class PlainTextReader(BaseTextReader, Serializable):
"""
Handles the typical case of reading plain text files,
with one sent per line.
"""
yaml_tag = u'!PlainTextReader'
def __init__(self, vocab=None, include_vocab_reference=False):
self.vocab = vocab
self.include_vocab_reference = include_vocab_reference
if vocab is not None:
self.vocab.freeze()
self.vocab.set_unk(Vocab.UNK_STR)
def read_sents(self, filename, filter_ids=None):
if self.vocab is None:
self.vocab = Vocab()
vocab_reference = self.vocab if self.include_vocab_reference else None
return six.moves.map(lambda l: SimpleSentenceInput([self.vocab.convert(word) for word in l.strip().split()] + \
[self.vocab.convert(Vocab.ES_STR)], vocab_reference),
self.iterate_filtered(filename, filter_ids))
def freeze(self):
self.vocab.freeze()
self.vocab.set_unk(Vocab.UNK_STR)
self.overwrite_serialize_param("vocab", self.vocab)
def count_words(self, trg_words):
trg_cnt = 0
for x in trg_words:
if type(x) == int:
trg_cnt += 1 if x != Vocab.ES else 0
else:
trg_cnt += sum([1 if y != Vocab.ES else 0 for y in x])
return trg_cnt
def vocab_size(self):
return len(self.vocab)
class SegmentationTextReader(PlainTextReader):
yaml_tag = '!SegmentationTextReader'
# TODO: document me
@serializable_init
def __init__(self, vocab=None, include_vocab_reference=False):
super().__init__(vocab=vocab,
include_vocab_reference=include_vocab_reference)
def read_sents(self, filename, filter_ids=None):
if self.vocab is None:
self.vocab = Vocab()
def convert(line, segmentation):
line = line.strip().split()
ret = AnnotatedSentenceInput(
list(map(self.vocab.convert, line)) +
[self.vocab.convert(Vocab.ES_STR)])
ret.annotate("segment", list(map(int,
segmentation.strip().split())))
return ret
if type(filename) != list:
try:
filename = ast.literal_eval(filename)
except:
logger.debug("Reading %s with a PlainTextReader instead..." %
filename)
return super(SegmentationTextReader, self).read_sents(filename)
max_id = None
if filter_ids is not None:
max_id = max(filter_ids)
filter_ids = set(filter_ids)
data = []
with open(filename[0], encoding='utf-8') as char_inp,\
open(filename[1], encoding='utf-8') as seg_inp:
for sent_count, (char_line,
seg_line) in enumerate(zip(char_inp, seg_inp)):
if filter_ids is None or sent_count in filter_ids:
data.append(convert(char_line, seg_line))
if max_id is not None and sent_count > max_id:
break
return data
def count_sents(self, filename):
return super(SegmentationTextReader, self).count_sents(filename[0])
class TreeTextReader(BaseTextReader, Serializable):
"""
Handles the typical case of reading plain text files,
with one sent per line.
Args:
vocab (Vocab): turns tokens strings into token IDs
include_vocab_reference (bool): TODO document me
"""
yaml_tag = '!TreeTextReader'
def __init__(self, vocab=None, include_vocab_reference=False):
self.vocab = vocab
self.include_vocab_reference = include_vocab_reference
if vocab is not None:
self.vocab.freeze()
self.vocab.set_unk(Vocab.UNK_STR)
def read_sents(self, filename, filter_ids=None):
def convert_binary_bracketing(parse, lowercase=False):
transitions = []
tokens = []
for word in parse.split(' '):
if word[0] != "(":
if word == ")":
transitions.append(1)
else:
# Downcase all words to match GloVe.
if lowercase:
tokens.append(word.lower())
else:
tokens.append(word)
transitions.append(0)
type="rb"
if type=="gt":
transitions=transitions
elif type=="lb":
transitions=lb_build(len(tokens))
elif type=="rb":
transitions=rb_build(len(tokens))
elif type=="bal":
transitions=balanced_transitions(len(tokens))
else:
print("Invalid")
return tokens, transitions
def get_tokens(parse):
return convert_binary_bracketing(parse)[0]
def get_transitions(parse):
return convert_binary_bracketing(parse)[1]
def lb_build(N):
if N==2:
return [0,0,1]
else:
return lb_build(N-1)+[0,1]
def rb_build(N):
return [0]*N+[1]*(N-1)
def balanced_transitions(N):
"""
Recursively creates a balanced binary tree with N
leaves using shift reduce transitions.
"""
if N == 3:
return [0, 0, 1, 0, 1]
elif N == 2:
return [0, 0, 1]
elif N == 1:
return [0]
else:
right_N = N // 2
left_N = N - right_N
return balanced_transitions(left_N) + balanced_transitions(right_N) + [1]
if self.vocab is None:
self.vocab = Vocab()
vocab_reference = self.vocab if self.include_vocab_reference else None
#import pdb;pdb.set_trace()
r1=map(lambda l: SimpleSentenceInput([self.vocab.convert(word) for word in get_tokens(l.strip())] + \
[self.vocab.convert(Vocab.ES_STR)], vocab_reference),
self.iterate_filtered(filename, filter_ids))
r2=map(lambda l: get_transitions(l.strip()),
self.iterate_filtered(filename, filter_ids))
return [r1,r2]
def count_words(self, trg_words):
trg_cnt = 0
for x in trg_words:
if type(x) == int:
trg_cnt += 1 if x != Vocab.ES else 0
else:
trg_cnt += sum([1 if y != Vocab.ES else 0 for y in x])
return trg_cnt
def vocab_size(self):
return len(self.vocab)
