Spaces:
Runtime error
Runtime error
File size: 8,214 Bytes
b6e5241 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 |
"""
Word-level perturbation generator.
Originally by https://github.com/awasthiabhijeet/PIE/tree/master/errorify
"""
import os
import math
import pickle
import random
import editdistance
from numpy.random import choice as npchoice
from collections import defaultdict
try:
dir_path = os.path.dirname(os.path.realpath(__file__))
except:
dir_path = '.'
VERBS = pickle.load(open(f'{dir_path}/verbs.p', 'rb'))
COMMON_INSERTS = set(pickle.load(open(f'{dir_path}/common_inserts.p', 'rb'))) #common inserts *to fix a sent*
COMMON_DELETES = pickle.load(open(f'{dir_path}/common_deletes.p','rb')) #common deletes *to fix a sent*
_COMMON_REPLACES = pickle.load(open(f'{dir_path}/common_replaces.p', 'rb')) #common replacements *to errorify a sent*
COMMON_REPLACES = {}
for src in _COMMON_REPLACES:
for tgt in _COMMON_REPLACES[src]:
if (src=="'re" and tgt=="are") or (tgt=="'re" and src=="are"):
continue
ED = editdistance.eval(tgt, src)
if ED > 2:
continue
longer = max(len(src), len(tgt))
if float(ED)/longer >= 0.5:
continue
if tgt not in COMMON_REPLACES:
COMMON_REPLACES[tgt] = {}
COMMON_REPLACES[tgt][src] = _COMMON_REPLACES[src][tgt]
VERBS_refine = defaultdict(list)
for src in VERBS:
for tgt in VERBS[src]:
ED = editdistance.eval(tgt, src)
if ED > 2:
continue
longer = max(len(src), len(tgt))
if float(ED)/longer >= 0.5:
continue
VERBS_refine[src].append(tgt)
class WordLevelPerturber_all:
def __init__(self, sentence: str):
self.original_sentence = sentence.rstrip()
self.sentence = self.original_sentence
self.tokenized = None
self.tokenize()
def tokenize(self):
self.tokenized = self.sentence.split()
def orig(self):
return self.original_sentence
def _insert(self):
"""Insert a commonly deleted word."""
if len(self.tokenized) > 0:
insertable = list(range(len(self.tokenized)))
index = random.choice(insertable)
plist = list(COMMON_DELETES.values())
plistsum = sum(plist)
plist = [x / plistsum for x in plist]
# Choose a word
ins_word = npchoice(list(COMMON_DELETES.keys()), p=plist)
self.tokenized.insert(index,ins_word)
return ' '.join(self.tokenized)
def _mod_verb(self, redir=True):
if len(self.tokenized) > 0:
verbs = [i for i, w in enumerate(self.tokenized) if w in VERBS]
if not verbs:
if redir:
return self._replace(redir=False)
return self.sentence
index = random.choice(verbs)
word = self.tokenized[index]
if not VERBS[word]:
return self.sentence
repl = random.choice(VERBS[word])
self.tokenized[index] = repl
return ' '.join(self.tokenized)
def _delete(self):
"""Delete a commonly inserted word."""
if len(self.tokenized) > 1:
toks_len = len(self.tokenized)
toks = self.tokenized
deletable = [i for i, w in enumerate(toks) if w in COMMON_INSERTS]
if not deletable:
return self.sentence
index = random.choice(deletable)
del self.tokenized[index]
return ' '.join(self.tokenized)
def _replace(self, redir=True):
if len(self.tokenized) > 0:
deletable = [i for i, w in enumerate(self.tokenized) if (w in COMMON_REPLACES)]
if not deletable:
if redir:
return self._mod_verb(redir=False)
return self.sentence
index = random.choice(deletable)
word = self.tokenized[index]
if not COMMON_REPLACES[word]:
return self.sentence
# Normalize probabilities
plist = list(COMMON_REPLACES[word].values())
plistsum = sum(plist)
plist = [x / plistsum for x in plist]
# Choose a word
repl = npchoice(list(COMMON_REPLACES[word].keys()), p=plist)
self.tokenized[index] = repl
return ' '.join(self.tokenized)
def perturb(self):
count = 1
orig_sent = self.sentence
for x in range(count):
perturb_probs = [.30,.30,.30,.10]
perturb_fun = npchoice([self._insert, self._mod_verb, self._replace, self._delete],p=perturb_probs)
self.sentence = perturb_fun()
self.tokenize()
res_sentence = self.sentence
self.sentence = self.original_sentence
self.tokenize()
return res_sentence
class WordLevelPerturber_refine:
def __init__(self, sentence: str):
self.original_sentence = sentence.rstrip()
self.sentence = self.original_sentence
self.tokenized = None
self.tokenize()
def tokenize(self):
self.tokenized = self.sentence.split()
def orig(self):
return self.original_sentence
def _insert(self):
"""Insert a commonly deleted word."""
if len(self.tokenized) > 0:
insertable = list(range(len(self.tokenized)))
index = random.choice(insertable)
plist = list(COMMON_DELETES.values())
plistsum = sum(plist)
plist = [x / plistsum for x in plist]
# Choose a word
ins_word = npchoice(list(COMMON_DELETES.keys()), p=plist)
self.tokenized.insert(index,ins_word)
return ' '.join(self.tokenized)
def _mod_verb(self, redir=True):
if len(self.tokenized) > 0:
verbs = [i for i, w in enumerate(self.tokenized) if w in VERBS_refine]
if not verbs:
if redir:
return self._replace(redir=False)
return self.sentence
index = random.choice(verbs)
word = self.tokenized[index]
if not VERBS_refine[word]:
return self.sentence
repl = random.choice(VERBS_refine[word])
self.tokenized[index] = repl
return ' '.join(self.tokenized)
def _delete(self):
"""Delete a commonly inserted word."""
if len(self.tokenized) > 1:
toks_len = len(self.tokenized)
toks = self.tokenized
deletable = [i for i, w in enumerate(toks) if (w in COMMON_INSERTS) and (i>0 and toks[i-1].lower() == toks[i].lower())]
if not deletable:
return self.sentence
index = random.choice(deletable)
del self.tokenized[index]
return ' '.join(self.tokenized)
def _replace(self, redir=True):
def _keep(i,w):
if w.lower() in {"not", "n't"}:
return True
return False
if len(self.tokenized) > 0:
deletable = [i for i, w in enumerate(self.tokenized) if (w in COMMON_REPLACES) and (not _keep(i,w))]
if not deletable:
if redir:
return self._mod_verb(redir=False)
return self.sentence
index = random.choice(deletable)
word = self.tokenized[index]
if not COMMON_REPLACES[word]:
return self.sentence
# Normalize probabilities
plist = list(COMMON_REPLACES[word].values())
plistsum = sum(plist)
plist = [x / plistsum for x in plist]
# Choose a word
repl = npchoice(list(COMMON_REPLACES[word].keys()), p=plist)
self.tokenized[index] = repl
return ' '.join(self.tokenized)
def perturb(self):
count = 1
orig_sent = self.sentence
for x in range(count):
perturb_probs = [.30,.30,.30,.10]
perturb_fun = npchoice([self._insert, self._mod_verb, self._replace, self._delete],p=perturb_probs)
self.sentence = perturb_fun()
self.tokenize()
res_sentence = self.sentence
self.sentence = self.original_sentence
self.tokenize()
return res_sentence
|