Mercurial > ift6266

annotate deep/amt/amt.py @ 583:ae77edb9df67

Find changesets by keywords (author, files, the commit message), revision number or hash, or revset expression.
DIRO techreport, sent to arXiv
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Sat, 18 Sep 2010 16:44:46 -0400
parents 7bdd412754ea
children
rev   line source
412
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
humel
parents: 403
diff changeset
1 # Script usage : python amt.py filname.cvs type
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2 """
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3 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv all
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4 Testing on : all
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5 Total entries : 300.0
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6 Turks per batch : 3
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7 Average test error : 45.3333333333%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
humel
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8 Error variance : 7.77777777778%
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9 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv 36
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10 Testing on : 36
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11 Total entries : 300.0
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12 Turks per batch : 3
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13 Average test error : 51.6666666667%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
humel
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14 Error variance : 3.33333333333%
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15 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv upper
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16 Testing on : upper
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17 Total entries : 63.0
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18 Turks per batch : 3
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19 Average test error : 53.9682539683%
6478eef4f8aa Added support for calculating the test error over different set of classes (lower,upper,digits,all,36)
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20 Error variance : 1.77777777778%
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21 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv lower
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22 Testing on : lower
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23 Total entries : 135.0
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24 Turks per batch : 3
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25 Average test error : 37.037037037%
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26 Error variance : 3.77777777778%
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27 [rifaisal@timide ../fix/ift6266/deep/amt]$ python amt.py pnist.csv digits
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28 Testing on : digits
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29 Total entries : 102.0
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30 Turks per batch : 3
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31 Average test error : 50.9803921569%
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32 Error variance : 1.33333333333%
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33 """
399
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34
402
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35 import csv,numpy,re,decimal
449
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36 from ift6266 import datasets
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37 from pylearn.io import filetensor as ft
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38
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39 fnist = open('nist_train_class_freq.ft','r')
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40 fp07 = open('p07_train_class_freq.ft','r')
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41 fpnist = open('pnist_train_class_freq.ft','r')
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42
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43 nist_freq_table = ft.read(fnist)
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44 p07_freq_table = ft.read(fp07)
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45 pnist_freq_table = ft.read(fpnist)
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46
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47 fnist.close();fp07.close();fpnist.close()
399
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48
402
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49 DATASET_PATH = { 'nist' : '/data/lisa/data/ift6266h10/amt_data/nist/',
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50 'p07' : '/data/lisa/data/ift6266h10/amt_data/p07/',
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51 'pnist' : '/data/lisa/data/ift6266h10/amt_data/pnist/' }
399
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52
449
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53 freq_tables = { 'nist' : nist_freq_table,
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54 'p07' : p07_freq_table,
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55 'pnist': pnist_freq_table }
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56
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57
402
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58 CVSFILE = None
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59 #PATH = None
403
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60 answer_labels = [ 'Answer.c'+str(i+1) for i in range(10) ]
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61 img_url = 'Input.image_url'
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62 turks_per_batch = 3
402
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63 image_per_batch = 10
412
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64 TYPE = None
399
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65
412
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66 def all_classes_assoc():
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67 answer_assoc = {}
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68 for i in range(0,10):
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69 answer_assoc[str(i)]=i
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70 for i in range(10,36):
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71 answer_assoc[chr(i+55)]=i
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72 for i in range(36,62):
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73 answer_assoc[chr(i+61)]=i
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74 return answer_assoc
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75
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76 def upper_classes_assoc():
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77 answer_assoc = {}
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78 for i in range(10,36):
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79 answer_assoc[chr(i+55)]=i
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80 return answer_assoc
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81
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82 def lower_classes_assoc():
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83 answer_assoc = {}
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84 for i in range(36,62):
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85 answer_assoc[chr(i+61)]=i
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86 return answer_assoc
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87
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88 def digit_classes_assoc():
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89 answer_assoc = {}
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90 for i in range(0,10):
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91 answer_assoc[str(i)]=i
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92 return answer_assoc
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93
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94 def tsix_classes_assoc():
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95 answer_assoc = {}
430
5777b5041ac9 fixed error computation for 36 classes
Dumitru Erhan <dumitru.erhan@gmail.com>
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96 for i in range(0,10):
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97 answer_assoc[str(i)]=i
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98 for i in range(10,36):
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99 answer_assoc[chr(i+55)]=i
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100 answer_assoc[chr(i+87)]=i
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101 return answer_assoc
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102
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103 def upper_label_assoc(ulabel):
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104 for i in range(len(ulabel)):
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105 if ulabel[i] < 10 or ulabel[i] > 35 :
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106 ulabel[i] = -1
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107 return ulabel
401
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108
412
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109 def lower_label_assoc(ulabel):
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110 for i in range(len(ulabel)):
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111 if ulabel[i] < 36 or ulabel[i] > 61 :
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112 ulabel[i] = -1
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113 return ulabel
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114
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115 def tsix_label_assoc(ulabel):
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116 for i in range(len(ulabel)):
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117 if ulabel[i] > 35 and ulabel[i] < 62 :
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118 ulabel[i] = ulabel[i] - 26
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119 return ulabel
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120
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121 def digit_label_assoc(ulabel):
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122 for i in range(len(ulabel)):
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123 if ulabel[i] < 0 or ulabel[i] > 9 :
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124 ulabel[i] = -1
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125
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126 return ulabel
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127
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128 def classes_answer(type):
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129 if type == 'all':
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130 return all_classes_assoc()
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131 elif type == '36':
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132 return tsix_classes_assoc()
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133 elif type == 'lower':
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134 return lower_classes_assoc()
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135 elif type == 'upper':
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136 return upper_classes_assoc()
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137 elif type == 'digits':
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138 return digit_classes_assoc()
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139 else:
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140 raise ('Inapropriate option for the type of classification :' + type)
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141
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142
449
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143 def test_error(assoc_type=TYPE,consensus=True):
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144 answer_assoc = classes_answer(assoc_type)
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145
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146 turks = []
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147 reader = csv.DictReader(open(CVSFILE), delimiter=',')
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148 entries = [ turk for turk in reader ]
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149
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150 errors = numpy.zeros((len(entries),))
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151 if len(entries) % turks_per_batch != 0 :
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152 raise Exception('Wrong number of entries or turks_per_batch')
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153
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154 total_uniq_entries = len(entries) / turks_per_batch
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155
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156
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157 error_variances = numpy.zeros((total_uniq_entries,))
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158
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159 if consensus:
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160 errors = numpy.zeros((total_uniq_entries,))
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161 num_examples = numpy.zeros((total_uniq_entries,))
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162 for i in range(total_uniq_entries):
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163 errors[i],num_examples[i] = get_turk_consensus_error(entries[i*turks_per_batch:(i+1)*turks_per_batch],assoc_type)
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164 error_variances[i] = errors[i*turks_per_batch:(i+1)*turks_per_batch].var()
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165 else:
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166 errors = numpy.zeros((len(entries),))
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167 num_examples = numpy.zeros((len(entries),))
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168 for i in range(total_uniq_entries):
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169 for t in range(turks_per_batch):
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170 errors[i*turks_per_batch+t],num_examples[i*turks_per_batch+t] = get_error(entries[i*turks_per_batch+t],assoc_type)
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171 error_variances[i] = errors[i*turks_per_batch:(i+1)*turks_per_batch].var()
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172
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173 percentage_error = 100. * errors.sum() / num_examples.sum()
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174 print 'Testing on : ' + str(assoc_type)
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175 print 'Total entries : ' + str(num_examples.sum())
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176 print 'Turks per batch : ' + str(turks_per_batch)
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177 print 'Average test error : ' + str(percentage_error) +'%'
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178 print 'Error variance : ' + str(error_variances.mean()*image_per_batch) +'%'
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179
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180
402
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181 def find_dataset(entry):
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182 file = parse_filename(entry[img_url])
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183 return file.split('_')[0]
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184
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185 def get_error(entry, type):
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186 answer_assoc = classes_answer(type)
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187 labels = get_labels(entry,type)
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188 test_error = 0
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189 cnt = 0
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190 for i in range(len(answer_labels)):
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191 if labels[i] == -1:
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192 cnt+=1
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193 else:
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194 answer = entry[answer_labels[i]]
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195 try:
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196 if answer_assoc[answer] != labels[i]:
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197 test_error+=1
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198 except:
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199 test_error+=1
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200 return test_error,image_per_batch-cnt
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201
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202 def get_turk_consensus_error(entries, type):
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203 answer_assoc = classes_answer(type)
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204 labels = get_labels(entries[0],type)
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205 test_error = 0
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206 cnt = 0
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207 answer= []
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208 freq_t = freq_tables[find_dataset(entries[0])]
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209 for i in range(len(answer_labels)):
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210 if labels[i] == -1:
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211 cnt+=1
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212 else:
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213 answers = [ entry[answer_labels[i]] for entry in entries ]
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214 if answers[0] != answers[1] and answers[1] != answers[2] and answers[0] != answers[2]:
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215 m = max([ freq_t[answer_assoc[answer]] for answer in answers])
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216 for answer in answers:
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217 if freq_t[answer_assoc[answer]] == m :
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218 a = answer
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219 else:
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220 for answer in answers:
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221 if answers.count(answer) > 1 :
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222 a =answer
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223 try:
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224 if answer_assoc[answer] != labels[i]:
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225 test_error+=1
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226 except:
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227 test_error+=1
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228 return test_error,image_per_batch-cnt
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229 def frequency_table():
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230 filenames = ['nist_train_class_freq.ft','p07_train_class_freq.ft','pnist_train_class_freq.ft']
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231 iterators = [datasets.nist_all(),datasets.nist_P07(),datasets.PNIST07()]
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232 for dataset,filename in zip(iterators,filenames):
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233 freq_table = numpy.zeros(62)
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234 for x,y in dataset.train(1):
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235 freq_table[int(y)]+=1
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236 f = open(filename,'w')
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237 ft.write(f,freq_table)
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238 f.close()
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239
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240 def get_labels(entry,type):
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241 file = parse_filename(entry[img_url])
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242 path = DATASET_PATH[find_dataset(entry)]
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243 f = open(path+file,'r')
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244 str_labels = re.sub("\s+", "",f.readline()).strip()[1:-2].split('.')
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245 unrestricted_labels = [ int(element) for element in str_labels ]
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246 if type == 'all':
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247 return unrestricted_labels
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248 elif type == '36':
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249 return tsix_label_assoc(unrestricted_labels)
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250 elif type == 'lower':
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251 return lower_label_assoc(unrestricted_labels)
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252 elif type == 'upper':
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253 return upper_label_assoc(unrestricted_labels)
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254 elif type == 'digits':
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255 return digit_label_assoc(unrestricted_labels)
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256 else:
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257 raise ('Inapropriate option for the type of classification :' + str(type))
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258
399
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259
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260 def parse_filename(string):
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261 filename = string.split('/')[-1]
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262 return filename.split('.')[0]+'.txt'
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263
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264 if __name__ =='__main__':
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265 import sys
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266 CVSFILE = sys.argv[1]
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267 test_error(sys.argv[2],int(sys.argv[3]))
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268 #frequency_table()
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269