def load_set(fname, emb, cache_dir=None):
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, _, _, _ = loader.load_anssel(fname)
e0, e1, s0, s1, y = loader.load_embedded(emb,
s0,
s1,
y,
balance=True,
ndim=1)
if save_cache:
with open(cache_filename, "wb") as f:
pickle.dump((e0, e1, y), f)
return (e0, e1, y)
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, 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(glove, fname, balance=False, subsample0=3):
s0, s1, labels, toklabels = loader.load_anssel(fname,
subsample0=subsample0)
print('(%s) Loaded dataset: %d' % (fname, len(s0)))
e0, e1, s0, s1, labels = loader.load_embedded(glove,
s0,
s1,
labels,
balance=balance)
return ([e0, e1], labels)
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, t = loader.load_anssel(fname, skip_oneclass=False)
# 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 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(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(fname, emb, cache_dir=None):
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)
e0, e1, s0, s1, y = loader.load_embedded(emb, s0, s1, y, balance=True, ndim=1)
if save_cache:
with open(cache_filename, "wb") as f:
pickle.dump((e0, e1, y), f)
return (e0, e1, y)
def load_set(glove, fname, balance=False, subsample0=3):
s0, s1, labels = loader.load_anssel(fname, subsample0=subsample0)
print('(%s) Loaded dataset: %d' % (fname, len(s0)))
e0, e1, s0, s1, labels = loader.load_embedded(glove, s0, s1, labels, balance=balance)
return ([e0, e1], labels)
