def __init__(self,
source_with_start=False, source_with_end = False, source_with_unk = False,
target_with_start=False, target_with_end=False, target_with_unk=False,
same_length = False
):
self.source_with_start = source_with_start
self.source_with_end = source_with_end
self.source_with_unk = source_with_unk
self.target_with_start = target_with_start
self.target_with_end = target_with_end
self.target_with_unk = target_with_unk
self.source_corpus = SequenceCorpus(source_with_start, source_with_end, source_with_unk)
self.target_corpus = SequenceCorpus(target_with_start, target_with_end, target_with_unk)
self.same_length = same_length
self.corpus = []
def __init__(self,
source_with_start=False,
source_with_end=False,
source_with_unk=False,
target_with_start=False,
target_with_end=False,
target_with_unk=False,
same_length=False):
self.source_with_start = source_with_start
self.source_with_end = source_with_end
self.source_with_unk = source_with_unk
self.target_with_start = target_with_start
self.target_with_end = target_with_end
self.target_with_unk = target_with_unk
self.source_corpus = SequenceCorpus(source_with_start, source_with_end,
source_with_unk)
self.target_corpus = SequenceCorpus(target_with_start, target_with_end,
target_with_unk)
self.same_length = same_length
self.corpus = []
from neural_machine.tasks.language.common.corpus.segmentor import *
from neural_machine.tasks.language.common.corpus.sequence_corpus import SequenceCorpus
from neural_machine.tasks.language.common.data_reader.bucket_iter import *
import sys
import logging
if __name__ == '__main__':
head = '%(asctime)-15s %(message)s'
logging.basicConfig(level=logging.DEBUG, format=head)
segmenter = SpaceSegmenter()
corpus = SequenceCorpus()
corpus.build(open(sys.argv[1], 'r'), segmenter)
cell_num = corpus.cell_num()
problem = LanguageModelProblem(corpus)
batch_size = 32
data_train = BucketIter(problem, batch_size)
val_corpus = corpus.make(open(sys.argv[2], 'r'), segmenter)
val_problem = LanguageModelProblem(val_corpus)
data_val = BucketIter(val_problem, batch_size)
arch_param = LanguageModelArchParam(num_hidden=200,
from neural_machine.tasks.language.common.corpus.segmentor import *
from neural_machine.tasks.language.common.corpus.sequence_corpus import SequenceCorpus
from neural_machine.tasks.language.common.data_reader.bucket_iter import *
import sys
import logging
if __name__ == '__main__':
head = '%(asctime)-15s %(message)s'
logging.basicConfig(level=logging.DEBUG, format=head)
segmenter = SpaceSegmenter()
corpus = SequenceCorpus()
corpus.build(open(sys.argv[1], 'r'), segmenter)
cell_num = corpus.cell_num()
problem = LanguageModelProblem(corpus)
batch_size = 32
data_train = BucketIter(problem, batch_size)
val_corpus = corpus.make(open(sys.argv[2], 'r'), segmenter)
val_problem = LanguageModelProblem(val_corpus)
data_val = BucketIter(val_problem, batch_size)
class SequencePairCorpus(object):
def __init__(self,
source_with_start=False, source_with_end = False, source_with_unk = False,
target_with_start=False, target_with_end=False, target_with_unk=False,
same_length = False
):
self.source_with_start = source_with_start
self.source_with_end = source_with_end
self.source_with_unk = source_with_unk
self.target_with_start = target_with_start
self.target_with_end = target_with_end
self.target_with_unk = target_with_unk
self.source_corpus = SequenceCorpus(source_with_start, source_with_end, source_with_unk)
self.target_corpus = SequenceCorpus(target_with_start, target_with_end, target_with_unk)
self.same_length = same_length
self.corpus = []
def build(self, data_file, source_segmenter, target_segmenter):
for line in data_file:
line = line.strip()
if not line:
continue
try:
src_seq, tgt_seq = line.split('\t')
except:
logging.error("no sequence pair found in sentence : {0} ".format(json.dumps(line)))
continue
if self.same_length and len(src_seq) != len(tgt_seq):
logging.error("src and tgt seq not in same length {0} {1} {2}".format(len(src_seq), len(tgt_seq), json.dumps(line)))
continue
src=self.source_corpus.update(src_seq, source_segmenter)
target=self.target_corpus.update(tgt_seq, target_segmenter)
self.corpus.append((src, target))
def make(self, data_file, source_segmenter, target_segmenter):
corpus = SequencePairCorpus(
self.source_with_start, self.source_with_end , self.source_with_unk ,
self.target_with_start, self.target_with_end, self.target_with_unk,
self.same_length
)
corpus.source_corpus = self.source_corpus.clone()
corpus.target_corpus = self.target_corpus.clone()
for line in data_file:
line = line.strip()
if not line:
continue
try:
src_seq, tgt_seq = line.split('\t')
except:
logging.error("no sequence pair found in sentence : {0} ".format(json.dumps(line)))
continue
if self.same_length and len(src_seq) != len(tgt_seq):
logging.error("src and tgt seq not in same length {0} {1} {2}".format(len(src_seq), len(tgt_seq), json.dumps(line)))
continue
src = self.source_corpus.predict(src_seq, source_segmenter)
target = self.target_corpus.predict(tgt_seq, target_segmenter)
corpus.corpus.append((src, target))
return corpus
def source_cell_num(self):
return self.source_corpus.cell_num()
def target_cell_num(self):
return self.target_corpus.cell_num()
def corpus_size(self):
return len(self.corpus)
class SequencePairCorpus(object):
def __init__(self,
source_with_start=False,
source_with_end=False,
source_with_unk=False,
target_with_start=False,
target_with_end=False,
target_with_unk=False,
same_length=False):
self.source_with_start = source_with_start
self.source_with_end = source_with_end
self.source_with_unk = source_with_unk
self.target_with_start = target_with_start
self.target_with_end = target_with_end
self.target_with_unk = target_with_unk
self.source_corpus = SequenceCorpus(source_with_start, source_with_end,
source_with_unk)
self.target_corpus = SequenceCorpus(target_with_start, target_with_end,
target_with_unk)
self.same_length = same_length
self.corpus = []
def build(self, data_file, source_segmenter, target_segmenter):
for line in data_file:
line = line.strip()
if not line:
continue
try:
src_seq, tgt_seq = line.split('\t')
except:
logging.error(
"no sequence pair found in sentence : {0} ".format(
json.dumps(line)))
continue
if self.same_length and len(src_seq) != len(tgt_seq):
logging.error(
"src and tgt seq not in same length {0} {1} {2}".format(
len(src_seq), len(tgt_seq), json.dumps(line)))
continue
src = self.source_corpus.update(src_seq, source_segmenter)
target = self.target_corpus.update(tgt_seq, target_segmenter)
self.corpus.append((src, target))
def make(self, data_file, source_segmenter, target_segmenter):
corpus = SequencePairCorpus(self.source_with_start,
self.source_with_end, self.source_with_unk,
self.target_with_start,
self.target_with_end, self.target_with_unk,
self.same_length)
corpus.source_corpus = self.source_corpus.clone()
corpus.target_corpus = self.target_corpus.clone()
for line in data_file:
line = line.strip()
if not line:
continue
try:
src_seq, tgt_seq = line.split('\t')
except:
logging.error(
"no sequence pair found in sentence : {0} ".format(
json.dumps(line)))
continue
if self.same_length and len(src_seq) != len(tgt_seq):
logging.error(
"src and tgt seq not in same length {0} {1} {2}".format(
len(src_seq), len(tgt_seq), json.dumps(line)))
continue
src = self.source_corpus.predict(src_seq, source_segmenter)
target = self.target_corpus.predict(tgt_seq, target_segmenter)
corpus.corpus.append((src, target))
return corpus
def source_cell_num(self):
return self.source_corpus.cell_num()
def target_cell_num(self):
return self.target_corpus.cell_num()
def corpus_size(self):
return len(self.corpus)