def load_set(self, fname, cache_dir=None):
# TODO: Make the cache-handling generic,
# and offer a way to actually pass cache_dir
save_cache = False
if cache_dir:
import os.path
fname_abs = os.path.abspath(fname)
from hashlib import md5
cache_filename = "%s/%s.p" % (
cache_dir, md5(fname_abs.encode("utf-8")).hexdigest())
try:
with open(cache_filename, "rb") as f:
return pickle.load(f)
except (IOError, TypeError, KeyError):
save_cache = True
s0, s1, y = loader.load_hypev(fname)
if self.vocab is None:
vocab = Vocabulary(s0 + s1) # FIXME: lower?
else:
vocab = self.vocab
si0 = vocab.vectorize(s0, spad=self.s0pad)
si1 = vocab.vectorize(s1, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
gr, y = self.merge_questions(gr)
if save_cache:
with open(cache_filename, "wb") as f:
pickle.dump((s0, s1, y, vocab, gr), f)
print("save")
return (gr, y, vocab)
def load_set(fname, vocab):
s0, s1, y = loader.load_snli(fname, vocab)
si0 = vocab.vectorize(s0, spad=spad)
si1 = vocab.vectorize(s1, spad=spad)
f0, f1 = nlp.sentence_flags(s0, s1, spad, spad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return (si0, si1, y, gr)
def load_set(fname, vocab=None, s0pad=s0pad, s1pad=s1pad, cache_dir=None, skip_oneclass=True):
""" Caching: If cache_dir is set: it tries to load finished dataset from it
(filename of cache is hash of fname), and if that fails, it will compute
dataset and try to save it."""
save_cache = False
if cache_dir:
fname_abs = os.path.abspath(fname)
from hashlib import md5
cache_filename = "%s/%s.p" % (cache_dir, md5(fname_abs.encode("utf-8")).hexdigest())
try:
with open(cache_filename, "rb") as f:
return pickle.load(f)
except (IOError, TypeError, KeyError):
save_cache=True
s0, s1, y, t = loader.load_anssel(fname, skip_oneclass=skip_oneclass)
# TODO: Make use of the t-annotations
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0, spad=s0pad)
si1 = vocab.vectorize(s1, spad=s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
if save_cache:
with open(cache_filename, "wb") as f:
pickle.dump((s0, s1, y, vocab, gr), f)
print("save")
return (s0, s1, y, vocab, gr)
def load_set(self, fname, cache_dir=None):
# TODO: Make the cache-handling generic,
# and offer a way to actually pass cache_dir
save_cache = False
if cache_dir:
import os.path
fname_abs = os.path.abspath(fname)
from hashlib import md5
cache_filename = "%s/%s.p" % (cache_dir, md5(fname_abs.encode("utf-8")).hexdigest())
try:
with open(cache_filename, "rb") as f:
return pickle.load(f)
except (IOError, TypeError, KeyError):
save_cache = True
skip_oneclass = self.c.get('skip_oneclass', True)
s0, s1, y, kw, akw, t = loader.load_anssel(fname, skip_oneclass=skip_oneclass)
# TODO: Make use of the t-annotations
if self.vocab is None:
vocab = Vocabulary(s0 + s1)
else:
vocab = self.vocab
si0 = vocab.vectorize(s0, spad=self.s0pad)
si1 = vocab.vectorize(s1, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1, kw=kw, akw=akw)
if save_cache:
with open(cache_filename, "wb") as f:
pickle.dump((s0, s1, y, vocab, gr), f)
print("save")
return (gr, y, vocab)
def load_set(fname, vocab=None):
s0, s1, y, _, _, _ = loader.load_anssel(fname)
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0)
si1 = vocab.vectorize(s1)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1)
return (s0, s1, y, vocab, gr)
def load_set(fname, vocab=None):
s0, s1, y, t = loader.load_anssel(fname)
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0)
si1 = vocab.vectorize(s1)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1)
return (s0, s1, y, vocab, gr)
def load_set(fname, vocab=None):
s0, s1, y = loader.load_hypev(fname)
# s0=questions, s1=answers
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0, spad=s0pad)
si1 = vocab.vectorize(s1, spad=s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return s0, s1, y, vocab, gr
def load_sent(q, a, vocab=None):
s0, s1, y = [q], [a], 1
# s0=questions, s1=answers
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0, spad=s0pad)
si1 = vocab.vectorize(s1, spad=s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1)
return gr
def load_set(fname, vocab=None):
s0, s1, y = loader.load_hypev(fname)
# s0=questions, s1=answers
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0, spad=s0pad)
si1 = vocab.vectorize(s1, spad=s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return s0, s1, y, vocab, gr
def load_set(fname, vocab=None, s0pad=s0pad, s1pad=s1pad):
s0, s1, y, t = loader.load_anssel(fname)
# TODO: Make use of the t-annotations
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0 = vocab.vectorize(s0, spad=s0pad)
si1 = vocab.vectorize(s1, spad=s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return (s0, s1, y, vocab, gr)
def load_set(self, fname, cache_dir=None):
# TODO: Make the cache-handling generic,
# and offer a way to actually pass cache_dir
save_cache = False
if cache_dir:
import os.path
fname_abs = os.path.abspath(fname)
from hashlib import md5
cache_filename = "%s/%s.p" % (
cache_dir, md5(fname_abs.encode("utf-8")).hexdigest())
try:
with open(cache_filename, "rb") as f:
return pickle.load(f)
except (IOError, TypeError, KeyError):
save_cache = True
skip_oneclass = self.c.get('skip_oneclass', True)
s0, s1, y, kw, akw, t = loader.load_anssel(fname,
skip_oneclass=skip_oneclass)
# TODO: Make use of the t-annotations
if self.vocab is None:
vocab = Vocabulary(s0 + s1,
prune_N=self.c['embprune'],
icase=self.c['embicase'])
else:
vocab = self.vocab
si0, sj0 = vocab.vectorize(s0, self.emb, spad=self.s0pad)
si1, sj1 = vocab.vectorize(s1, self.emb, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0,
si1,
sj0,
sj1,
None,
None,
y,
f0,
f1,
s0,
s1,
kw=kw,
akw=akw)
if save_cache:
with open(cache_filename, "wb") as f:
pickle.dump((s0, s1, y, vocab, gr), f)
print("save")
return (gr, y, vocab)
def load_set(self, fname):
s0, s1, y = loader.load_msrpara(fname)
if self.vocab is None:
vocab = Vocabulary(s0 + s1)
else:
vocab = self.vocab
si0 = vocab.vectorize(s0, spad=self.s0pad)
si1 = vocab.vectorize(s1, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return (gr, y, vocab)
def load_set(self, fname, cache_dir=None):
s0, s1, y = loader.load_hypev(fname)
if self.vocab is None:
vocab = Vocabulary(s0 + s1)
else:
vocab = self.vocab
si0 = vocab.vectorize(s0, spad=self.s0pad)
si1 = vocab.vectorize(s1, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, y, f0, f1, s0, s1)
return (gr, y, vocab)
def load_set(self, fname):
s0, s1, y = loader.load_sick2014(fname, mode='entailment')
if self.vocab is None:
vocab = Vocabulary(s0 + s1, prune_N=self.c['embprune'], icase=self.c['embicase'])
else:
vocab = self.vocab
si0, sj0 = vocab.vectorize(s0, self.emb, spad=self.s0pad)
si1, sj1 = vocab.vectorize(s1, self.emb, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, y, f0, f1, s0, s1)
return (gr, y, vocab)
def links_to_graph(self, links):
s0 = []
s1 = []
labels = []
for link in links:
s0l, s1l, labelsl = self.link_to_s(link)
s0 += s0l
s1 += s1l
labels += labelsl
si0 = self.vocab.vectorize(s0, spad=self.s0pad)
si1 = self.vocab.vectorize(s1, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, np.array(labels), f0, f1)
return gr
def links_to_graph(self, links):
s0 = []
s1 = []
labels = []
for link in links:
s0l, s1l, labelsl = self.link_to_s(link)
s0 += s0l
s1 += s1l
labels += labelsl
si0 = self.vocab.vectorize(s0, spad=self.s0pad)
si1 = self.vocab.vectorize(s1, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, np.array(labels), f0, f1)
return gr
def load_set(self, fname):
s0, s1, y = loader.load_sick2014(fname, mode='entailment')
if self.vocab is None:
vocab = Vocabulary(s0 + s1,
prune_N=self.c['embprune'],
icase=self.c['embicase'])
else:
vocab = self.vocab
si0, sj0 = vocab.vectorize(s0, self.emb, spad=self.s0pad)
si1, sj1 = vocab.vectorize(s1, self.emb, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, y, f0, f1, s0,
s1)
return (gr, y, vocab)
def load_set(fname, emb, vocab=None):
s0, s1, y, _, _, _ = loader.load_anssel(fname)
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0, sj0 = vocab.vectorize(s0, emb)
si1, sj1 = vocab.vectorize(s1, emb)
se0 = emb.map_jset(sj0)
se1 = emb.map_jset(sj1)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, sj0, sj1, se0, se1, y, f0, f1)
# XXX: Pre-generating the whole (se0, se1) produces a *big* memory footprint
# for the dataset. In KeraSTS, we solve this by using fit_generator (also
# because of epoch_fract) and embed just per-batch.
return (s0, s1, y, vocab, gr)
def load_set(fname, emb, vocab=None):
s0, s1, y, _, _, _ = loader.load_anssel(fname)
if vocab is None:
vocab = Vocabulary(s0 + s1)
si0, sj0 = vocab.vectorize(s0, emb)
si1, sj1 = vocab.vectorize(s1, emb)
se0 = emb.map_jset(sj0)
se1 = emb.map_jset(sj1)
f0, f1 = nlp.sentence_flags(s0, s1, s0pad, s1pad)
gr = graph_input_anssel(si0, si1, sj0, sj1, se0, se1, y, f0, f1)
# XXX: Pre-generating the whole (se0, se1) produces a *big* memory footprint
# for the dataset. In KeraSTS, we solve this by using fit_generator (also
# because of epoch_fract) and embed just per-batch.
return (s0, s1, y, vocab, gr)
def load_set(self, fname, lists=None):
if lists:
s0, s1, y = lists
else:
# s0, s1, y = loader.load_msrpara(fname) #set it free is we decide not to use quora dataset
s0, s1, y = loader.load_quora(fname)
if self.vocab is None:
vocab = Vocabulary(s0 + s1,
prune_N=self.c['embprune'],
icase=self.c['embicase'])
else:
vocab = self.vocab
si0, sj0 = vocab.vectorize(s0, self.emb, spad=self.s0pad)
si1, sj1 = vocab.vectorize(s1, self.emb, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_nparray_anssel(
graph_input_anssel(si0, si1, sj0, sj1, None, None, y, f0, f1))
return (gr, y, vocab)
def load_set(self, fname, cache_dir=None):
si0, si1, sj0, sj1, f0, f1, labels = cPickle.load(open(fname, "rb"))
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, labels, f0, f1)
return (gr, labels, self.vocab)
def load_set(self, fname):
si0, si1, sj0, sj1, f0_, f1_, labels = pickle.load(open(fname, "rb"))
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, labels, f0_, f1_)
return (gr, labels, self.vocab)
def load_set(self, fname):
si0, si1, sj0, sj1, f0_, f1_, labels = pickle.load(open(fname, "rb"))
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, labels, f0_,
f1_)
return (gr, labels, self.vocab)
def load_set(self, fname, cache_dir=None, lists=None):
# TODO: Make the cache-handling generic,
# and offer a way to actually pass cache_dir
save_cache = False
if cache_dir:
import os.path
fname_abs = os.path.abspath(fname)
from hashlib import md5
cache_filename = "%s/%s.p" % (cache_dir, md5(fname_abs.encode("utf-8")).hexdigest())
try:
with open(cache_filename, "rb") as f:
return pickle.load(f)
except (IOError, TypeError, KeyError):
save_cache = True
if lists is not None:
s0, s1, y, qids, xtra, types = lists
else:
xtra = None
if '/mc' in fname:
s0, s1, y, qids, types = loader.load_mctest(fname)
else:
s0, s1, y, qids = loader.load_hypev(fname)
try:
dsfile = re.sub('\.([^.]*)$', '_aux.tsv', fname) # train.tsv -> train_aux.tsv
with open(dsfile) as f:
rows = csv.DictReader(f, delimiter='\t')
xtra = loader.load_hypev_xtra(rows)
print(dsfile + ' loaded and available')
except Exception as e:
if self.c['aux_r'] or self.c['aux_c']:
raise e
types = None
if self.vocab is None:
vocab = Vocabulary(s0 + s1, prune_N=self.c['embprune'], icase=self.c['embicase'])
else:
vocab = self.vocab
# mcqtypes pruning must happen *after* Vocabulary has been constructed!
if types is not None:
s0 = [x for x, t in zip(s0, types) if t in self.c['mcqtypes']]
s1 = [x for x, t in zip(s1, types) if t in self.c['mcqtypes']]
y = [x for x, t in zip(y, types) if t in self.c['mcqtypes']]
qids = [x for x, t in zip(qids, types) if t in self.c['mcqtypes']]
print('Retained %d questions, %d hypotheses (%s types)' % (len(set(qids)), len(set([' '.join(s) for s in s0])), self.c['mcqtypes']))
si0, sj0 = vocab.vectorize(s0, self.emb, spad=self.s0pad)
si1, sj1 = vocab.vectorize(s1, self.emb, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, y, f0, f1, s0, s1)
if qids is not None:
gr['qids'] = qids
if xtra is not None:
gr['#'] = xtra['#']
gr['@'] = xtra['@']
gr, y = self.merge_questions(gr)
if save_cache:
with open(cache_filename, "wb") as f:
pickle.dump((s0, s1, y, vocab, gr), f)
print("save")
return (gr, y, vocab)
def load_set(self, fname):
si0, si1, f0, f1, labels = pickle.load(open(fname, "rb"))
gr = graph_input_anssel(si0, si1, labels, f0, f1)
return (gr, labels, self.vocab)
def load_set(self, fname):
si0, si1, f0, f1, labels = pickle.load(open(fname, "rb"))
gr = graph_input_anssel(si0, si1, labels, f0, f1)
return (gr, labels, self.vocab)
def load_set(fname, vocab):
si0, si1, f0, f1, labels = cPickle.load(open(fname, "rb"))
gr = graph_input_anssel(si0, si1, labels, f0, f1)
return gr
def load_set(self, fname, cache_dir=None, lists=None):
# TODO: Make the cache-handling generic,
# and offer a way to actually pass cache_dir
save_cache = False
if cache_dir:
import os.path
fname_abs = os.path.abspath(fname)
from hashlib import md5
cache_filename = "%s/%s.p" % (
cache_dir, md5(fname_abs.encode("utf-8")).hexdigest())
try:
with open(cache_filename, "rb") as f:
return pickle.load(f)
except (IOError, TypeError, KeyError):
save_cache = True
if lists is not None:
s0, s1, y, qids, xtra, types = lists
else:
xtra = None
if '/mc' in fname:
s0, s1, y, qids, types = loader.load_mctest(fname)
else:
s0, s1, y, qids = loader.load_hypev(fname)
try:
dsfile = re.sub('\.([^.]*)$', '_aux.tsv',
fname) # train.tsv -> train_aux.tsv
with open(dsfile) as f:
rows = csv.DictReader(f, delimiter='\t')
xtra = loader.load_hypev_xtra(rows)
print(dsfile + ' loaded and available')
except Exception as e:
if self.c['aux_r'] or self.c['aux_c']:
raise e
types = None
if self.vocab is None:
vocab = Vocabulary(s0 + s1,
prune_N=self.c['embprune'],
icase=self.c['embicase'])
else:
vocab = self.vocab
# mcqtypes pruning must happen *after* Vocabulary has been constructed!
if types is not None:
s0 = [x for x, t in zip(s0, types) if t in self.c['mcqtypes']]
s1 = [x for x, t in zip(s1, types) if t in self.c['mcqtypes']]
y = [x for x, t in zip(y, types) if t in self.c['mcqtypes']]
qids = [x for x, t in zip(qids, types) if t in self.c['mcqtypes']]
print(
'Retained %d questions, %d hypotheses (%s types)' %
(len(set(qids)), len(set([' '.join(s)
for s in s0])), self.c['mcqtypes']))
si0, sj0 = vocab.vectorize(s0, self.emb, spad=self.s0pad)
si1, sj1 = vocab.vectorize(s1, self.emb, spad=self.s1pad)
f0, f1 = nlp.sentence_flags(s0, s1, self.s0pad, self.s1pad)
gr = graph_input_anssel(si0, si1, sj0, sj1, None, None, y, f0, f1, s0,
s1)
if qids is not None:
gr['qids'] = qids
if xtra is not None:
gr['#'] = xtra['#']
gr['@'] = xtra['@']
gr, y = self.merge_questions(gr)
if save_cache:
with open(cache_filename, "wb") as f:
pickle.dump((s0, s1, y, vocab, gr), f)
print("save")
return (gr, y, vocab)
