def test_v_svr(prompt_idx, gamma=None):
args = _parse_args()
dataset = AESDataset(args.dataset_dir,
prompt_num=PROMPT_NUM,
is_cross_dataset=False)
x_train_list, y_train_list = dataset.get_train()
x_dev_list, y_dev_list = dataset.get_dev()
x_test_list, _, _ = dataset.get_test()
train_len, dev_len, test_len = len(x_train_list[prompt_idx-1]), len(x_dev_list[prompt_idx-1]),\
len(x_test_list[prompt_idx-1])
y, x = svm_read_problem(SVM_SCALE_DIR + '/prompt@' + str(prompt_idx) +
'-scale.txt')
x_train, y_train = x[:train_len], y[:train_len]
x_dev, y_dev = x[train_len:train_len + dev_len], y[train_len:train_len +
dev_len]
x_test = x[train_len + dev_len:]
if gamma:
param = f'-s 4 -t 2 -c 1000 -n 0.1 -g {gamma}'
else:
param = f'-s 4 -t 2 -c 1000 -n 0.1'
svm_model = svm_train(y_train + y_dev, x_train + x_dev, param)
p_label, p_acc, p_val = svm_predict(np.zeros(shape=len(x_test)), x_test,
svm_model)
p_label = np.round(p_label)
dev_label, dev_acc, dev_val = svm_predict(y_dev, x_dev, svm_model)
dev_kappa = kappa(y_true=y_dev, y_pred=dev_label, weights='quadratic')
print(f'Dev kappa: {dev_kappa}')
return dev_kappa, p_label
def predict(self, x_test, is_dev):
# x_test = self._model.preprocess_data(x_test)
# x_test = self.preprocess_data(x_test, is_test=True)
y_pred = self._model.predict(x_test)
if is_dev:
return y_pred
x_dev, y_dev = self._dev_set
# x_dev = self.preprocess_data(x_dev, is_test=True)
y_pred_dev = self._model.predict(x_dev)
kap = kappa(y_true=y_dev, y_pred=y_pred_dev)
if self._k_best_kappa[-1] < kap:
self._k_best_kappa[-1] = kap
self._k_best_predicts[-1] = y_pred
self._k_best_predicts_dev[-1] = y_pred_dev
# sort k_best
for idx, (kap, pred, pred_dev) in enumerate(
sorted(zip(self._k_best_kappa, self._k_best_predicts,
self._k_best_predicts_dev),
key=lambda x: x[0],
reverse=True)):
self._k_best_kappa[idx] = kap
self._k_best_predicts[idx] = pred
self._k_best_predicts_dev[idx] = pred_dev
return self._blending_ensemble()
def objective(hyperparams):
model = gbr(**params, **hyperparams)
model.fit(x_train, y_train)
y_pred_dev = model.predict(x_dev)
score = kappa(y_true=y_pred_dev, y_pred=y_dev)
return {'loss': -score, 'status': STATUS_OK}
def objective(hyperparams):
model = XGBClassifier(**params, **hyperparams)
# model.fit(x_train, y_train, eval_set=[(x_dev, y_dev)], early_stopping_rounds=50, eval_metric='merror', verbose=True)
model.fit(X=x_train, y=y_train, eval_metric=self._metrics)
y_pred_dev = model.predict(x_dev)
score = kappa(y_true=y_pred_dev, y_pred=y_dev)
return {'loss': -score, 'status': STATUS_OK}
def _dev(umodel, dataset):
# Validate on model
timer = Timer()
x_dev, y_dev = dataset
y_pred = umodel.predict(x_dev, is_dev=True)
score = kappa(y_true=y_dev, y_pred=y_pred)
duration = timer.get_duration()
LOGGER.info(
f"Finished validating the model. time spent {duration} sec. Validate score: {score}"
)
return score
def do_ingestion():
"""main entry"""
LOGGER.info('===== Start integration program.')
# Parse directories from input arguments
LOGGER.info('===== Initialize args.')
args = _parse_args()
_init_python_path(args)
dataset = AESDataset(args.dataset_dir,
prompt_num=PROMPT_NUM,
is_cross_dataset=IS_CROSS_DATASET)
x_train_list, y_train_list = dataset.get_train()
x_dev_list, y_dev_list = dataset.get_dev()
essay_list, essay_id_list, essay_set_list = dataset.get_test()
score_list = []
prediction_list = []
for i in range(PROMPT_NUM):
log_prompt(entry="Begin handling ", prompt=i + 1)
x_train, y_train = x_train_list[i], y_train_list[i]
x_dev, y_dev = x_dev_list[i], y_dev_list[i]
essay, essay_id, essay_set = essay_list[i], essay_id_list[
i], essay_set_list[i]
umodel = Model(prompt=i + 1, max_iter=1)
# LOGGER.info("===== Check model methods =====")
# _check_umodel_methed(umodel)
dev_score, pred_result = None, None
while not umodel.done_training:
LOGGER.info(f"===== Begin training model =====")
_train(umodel, (x_train, y_train), (x_dev, y_dev))
LOGGER.info("===== Begin predicting on test set =====")
pred_result, pred_result_dev = _predict(
umodel, (essay, essay_id, essay_set))
pred_result_dev = np.round(pred_result_dev)
dev_score = kappa(y_true=y_dev, y_pred=pred_result_dev)
log(f"--------------Prompt{i+1} is done, and the dev_score is {dev_score}-------------"
)
score_list.append(dev_score)
prediction_list.append(pred_result)
# save result
score_file = os.path.join(
args.output_dir,
"score-" + time.strftime("%Y-%m-%d@%H-%M-%S") + '.txt')
prediction_file = os.path.join(
args.output_dir,
"prediction-" + time.strftime("%Y-%m-%d@%H-%M-%S") + '.txt')
LOGGER.info("===== Begin Saving prediction =====")
# with open(score_file, 'w', encoding='utf8') as fout:
# score_list = [str(score) for score in score_list]
# fout.write('\n'.join(score_list) + '\n')
with open(prediction_file, 'w', encoding='utf') as fout:
for prediction in prediction_list:
for idx in range(len(prediction[0])):
fout.write(
str(prediction[0][idx]) + '\t' + str(prediction[1][idx]) +
'\t' + str(prediction[2][idx]) + '\n')
with open(score_file, 'w', encoding='utf') as fout1:
tot = 0.0
for idx in range(len(score_list)):
tot += score_list[idx]
fout1.write(str(idx + 1) + '\t' + str(score_list[idx]) + '\n')
avg = tot * 1.0 / PROMPT_NUM
fout1.write("avg_score: " + str(avg) + '\n')
LOGGER.info("[Ingestion terminated]")
def embedding_predicts(wordvec_dict):
args = _parse_args()
dataset = AESDataset(args.dataset_dir, prompt_num=PROMPT_NUM, is_cross_dataset=IS_CROSS_DATASET, use_correct=True)
x_train_list, y_train_list = dataset.get_train()
x_dev_list, y_dev_list = dataset.get_dev()
essay_list, essay_id_list, essay_set_list = dataset.get_test()
cleaned_dir = ROOT_DIR + '/essay_data/cleaned'
cleaned_path = os.path.join(cleaned_dir, 'cleaned.txt')
os.makedirs(cleaned_dir, exist_ok=True)
if IS_CROSS_DATASET:
x_train_cleaned = cleanup_essays(x_train_list, logging=True)
x_dev_cleaned = cleanup_essays(x_dev_list, logging=True)
x_test_cleaned = cleanup_essays(essay_list, logging=True)
else:
if not os.path.exists(cleaned_path):
x_train_cleaned = [cleanup_essays(x_train_list[i], logging=True) for i in range(PROMPT_NUM)]
x_dev_cleaned = [cleanup_essays(x_dev_list[i], logging=True) for i in range(PROMPT_NUM)]
x_test_cleaned = [cleanup_essays(essay_list[i], logging=True) for i in range(PROMPT_NUM)]
fout = open(cleaned_path, 'w', encoding='utf8')
for i in range(PROMPT_NUM):
fout.write('\n'.join(x_train_cleaned[i]) + '\n')
fout.write('\n'.join(x_dev_cleaned[i]) + '\n')
fout.write('\n'.join(x_test_cleaned[i]) + '\n')
fout.close()
else:
x_train_cleaned, x_dev_cleaned, x_test_cleaned = [], [], []
begin_idx = 0
with open(cleaned_path, 'r', encoding='utf8') as fin:
cleaned_essays = [line.strip() for line in fin]
for prompt_i in range(PROMPT_NUM):
x_train_cleaned.append(cleaned_essays[begin_idx:begin_idx+len(x_train_list[prompt_i])])
begin_idx += len(x_train_list[prompt_i])
x_dev_cleaned.append(cleaned_essays[begin_idx:begin_idx+len(x_dev_list[prompt_i])])
begin_idx += len(x_dev_list[prompt_i])
x_test_cleaned.append(cleaned_essays[begin_idx:begin_idx+len(essay_list[prompt_i])])
begin_idx += len(essay_list[prompt_i])
prompt_cnt = 0
k_list = []
use_regression = True
model_lib = {
# LSTM_MODEL: Lstm,
# CNN_MODEL: Cnn,
CNN_MULTIPLE: CnnMulInputs,
LSTM_MULTIPLE: LstmMulInputs,
# CRNN_MODEL: crnn
}
repeat_num = 6
prompt_predicts = []
for i in range(0, PROMPT_NUM):
prompt_cnt += 1
x_train_vec = np.array([create_average_vec(essay, text_dim=TEXT_DIM, wordvec_dict=wordvec_dict)
for essay in x_train_cleaned[i]])
x_dev_vec = np.array([create_average_vec(essay, text_dim=TEXT_DIM, wordvec_dict=wordvec_dict)
for essay in x_dev_cleaned[i]])
x_test_vec = np.array([create_average_vec(essay, text_dim=TEXT_DIM, wordvec_dict=wordvec_dict)
for essay in x_test_cleaned[i]])
x_train_seq_vec = np.array([create_sequence_vec(essay, text_dim=TEXT_DIM, wordvec_dict=wordvec_dict)
for essay in x_train_cleaned[i]])
x_dev_seq_vec = np.array([create_sequence_vec(essay, text_dim=TEXT_DIM, wordvec_dict=wordvec_dict)
for essay in x_dev_cleaned[i]])
x_test_seq_vec = np.array([create_sequence_vec(essay, text_dim=TEXT_DIM, wordvec_dict=wordvec_dict)
for essay in x_test_cleaned[i]])
y_train = y_train_list[i]
y_dev = y_dev_list[i]
max_class, min_class = max(y_train), min(y_train)
if use_regression:
output_dim = 1
else:
output_dim = max_class + 1
hisk_dir = ROOT_DIR + '/essay_data/HISK/output'
hisk_all_dir = ROOT_DIR + '/essay_data/HISK/output-all'
hisk_all = [np.array(line.strip().split()).astype(int) for line
in open(hisk_all_dir + '/prompt@' + str(i+1) + '.txt', 'r', encoding='utf8')]
hisk_train = [np.array(line.strip().split()).astype(int) for line
in open(hisk_dir+'/prompt@' + str(i+1) + '-train.txt', 'r', encoding='utf8')]
hisk_dev = [np.array(line.strip().split()).astype(int) for line
in open(hisk_dir+'/prompt@' + str(i+1) + '-dev.txt', 'r', encoding='utf8')]
hisk_test = [np.array(line.strip().split()).astype(int) for line
in open(hisk_dir+'/prompt@' + str(i+1) + '-test.txt', 'r', encoding='utf8')]
hisk_train, hisk_dev, hisk_test = np.array(hisk_train), np.array(hisk_dev), np.array(hisk_test)
sscalar = StandardScaler()
hisk_all = sscalar.fit_transform(hisk_all)
hisk_train, hisk_dev, hisk_test = np.array(hisk_all[:len(y_train)]), np.array(hisk_all[len(y_train):len(y_train)+len(y_dev)]),\
np.array(hisk_all[-len(essay_list[i]):])
x_train_vec = np.concatenate([x_train_vec, hisk_train], axis=-1)
x_dev_vec = np.concatenate([x_dev_vec, hisk_dev], axis=-1)
x_test_vec = np.concatenate([x_test_vec, hisk_test], axis=-1)
x_train_vec = hisk_train
x_dev_vec = hisk_dev
x_test_vec = hisk_test
x_train_vec = x_train_seq_vec
x_dev_vec = x_dev_seq_vec
x_test_vec = x_test_seq_vec
print(f'Prompt@{i+1}, num_classes: {max_class-min_class+1}; '
f'x_train shape: {np.array(x_train_vec).shape}, y_train shape: {np.array(y_train).shape}; '
f'x_dev shape: {np.array(x_dev_vec).shape}, y_dev shape: {np.array(y_dev).shape}; '
f'x_test shape: {np.array(x_test_vec).shape}, y_test shape: {np.array(essay_list[i]).shape}')
total_predicts = []
for model_name in model_lib.keys():
predicts_list = []
dev_predicts_list = []
for idx in range(repeat_num):
x_train_input = x_train_vec
x_dev_input = x_dev_vec
x_test_input = x_test_vec
my_model = model_lib[model_name]()
if 'mul' in model_name:
my_model.init_model(prompt=i+1,
input_shape1=x_train_vec.shape[1:], input_shape2=np.array(hisk_train).shape[-1],
output_dim=output_dim)
x_train_input = [x_train_vec, hisk_train]
x_dev_input = [x_dev_vec, hisk_dev]
x_test_input = [x_test_vec, hisk_test]
else:
my_model.init_model(input_shape=x_train_vec.shape[1:], output_dim=output_dim)
my_model.fit(x_train_input, y_train, x_dev_input, y_dev, train_loop_num=1)
predicts = np.round(my_model.predict(x_test_input)).reshape(-1, 1)
dev_predicts = np.round(my_model.predict(x_dev_input)).reshape(-1, 1)
# predicts = mmscaler.inverse_transform(predicts)
predicts_list.append(predicts)
dev_predicts_list.append(dev_predicts)
dev_kappa_list = []
for dev_predict in dev_predicts_list:
dev_kappa = kappa(y_true=y_dev, y_pred=dev_predict, weights="quadratic")
dev_kappa_list.append(dev_kappa)
aver_dev_kappa = np.mean(dev_kappa_list)
cmp_kapaa, cmp_kappa_list = aver_dev_kappa, dev_kappa_list
selected_list = [predict for predict, kp in zip(predicts_list, cmp_kappa_list) if kp >= cmp_kapaa]
aver_predicts = np.mean(np.concatenate(selected_list, axis=-1), axis=-1)
total_predicts.append(aver_predicts.reshape(-1, 1))
ensemble_predicts = np.mean(np.concatenate(total_predicts, axis=-1), axis=-1)
prompt_predicts.append(ensemble_predicts)
os.makedirs(ROOT_DIR + '/result_output', exist_ok=True)
save_predicts(prompt_predicts)
