def compute_string_kernel():
args = _parse_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()
# train_len = sum(list(map(len, x_train_list)))
# dev_len = sum(list(map(len, x_dev_list)))
# test_len = sum(list(map(len, essay_list)))
# print(train_len, dev_len, test_len)
for idx in range(PROMPT_NUM):
x_train, y_train = x_train_list[idx], y_train_list[idx]
x_dev, y_dev = x_dev_list[idx], y_dev_list[idx]
essay_test, essay_id_test, essay_set_test = essay_list[idx], essay_id_list[idx], essay_set_list[idx]
all_prompt_essays = np.concatenate([x_train, x_dev, essay_test], axis=0)
prompt_write_into_txt(essays_of_prompt=all_prompt_essays, prompt=idx+1)
hisk_input_dir = ROOT_DIR + '/essay_data/HISK/input'
hisk_output_dir = ROOT_DIR + '/essay_data/HISK/output1'
os.makedirs(hisk_output_dir, exist_ok=True)
# Compute kernel string matrix
for idx in range(PROMPT_NUM):
# kernel_type can be "intersection", "presence" or "spectrum"
essays_input = os.path.join(hisk_input_dir, get_prompt_str(prompt=idx+1))
essays_output = os.path.join(hisk_output_dir, get_prompt_str(prompt=idx+1))
execute_compute_string_kernel_command(kernel_type='intersection',
input_file_1=essays_input,
input_file_2=None,
output_file=essays_output)
'''
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 correct_essays(save_path=None):
args = _parse_args()
dataset = AESDataset(args.dataset_dir, prompt_num=PROMPT_NUM, is_cross_dataset=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()
all_essays = np.concatenate((x_train_list, x_dev_list, essay_list), axis=0)
corrects = correct_language(essay_list=all_essays, save_path=save_path)
return corrects
def save_predicts(all_predicts):
fout = open(ROOT_DIR + '/result_output/predicts-' + time.strftime("%Y-%m-%d@%H-%M-%S") + '.tsv',
'w', encoding='utf8')
args = _parse_args()
dataset = AESDataset(args.dataset_dir, prompt_num=PROMPT_NUM, is_cross_dataset=IS_CROSS_DATASET)
essay_list, essay_id_list, essay_set_list = dataset.get_test()
for i in range(PROMPT_NUM):
essay_ids = essay_id_list[i]
essay_sets = essay_set_list[i]
for idx, prediction in enumerate(all_predicts[i]):
fout.write(f'{essay_ids[idx]}\t{essay_sets[idx]}\t{prediction}\n')
fout.close()
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)
def convert_to_libsvm_input_format(is_contain_test=True,
is_scale_y=False,
split_train_dev=False):
hisk_output_dir = os.path.join(ROOT_DIR, 'essay_data/HISK/output-all')
libsvm_input_dir = LIBSVM_DIR
os.makedirs(libsvm_input_dir, exist_ok=True)
args = _parse_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()
x_test_list, x_test_ids, x_test_sets = dataset.get_test()
for file in os.listdir(hisk_output_dir):
hisk_file = os.path.join(hisk_output_dir, file)
if is_contain_test:
libsvm_file = os.path.join(
libsvm_input_dir,
file.__str__().split('.')[0] + '-libsvm.txt')
else:
libsvm_file = os.path.join(
libsvm_input_dir,
file.__str__().split('.')[0] + '-libsvm-notest.txt')
if os.path.exists(libsvm_file) and not split_train_dev:
continue
prompt_idx = int(re.findall(r'\d+', file.__str__())[0])
if is_contain_test:
y_test = np.zeros(shape=len(x_test_list[prompt_idx - 1]))
y_labels = np.concatenate([
y_train_list[prompt_idx - 1], y_dev_list[prompt_idx - 1],
y_test
],
axis=0)
else:
y_labels = np.concatenate(
[y_train_list[prompt_idx - 1], y_dev_list[prompt_idx - 1]],
axis=0)
if is_scale_y:
mm_scaler = MinMaxScaler(feature_range=(0, 1))
y_labels = mm_scaler.fit_transform(
np.array(y_labels).reshape(-1, 1)).reshape(-1)
if DEBUG:
print(f'prompt@{prompt_idx} shape: {y_labels.shape}')
with open(hisk_file, 'r',
encoding='utf8') as fin, open(libsvm_file,
'w',
encoding='utf8') as fout:
x_feas = [line.strip().split() for line in fin][:len(y_labels)]
for idx, x_fea in enumerate(x_feas):
fea_idx = 1
fout.write(str(y_labels[idx]))
for each_fea in x_fea:
fout.write(' ' + str(fea_idx) + ':' + each_fea)
fea_idx += 1
fout.write('\n')
if split_train_dev:
libsvm_train_file = os.path.join(
libsvm_input_dir,
file.__str__().split('.')[0] + '-libsvm-train.txt')
libsvm_dev_file = os.path.join(
libsvm_input_dir,
file.__str__().split('.')[0] + '-libsvm-dev.txt')
with open(libsvm_file, 'r', encoding='utf8') as fin:
fout1, fout2 = open(libsvm_train_file, 'w',
encoding='utf8'), open(libsvm_dev_file,
'w',
encoding='utf8')
train_len = len(x_train_list[prompt_idx - 1])
for idx, line in enumerate(fin):
if idx < train_len:
fout1.write(line.strip() + '\n')
else:
fout2.write(line.strip() + '\n')
fout1.close()
fout2.close()
execute_scale_command()