Spaces:
Runtime error
Runtime error
File size: 9,964 Bytes
2fc2c1f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 |
#!/usr/local/bin/python3
# avenir-python: Machine Learning
# Author: Pranab Ghosh
#
# 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
#
# http://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.
import os
import sys
from random import randint
import random
import time
from datetime import datetime
import re, string, unicodedata
import spacy
import torch
from collections import defaultdict
import pickle
import numpy as np
import re
from sentence_transformers import CrossEncoder
sys.path.append(os.path.abspath("../lib"))
from util import *
from mlutil import *
"""
neural language model
"""
class NeuralLangModel(object):
def __init__(self):
"""
initialize
"""
self.dexts = None
def loadDocs(self, fpaths):
"""
loads documents from one file
"""
fPaths = fpaths.split(",")
if len(fPaths) == 1:
if os.path.isfile(fPaths[0]):
#one file
print("got one file from path")
dnames = fpaths
docStr = getOneFileContent(fPaths[0])
dtexts = [docStr]
else:
#all files under directory
print("got all files under directory from path")
dtexts, dnames = getFileContent(fPaths[0])
print("found following files")
for dt, dn in zip(dtexts, dnames):
print(dn + "\t" + dt[:40])
else:
#list of files
print("got list of files from path")
dnames = fpaths
dtexts = list(map(getOneFileContent, fpaths))
ndocs = (dtexts, dnames)
return ndocs
#Encoded doc
class EncodedDoc:
def __init__(self, dtext, dname, drank=None):
"""
initialize
"""
self.dtext = dtext
self.dname = dname
self.drank = drank
self.denc = None
self.score = None
def encode(self, nlp):
"""
encode
"""
self.denc = nlp(self.dtext)
#similarity at token and sentence level for BERT encoding
class SemanticSearch:
def __init__(self, docs=None):
"""
initialize
"""
print("loading BERT transformer model")
self.nlp = spacy.load("en_trf_bertbaseuncased_lg")
self.docs = docs if docs is not None else list()
def docAv(self,qu, doc):
"""
whole doc similarity
"""
return qu.similarity(doc)
def tokSimAv(self, qu, doc):
"""
token pair wise average
"""
qts = simAll(qu, doc)
asi = numpy.mean(qts)
return asi
def tokSimMed(self, qu, doc):
"""
token pair wise average
"""
qts = simAll(qu, doc)
asi = numpy.median(qts)
return asi
def tokSimMax(self, qu, doc):
"""
token pair wise max (tsma)
"""
qte = self. __getTensor(qu)
dte = self. __getTensor(doc)
return self.simMax(qte, dte)
def tokSimAvMax(self, qu, doc):
"""
token max then average (tsavm)
"""
qte = self. __getTensor(qu)
dte = self. __getTensor(doc)
return self.simAvMax(qte, dte)
def tokSimMaxAv(self, qu, doc):
"""
token average and then max
"""
qte = self. __getTensor(qu)
dte = self. __getTensor(doc)
return self.simMaxAv(qte, dte)
def sentSimAv(self, qu, doc):
"""
sentence wise average
"""
qse, dse = self.__sentEnc(qu, doc)
sims = self.simAll(qse, dse)
return numpy.mean(sims)
def sentSimMed(self, qu, doc):
"""
sentence wise average (ssma)
"""
qse, dse = self.__sentEnc(qu, doc)
sims = self.simAll(qse, dse)
return numpy.median(sims)
def sentSimMax(self, qu, doc):
"""
sentence wise average (ssma)
"""
qse, dse = self.__sentEnc(qu, doc)
sims = self.simAll(qse, dse)
return numpy.maximum(sims)
def sentSimAvMax(self, qu, doc):
"""
sentence max then average (tsavm)
"""
qse, dse = self.__sentEnc(qu, doc)
return self.simAvMax(qse, dse)
def sentSimMaxAv(self, qu, doc):
"""
sentence average and then max
"""
qse, dse = self.__sentEnc(qu, doc)
return self.simMaxAv(qse, dse)
def simMax(self, qte, dte):
"""
max similarity between 2 elements
"""
msi = 0
for qt in qte:
for dt in dte:
si = cosineSimilarity(qt, dt)
if not math.isnan(si) and si > msi:
msi = si
return msi
def simAvMax(self, qte, dte):
"""
max then average (tsavm)
"""
qts = list()
for qt in qte:
msi = 0
for dt in dte:
si = cosineSimilarity(qt, dt)
if not math.isnan(si) and si > msi:
msi = si
qts.append(msi)
amsi = numpy.mean(numpy.array(qts))
return amsi
def simMaxAv(self, lqe, lde):
"""
average and then max
"""
masi = 0
for qe in lqe:
qes = list()
for de in lde:
si = cosineSimilarity(qe, de)
if not math.isnan(si):
qes.append(si)
av = numpy.mean(numpy.array(qes))
if av > masi:
masi = av
return masi
def simAll(self, lqe, lde):
"""
all similarity
"""
qes = list()
for qe in lqe:
for de in lde:
si = cosineSimilarity(qe, de)
if not math.isnan(si):
qes.append(si)
return numpy.array(qes)
def __sentEnc(self, qu, doc):
"""
sentence encoding for query and doc
"""
qstr = qu._.trf_word_pieces_
qte = zip(qstr, qu._.trf_last_hidden_state)
qse = list()
for t, v in qte:
if t == "[CLS]":
qse.append(v)
dstr = doc._.trf_word_pieces_
dte = zip(dstr, doc._.trf_last_hidden_state)
dse = list()
for t, v in dte:
if t == "[CLS]":
dse.append(v)
enp = (numpy.array(qse), numpy.array(dse))
return enp
def __getTensor(self, toks):
"""
tensors from tokens
"""
return list(map(lambda t: t.tensor, toks))
def addDocs(self, docs):
"""
add named doc content
"""
self.docs.extend(docs)
def loadDocs(self, fpaths):
"""
loads documents from one file
"""
fPaths = fpaths.split(",")
if len(fPaths) == 1:
if os.path.isfile(fPaths[0]):
#one file
print("one file")
dnames = fpaths
docStr = getOneFileContent(fPaths[0])
dtexts = [docStr]
else:
#all files under directory
print("all files under directory")
dtexts, dnames = getFileContent(fPaths[0])
print("found following files")
for dt, dn in zip(dtexts, dnames):
print(dn + "\t" + dt[:40])
else:
#list of files
print("list of files")
dnames = fpaths
dtexts = list(map(getOneFileContent, fpaths))
docs = list(map(lambda dtext, dname : EncodedDoc(dtext, dname), zip(dtexts, dnames)))
self.docs.extend(docs)
def search(self, qstr, algo, gdranks=None):
"""
tensors from tokens
"""
qv = self.nlp(qstr)
res = list()
for d in self.docs:
dn = d.dname
if d.denc == None:
d.encode(self.nlp)
dv = d.denc
if algo == "ds":
si = self.docAv(qv, dv)
elif algo == "tsa":
si = self.tokSimAv(qv, dv)
elif algo == "tsme":
si = self.tokSimMed(qv, dv)
elif algo == "tsma":
si = self.tokSimMax(qv, dv)
elif algo == "tsavm":
si = self.tokSimAvMax(qv, dv)
elif algo == "tsmav":
si = self.tokSimMaxAv(qv, dv)
elif algo == "ssa":
si = self.sentSimAv(qv, dv)
elif algo == "ssme":
si = self.sentSimMed(qv, dv)
elif algo == "ssma":
si = self.sentSimMax(qv, dv)
elif algo == "ssavm":
si = self.sentSimAvMax(qv, dv)
elif algo == "ssmav":
si = self.sentSimMaxAv(qv, dv)
else:
si = -1.0
print("invalid semilarity algo")
#print("{} score {:.6f}".format(dn, si))
d.score = si
r = (dn, si)
res.append(r)
#search score for each document
res.sort(key=lambda r : r[1], reverse=True)
print("\nsorted search result")
print("query: {} matching algo: {}".format(qstr, algo))
for r in res:
print("{} score {:.3f}".format(r[0], r[1]))
#rank order if gold truuth rank provided
if gdranks is not None:
i = 0
count = 0
for d in gdranks:
while i < len(gdranks):
if d == res[i][0]:
count += 1
i += 1
break;
i += 1
ro = count / len(gdranks)
print("rank order {:.3f}".format(ro))
#similarity at passage or paragraph level using sbertcross encoder
class SemanticSimilaityCrossEnc(NeuralLangModel):
def __init__(self, docs=None):
self.dparas = None
self.scores = None
print("loading cross encoder")
self.model = CrossEncoder("cross-encoder/ms-marco-TinyBERT-L-2")
print("done loading cross encoder")
super(NeuralLangModel, self).__init__()
def paraSimilarity(self, dtext, fpaths, minParNl=1):
"""
returns paragarph pair similarity across 2 documents
"""
dtexts, dnames = self.loadDocs(fpaths)
if dtext is None:
assertEqual(len(dtexts), 2, "exactly 2 files needed")
self.dtexts = dtexts
else:
assertEqual(len(dtexts), 1, "exactly 1 file needed")
self.dtexts = list()
self.dtexts.append(dtext)
self.dtexts.append(dtexts[0])
self.dparas = list()
for text in self.dtexts:
regx = "\n+" if minParNl == 1 else "\n{2,}"
paras = re.split(regx, text.replace("\r\n", "\n"))
print("no of paras {}".format(len(paras)))
self.dparas.append(paras)
tinp = list()
for para1 in self.dparas[0]:
inp = list(map(lambda para2: [para1, para2], self.dparas[1]))
tinp.extend(inp)
print("input shape " + str(np.array(tinp).shape))
scores = self.model.predict(tinp)
print("score shape " + str(np.array(scores).shape))
#assertEqual(len(scores), len(self.dparas[0]) * len(self.dparas[1]), "no of scores don't match no of paragraph pairs")
print(scores)
i = 0
print("text paragraph pair wise similarity")
for para1 in self.dparas[0]:
for para2 in self.dparas[1]:
print("first: {}\t second: {}\t score: {:.6f}".format(para1[:20], para2[:20], scores[i]))
i += 1
self.scores = scores
def avMaxScore(self):
"""
"""
pass
def ner(text, nlp):
#nlp = spacy.load("en_core_web_md")
doc = nlp(text)
for ent in doc.ents:
print(ent.text, ent.start_char, ent.end_char, ent.label_)
|