-
Notifications
You must be signed in to change notification settings - Fork 0
/
utils.py
77 lines (59 loc) · 2.53 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import math
import numpy as np
def pad_sents(sents, pad_token):
""" Pad list of sentences according to the longest sentence in the batch.
@param sents (list[list[int]]): list of sentences, where each sentence
is represented as a list of words
@param pad_token (str): padding token
@returns sents_padded (list[list[int]]): list of sentences where sentences shorter
than the max length sentence are padded out with the pad_token, such that
each sentences in the batch now has equal length.
"""
sents_padded = []
max_sentence_length= 0
for sentence in sents:
sentence_length = len(sentence)
if sentence_length > max_sentence_length:
max_sentence_length = sentence_length
for sentence in sents:
sentence_length = len(sentence)
sentence_padded = sentence
for i in range(max_sentence_length - sentence_length):
sentence_padded.append(pad_token)
sents_padded.append(sentence_padded)
### YOUR CODE HERE (~6 Lines)
### END YOUR CODE
return sents_padded
def read_corpus(file_path, source):
""" Read file, where each sentence is dilineated by a `\n`.
@param file_path (str): path to file containing corpus
@param source (str): "tgt" or "src" indicating whether text
is of the source language or target language
"""
data = []
for line in open(file_path):
sent = line.strip().split(' ')
# only append <s> and </s> to the target sentence
if source == 'tgt':
sent = ['<s>'] + sent + ['</s>']
data.append(sent)
return data
def batch_iter(data, batch_size, shuffle=False):
""" Yield batches of source and target sentences reverse sorted by length (largest to smallest).
@param data (list of (src_sent, tgt_sent)): list of tuples containing source and target sentence
@param batch_size (int): batch size
@param shuffle (boolean): whether to randomly shuffle the dataset
"""
batch_num = math.ceil(len(data) / batch_size)
index_array = list(range(len(data)))
if shuffle:
np.random.shuffle(index_array)
for i in range(batch_num):
indices = index_array[i * batch_size: (i + 1) * batch_size]
examples = [data[idx] for idx in indices]
examples = sorted(examples, key=lambda e: len(e[0]), reverse=True)
src_sents = [e[0] for e in examples]
tgt_sents = [e[1] for e in examples]
yield src_sents, tgt_sents