def test(model):
model = load_model(model, model_name, args.gpu)
print('\n\n-------------------------------------------')
print('Testing')
print('-------------------------------------------')
test_iter = enumerate(chat_data.get_iter('test'))
if not args.no_tqdm:
test_iter = tqdm(test_iter)
test_iter.set_description_str('Testing')
test_iter.total = chat_data.n_test // chat_data.batch_size
test_loss = 0.0
extrema = []
gm = []
emb_avg_all = []
predicted_s = []
orig_s = []
f1_score = 0.0
for it, mb in test_iter:
q, q_c, a, q_m, a_m, kb, kb_m, sentient, v_m, teams = mb
pred, loss = model.evaluate_batch(q, q_c, a, q_m, a_m, kb, kb_m,
sentient)
pred = pred.transpose(0, 1).contiguous()
a = a.transpose(0, 1).contiguous()
s_g = get_sentences(a, teams)
s_p = get_sentences(pred, teams)
e_a, v_e, g_m = metrics.get_metrics_batch(s_g, s_p)
f1_score += compute_f1(s_g, s_p, global_entity_list, teams)
emb_avg_all.append(e_a)
extrema.append(v_e)
gm.append(g_m)
predicted_s.append(s_p)
orig_s.append(s_g)
test_loss += loss.item()
print("Vector extrema:" + str(np.average(extrema)))
print("Greedy Matching:" + str(np.average(gm)))
print("Embedding Average on Test:{:.6f}".format(np.average(emb_avg_all)))
print('\n\n-------------------------------------------')
print('-------------------------------------------')
predicted_s = [q for ques in predicted_s for q in ques]
orig_s = [q for ques in orig_s for q in ques]
moses_bleu = get_moses_multi_bleu(predicted_s, orig_s, lowercase=True)
print("Moses Bleu:" + str(moses_bleu))
print("F1 score: ", f1_score / len(test_iter))
test_out['original_response'] = orig_s
test_out['predicted_response'] = predicted_s
print('Saving the test predictions......')
test_out.to_csv(test_results, index=False)
def __init__(self):
#userdictionary
super().__init__()
self.dialogue_hist = defaultdict(list)
# Set random seed
np.random.seed(args.randseed)
torch.manual_seed(args.randseed)
if args.gpu:
torch.cuda.manual_seed(args.randseed)
if os.path.isfile(args.stoi):
self.stoi = np.load(args.stoi, allow_pickle=True).item()
self.itos = {v: k for k, v in self.stoi.items()}
# Get data
# chat_data = DialogBatcher(gpu=args.gpu)
self.model = KGSentient(hidden_size=args.hidden_size,
max_r=args.resp_len,
gpu=args.gpu,
n_words=len(self.stoi) + 1,
emb_dim=args.words_dim,
kb_max_size=200,
b_size=args.batch_size,
lr=args.lr,
dropout=args.rnn_dropout,
emb_drop=args.emb_drop,
teacher_forcing_ratio=args.teacher_forcing,
sos_tok=self.stoi['<sos>'],
eos_tok=self.stoi['<eos>'],
itos=self.itos,
first_kg_token=self.stoi['o0'])
if args.gpu:
self.model = self.model.cuda()
self.model_name = 'Sentient_model2'
self.model = load_model(self.model, self.model_name, gpu=args.gpu)
self.bot = BotAgent(self.model, stoi=self.stoi, itos=self.itos)
def load_convnet():
model = load_model()
model.summary()
return model