def langModelFeat(self, argString, preprocessReq=0):
'''
Extracts n-gram Language Model preplexity features.
'''
ngramOrder = 3
langModel = 0
# Binary1/0,ngramOrder,LMFilePath(ifBinary1)
arguments = argString.split(',')
if (int(arguments[0])):
# Use given langModel
langModel = "\"{0}\"".format(arguments[-1])
ngramOrder = int(arguments[1])
if preprocessReq:
# Request all preprocessing functions to be prepared
if not langModel:
langModel = self.preprocessor.buildLanguageModel(ngramOrder)
self.preprocessor.getInputFileName()
self.preprocessor.getBinariesPath()
return 1
sentsFile = self.preprocessor.getInputFileName()
srilmBinary, kenlm = self.preprocessor.getBinariesPath()
if not langModel:
langModel = self.preprocessor.buildLanguageModel(ngramOrder)
if srilmBinary and not kenlm:
pplFile = "tempLang{0}{1}.ppl".format(os.path.basename(sentsFile),
ngramOrder)
command = "\"{0}ngram\" -order {1} -lm {2} -ppl {3} -debug 1 -unk> {4}".format(
srilmBinary, ngramOrder, langModel, sentsFile, pplFile)
subprocess.call(command, shell=True)
probab = self.extractValues(pplFile,
self.preprocessor.getSentCount())
os.remove(pplFile)
return sparse.lil_matrix(probab)
else:
try:
__import__('imp').find_module('kenlm')
import kenlm
model = kenlm.Model(langModel)
probab = []
for sent in self.preprocessor.getPlainSentences():
probab.append([
model.score(sent, bos=True, eos=True),
model.perplexity(sent)
])
output = sparse.lil_matrix(probab)
return output
except ImportError:
import pynlpl.lm.lm as pineApple
arpaLM = pineApple.ARPALanguageModel(langModel)
probab = []
for sent in self.preprocessor.gettokenizeSents():
probab.append([arpaLM.score(sent)])
output = sparse.lil_matrix(probab)
return output
def getPynlplScores(self, sentences, langModel):
import pynlpl.lm.lm as pineApple
arpaLM = pineApple.ARPALanguageModel(langModel)
probab = []
print("Using Pineapple")
for sent in sentences:
probab.append([arpaLM.score(sent)])
return sparse.lil_matrix(probab)
def backwardLangModelPOSFeat(self, argString, preprocessReq=0):
'''
Extracts n-gram POS language model preplexity features.
'''
ngramOrder = 3
langModel = ""
taggedInput = ""
taggedCorpus = ""
# TaggedInput1/0,LM0/1,taggedCorpus0/1,ngramOrder(,TaggedPOSfile(ifTaggedInp1),
# LMFilePath(ifLM1),taggedCorpus(if LM0&TaggedCorpus1))
arguments = argString.split(',')
if int(arguments[0]):
# Use file of tagged sents (last argument)
taggedInput = "\"{0}\"".format(arguments[4])
if int(arguments[1]):
# Next argument
langModel = "\"{0}\"".format(arguments[4 + int(arguments[0])])
elif int(arguments[2]):
taggedCorpus = "\"{0}\"".format(arguments[4 + int(arguments[0])])
ngramOrder = int(arguments[3])
if preprocessReq:
# Request all preprocessing functions to be prepared
if not taggedInput:
taggedInput = self.prep_servs.dumpTokensTofile(
dumpFile="{0}_tagged_Input.txt".format(
self.preprocessor.getInputFileName()),
tokenSents=self.preprocessor.getPOStagged())
if not langModel:
if not taggedCorpus:
taggedCorpus = self.prep_servs.dumpTokensTofile(
dumpFile="{0}_tagged_Corpus.txt".format(
self.preprocessor.getCorpusLMName()),
tokenSents=self.prep_servs.tagPOSfromFile(
self.preprocessor.getCorpusLMName()))
# If tagged corpus is empty, just use
langModel = self.preprocessor.buildBackwardLanguageModel(
ngramOrder, taggedCorpus, False)
return 1
if not taggedInput:
taggedInput = "{0}_tagged_Input.txt".format(
self.preprocessor.getInputFileName())
if not langModel:
if not taggedCorpus:
taggedCorpus = "{0}_tagged_Corpus.txt".format(
self.preprocessor.getCorpusLMName())
langModel = self.preprocessor.buildBackwardLanguageModel(
ngramOrder, taggedCorpus, False)
srilmBinary, kenlm = self.preprocessor.getBinariesPath()
if srilmBinary and not kenlm:
pplFile = "tempLang{0}_reversed_{1}.ppl".format(
os.path.basename(taggedInput[1:-1]), ngramOrder)
# strip the quotes from the taggedInput filename
# so that os.path.basename works correctly
command = "\"{0}ngram\" -order {1} -lm {2} -ppl {3} -debug 1 -unk -reverse > {4}".format(
srilmBinary, ngramOrder, langModel, taggedInput, pplFile)
subprocess.call(command, shell=True)
probab = self.extractValues(pplFile,
self.preprocessor.getSentCount())
os.remove(pplFile)
return sparse.lil_matrix(probab)
else:
try:
__import__('imp').find_module('kenlm')
import kenlm
model = kenlm.Model(langModel)
probab = []
for sent in self.preprocessor.getPOStagged():
probab.append([
model.score(sent, bos=True, eos=True),
model.perplexity(sent)
])
output = sparse.lil_matrix(probab)
return output
except ImportError:
import pynlpl.lm.lm as pineApple
arpaLM = pineApple.ARPALanguageModel(langModel)
probab = []
for sent in self.preprocessor.getPOStagged():
probab.append([arpaLM.score(sent)])
output = sparse.lil_matrix(probab)
return output
def __init__(self, bigramARPAFilname: str):
self.bigrams = lm.ARPALanguageModel(bigramARPAFilname, base_e=False)
# gather_captions_to_text(caption_dir, out_fpath)
# building 3-g LM
# https://cmusphinx.github.io/wiki/tutoriallm/#training-an-arpa-model-with-srilm
# $ ~/tools/kaldi/tools/srilm/lm/bin/i686-m64/ngram-count -kndiscount -interpolate -text ../clotho-dataset/lm/dev.txt -lm ../clotho-dataset/lm/dev.lm
# $ ~/tools/kaldi/tools/srilm/lm/bin/i686-m64/ngram -lm ../clotho-dataset/lm/dev.lm -ppl ../clotho-dataset/lm/eva.txt
# file ../clotho-dataset/lm/eva.txt: 5225 sentences, 64350 words, 0 OOVs
# 0 zeroprobs, logprob= -114470.2 ppl= 44.18532 ppl1= 60.09924
# $ ~/tools/kaldi/tools/srilm/lm/bin/i686-m64/ngram -lm ../clotho-dataset/lm/dev.lm -prune 1e-8 -write-lm ../clotho-dataset/lm/dev_pruned.lm
# $ ~/tools/kaldi/tools/srilm/lm/bin/i686-m64/ngram -lm ../clotho-dataset/lm/dev_pruned.lm -ppl ../clotho-dataset/lm/eva.txt
# file ../clotho-dataset/lm/eva.txt: 5225 sentences, 64350 words, 0 OOVs
# 0 zeroprobs, logprob= -114658.9 ppl= 44.46208 ppl1= 60.50634
# lm_path=b'../clotho-dataset/lm/dev.lm'
#
# lm = LM(lm_path, lower=False)
#
#
# print(len([b"man", b"a"]))
# print(lm.logprob_strings(lm, b"is", [b"man", b"a"]))
lm = lm.ARPALanguageModel('../clotho-dataset/lm/dev.lm')
print("man", lm.scoreword("man"))
print("a man", lm.scoreword("man", history=("a",)))
print("a hyman", lm.scoreword("hyman", history=("a",)))
def __init__(self,
vocab_size,
embedding_dim=128,
decoder_hidden_size_1=128,
decoder_hidden_size_2=128,
query_size=128,
value_size=128,
key_size=128,
isAttended=False,
beam_size=10,
use_lm_bigram=False,
use_lm_trigram=False,
lm_weight=0.,
teacher_forcing_ratio=0.9,
word2index=None,
index2word=None,
vocab=None,
device='cpu'):
super(BeamDecoder, self).__init__()
print("BeamDecoder VOCAB SIZE:", vocab_size)
self.beam_size = beam_size
self.use_lm_bigram = use_lm_bigram
self.use_lm_trigram = use_lm_trigram
if use_lm_bigram or use_lm_trigram:
self.my_lm = lm_object.ARPALanguageModel(lm_dir + 'dev_pruned.lm')
# self.my_lm = lm_object.ARPALanguageModel(lm_dir + 'dev.lm')
# self.my_lm = lm_object.ARPALanguageModel(lm_dir + 'dev_eva.lm')
self.lm_w = lm_weight
self.embedding = nn.Embedding(vocab_size,
embedding_dim,
padding_idx=word2index['<eos>'])
self.lstm1 = nn.LSTMCell(input_size=embedding_dim + value_size,
hidden_size=decoder_hidden_size_1)
self.lstm2 = nn.LSTMCell(input_size=decoder_hidden_size_1,
hidden_size=decoder_hidden_size_2)
# self.decoder_hidden_size_1 = decoder_hidden_size_1
# self.decoder_hidden_size_2 = decoder_hidden_size_2
self.query_size = query_size
assert query_size == value_size and query_size == key_size, "ERROR: decoder, query_size!=key_size or query_size!=value_size"
self.query_network = nn.Linear(decoder_hidden_size_2, query_size)
# print("hidden size", hidden_dim)
self.teacher_forcing_ratio = teacher_forcing_ratio
self.gumbel_noise_weight = 1.0
# https://casmls.github.io/general/2017/02/01/GumbelSoftmax.html
self.vocab_size = vocab_size
self.word2index = word2index
# print("word2index", word2index)
self.index2word = index2word
self.vocab = vocab
self.DEVICE = device
self.isAttended = isAttended
if isAttended:
self.attention = Attention()
self.character_prob = nn.Linear(decoder_hidden_size_2 + query_size,
vocab_size)
