def create_lm(path_to_data, grades, f_type):
dataset = prepare_dataset(path_to_data, grades)
client = MongoClient('mongodb://localhost:27017/')
for grade in grades:
print grade + " grade"
start_total = time.time()
client.drop_database(f_type + '_' + grade)
db = client[f_type + '_' + grade]
for n in xrange(1, 3):
print str(n) + " gram"
start = time.time()
fd_dict = dict()
# TODO separate dataset on grades
for text in dataset:
if text.grade == grade:
fd_dict[text.name] = FreqDist()
fd_dict['all'] = FreqDist()
for text in dataset:
if text.grade == grade:
tokens = nltk.word_tokenize(text.data)
tokens_l = [token.lower() for token in tokens]
for key in fd_dict:
if key != text.name:
n_grams = ngrams(tokens_l, n)
fd_dict[key].update(n_grams)
for key in fd_dict:
lm_collection = db[key]
fd = fd_dict[key]
sgt = SimpleGoodTuringProbDist(fd)
prob_many = list()
for fd_key in fd:
prob_many.append({"type": fd_key, "n-gram": n, "count": fd[fd_key], "prob": sgt.prob(fd_key)})
if prob_many:
lm_collection.insert_many(prob_many)
print str(time.time() - start) + " sec"
print str(time.time() - start_total) + " sec total"
def _estimator(fdist, bins):
"""
Default estimator function using a SimpleGoodTuringProbDist.
"""
# can't be an instance method of NgramModel as they
# can't be pickled either.
return SimpleGoodTuringProbDist(fdist)
def _setTrigram(self, listInput):
trigramList = ngrams(listInput, 3)
trigramFreq = self.countProbability(trigramList)
outerTrigram = {}
size = len(listInput)
sgtTri = SimpleGoodTuringProbDist(trigramFreq)
for trigram in trigramFreq:
b = trigram[0:2]
if b in outerTrigram:
innerTrigram = outerTrigram[b]
innerTrigram[b] = (sgtTri.prob(trigram) / size)
else:
innerTrigram = {}
#print(trigram[2])
innerTrigram[trigram[2]] = (sgtTri.prob(trigram) / size)
outerTrigram[b] = innerTrigram
return outerTrigram
def _setTrigram(self, listInput):
trigramList = ngrams(listInput,3)
trigramFreq = self.countProbability(trigramList)
outerTrigram = {}
size = len(listInput)
sgtTri = SimpleGoodTuringProbDist(trigramFreq)
for trigram in trigramFreq:
b = trigram[0:2]
if b in outerTrigram:
innerTrigram = outerTrigram[b]
innerTrigram[b] = (sgtTri.prob(trigram)/size)
else:
innerTrigram = {}
#print(trigram[2])
innerTrigram[trigram[2]] = (sgtTri.prob(trigram)/size)
outerTrigram[b] = innerTrigram
return outerTrigram
def __init__(self, tokens, frequencies, debug=DEBUG, stop=STOP):
self.freqdist = frequencies
self.tokens = tokens | set(self.freqdist)
self.DEBUG = debug
self.STOP = stop
if len(self.freqdist) == 0:
raise ValueError("No frequencies given!")
while not self.freqdist.hapaxes():
warnings.warn("no hapaxes present -- shifting distribution down")
min_freq = min(self.freqdist.values()) - 1
for k in self.freqdist:
self.freqdist[k] -= min_freq
if min(self.freqdist.values()) != 0:
warnings.warn("no unseen present -- adding dummy category")
self.tokens.add("<dummy>")
self.sgt = SimpleGoodTuringProbDist(self.freqdist,
bins=len(self.tokens))
self._get_probabilities()
def train(train_set, word_types, tag_set):
"""
Training...
Called this way, the HMM knows the whole set of tags and the whole set of words (no "unknown" word and/or tag during test)
"""
trainer = HiddenMarkovModelTrainer(list(tag_set), list(
word_types)) # tag_set and word_types are sets: I need to create lists
# GoodTuring smoothing
# see: https://nltk.googlecode.com/svn/trunk/doc/api/nltk.probability.SimpleGoodTuringProbDist-class.html
# http://en.wikipedia.org/wiki/Additive_smoothing
hmm = trainer.train_supervised(
train_set,
estimator=lambda fd, bins: SimpleGoodTuringProbDist(fd, bins))
return hmm
def _setBigram(self, listInput):
bigramList = ngrams(listInput, 2)
bigramFreq = self.countProbability(bigramList)
outerBigram = {}
size = len(listInput)
sgtBig = SimpleGoodTuringProbDist(bigramFreq)
for bigram in bigramFreq:
b = bigram[0]
#print(b)
if b in outerBigram:
innerBigram1 = outerBigram[b]
innerBigram1[b] = (sgtBig.prob(bigram) / size)
# print(bigramFreq[bigram]/size)
else:
innerBigram = {}
innerBigram[bigram[1]] = (sgtBig.prob(bigram) / size)
outerBigram[b] = innerBigram
#print(bigramFreq[bigram]/size)
return outerBigram
def _setBigram(self, listInput):
bigramList = ngrams(listInput, 2)
bigramFreq = self.countProbability(bigramList)
outerBigram = {}
size = len(listInput)
sgtBig = SimpleGoodTuringProbDist(bigramFreq)
for bigram in bigramFreq:
b = bigram[0]
#print(b)
if b in outerBigram :
innerBigram1 = outerBigram[b]
innerBigram1[b] = (sgtBig.prob(bigram)/size)
# print(bigramFreq[bigram]/size)
else:
innerBigram = {}
innerBigram[bigram[1]] = (sgtBig.prob(bigram)/size)
outerBigram[b] = innerBigram
#print(bigramFreq[bigram]/size)
return outerBigram
def goodturing_estimator(freqdist):
return SimpleGoodTuringProbDist(freqdist)
parser.add_argument("--word-type")
parser.add_argument("-n", type=int) #n-grams
group = parser.add_mutually_exclusive_group()
group.add_argument("--laplace", action="store_true")
group.add_argument("--good-turing", action="store_true")
parser.add_argument("--unknown-word-freq", type=int)
parser.add_argument("-o", required=True)
parsed = parser.parse_args()
estimator = None
if parsed.laplace:
estimator = lambda fdist, bins: LaplaceProbDist(fdist)
elif parsed.good_turing:
estimator = lambda fdist, bins: SimpleGoodTuringProbDist(fdist, bins=1e5)
words = []
directory = parsed.src_texts
n = parsed.n
output = parsed.o
for filename in os.listdir(directory):
with open (directory+"/"+filename, "r") as file:
inp = file.read()
if parsed.text_encoding:
inp = inp.decode(parsed.text_encoding)
if filename != ".DS_Store":
if parsed.word_type == "stem":
stemmer = Stemmer.Stemmer('russian')
from nltk.book import text1
from collections import Counter
from nltk.probability import SimpleGoodTuringProbDist
from openpyxl import Workbook
#Create your bigrams
bgs = nltk.bigrams(text1)
#compute frequency distribution for bigrams
fdistB = nltk.FreqDist(bgs)
#compute frequency distribution for unigrams
fdistU = nltk.FreqDist(text1)
#apply simple good turing smoothing method
sgt = SimpleGoodTuringProbDist(fdistB)
#most common 25 unigrams
mostCommon = fdistU.most_common(25)
#initialize dataframe for the excel sheet
column = []
for k, v in mostCommon:
column.append(k)
#data frame
df = pd.DataFrame(index=column, columns=column)
#fill data frame with probability data
for k, v in mostCommon:
def _estimator(fdist, bins):
return SimpleGoodTuringProbDist(fdist)
from __future__ import division
from nltk.corpus.reader import ConllCorpusReader
from nltk.probability import FreqDist, DictionaryProbDist, LaplaceProbDist, SimpleGoodTuringProbDist, MLEProbDist
conllreader = ConllCorpusReader(".", "de-train.tt", ('words', 'pos')) # getting a train corpus from file
states = ('VERB', 'NOUN', 'PRON', 'ADJ', 'ADV', 'ADP', 'CONJ', 'DET', 'NUM', 'PRT', 'X', '.') # list of 12 POS tags
sentslen = len(conllreader.tagged_sents()) # getting number of sentences
tagfdist = FreqDist(pair[1] for pair in conllreader.tagged_words()) # getting frequence of (word,tag)
firsttagfdist = FreqDist(pair[0][1] for pair in conllreader.tagged_sents()) # getting frequence of first tags
A0j = DictionaryProbDist(dict(map(lambda (k, x): (k, x/sentslen), firsttagfdist.iteritems())))
A0jLap = LaplaceProbDist(firsttagfdist)
A0jGT = SimpleGoodTuringProbDist(firsttagfdist)
A0jMLE = MLEProbDist(firsttagfdist)
TagPair = []
words = conllreader.tagged_words()
for i in range(0, len(words)-1):
TagPair.append((words[i][1], words[i+1][1]))
TagPairfdist = FreqDist(TagPair)
Aij = DictionaryProbDist(dict(map(lambda (k, x): (k, x/tagfdist.get(k[0])), TagPairfdist.iteritems())))
AijLap = LaplaceProbDist(TagPairfdist)
AijGT = SimpleGoodTuringProbDist(TagPairfdist)
AijMLE = MLEProbDist(TagPairfdist)
TagWordfdist = FreqDist(conllreader.tagged_words())
Biw = DictionaryProbDist(dict(map(lambda (k, x): (k, x/tagfdist.get(k[1])), TagWordfdist.iteritems())))
BiwLap = LaplaceProbDist(TagWordfdist)
BiwGT = SimpleGoodTuringProbDist(TagWordfdist)
def simple_good_turing_estimator(fdist, bins):
return SimpleGoodTuringProbDist(fdist, bins=bins)
current_line = test_file[i]
word_tag = current_line.split('\t\t')
words.add(word_tag[0])
tags.add(word_tag[1])
current_sentence.append((word_tag[0], word_tag[1]))
if word_tag[0] == '.':
test.append(current_sentence)
current_sentence = []
tags = list(tags)
words = list(words)
trainer = hmm.HiddenMarkovModelTrainer(tags, words)
# tagger = trainer.train_supervised(train, estimator=lambda fd, bins: LidstoneProbDist(fd, 0.1, bins))
# tagger = trainer.train_supervised(train, estimator=lambda fd, bins: MLEProbDist(fd))
tagger = trainer.train_supervised(
train, estimator=lambda fd, bins: SimpleGoodTuringProbDist(fd, bins))
# tagger = trainer.train_supervised(train, estimator=lambda fd, bins: WittenBellProbDist(fd, bins))
# tagger = trainer.train_supervised(train, estimator=lambda fd, bins: KneserNeyProbDist(fd, bins))
print("here")
predicted = []
real = []
for i in range(0, len(test) - 1):
current = list(zip(*test[i]))
tagged = tagger.tag(list(current[0]))
current_tags = list(list(zip(*tagged))[1])
predicted += current_tags
real += list(current[1])
print(tags)
confusion = confusion_matrix(predicted, real, labels=tags)
class Simulator(object):
def __init__(self, tokens, frequencies, debug=DEBUG, stop=STOP):
self.freqdist = frequencies
self.tokens = tokens | set(self.freqdist)
self.DEBUG = debug
self.STOP = stop
if len(self.freqdist) == 0:
raise ValueError("No frequencies given!")
while not self.freqdist.hapaxes():
warnings.warn("no hapaxes present -- shifting distribution down")
min_freq = min(self.freqdist.values()) - 1
for k in self.freqdist:
self.freqdist[k] -= min_freq
if min(self.freqdist.values()) != 0:
warnings.warn("no unseen present -- adding dummy category")
self.tokens.add("<dummy>")
self.sgt = SimpleGoodTuringProbDist(self.freqdist,
bins=len(self.tokens))
self._get_probabilities()
def _get_probabilities(self):
self.probabilities = {}
for t in self.tokens:
self.probabilities[t] = self.sgt.prob(t)
return self.probabilities
def dump(self): # pragma: no cover
for t in self.tokens:
print("\t".join([
"%30s" % t,
'%d' % self.freqdist[t],
'%0.4f' % self.sgt.prob(t)
]))
print("HAPAXES: %r" % self.freqdist.hapaxes())
# freq of freqs:
print("FreqDist: %r" % sorted(self.freqdist.values(), reverse=True))
print("Slope: %r" % self.sgt._slope)
print("Switch at: %r" % self.sgt._switch_at)
def simulate(self, n=1000):
keys = [k for k in self.probabilities]
probs = [self.probabilities[k] for k in keys]
# set up
itercount = 0 # stopping safety valve
transcript = []
complete = False # have we seen everything?
seen = Counter({k: 0 for k in self.tokens}) # tokens we've seen
while not complete:
for char in np.random.choice(keys, n, replace=True, p=probs)[0:n]:
transcript.append(char)
seen[char] += 1
complete = all(v > 0 for v in seen.values())
# if self.DEBUG:
# print(itercount, len(transcript))
#
if complete:
return len(transcript)
if itercount >= self.STOP:
raise StopIteration("Abort Abort!")
itercount += 1
raise StopIteration("Failed")
