/
digit.py
199 lines (137 loc) · 4.35 KB
/
digit.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
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
import os
import csv
import cPickle
import numpy as np
from sklearn.cross_validation import KFold
from sklearn.ensemble import RandomForestClassifier
from scipy import stats
from multiprocessing import Pool
from pilutil import imresize
import overfeat
import pylab as pl
class DigitData():
def __init__(self, trainfile, testfile, sampleresult):
self.n_dim = 0
self.n_class = 0
self.n_train = 0
self.n_test = 0
self.savefile = 'result.csv'
self.header = self.get_header(sampleresult)
self.data_trn = self.load_data(trainfile, 'train')
self.data_tst = self.load_data(testfile, 'test')
def get_header(self, samplefile):
f1 = open(samplefile, 'rb')
content = csv.reader(f1)
raw_data = []
for r in content:
raw_data.append(r)
f1.close()
header = raw_data[0]
return header
def load_data(self, filename, type):
with open(filename, 'rb') as f1:
content = csv.reader(f1)
data = []
for r in content:
data.append(r)
if type is 'train':
X = np.asarray(data)[1:, 1:].astype(np.uint8)
Y = np.asarray(data)[1:, 0].astype(np.int)
return X, Y
elif type is 'test':
X = np.asarray(data)[1:, :].astype(np.uint8)
return X
else:
raise NameError('Unknown data type')
def save_data(self, result, filename=None):
'''
save result into a csv file
'''
if filename is None:
filename = self.savefile
data = []
data.append(self.header)
for i in range(len(result)):
data.append([str(i+1), str(result[i])])
if os.path.isfile(filename):
os.remove(filename)
with open(filename, 'wb') as f:
c = csv.writer(f)
c.writerows(data)
def extract_feat(self, imgs, ftype):
'''
extract features
'''
if ftype is 'hog':
from skimage.feature import hog
L = np.sqrt(len(imgs[0])).astype(int)
X_im = [imresize(arr.reshape((L, L)), (64, 64)) for arr in imgs]
pool = Pool(processes=8)
X = pool.map(hog, X_im)
pool.close()
return np.asarray(X)
elif ftype is 'overfeat':
overfeat.init('OverFeat/data/default/net_weight_0', 1)
L = np.sqrt(len(imgs[0])).astype(int)
imgs_color = [imresize(arr.reshape((L, L)), (231, 231)) for arr in imgs]
if len(imgs_color[0].shape) != 3:
cmap = pl.get_cmap('jet')
imgs_color = [np.delete(cmap(im/255.), 3, 2) for im in imgs_color]
imgs_roll = [im.transpose((2, 0, 1)).astype(np.float32) for im in imgs_color]
feats = np.zeros((len(imgs_roll), 4096), dtype = float)
for i in range(len(imgs_roll)):
b = overfeat.fprop(imgs_roll[i])
f22 = overfeat.get_output(22)
f22 = np.asarray(f22).squeeze().astype(np.float)
feats[i, :] = f22
return feats
elif ftype is 'pix':
return imgs
else:
raise NameError('{0} is not implemented!'.format(ftype))
class DigitModel():
def __init__(self, X, Y, Mopts):
self.X = X
self.Y = Y
self.Mopts = Mopts
def print_acc(self, predict, label):
'''
print the accuracy of prediction
'''
print sum(map(lambda x, y: x == y, predict, label))*1./len(label)
def train(self):
if self.Mopts['type'] is 'RF':
rf = RandomForestClassifier(self.Mopts['nTree'], n_jobs=8)
rf.fit(self.X, self.Y)
self.print_acc(rf.predict(self.X), self.Y)
self.model = rf
def cv_train(self, nFold):
cv = KFold(n=len(self.X), n_folds=nFold, indices=True)
self.model = []
for train, valid in cv:
self.model.append(RandomForestClassifier(self.Mopts['nTree'], n_jobs=8))
self.model[-1].fit(self.X[train], self.Y[train])
self.print_acc(self.model[-1].predict(self.X[valid]), self.Y[valid])
def test(self, X_test):
if type(self.model) is not list:
Y_pred = self.model.predict(X_test)
return Y_pred
else:
nFold = len(self.model)
#Y_pred_cv = np.zeros((nFold, X_test.shape[0]))
Y_pred_cv = np.asarray([rf.predict(X_test) for rf in self.model])
Y_pred = stats.mode(Y_pred_cv)[0].T.astype(np.int)
return Y_pred
if __name__ == '__main__':
digit = DigitData( trainfile = 'train.csv',
testfile = 'test.csv',
sampleresult = 'rf_benchmark.csv')
images_trn, labels_trn = digit.data_trn
feats_trn = digit.extract_feat(imgs = images_trn, ftype='overfeat')
feats_tst = digit.extract_feat(imgs = digit.data_tst, ftype='overfeat')
model = DigitModel( X = feats_trn,
Y = labels_trn,
Mopts = {'type': 'RF', 'nTree': 100})
model.cv_train(10)
labels_tst = model.test(feats_tst)
digit.save_data(labels_tst)