/
train.py
148 lines (125 loc) · 6.1 KB
/
train.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
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
''' Script for task 1 training'''
import argparse
import os
import time
import numpy as np
import joblib
from vad_utils import read_label_from_file
from evaluate import get_metrics
from utils import sklearn_dataset, read_json, save_json, build_model
"""----------------------------- Training options -----------------------------"""
parser = argparse.ArgumentParser(description='VAD Training')
parser.add_argument('--f_size', type=float, default=0.032,
help='frame size')
parser.add_argument('--f_shift', type=float, default=0.008,
help='frame shift')
parser.add_argument('--task', type=int, default=1,
help='task id')
parser.add_argument('--exp', type=str, default='svm_not_normalized',
help='Experiment ID')
# parser.add_argument('--save_name', type=str, default='train',
# help='file name while saving data for lazing loading')
"""----------------------------- Only meaningful in task1 -----------------------------"""
parser.add_argument('--model', type=str, default='svm',
help='what kind of model to use, supported: svm, linear, ridge, logistic, lasso')
parser.add_argument('--scaler', default=False, action='store_true',
help='whether to normalize the input data')
parser.add_argument('--cv', type=int, default=None,
help='cv value, only supported while using ridge, logistic or lasso')
parser.add_argument('--tol', type=float, default=0.001,
help='Tolerance for stopping criterion when using SVM.')
parser.add_argument('--c', type=float, default=1.0,
help='Regularization parameter when using SVM.')
"""----------------------------- Only meaningful in task2 -----------------------------"""
parser.add_argument('--n_cpnt', type=int, default=2,
help='The number of mixture components.')
args = parser.parse_args()
frame_size = args.f_size
frame_shift = args.f_shift
prefix_train = './task' + str(args.task) + '/' + 'train' + '_' + str(frame_size) + '_' + str(frame_shift)
prefix_val = './task' + str(args.task) + '/' + 'val' + '_' + str(frame_size) + '_' + str(frame_shift)
train_features_path = prefix_train + '_features.npy'
train_target_path = prefix_train + '_target.npy'
val_features_path = prefix_val + '_features.npy'
val_target_path = prefix_val + '_target.npy'
exp_id = args.exp + '_' + str(frame_size) + '_' + str(frame_shift)
train_label_path = './task' + str(args.task) + '/train_label' + '_' + str(frame_size) + '_' + str(frame_shift) + '.json'
val_label_path = './task' + str(args.task) + '/val_label' + '_' + str(frame_size) + '_' + str(frame_shift) + '.json'
def train(m, X, y):
if args.task == 1:
m.fit(X, y)
score = m.score(X, y)
else:
score1 = m[0].fit(X[y >= 0.5])
score2 = m[1].fit(X[y < 0.5])
score = [score1, score2]
return score
def validate(m, X, y):
if args.task == 1:
y_pred = m.predict(X).tolist()
else:
y_pred1 = m[0].score_samples(X)
y_pred2 = m[1].score_samples(X)
y_pred = (y_pred1 > y_pred2).tolist()
y_true = y.tolist()
auc, eer = get_metrics(y_pred, y_true)
return auc, eer
def exp(m, features_train, target_train, features_val, target_val, name):
print('Experiment Name: ', name)
start_time = time.time()
score = train(m, features_train, target_train)
print('Training score :', score)
print('Training time :', time.time() - start_time)
print('-----------------------------------------')
joblib.dump(m, './models/' + name + '.pkl')
train_auc, train_err = validate(m, features_train, target_train)
print('Taining AUC :', train_auc)
print('Taining ERR :', train_err)
if target_val is not None:
print('-----------------------------------------')
val_auc, val_eer = validate(m, features_val, target_val)
print('Val AUC :', val_auc)
print('Val ERR :', val_eer)
def main():
if not os.path.exists(train_label_path):
print('loading training labels...')
train_label_file = "data/dev_label.txt" if args.task == 1 else "data/train_label.txt"
train_label = read_label_from_file(train_label_file, frame_size=frame_size, frame_shift=frame_shift)
save_json(train_label, train_label_path)
else:
print('lazy loading training labels...')
train_label = read_json(train_label_path)
features_train, target_train = sklearn_dataset(
train_label, task=args.task, mode='train',
frame_size=frame_size, frame_shift=frame_shift,
features_path=train_features_path, target_path=train_target_path
)
'''optional'''
# from sklearn.manifold import TSNE
# import matplotlib
# matplotlib.use('Agg')
# import matplotlib.pyplot as plt
# X_embedded = TSNE(n_components=2).fit_transform(features_train[0::500,:])
# plt.scatter(X_embedded[:,0], X_embedded[:,1],c=target_train[0::500])
# plt.savefig('vis.png')
'''optional'''
if args.task == 2:
if not os.path.exists(val_label_path):
print('loading validation labels...')
val_label_file = "data/dev_label.txt"
val_label = read_label_from_file(val_label_file, frame_size=frame_size, frame_shift=frame_shift)
save_json(val_label, val_label_path)
else:
print('lazy loading validation labels...')
val_label = read_json(val_label_path)
features_val, target_val = sklearn_dataset(
val_label, task=args.task, mode='val',
frame_size=frame_size, frame_shift=frame_shift,
features_path=val_features_path, target_path=val_target_path
)
else:
features_val, target_val = None, None
m = build_model(args)
exp(m, features_train, target_train, features_val, target_val, exp_id)
if __name__ == '__main__':
main()