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reader.py
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reader.py
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# Copyright 2019 DeepMind Technologies Limited and Google LLC
#
# 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
#
# https://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.
"""Utilities for parsing text files."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import os
from absl import logging
import numpy as np
from tensorflow.compat.v1.io import gfile
# sequences: [N, MAX_TOKENS_SEQUENCE] array of int32
# lengths: [N, 2] array of int32, such that
# lengths[i, 0] is the number of non-pad tokens in sequences[i, :]
FILENAMES = {
"emnlp2017": ("train.json", "valid.json", "test.json"),
}
# EMNLP2017 sentences have max length 50, add one for a PAD token so that all
# sentences end with PAD.
MAX_TOKENS_SEQUENCE = {"emnlp2017": 52}
UNK = "<unk>"
PAD = " "
PAD_INT = 0
def tokenize(sentence):
"""Split a string into words."""
return sentence.split(" ") + [PAD]
def _build_vocab(json_data):
"""Builds full vocab from json data."""
vocab = collections.Counter()
for sentence in json_data:
tokens = tokenize(sentence["s"])
vocab.update(tokens)
for title in sentence["t"]:
title_tokens = tokenize(title)
vocab.update(title_tokens)
# Most common words first.
count_pairs = sorted(list(vocab.items()), key=lambda x: (-x[1], x[0]))
words, _ = list(zip(*count_pairs))
words = list(words)
if UNK not in words:
words = [UNK] + words
word_to_id = dict(list(zip(words, list(range(len(words))))))
# Tokens are now sorted by frequency. There's no guarantee that `PAD` will
# end up at `PAD_INT` index. Enforce it by swapping whatever token is
# currently at the `PAD_INT` index with the `PAD` token.
word = list(word_to_id.keys())[list(word_to_id.values()).index(PAD_INT)]
word_to_id[PAD], word_to_id[word] = word_to_id[word], word_to_id[PAD]
assert word_to_id[PAD] == PAD_INT
return word_to_id
def string_sequence_to_sequence(string_sequence, word_to_id):
result = []
for word in string_sequence:
if word in word_to_id:
result.append(word_to_id[word])
else:
result.append(word_to_id[UNK])
return result
def _integerize(json_data, word_to_id, dataset):
"""Transform words into integers."""
sequences = np.full((len(json_data), MAX_TOKENS_SEQUENCE[dataset]),
word_to_id[PAD], np.int32)
sequence_lengths = np.zeros(shape=(len(json_data)), dtype=np.int32)
for i, sentence in enumerate(json_data):
sequence_i = string_sequence_to_sequence(
tokenize(sentence["s"]), word_to_id)
sequence_lengths[i] = len(sequence_i)
sequences[i, :sequence_lengths[i]] = np.array(sequence_i)
return {
"sequences": sequences,
"sequence_lengths": sequence_lengths,
}
def get_raw_data(data_path, dataset, truncate_vocab=20000):
"""Load raw data from data directory "data_path".
Reads text files, converts strings to integer ids,
and performs mini-batching of the inputs.
Args:
data_path: string path to the directory where simple-examples.tgz has been
extracted.
dataset: one of ["emnlp2017"]
truncate_vocab: int, number of words to keep in the vocabulary.
Returns:
tuple (train_data, valid_data, vocabulary) where each of the data
objects can be passed to iterator.
Raises:
ValueError: dataset not in ["emnlp2017"].
"""
if dataset not in FILENAMES:
raise ValueError("Invalid dataset {}. Valid datasets: {}".format(
dataset, list(FILENAMES.keys())))
train_file, valid_file, _ = FILENAMES[dataset]
train_path = os.path.join(data_path, train_file)
valid_path = os.path.join(data_path, valid_file)
with gfile.GFile(train_path, "r") as json_file:
json_data_train = json.load(json_file)
with gfile.GFile(valid_path, "r") as json_file:
json_data_valid = json.load(json_file)
word_to_id = _build_vocab(json_data_train)
logging.info("Full vocab length: %d", len(word_to_id))
# Assume the vocab is sorted by frequency.
word_to_id_truncated = {
k: v for k, v in word_to_id.items() if v < truncate_vocab
}
logging.info("Truncated vocab length: %d", len(word_to_id_truncated))
train_data = _integerize(json_data_train, word_to_id_truncated, dataset)
valid_data = _integerize(json_data_valid, word_to_id_truncated, dataset)
return train_data, valid_data, word_to_id_truncated
def iterator(raw_data, batch_size, random=False):
"""Looping iterators on the raw data."""
sequences = raw_data["sequences"]
sequence_lengths = raw_data["sequence_lengths"]
num_examples = sequences.shape[0]
indice_range = np.arange(num_examples)
if random:
while True:
indices = np.random.choice(indice_range, size=batch_size, replace=True)
yield {
"sequence": sequences[indices, :],
"sequence_length": sequence_lengths[indices],
}
else:
start = 0
while True:
sequence = sequences[start:(start + batch_size), :]
sequence_length = sequence_lengths[start:(start + batch_size)]
start += batch_size
if start + batch_size > num_examples:
start = (start + batch_size) % num_examples
yield {
"sequence": sequence,
"sequence_length": sequence_length,
}