def get_score():
if not request.json["s0"]:
return jsonify({"matches": []}), 200
s0toks = request.json["s0"].split(" ")
s1toks = [s1["text"].split(" ") for s1 in request.json["s1"]]
l_s0 = [s0toks for s1 in s1toks]
l_s1 = s1toks
l_y = [0.5 for s1 in s1toks]
l_qids = [0 for s1 in s1toks]
if len(request.json["s1"][0]) > 1:
l_xtra = loader.load_hypev_xtra(request.json["s1"])
else:
l_xtra = None
l_types = None
lists = l_s0, l_s1, l_y, l_qids, l_xtra, l_types
s0, s1, y, qids, xtra, types = lists
gr, y, _ = task.load_set(None, lists=lists)
cl, rel, sc = [], [], []
for ogr in task.sample_pairs(gr, 16384, shuffle=False, once=True):
cl += list(model.predict(ogr)["class"])
rel += list(model.predict(ogr)["rel"])
sc += list(model.predict(ogr)["score"])
return (
jsonify(
{
"score": sc[0].tolist()[0],
"class": [x[0] for x in cl[0].tolist()],
"rel": [x[0] for x in rel[0].tolist()],
}
),
200,
)
def get_score():
if not request.json['s0']:
return jsonify({'matches': []}), 200
s0toks = request.json['s0'].split(' ')
s1toks = [s1['text'].split(' ') for s1 in request.json['s1']]
l_s0 = [s0toks for s1 in s1toks]
l_s1 = s1toks
l_y = [0.5 for s1 in s1toks]
l_qids = [0 for s1 in s1toks]
if len(request.json['s1'][0]) > 1:
l_xtra = loader.load_hypev_xtra(request.json['s1'])
else:
l_xtra = None
l_types = None
lists = l_s0, l_s1, l_y, l_qids, l_xtra, l_types
s0, s1, y, qids, xtra, types = lists
gr, y, _ = task.load_set(None, lists=lists)
cl, rel, sc = [], [], []
for ogr in task.sample_pairs(gr, 16384, shuffle=False, once=True):
cl += list(model.predict(ogr)['class'])
rel += list(model.predict(ogr)['rel'])
sc += list(model.predict(ogr)['score'])
return jsonify({'score': sc[0].tolist()[0], 'class': [x[0] for x in cl[0].tolist()], 'rel': [x[0] for x in rel[0].tolist()]}), 200
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, 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)