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train_bert_all.py
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train_bert_all.py
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import time
import torch
import transformers as ppb
import warnings
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.metrics import cohen_kappa_score
from baseline_keras import get_model
from preprocess import prepare_data
from visualize import plot_accuracy_curve
# use_cuda = True
# if use_cuda and torch.cuda.is_available():
# torch.cuda()
#####
#####
# Hyperparameters
Hidden_dim1=300
Hidden_dim2=64
return_sequences = True
dropout=0.5
recurrent_dropout=0.4
input_size=768
activation='relu'
optimizer = 'adam'
loss_function = 'mean_square_error'
batch_size= 64
epoch =70
model_name = "BiLSTM"
#####
####
dataset_path='./data/training_set_rel3.tsv'
def train_bert():
warnings.filterwarnings('ignore')
cv = KFold(n_splits=5, shuffle=True)
X,y,_ = prepare_data(dataset_path=dataset_path)
cv_data = cv.split(X)
results = []
prediction_list = []
fold_count = 1
cuda = torch.device('cuda')
# For DistilBERT:
# model_class, tokenizer_class, pretrained_weights = (ppb.DistilBertModel, ppb.DistilBertTokenizer, 'distilbert-base-uncased')
## Want BERT instead of distilBERT? Uncomment the following line:
model_class, tokenizer_class, pretrained_weights = (ppb.BertModel, ppb.BertTokenizer, 'bert-base-uncased')
# Load pretrained model/tokenizer
tokenizer = tokenizer_class.from_pretrained(pretrained_weights)
model = model_class.from_pretrained(pretrained_weights)
with torch.cuda.device(cuda):
for traincv, testcv in cv_data:
torch.cuda.empty_cache()
print("\n--------Fold {}--------\n".format(fold_count))
# get the train and test from the dataset.
X_train, X_test, y_train, y_test = X.iloc[traincv], X.iloc[testcv], y.iloc[traincv], y.iloc[testcv]
train_essays = X_train['essay']
# print("y_train",y_train)
test_essays = X_test['essay']
# model = model.cuda()
# y_train = torch.tensor(y_train,dtype=torch.long)
sentences = []
tokenize_sentences = []
train_bert_embeddings = []
# bert_embedding = BertEmbedding()
# for essay in train_essays:
# # get all the sentences from the essay
# sentences += essay_to_sentences(essay, remove_stopwords = True)
# sentences = pd.Series(sentences)
# print(train_essays)
tokenized_train = train_essays.apply((lambda x: tokenizer.encode(x, add_special_tokens=True, max_length=40)))
tokenized_test = test_essays.apply((lambda x: tokenizer.encode(x, add_special_tokens=True, max_length=40)))
## train
max_len = 0
for i in tokenized_train.values:
if len(i) > max_len:
max_len = len(i)
padded_train = np.array([i + [0] * (max_len - len(i)) for i in tokenized_train.values])
attention_mask_train = np.where(padded_train != 0, 1, 0)
train_input_ids = torch.tensor(padded_train)
train_attention_mask = torch.tensor(attention_mask_train)
with torch.no_grad():
last_hidden_states_train = model(train_input_ids, attention_mask=train_attention_mask)
train_features = last_hidden_states_train[0][:, 0, :].numpy()
## test
max_len = 0
for i in tokenized_test.values:
if len(i) > max_len:
max_len = len(i)
padded_test = np.array([i + [0] * (max_len - len(i)) for i in tokenized_test.values])
attention_mask_test = np.where(padded_test != 0, 1, 0)
test_input_ids = torch.tensor(padded_test)
test_attention_mask = torch.tensor(attention_mask_test)
with torch.no_grad():
last_hidden_states_test = model(test_input_ids, attention_mask=test_attention_mask)
test_features = last_hidden_states_test[0][:, 0, :].numpy()
train_x, train_y = train_features.shape
test_x, test_y = test_features.shape
trainDataVectors = np.reshape(train_features, (train_x, 1, train_y))
testDataVectors = np.reshape(test_features, (test_x, 1, test_y))
# print(trainDataVectors)
# print(testDataVectors)
lstm_model = get_model(Hidden_dim1=Hidden_dim1, Hidden_dim2=Hidden_dim2, return_sequences = return_sequences,
dropout=dropout, recurrent_dropout=recurrent_dropout, input_size=input_size, activation=activation,
loss_function=loss_function, optimizer=optimizer, model_name=model_name)
history = lstm_model.fit(trainDataVectors, y_train, batch_size=batch_size, epochs=epoch)
plot_accuracy_curve(history)
y_pred = lstm_model.predict(testDataVectors)
y_pred = np.around(y_pred)
# y_pred.dropna()s
np.nan_to_num(y_pred)
# evaluate the model
# print(y_pred)
result = cohen_kappa_score(y_test.values, y_pred, weights='quadratic')
print("Kappa Score: {}".format(result))
results.append(result)
fold_count += 1
print("Average kappa score value is : {}".format(np.mean(np.asarray(results))))
# print(features.shape)
if __name__ == '__main__':
train_bert()