.put(self.current_data_root) self._epochs_completed += 1 def data_generator(self, epochs_between_evals): """Yields examples during local training.""" assert not self._stream_files assert self._is_training or epochs_between_evals == 1 if self._is_training: for _ in range(self._batches_per_epoch * epochs_between_evals): yield self._result_queue.get(timeout=300) else: if self._result_reuse: assert len(self._result_reuse) == self._batches_per_epoch for i in self._result_reuse: yield i else: # First epoch. for _ in range(self._batches_per_epoch * epochs_between_evals): result = self._result_queue.get(timeout=300) self._result_reuse.append(result) yield result def increment_request_epoch(self): self._epochs_requested += 1 def get_dataset(self, batch_size, epochs_between_evals): """Construct the dataset to be used for training and eval. For local training, data is provided through Dataset.from_generator. For remote training (TPUs) the data is first serialized to files and then sent to the TPU through a StreamingFilesDataset. Args: batch_size: The per-replica batch size of the dataset. epochs_between_evals: How many epochs worth of data to yield. (Generator mode only.) """ self.increment_request_epoch() if self._stream_files: if epochs_between_evals > 1: raise ValueError("epochs_between_evals > 1 not supported for file " "based dataset.") epoch_data_dir = self._result_queue.get(timeout=300) if not self._is_training: self._result_queue.put(epoch_data_dir) # Eval data is reused. file_pattern = os.path.join(epoch_data_dir, rconst.SHARD_TEMPLATE.format("*")) dataset = StreamingFilesDataset( files=file_pattern, worker_job=popen_helper.worker_job(), num_parallel_reads=rconst.NUM_FILE_SHARDS, num_epochs=1, sloppy=not self._deterministic) map_fn = functools.partial( self.deserialize, batch_size=batch_size, is_training=self._is_training) dataset = dataset.map(map_fn, num_parallel_calls=16) else: types = { movielens.USER_COLUMN: rconst.USER_DTYPE, movielens.ITEM_COLUMN: rconst.ITEM_DTYPE } shapes = { movielens.USER_COLUMN: tf.TensorShape([batch_size, 1]), movielens.ITEM_COLUMN: tf.TensorShape([batch_size, 1]) } if self._is_training: types[rconst.VALID_POINT_MASK] = bool shapes[rconst.VALID_POINT_MASK] = tf.TensorShape([batch_size, 1]) types = (types, bool) shapes = (shapes, tf.TensorShape([batch_size, 1])) else: types[rconst.DUPLICATE_MASK] = bool shapes[rconst.DUPLICATE_MASK] = tf.TensorShape([batch_size, 1]) data_generator = functools.partial( self.data_generator, epochs_between_evals=epochs_between_evals) dataset = tf.data.Dataset.from_generator( generator=data_generator, output_types=types, output_shapes=shapes) return dataset.prefetch(16) def make_input_fn(self, batch_size): """Create an input_fn which checks for batch size consistency.""" def input_fn(params): """Returns batches for training.""" # Estimator passes batch_size during training and eval_batch_size during # eval. param_batch_size = ( params["batch_size"] if self._is_training else params.get("eval_batch_size") or params["batch_size"]) if batch_size != param_batch_size: raise ValueError("producer batch size ({}) differs from params batch " "size ({})".format(batch_size, param_batch_size)) epochs_between_evals = ( params.get("epochs_between_evals", 1) if self._is_training else 1) return self.get_dataset( batch_size=batch_size, epochs_between_evals=epochs_between_evals) return input_fn class BaseDataConstructor(threading.Thread): """Data constructor base class. This class manages the control flow for constructing data. It is not meant to be used directly, but instead subclasses should implement the following two methods: self.construct_lookup_variables self.lookup_negative_items """ def __init__( self, maximum_number_epochs, # type: int num_users, # type: int num_items, # type: int user_map, # type: dict item_map, # type: dict train_pos_users, # type: np.ndarray train_pos_items, # type: np.ndarray train_batch_size, # type: int batches_per_train_step, # type: int num_train_negatives, # type: int eval_pos_users, # type: np.ndarray eval_pos_items, # type: np.ndarray eval_batch_size, # type: int batches_per_eval_step, # type: int stream_files, # type: bool deterministic=False, # type: bool epoch_dir=None, # type: str num_train_epochs=None, # type: int create_data_offline=False # type: bool ): # General constants self._maximum_number_epochs = maximum_number_epochs self._num_users = num_users self._num_items = num_items self.user_map = user_map self.item_map = item_map self._train_pos_users = train_pos_users self._train_pos_items = train_pos_items self.train_batch_size = train_batch_size self._num_train_negatives = num_train_negatives self._batches_per_train_step = batches_per_train_step self._eval_pos_users = eval_pos_users self._eval_pos_items = eval_pos_items self.eval_batch_size = eval_batch_size self.num_train_epochs = num_train_epochs self.create_data_offline = create_data_offline # Training if self._train_pos_users.shape != self._train_pos_items.shape: raise ValueError( "User positives ({}) is different from item positives ({})".format( self._train_pos_users.shape, self._train_pos_items.shape)) (self._train_pos_count,) = self._train_pos_users.shape self._elements_in_epoch = (1 + num_train_negatives) * self._train_pos_count self.train_batches_per_epoch = self._count_batches(self._elements_in_epoch, train_batch_size, batches_per_train_step) # Evaluation if eval_batch_size % (1 + rconst.NUM_EVAL_NEGATIVES): raise ValueError("Eval batch size {} is not divisible by {}".format( eval_batch_size, 1 + rconst.NUM_EVAL_NEGATIVES)) self._eval_users_per_batch = int(eval_batch_size // (1 + rconst.NUM_EVAL_NEGATIVES)) self._eval_elements_in_epoch = num_users * (1 + rconst.NUM_EVAL_NEGATIVES) self.eval_batches_per_epoch = self._count_batches( self._eval_elements_in_epoch, eval_batch_size, batches_per_eval_step) # Intermediate artifacts self._current_epoch_order = np.empty(shape=(0,)) self._shuffle_iterator = None self._shuffle_with_forkpool = not stream_files if stream_files: self._shard_root = epoch_dir or tempfile.mkdtemp(prefix="ncf_") if not create_data_offline: atexit.register(tf.io.gfile.rmtree, self._shard_root) else: self._shard_root = None self._train_dataset = DatasetManager(True, stream_files, self.train_batches_per_epoch, self._shard_root, deterministic, num_train_epochs) self._eval_dataset = DatasetManager(False, stream_files, self.eval_batches_per_epoch, self._shard_root, deterministic, num_train_epochs) # Threading details super(BaseDataConstructor, self).__init__() self.daemon = True self._stop_loop = False self._fatal_exception = None self.deterministic = deterministic def __str__(self): multiplier = ("(x{} devices)".format(self._batches_per_train_step) if self._batches_per_train_step > 1 else "") summary = SUMMARY_TEMPLATE.format( spacer=" ", num_users=self._num_users, num_items=self._num_items, train_pos_ct=self._train_pos_count, train_batch_size=self.train_batch_size, train_batch_ct=self.train_batches_per_epoch, eval_pos_ct=self._num_users, eval_batch_size=self.eval_batch_size, eval_batch_ct=self.eval_batches_per_epoch, multiplier=multiplier) return super(BaseDataConstructor, self).__str__() + "\n" + summary @staticmethod def _count_batches(example_count, batch_size, batches_per_step): """Determine the number of batches, rounding up to fill all devices.""" x = (example_count + batch_size - 1) // batch_size return (x + batches_per_step - 1) // batches_per_step * batches_per_step def stop_loop(self): self._stop_loop = True def construct_lookup_variables(self): """Perform any one time pre-compute work.""" raise NotImplementedError def lookup_negative_items(self, **kwargs): """Randomly sample negative items for given users.""" raise NotImplementedError def _run(self): atexit.register(self.stop_loop) self._start_shuffle_iterator() self.construct_lookup_variables() self._construct_training_epoch() self._construct_eval_epoch() for _ in range(self._maximum_number_epochs - 1): self._construct_training_epoch() self.stop_loop() def run(self): try: self._run() except Exception as e: # The Thread base class swallows stack traces, so unfortunately it is # necessary to catch and re-raise to get debug output traceback.print_exc() self._fatal_exception = e sys.stderr.flush() raise def _start_shuffle_iterator(self): if self._shuffle_with_forkpool: pool = popen_helper.get_forkpool(3, closing=False) else: pool = popen_helper.get_threadpool(1, closing=False) atexit.register(pool.close) args = [(self._elements_in_epoch, stat_utils.random_int32()) for _ in range(self._maximum_number_epochs)] imap = pool.imap if self.deterministic else pool.imap_unordered self._shuffle_iterator = imap(stat_utils.permutation, args) def _get_training_batch(self, i): """Construct a single batch of training data. Args: i: The index of the batch. This is used when stream_files=True to assign data to file shards. """ batch_indices = self._current_epoch_order[i * self.train_batch_size:(i + 1) * self.train_batch_size] (mask_start_index,) = batch_indices.shape batch_ind_mod = np.mod(batch_indices, self._train_pos_count) users = self._train_pos_users[batch_ind_mod] negative_indices = np.greater_equal(batch_indices, self._train_pos_count) negative_users = users[negative_indices] negative_items = self.lookup_negative_items(negative_users=negative_users) items = self._train_pos_items[batch_ind_mod] items[negative_indices] = negative_items labels = np.logical_not(negative_indices) # Pad last partial batch pad_length = self.train_batch_size - mask_start_index if pad_length: # We pad with arange rather than zeros because the network will still # compute logits for padded examples, and padding with zeros would create # a very "hot" embedding key which can have performance implications. user_pad = np.arange(pad_length, dtype=users.dtype) % self._num_users item_pad = np.arange(pad_length, dtype=items.dtype) % self._num_items label_pad = np.zeros(shape=(pad_length,), dtype=labels.dtype) users = np.concatenate([users, user_pad]) items = np.concatenate([items, item_pad]) labels = np.concatenate([labels, label_pad]) self._train_dataset.put( i, { movielens.USER_COLUMN: np.reshape(users, (self.train_batch_size, 1)), movielens.ITEM_COLUMN: np.reshape(items, (self.train_batch_size, 1)), rconst.MASK_START_INDEX: np.array(mask_start_index, dtype=np.int32), "labels": np.reshape(labels, (self.train_batch_size, 1)), }) def _wait_to_construct_train_epoch(self): count = 0 while self._train_dataset.buffer_reached() and not self._stop_loop: time.sleep(0.01) count += 1 if count >= 100 and np.log10(count) == np.round(np.log10(count)): logging.info( "Waited {} times for training data to be consumed".format(count)) def _construct_training_epoch(self): """Loop to construct a batch of training data.""" if not self.create_data_offline: self._wait_to_construct_train_epoch() start_time = timeit.default_timer() if self._stop_loop: return self._train_dataset.start_construction() map_args = list(range(self.train_batches_per_epoch)) self._current_epoch_order = next(self._shuffle_iterator) get_pool = ( popen_helper.get_fauxpool if self.deterministic else popen_helper.get_threadpool) with get_pool(6) as pool: pool.map(self._get_training_batch, map_args) self._train_dataset.end_construction() logging.info("Epoch construction complete. Time: {:.1f} seconds".format( timeit.default_timer() - start_time)) @staticmethod def _assemble_eval_batch(users, positive_items, negative_items, users_per_batch): """Construct duplicate_mask and structure data accordingly. The positive items should be last so that they lose ties. However, they should not be masked out if the true eval positive happens to be selected as a negative. So instead, the positive is placed in the first position, and then switched with the last element after the duplicate mask has been computed. Args: users: An array of users in a batch. (should be identical along axis 1) positive_items: An array (batch_size x 1) of positive item indices. negative_items: An array of negative item indices. users_per_batch: How many users should be in the batch. This is passed as an argument so that ncf_test.py can use this method. Returns: User, item, and duplicate_mask arrays. """ items = np.concatenate([positive_items, negative_items], axis=1) # We pad the users and items here so that the duplicate mask calculation # will include padding. The metric function relies on all padded elements # except the positive being marked as duplicate to mask out padded points. if users.shape[0] < users_per_batch: pad_rows = users_per_batch - users.shape[0] padding = np.zeros(shape=(pad_rows, users.shape[1]), dtype=np.int32) users = np.concatenate([users, padding.astype(users.dtype)], axis=0) items = np.concatenate([items, padding.astype(items.dtype)], axis=0) duplicate_mask = stat_utils.mask_duplicates(items, axis=1).astype(bool) items[:, (0, -1)] = items[:, (-1, 0)] duplicate_mask[:, (0, -1)] = duplicate_mask[:, (-1, 0)] assert users.shape == items.shape == duplicate_mask.shape return users, items, duplicate_mask def _get_eval_batch(self, i): """Construct a single batch of evaluation data. Args: i: The index of the batch. """ low_index = i * self._eval_users_per_batch high_index = (i + 1) * self._eval_users_per_batch users = np.repeat( self._eval_pos_users[low_index:high_index, np.newaxis], 1 + rconst.NUM_EVAL_NEGATIVES, axis=1) positive_items = self._eval_pos_items[low_index:high_index, np.newaxis] negative_items = ( self.lookup_negative_items(negative_users=users[:, :-1]).reshape( -1, rconst.NUM_EVAL_NEGATIVES)) users, items, duplicate_mask = self._assemble_eval_batch( users, positive_items, negative_items, self._eval_users_per_batch) self._eval_dataset.put( i, { movielens.USER_COLUMN: np.reshape(users.flatten(), (self.eval_batch_size, 1)), movielens.ITEM_COLUMN: np.reshape(items.flatten(), (self.eval_batch_size, 1)), rconst.DUPLICATE_MASK: np.reshape(duplicate_mask.flatten(), (self.eval_batch_size, 1)), }) def _construct_eval_epoch(self): """Loop to construct data for evaluation.""" if self._stop_loop: return start_time = timeit.default_timer() self._eval_dataset.start_construction() map_args = [i for i in range(self.eval_batches_per_epoch)] get_pool = ( popen_helper.get_fauxpool if self.deterministic else popen_helper.get_threadpool) with get_pool(6) as pool: pool.map(self._get_eval_batch, map_args) self._eval_dataset.end_construction() logging.info("Eval construction complete. Time: {:.1f} seconds".format( timeit.default_timer() - start_time)) def make_input_fn(self, is_training): # It isn't feasible to provide a foolproof check, so this is designed to # catch most failures rather than provide an exhaustive guard. if self._fatal_exception is not None: raise ValueError("Fatal exception in the data production loop: {}".format( self._fatal_exception)) return (self._train_dataset.make_input_fn(self.train_batch_size) if is_training else self._eval_dataset.make_input_fn( self.eval_batch_size)) def increment_request_epoch(self): self._train_dataset.increment_request_epoch() class DummyConstructor(threading.Thread): """Class for running with synthetic data.""" def __init__(self, *args, **kwargs): super(DummyConstructor, self).__init__(*args, **kwargs) self.train_batches_per_epoch = rconst.SYNTHETIC_BATCHES_PER_EPOCH self.eval_batches_per_epoch = rconst.SYNTHETIC_BATCHES_PER_EPOCH def run(self): pass def stop_loop(self): pass def increment_request_epoch(self): pass @staticmethod def make_input_fn(is_training): """Construct training input_fn that uses synthetic data.""" def input_fn(params): """Returns dummy input batches for training.""" # Estimator passes batch_size during training and eval_batch_size during # eval. batch_size = ( params["batch_size"] if is_training else params.get("eval_batch_size") or params["batch_size"]) num_users = params["num_users"] num_items = params["num_items"] users = tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=num_users) items = tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=num_items) if is_training: valid_point_mask = tf.cast( tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=2), tf.bool) labels = tf.cast( tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=2), tf.bool) data = { movielens.USER_COLUMN: users, movielens.ITEM_COLUMN: items, rconst.VALID_POINT_MASK: valid_point_mask, }, labels else: dupe_mask = tf.cast( tf.random.uniform([batch_size, 1], dtype=tf.int32, minval=0, maxval=2), tf.bool) data = { movielens.USER_COLUMN: users, movielens.ITEM_COLUMN: items, rconst.DUPLICATE_MASK: dupe_mask, } dataset = tf.data.Dataset.from_tensors(data).repeat( rconst.SYNTHETIC_BATCHES_PER_EPOCH * params["batches_per_step"]) dataset = dataset.prefetch(32) return dataset return input_fn class MaterializedDataConstructor(BaseDataConstructor): """Materialize a table of negative examples for fast negative generation. This class creates a table (num_users x num_items) containing all of the negative examples for each user. This table is conceptually ragged; that is to say the items dimension will have a number of unused elements at the end equal to the number of positive elements for a given user. For instance: num_users = 3 num_items = 5 positives = [[1, 3], [0], [1, 2, 3, 4]] will generate a negative table: [ [0 2 4 int32max int32max], [1 2 3 4 int32max], [0 int32max int32max int32max int32max], ] and a vector of per-user negative counts, which in this case would be: [3, 4, 1] When sampling negatives, integers are (nearly) uniformly selected from the range [0, per_user_neg_count[user]) which gives a column_index, at which point the negative can be selected as: negative_table[user, column_index] This technique will not scale; however MovieLens is small enough that even a pre-compute which is quadratic in problem size will still fit in memory. A more scalable lookup method is in the works. """ def __init__(self, *args, **kwargs): super(MaterializedDataConstructor, self).__init__(*args, **kwargs) self._negative_table = None self._per_user_neg_count = None def construct_lookup_variables(self): # Materialize negatives for fast lookup sampling. start_time = timeit.default_timer() inner_bounds = np.argwhere(self._train_pos_users[1:] - self._train_pos_users[:-1])[:, 0] + 1 (upper_bound,) = self._train_pos_users.shape index_bounds = [0] + inner_bounds.tolist() + [upper_bound] self._negative_table = np.zeros( shape=(self._num_users, self._num_items), dtype=rconst.ITEM_DTYPE) # Set the table to the max value to make sure the embedding lookup will fail # if we go out of bounds, rather than just overloading item zero. self._negative_table += np.iinfo(rconst.ITEM_DTYPE).max assert self._num_items < np.iinfo(rconst.ITEM_DTYPE).max # Reuse arange during generation. np.delete will make a copy. full_set = np.arange(self._num_items, dtype=rconst.ITEM_DTYPE) self._per_user_neg_count = np.zeros( shape=(self._num_users,), dtype=np.int32) # Threading does not improve this loop. For some reason, the np.delete # call does not parallelize well. Multiprocessing incurs too much # serialization overhead to be worthwhile. for i in range(self._num_users): positives = self._train_pos_items[index_bounds[i]:index_bounds[i + 1]] negatives = np.delete(full_set, positives) self._per_user_neg_count[i] = self._num_items - positives.shape[0] self._negative_table[i, :self._per_user_neg_count[i]] = negatives logging.info("Negative sample table built. Time: {:.1f} seconds".format( timeit.default_timer() - start_time)) def lookup_negative_items(self, negative_users, **kwargs): negative_item_choice = stat_utils.very_slightly_biased_randint( self._per_user_neg_count[negative_users]) return self._negative_table[negative_users, negative_item_choice] class BisectionDataConstructor(BaseDataConstructor): """Use bisection to index within positive examples. This class tallies the number of negative items which appear before each positive item for a user. This means that in order to select the ith negative item for a user, it only needs to determine which two positive items bound it at which point the item id for the ith negative is a simply algebraic expression. """ def __init__(self, *args, **kwargs): super(BisectionDataConstructor, self).__init__(*args, **kwargs) self.index_bounds = None self._sorted_train_pos_items = None self._total_negatives = None def _index_segment(self, user): lower, upper = self.index_bounds[user:user + 2] items = self._sorted_train_pos_items[lower:upper] negatives_since_last_positive = np.concatenate( [items[0][np.newaxis], items[1:] - items[:-1] - 1]) return np.cumsum(negatives_since_last_positive) def construct_lookup_variables(self): start_time = timeit.default_timer() inner_bounds = np.argwhere(self._train_pos_users[1:] - self._train_pos_users[:-1])[:, 0] + 1 (upper_bound,) = self._train_pos_users.shape self.index_bounds = np.array([0] + inner_bounds.tolist() + [upper_bound]) # Later logic will assume that the users are in sequential ascending order. assert np.array_equal(self._train_pos_users[self.index_bounds[:-1]], np.arange(self._num_users)) self._sorted_train_pos_items = self._train_pos_items.copy() for i in range(self._num_users): lower, upper = self.index_bounds[i:i + 2] self._sorted_train_pos_items[lower:upper].sort() self._total_negatives = np.concatenate( [self._index_segment(i) for i in range(self._num_users)]) logging.info("Negative total vector built. Time: {:.1f} seconds".format( timeit.default_timer() - start_time)) def lookup_negative_items(self, negative_users, **kwargs): output = np.zeros(shape=negative_users.shape, dtype=rconst.ITEM_DTYPE) - 1 left_index = self.index_bounds[negative_users] right_index = self.index_bounds[negative_users + 1] - 1 num_positives = right_index - left_index + 1 num_negatives = self._num_items - num_positives neg_item_choice = stat_utils.very_slightly_biased_randint(num_negatives) # Shortcuts: # For points where the negative is greater than or equal to the tally before # the last positive point there is no need to bisect. Instead the item id # corresponding to the negative item choice is simply: # last_postive_index + 1 + (neg_choice - last_negative_tally) # Similarly, if the selection is less than the tally at the first positive # then the item_id is simply the selection. # # Because MovieLens organizes popular movies into low integers (which is # preserved through the preprocessing), the first shortcut is very # efficient, allowing ~60% of samples to bypass the bisection. For the same # reason, the second shortcut is rarely triggered (<0.02%) and is therefore # not worth implementing. use_shortcut = neg_item_choice >= self._total_negatives[right_index] output[use_shortcut] = ( self._sorted_train_pos_items[right_index] + 1 + (neg_item_choice - self._total_negatives[right_index]))[use_shortcut] if np.all(use_shortcut): # The bisection code is ill-posed when there are no elements. return output not_use_shortcut = np.logical_not(use_shortcut) left_index = left_index[not_use_shortcut] right_index = right_index[not_use_shortcut] neg_item_choice = neg_item_choice[not_use_shortcut] num_loops = np.max( np.ceil(np.log2(num_positives[not_use_shortcut])).astype(np.int32)) for i in range(num_loops): mid_index = (left_index + right_index) // 2 right_criteria = self._total_negatives[mid_index] > neg_item_choice left_criteria = np.logical_not(right_criteria) right_index[right_criteria] = mid_index[right_criteria] left_index[left_criteria] = mid_index[left_criteria] # Expected state after bisection pass: # The right index is the smallest index whose tally is greater than the # negative item choice index. assert np.all((right_index - left_index) <= 1) output[not_use_shortcut] = ( self._sorted_train_pos_items[right_index] - (self._total_negatives[right_index] - neg_item_choice)) assert np.all(output >= 0) return output def get_constructor(name): if name == "bisection": return BisectionDataConstructor if name == "materialized": return MaterializedDataConstructor raise ValueError("Unrecognized constructor: {}".format(name))