def tag_test(test_feature_file, trained_model_file, tag_test_set_file):
fr = codecs.open(test_feature_file, 'r', 'utf-8')
fw = codecs.open(tag_test_set_file, 'w', 'utf-8')
m = MaxentModel()
m.load(trained_model_file)
contents = fr.read()
feature_list = contents.split('\r')
feature_list.remove('\n')
for feature in feature_list:
if (feature == 'split'):
fw.write('\n\n\n')
continue
str_feature = []
u_feature = feature.split(' ')
for item in u_feature:
str_feature.append(item.encode('utf-8'))
label_prob_list = m.eval_all(str_feature)
label = max_prob(label_prob_list)
try:
new_tag = str_feature[2].split('=')[1] + '/' + label
except IndexError:
print str_feature
fw.write(new_tag.decode('utf-8'))
pre_tag = label
return feature_list
def tag_test(test_feature_file, trained_model_file, tag_test_set_file):
fin = codecs.open(test_feature_file, 'r', 'utf-8')
fout = codecs.open(tag_test_set_file, 'w', 'utf-8')
m = MaxentModel()
m.load(trained_model_file)
contents = fin.read()
feature_list = contents.split('\r')
feature_list.remove('\n')
for feature in feature_list:
if (feature == 'split'):
fout.write('\n\n\n')
continue
str_feature = []
u_feature = feature.split(' ')
for item in u_feature:
str_feature.append(item.encode('utf-8'))
label_prob_list = m.eval_all(str_feature)
label = max_prob(label_prob_list)
try:
new_tag = str_feature[2].split('=')[1] + '/' + label
except IndexError:
print
str_feature
fout.write(new_tag.decode('utf-8'))
pre_tag = label
return feature_list
def test():
maxent.set_verbose(1)
m = MaxentModel()
m.begin_add_event()
m.add_event(['1'], '1')
m.add_event(['2'], '2')
m.add_event(['3'], '3')
m.end_add_event()
m.train(30, 'lbfgs', 2, 1e-03)
for x in map(str, range(1,4)):
print "tested on:", x, "predicted:", m.eval_all([x])
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
# Imports
import sys, os
# Load MaxEnt models
corpusPath = os.environ.get('CORPUS_PATH')
from maxent import MaxentModel
maxEntModel = MaxentModel()
maxEntModel.load(corpusPath+'/model_markers.txt')
for trainLine in sys.stdin.readlines():
trainCols = trainLine.split('\t')
modelMarkerProbas = maxEntModel.eval_all(trainCols[1:])
probaFeats = []
for modelMarkerProba in modelMarkerProbas:
if modelMarkerProba[1] > 0.00001:
probaFeats.append(modelMarkerProba[0] + ':' + str(modelMarkerProba[1]))
print trainCols[0] + '\t' + '\t'.join(probaFeats)
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
# Imports
import sys, os
# Load MaxEnt models
corpusPath = os.environ.get('CORPUS_PATH')
from maxent import MaxentModel
maxEntModel = MaxentModel()
maxEntModel.load(corpusPath + '/model_markers.txt')
for trainLine in sys.stdin.readlines():
trainCols = trainLine.split('\t')
modelMarkerProbas = maxEntModel.eval_all(trainCols[1:])
probaFeats = []
for modelMarkerProba in modelMarkerProbas:
if modelMarkerProba[1] > 0.00001:
probaFeats.append(modelMarkerProba[0] + ':' +
str(modelMarkerProba[1]))
print trainCols[0] + '\t' + '\t'.join(probaFeats)
class MMEMAlgorithm(object):
#реализация алгоритма на основе HMM
def __init__(self,compute_features, N_filter_func = N_default):
self.filter_func = N_filter_func
self.me = MaxentModel()
self.num_train_iters = 2000
self.compute_features = compute_features
def load_memm_model(self, filename):
self.me.load( filename )
def init(self):
pass
def train_model_file_list(self, corpus_filelist, ambiguity_dir ):
self.me.begin_add_event()
for corpus_file in corpus_filelist:
print "Training on file {0}".format( corpus_file )
sentence = []
morph_analys_file = os.path.join( ambiguity_dir, os.path.basename( corpus_file ) )
morph_analys_tokens = get_tokens_from_file(morph_analys_file, N_filter_func = self.filter_func ) if os.path.exists( morph_analys_file ) else None
if morph_analys_tokens:
print "Using mystem features on file {0}".format( morph_analys_file )
gold_tokens = get_tokens_from_file(corpus_file, N_filter_func = self.filter_func )
for corpus_token in gold_tokens:
morph_analys_token = morph_analys_tokens.next() if morph_analys_tokens else None
gold_token_word = corpus_token[0].word
morph_analys_token_word = morph_analys_token[0].word if morph_analys_token else None
if morph_analys_token_word:
if gold_token_word != morph_analys_token_word:
'''
if ('-' in gold_token_word and '-' not in morph_analys_token_word) or ('\'' in gold_token_word and '\'' not in morph_analys_token_word):
morph_analys_token = morph_analys_tokens.next()
if ('.' in gold_token_word):
cnt_dots = '.'.count( gold_token_word )
for i in xrange( 0, cnt_dots ):
morph_analys_token = morph_analys_tokens.next()
'''
print >>sys.stderr, u"Start skipping sentence. Gold token wordform {0} morph token wordform {1}".format( gold_token_word, morph_analys_token_word )
sentence = []
try:
next_gold = gold_tokens.next()
while( next_gold != [EOS_TOKEN] ):
next_gold = gold_tokens.next()
next_gold = gold_tokens.next()
next_morph = morph_analys_tokens.next()
while( next_morph[0].word != next_gold[0].word ):
next_morph = morph_analys_tokens.next()
except StopIteration:
break
if corpus_token[0] == EOS_TOKEN and len(sentence) > 0:
words = [token[0].word for token in sentence]
labels = [token[0].gram for token in sentence]
for i,token_info in enumerate( sentence ):
gold_token = token_info[0]
morph_analysises = [token.gram for token in token_info[1]] if token_info[1] and morph_analys_token else None
if token_info[1] is not None:
if gold_token.word != token_info[1][0].word:
print >>sys.stderr, u"Cannot match gold token and morph analysis token\n gold token : {0} morph analysis token : {1}".format( gold_token.word, token_info[1][0].word )
morph_analysises = None
word_features = list( self.compute_features( sentence = words, i = i , prev_label= labels[ i - 1 ] if i >0 else None, analysises = morph_analysises, labels = labels) )
gold_token_gram = gold_token.gram.encode('utf-8')
self.me.add_event(word_features, gold_token_gram )
sentence = []
else:
sentence.append( (corpus_token[0], morph_analys_token) )
self.me.end_add_event()
maxent.set_verbose(1)
self.me.train( self.num_train_iters, 'lbfgs', 0.0 )
maxent.set_verbose(0)
def train_model(self, corpus_dir, ambiguity_dir ):
self.me.begin_add_event()
#self.B = train_B_corpus(corpus_dir = corpus_dir,N_filter_func = N_filter_func)
sentence = []
corpus_files = get_corpus_files(corpus_dir)
for corpus_file in corpus_files:
morph_analys_file = os.path.join( ambiguity_dir, os.path.basename( corpus_file ) )
morph_analys_tokens = get_tokens_from_file(morph_analys_file, N_filter_func = self.filter_func )
for corpus_token in get_tokens_from_file(corpus_file, N_filter_func = self.filter_func ):
morph_analys_token = morph_analys_tokens.next()
if corpus_token[0] == EOS_TOKEN:
words = [token[0].word for token in sentence]
labels = [token[0].gram for token in sentence]
for i,token_info in enumerate( sentence ):
gold_token = token_info[0]
morph_analysises = [token.gram for token in token_info[1]]
if gold_token.word != token_info[1][0].word:
print >>sys.stderr, u"Cannot match gold token and morph analysis token\n gold token : {0} morph analysis token : {1}".format( gold_token.word, token_info[1][0].word )
morph_analysises = None
word_features = list( self.compute_features( sentence = words, i = i , prev_label= labels[ i - 1 ] if i >0 else None, analysises = morph_analysises, labels = labels) )
gold_token_gram = gold_token.gram.encode('utf-8')
self.me.add_event(word_features, gold_token_gram )
sentence = []
else:
sentence.append( (corpus_token[0], morph_analys_token) )
self.me.end_add_event()
maxent.set_verbose(1)
self.me.train( 50, 'lbfgs', 0.0 )
maxent.set_verbose(0)
def load_model(self, memm_filename):
self.me.load( memm_filename )
def save_model(self, memm_filename):
self.me.save( memm_filename )
#dump_object( B_stat_filename, self.B )
def remove_ambiguity_file(self, file, outfile):
out_f = codecs.open( outfile, 'w', 'utf-8' )
sentence = []
for token in get_tokens_from_file(file, N_filter_func= self.filter_func):
if len(token) == 1 and token[0] == EOS_TOKEN:
if len(sentence)>0:
no_ambig_tokens = self.remove_ambiguity( sentence )
for no_ambig_token in no_ambig_tokens:
out_f.write( u"{0}\t{1}={2}\r\n".format(no_ambig_token[0], 'nolemma', no_ambig_token[1] ) )
out_f.write('\r\n')
sentence = []
continue
else:
sentence = []
continue
sentence.append( (token[0].word, token) )
out_f.close()
def remove_ambiguity_dir(self, dir):
pass
def remove_ambiguity(self, variants):
"""
Структура variants = [ (word_form, [tokens ]), (...) , ( ) ]
"""
words = [variant[0] for variant in variants]
analysises = [[token.gram for token in variant[1]] for variant in variants ]
viterbi_layers = [ None for i in xrange(len(words)) ]
viterbi_backpointers = [ None for i in xrange(len(words) + 1) ]
# Compute first layer directly.
viterbi_layers[0] = self.me.eval_all(list(self.compute_features(sentence=words, i = 0 , prev_label= None, analysises = analysises[0], labels = None ) ) )
filtered_viterbi_layer = dict( (k, v) for k, v in viterbi_layers[0] if k in analysises[0] )
viterbi_layer_0_prob = sum( [v for v in filtered_viterbi_layer.values() ] )
viterbi_layers[0] = dict( (k, math.log(v/viterbi_layer_0_prob) ) for k, v in filtered_viterbi_layer.items() )
viterbi_backpointers[0] = dict( (k, None) for k, v in viterbi_layers[0].iteritems() )
# Compute intermediate layers.
for i in xrange(1, len(words)):
viterbi_layers[i] = defaultdict(lambda: float("-inf"))
viterbi_backpointers[i] = defaultdict(lambda: None)
for prev_label, prev_logprob in viterbi_layers[i - 1].iteritems():
features = self.compute_features(sentence=words,i= i, prev_label= prev_label, analysises = analysises[i], labels = None)
features = list(features)
distribution = self.me.eval_all(features)
distribution = dict( (label, prob) for label, prob in distribution if label in analysises[i])
distribution_sum = sum( [v for v in distribution.values() ] )
distribution = dict( (k, v/ distribution_sum) for k, v in distribution.items() )
for label, prob in distribution.items():
logprob = math.log(prob)
if prev_logprob + logprob > viterbi_layers[i][label]:
viterbi_layers[i][label] = prev_logprob + logprob
viterbi_backpointers[i][label] = prev_label
# Most probable endpoint.
max_logprob = float("-inf")
max_label = None
for label, logprob in viterbi_layers[len(words) - 1].iteritems():
if logprob > max_logprob:
max_logprob = logprob
max_label = label
# Most probable sequence.
path = []
label = max_label
for i in reversed(xrange(len(words))):
path.insert(0, label)
try:
label = viterbi_backpointers[i][label]
except KeyError:
pass
return zip(words,path)
