test_model.py
3.83 KB
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
__author__ = 'chunk'
from sklearn import cross_validation
from ..common import *
from ..mdata import ILSVRC, ILSVRC_S
from ..mmodel.svm import SVM
import gzip
import cPickle
timer = Timer()
package_dir = os.path.dirname(os.path.abspath(__file__))
def test_SVM_ILSVRC():
timer.mark()
dil = ILSVRC.DataILSVRC(base_dir='/data/hadoop/ImageNet/ILSVRC/ILSVRC2013_DET_val',
category='Train_5000_0.05_orig')
X, Y = dil.load_data(mode='local') #
# X, Y = dil.load_data(mode='hbase') #
# X, Y = dil.load_data(mode='spark') #
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.4,
random_state=0)
print np.array(Y).shape, np.array(X).shape
print np.array(X_train).shape, np.array(Y_train).shape
print np.array(X_test).shape, np.array(Y_test).shape
timer.report()
timer.mark()
msvm = SVM.ModelSVM(toolset='sklearn') # 4.884247s 0.777853030816
# msvm = SVM.ModelSVM(toolset='opencv') #
# msvm = SVM.ModelSVM(toolset='libsvm') #
# msvm = SVM.ModelSVM(toolset='spark')
msvm.train(X_train, Y_train)
timer.report()
timer.mark()
print msvm.test(X_test, Y_test) #
timer.report() #
# timer.mark()
# print 'or like this:'
# scores = cross_validation.cross_val_score(msvm.model, X, Y)
# print scores
# timer.report()
def test_SVM_ILSVRC_HBASE():
timer.mark()
# dil = ILSVRC.DataILSVRC(base_dir='ILSVRC2013_DET_val', category='Train_3')
# X, Y = dil.load_data(mode='hbase') # pass
dils = ILSVRC_S.DataILSVRC_S(base='ILSVRC2013_DET_val', category='Test_1')
X, Y = dils.load_data(mode='hbase') # pass
dil = ILSVRC.DataILSVRC(base_dir='/data/hadoop/ImageNet/ILSVRC/ILSVRC2013_DET_val',
category='Test_1')
X1, Y1 = dil.load_data(mode='local')
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.4,
random_state=0)
print Y, np.sum(np.array(Y) == 0), np.sum(np.array(Y) == 1)
print np.array(Y).shape, np.array(X).shape
print np.array(X_train).shape, np.array(Y_train).shape
print np.array(X_test).shape, np.array(Y_test).shape
timer.report()
timer.mark()
msvm = SVM.ModelSVM(toolset='sklearn') # 4.884247s 0.777853030816
# msvm = SVM.ModelSVM(toolset='opencv') #
# msvm = SVM.ModelSVM(toolset='libsvm') #
# msvm = SVM.ModelSVM(toolset='spark')
msvm.train(X_train, Y_train)
timer.report()
timer.mark()
print msvm.test(X_test, Y_test) #
timer.report() #
timer.mark()
print msvm.test(X1, Y1) #
timer.report() #
# timer.mark()
# print 'or like this:'
# scores = cross_validation.cross_val_score(msvm.model, X, Y)
# print scores
# timer.report()
def test_SVM_ILSVRC_SPARK():
timer.mark()
dils = ILSVRC_S.DataILSVRC_S(base='ILSVRC2013_DET_val', category='Test_1')
rdd_dataset = dils.load_data(mode='spark') # pass
timer.report()
timer.mark()
# msvm = SVM.ModelSVM(toolset='sklearn') #
# msvm = SVM.ModelSVM(toolset='opencv') #
# msvm = SVM.ModelSVM(toolset='libsvm') #
msvm = SVM.ModelSVM(toolset='spark', sc=dils.sparker)
msvm.train(rdd_dataset)
timer.report()
dataset = rdd_dataset.collect()
length = len(dataset)
X_test, Y_test = [dataset[i].features for i in range(length)], [dataset[i].label for i in
range(length)]
timer.mark()
print msvm.test(dils.sparker.sc.parallelize(X_test), Y_test) #
timer.report() #
def test_SVM_ILSVRC_S():
test_SVM_ILSVRC_HBASE()
# test_SVM_ILSVRC_SPARK()
if __name__ == '__main__':
# test_SVM_CV()
test_SVM_ILSVRC()
print 'helllo'