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 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 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 test_lookup_composer(self):
enc = self.segmenting_encoder
word_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
word_vocab.freeze()
enc.segment_composer = LookupComposer(word_vocab=word_vocab,
src_vocab=self.src_reader.vocab,
hidden_dim=self.layer_dim)
enc.transduce(self.inp_emb(0))
def test_add_multiple_segment_composer(self):
enc = self.segmenting_encoder
word_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
word_vocab.freeze()
enc.segment_composer = SumMultipleComposer(composers=[
LookupComposer(word_vocab=word_vocab,
src_vocab=self.src_reader.vocab,
hidden_dim=self.layer_dim),
CharNGramComposer(word_vocab=word_vocab,
src_vocab=self.src_reader.vocab,
hidden_dim=self.layer_dim)
])
enc.transduce(self.inp_emb(0))
def setUp(self):
xnmt.events.clear()
self.hyp = ["the taro met the hanako".split()]
self.ref = ["taro met hanako".split()]
vocab = Vocab()
self.hyp_id = list(map(vocab.convert, self.hyp[0]))
self.ref_id = list(map(vocab.convert, self.ref[0]))
def read_sents(self, filename, filter_ids=None):
"""
:param filename: data file
:param filter_ids: only read sentences with these ids (0-indexed)
:returns: iterator over sentences from filename
"""
if self.vocab is None:
self.vocab = Vocab()
return self.iterate_filtered(filename, filter_ids)
def read_sents(self, filename: str, filter_ids: Sequence[int] = None) -> Iterator[xnmt.input.Input]:
"""
Read sentences and return an iterator.
Args:
filename: data file
filter_ids: only read sentences with these ids (0-indexed)
Returns: iterator over sentences from filename
"""
if self.vocab is None:
self.vocab = Vocab()
return self.iterate_filtered(filename, filter_ids)
class PlainTextReader(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 = '!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 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 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)
seed = 13
random.seed(seed)
np.random.seed(seed)
EXP_DIR = os.path.dirname(__file__)
EXP = "programmatic"
model_file = f"{EXP_DIR}/models/{EXP}.mod"
log_file = f"{EXP_DIR}/logs/{EXP}.log"
xnmt.tee.set_out_file(log_file)
ParamManager.init_param_col()
ParamManager.param_col.model_file = model_file
src_vocab = Vocab(vocab_file="examples/data/head.ja.vocab")
trg_vocab = Vocab(vocab_file="examples/data/head.en.vocab")
batcher = SrcBatcher(batch_size=64)
inference = SimpleInference(batcher=batcher)
layer_dim = 512
model = DefaultTranslator(
src_reader=PlainTextReader(vocab=src_vocab),
trg_reader=PlainTextReader(vocab=trg_vocab),
src_embedder=SimpleWordEmbedder(emb_dim=layer_dim,
vocab_size=len(src_vocab)),
encoder=BiLSTMSeqTransducer(input_dim=layer_dim,
hidden_dim=layer_dim,
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]
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)