def entity_build(request):
""" """
site = request.matchdict['code']
eid = request.matchdict['id']
claims, site = verify_access(request, site=site)
e = Entity(request)
e.build()
return { 'started': True, 'name': site['name'], 'entity': eid }
def entity_build(request):
""" """
site = request.matchdict['code']
eid = request.matchdict['id']
claims, site = verify_access(request, site=site)
e = Entity(request)
e.build()
return {'started': True, 'name': site['name'], 'entity': eid}
def _extract(params, data):
train_batches, dev_batches, test_batches, vocabs, embedding = data
hate_train_batches = [train for train in train_batches if train["labels"] == 1]
hate_dev_batches = [dev for dev in dev_batches if dev["labels"] == 1]
hate_test_batches = [test for test in test_batches if test["labels"] == 1]
t_weights = np.array([1 - (Counter([train["target_label"] for train in hate_train_batches])[i] /
len(hate_train_batches)) for i in range(8)])
a_weights = np.array([1 - (Counter([train["action_label"] for train in hate_train_batches])[i] /
len(hate_train_batches)) for i in range(4)])
entity = Entity(params, vocabs, embedding)
entity.build()
if args.goal == "train":
entity.run_model(BatchIt(hate_train_batches, params["batch_size"], vocabs),
BatchIt(hate_dev_batches, params["batch_size"], vocabs),
BatchIt(hate_test_batches, params["batch_size"], vocabs),
(t_weights, a_weights))
elif args.goal == "predict":
unlabeled_batches = _load_unlabeled(params, args, vocabs)
target, action = entity.predict(unlabeled_batches, (t_weights, a_weights))
pickle.dump(target, open("Data/" + args.dataset + "/targets.pkl", "wb"))
pickle.dump(action, open("Data/" + args.dataset + "/actions.pkl", "wb"))
