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utils.py
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utils.py
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#!/usr/bin/env python
from __future__ import division
from __future__ import print_function
import collections
import io
import json
import os
import numpy as np
import progressbar
import chainer
from chainer import cuda
from chainer.dataset import convert
class UnkDropout(chainer.dataset.DatasetMixin):
def __init__(self, dataset, unk, ratio=0.01):
self.dataset = dataset
self.unk = unk
self.ratio = ratio
def __len__(self):
return len(self.dataset)
def get_example(self, i):
x, y = self.dataset[i]
if chainer.config.train:
rand = np.random.rand(x.size - 2) < self.ratio
# keep bos and eos
_x = x.copy()
_x[1:-1] = np.where(rand, self.unk, _x[1:-1])
return (_x, y)
return (x, y)
def convert_xt_batch_seq(xt_batch_seq, gpu):
batchsize = len(xt_batch_seq[0])
seq_len = len(xt_batch_seq)
xt_batch_seq = np.array(xt_batch_seq, 'i')
# (bproplen, batch, 2)
xt_batch_seq = convert.to_device(gpu, xt_batch_seq)
xp = cuda.get_array_module(xt_batch_seq)
x_seq_batch = xp.split(
xt_batch_seq[:, :, 0].T.reshape(batchsize * seq_len),
batchsize, axis=0)
t_seq_batch = xp.split(
xt_batch_seq[:, :, 1].T.reshape(batchsize * seq_len),
batchsize, axis=0)
return x_seq_batch, t_seq_batch
def count_words_from_file(counts, file_path):
bar = progressbar.ProgressBar()
for l in bar(io.open(file_path, encoding='utf-8')):
# TODO: parallel
if l.strip():
words = l.strip().split()
for word in words:
counts[word] += 1
return counts
def count_words(dataset, alpha=0.4):
counts = collections.defaultdict(int)
for w in dataset:
counts[w] += 1
counts = [counts[i] for i in range(len(counts))]
counts = np.array(counts, 'f')
counts /= counts.sum()
counts = counts ** alpha
counts = counts.tolist()
return counts
def make_chain_dataset(file_path, vocab={}, update_vocab=False,
chain_length=2):
dataset = []
chain = []
unk_id = vocab['<unk>']
def make_array(chain):
array_chain = []
for words in chain:
tokens = []
for word in words:
if update_vocab:
if word not in vocab:
vocab[word] = len(vocab)
tokens.append(vocab.get(word, unk_id))
array_chain.append(np.array(tokens, 'i'))
return array_chain
for line in io.open(file_path, encoding='utf-8'):
if not line.strip():
if len(chain) >= chain_length:
dataset.append(make_array(chain))
chain = []
continue
words = line.strip().split() + ['<eos>']
chain.append(words)
if len(chain) >= chain_length:
dataset.append(make_array(chain))
return dataset, vocab
def tokenize_text(file_path, vocab={}, update_vocab=False):
tokens = []
unk_id = vocab['<unk>']
with io.open(file_path, encoding='utf-8') as f:
for line in f:
words = line.split() + ['<eos>']
for word in words:
if update_vocab:
if word not in vocab:
vocab[word] = len(vocab)
tokens.append(vocab.get(word, unk_id))
return tokens, vocab
def get_wikitext_words_and_vocab(
name='wikitext-2', base_dir='datasets', vocab=None):
assert(name in ['wikitext-2', 'wikitext-103'])
base_dir2 = os.path.join(base_dir, name)
predata_path = os.path.join(base_dir2, 'preprocessed_data.json')
if os.path.exists(predata_path) and vocab is None:
train, valid, test, vocab = json.load(open(predata_path))
else:
prepared_vocab = (vocab is not None)
if not prepared_vocab:
vocab = {'<eos>': 0, '<unk>': 1}
train, vocab = tokenize_text(
os.path.join(base_dir2, 'wiki.train.tokens'),
vocab, update_vocab=not prepared_vocab)
valid, _ = tokenize_text(
os.path.join(base_dir2, 'wiki.valid.tokens'),
vocab, update_vocab=False)
test, _ = tokenize_text(
os.path.join(base_dir2, 'wiki.test.tokens'),
vocab, update_vocab=False)
json.dump([train, valid, test, vocab], open(predata_path, 'w'))
return train, valid, test, vocab