-
Notifications
You must be signed in to change notification settings - Fork 16
/
Copy pathtrain_word2vec_sets.py
110 lines (102 loc) · 3.75 KB
/
train_word2vec_sets.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
#@title
# Individual sets
import numpy as np
import warnings
from preprocess import prepare_data
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 utils import *
from visualize import plot_accuracy_curve
warnings.filterwarnings('ignore')
import tensorflow as tf
# Hyperparameters for word2vec
num_features = 200
min_word_count = 40
num_workers = 4
context = 10
downsampling = 1e-3
epochs = 30
# Hyperpaprameters for LSTM
Hidden_dim1=100
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"
output_dims=1
#####
####
dataset_path='./data/training_set_rel3.tsv'
def train_word2vec_sets():
set_count = 1
all_sets_score = []
data, target, sets = prepare_data(dataset_path=dataset_path)
for s in sets:
print("\n--------SET {}--------\n".format(set_count))
set_count +=1
X = s
y = s['domain1_score']
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
# hyperparameters for word2vec
most_common_words= []
print(X.shape)
print(y.shape)
for traincv, testcv in cv_data:
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']
#y_train = torch.tensor(y_train,dtype=torch.long)
train_sentences = []
# print("train_essay ",train_essays.shape)
#print(X_train.shape,y_train.shape)
for essay in train_essays:
# get all the sentences from the essay
train_sentences.append(essay_to_wordlist(essay, remove_stopwords = True))
# word2vec embedding
print("Converting sentences to word2vec model")
model,_ = build_word2vec(train_sentences, num_workers, num_features, min_word_count, context,
downsampling)
top10 = collections.defaultdict(int)
# print("train_sentencesshap",len(train_sentences))
trainDataVecs = np.array(getAvgFeatureVecs(train_sentences, model, num_features))
test_sentences = []
for essay_v in test_essays:
test_sentences.append(essay_to_wordlist(essay_v, remove_stopwords=True))
testDataVecs = np.array(getAvgFeatureVecs(test_sentences, model, num_features))
trainDataVectors = np.reshape(trainDataVecs, (trainDataVecs.shape[0], 1, trainDataVecs.shape[1]))
testDataVectors = np.reshape(testDataVecs, (testDataVecs.shape[0], 1, testDataVecs.shape[1]))
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, model_name=model_name, optimizer=optimizer,
loss_function=loss_function)
history = lstm_model.fit(trainDataVectors, y_train, batch_size=batch_size, epochs=epoch)
# print(trainDataVectors.shape)
# print(y_train.shape)
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)
np.nan_to_num(y_pred)
result = cohen_kappa_score(y_test.values, y_pred, weights='quadratic')
print("Kappa Score: {}".format(result))
results.append(result)
fold_count += 1
tf.keras.backend.clear_session()
print("Average kappa score value is : {}".format(np.mean(np.asarray(results))))
all_sets_score.append(results)