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"))