File size: 3,784 Bytes
9f61031 | 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | """Utils for language models."""
import re
import numpy as np
import json
# ------------------------
# utils for shakespeare dataset
ALL_LETTERS = "\n !\"&'(),-.0123456789:;>?ABCDEFGHIJKLMNOPQRSTUVWXYZ[]abcdefghijklmnopqrstuvwxyz}"
NUM_LETTERS = len(ALL_LETTERS)
print('num of letters:', NUM_LETTERS)
def letter_to_index(letter):
'''returns one-hot representation of given letter
'''
index = ALL_LETTERS.find(letter)
return index
def _one_hot(index, size):
'''returns one-hot vector with given size and value 1 at given index
'''
vec = [0 for _ in range(size)]
vec[int(index)] = 1
return vec
def letter_to_vec(letter):
'''returns one-hot representation of given letter
'''
index = ALL_LETTERS.find(letter)
return _one_hot(index, NUM_LETTERS)
def word_to_indices(word):
'''returns a list of character indices
Args:
word: string
Return:
indices: int list with length len(word)
'''
indices = []
for c in word:
indices.append(ALL_LETTERS.find(c))
return indices
# ------------------------
# utils for sent140 dataset
def split_line(line):
'''split given line/phrase into list of words
Args:
line: string representing phrase to be split
Return:
list of strings, with each string representing a word
'''
return re.findall(r"[\w']+|[.,!?;]", line)
def _word_to_index(word, indd):
'''returns index of given word based on given lookup dictionary
returns the length of the lookup dictionary if word not found
Args:
word: string
indd: dictionary with string words as keys and int indices as values
'''
if word in indd:
return indd[word]
else:
return len(indd)
def line_to_indices(line, word2id, max_words=25):
'''converts given phrase into list of word indices
if the phrase has more than max_words words, returns a list containing
indices of the first max_words words
if the phrase has less than max_words words, repeatedly appends integer
representing unknown index to returned list until the list's length is
max_words
Args:
line: string representing phrase/sequence of words
word2id: dictionary with string words as keys and int indices as values
max_words: maximum number of word indices in returned list
Return:
indl: list of word indices, one index for each word in phrase
'''
unk_id = len(word2id)
line_list = split_line(line) # split phrase in words
indl = [word2id[w] if w in word2id else unk_id for w in line_list[:max_words]]
indl += [unk_id]*(max_words-len(indl))
return indl
def bag_of_words(line, vocab):
'''returns bag of words representation of given phrase using given vocab
Args:
line: string representing phrase to be parsed
vocab: dictionary with words as keys and indices as values
Return:
integer list
'''
bag = [0]*len(vocab)
words = split_line(line)
for w in words:
if w in vocab:
bag[vocab[w]] += 1
return bag
def get_word_emb_arr(path):
with open(path, 'r') as inf:
embs = json.load(inf)
vocab = embs['vocab']
word_emb_arr = np.array(embs['emba'])
indd = {}
for i in range(len(vocab)):
indd[vocab[i]] = i
vocab = {w: i for i, w in enumerate(embs['vocab'])}
return word_emb_arr, indd, vocab
def val_to_vec(size, val):
"""Converts target into one-hot.
Args:
size: Size of vector.
val: Integer in range [0, size].
Returns:
vec: one-hot vector with a 1 in the val element.
"""
assert 0 <= val < size
vec = [0 for _ in range(size)]
vec[int(val)] = 1
return vec
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