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annotate pylearn/datasets/utlc.py @ 1428:3823dbfff6cf

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add parameter to randomize the valid and test data.
author Frederic Bastien <nouiz@nouiz.org>
date Tue, 08 Feb 2011 12:57:15 -0500
parents a36d3a406c59
children b0141efbf6a2
rev   line source
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1 """
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2 user should use the load _ndarray_dataset or load_sparse_dataset function
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3
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4 See the file ${PYLEARN_DATA_ROOT}/UTCL/README for detail on the datasets.
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5
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6 See the end of this file for an example.
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7 """
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8
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9 import cPickle
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10 import gzip
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11 import os
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12
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13 import numpy
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14 import theano
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15
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16 import pylearn.io.filetensor as ft
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17 import config
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18
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19 def load_ndarray_dataset(name, normalize=True, transfer=False,
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20 normalize_on_the_fly=False, randomize_valid=False,
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21 randomize_test=False):
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22 """ Load the train,valid,test data for the dataset `name`
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23 and return it in ndarray format.
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24
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25 :param normalize: If True, we normalize the train dataset
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26 before returning it
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27 :param transfer: If True also return the transfer label(currently only available for ule)
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28 :param normalize_on_the_fly: If True, we return a Theano Variable that will give
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29 as output the normalized value. If the user only
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30 take a subtensor of that variable, Theano optimization
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31 should make that we will only have in memory the subtensor
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32 portion that is computed in normalized form. We store
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33 the original data in shared memory in its original dtype.
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34
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35 This is usefull to have the original data in its original
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36 dtype in memory to same memory. Especialy usefull to
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37 be able to use rita and harry with 1G per jobs.
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38 :param randomize_valid: Do we randomize the order of the valid set?
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39 We always use the same random order
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40 If False, return in the same order as downloaded on the web
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41 :param randomize_test: Do we randomize the order of the test set?
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42 We always use the same random order
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43 If False, return in the same order as downloaded on the web
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44 """
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45 assert not (normalize and normalize_on_the_fly), "Can't normalize in 2 way at the same time!"
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46
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47 assert name in ['avicenna','harry','rita','sylvester','ule']
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48 common = os.path.join('UTLC','filetensor',name+'_')
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49 trname,vname,tename = [config.get_filepath_in_roots(common+subset+'.ft.gz',
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50 common+subset+'.ft')
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51 for subset in ['train','valid','test']]
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52
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53 train = load_filetensor(trname)
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54 valid = load_filetensor(vname)
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55 test = load_filetensor(tename)
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56 if randomize_valid:
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57 rng = numpy.random.RandomState([1,2,3,4])
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58 perm = rng.permutation(valid.shape[0])
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59 valid = valid[perm]
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60 if randomize_test:
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61 rng = numpy.random.RandomState([1,2,3,4])
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62 perm = rng.permutation(test.shape[0])
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63 test = test[perm]
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64
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65 if normalize or normalize_on_the_fly:
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66 if normalize_on_the_fly:
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67 train = theano.shared(train, borrow=True, name=name+"_train")
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68 valid = theano.shared(valid, borrow=True, name=name+"_valid")
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69 test = theano.shared(test, borrow=True, name=name+"_test")
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70 else:
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71 train = numpy.asarray(train, theano.config.floatX)
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72 valid = numpy.asarray(valid, theano.config.floatX)
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73 test = numpy.asarray(test, theano.config.floatX)
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74
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75 if name == "ule":
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76 train /= 255
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77 valid /= 255
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78 test /= 255
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79 elif name in ["avicenna", "sylvester"]:
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80 if name == "avicenna":
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81 train_mean = 514.62154022835455
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82 train_std = 6.829096494224145
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83 else:
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84 train_mean = 403.81889927027686
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85 train_std = 96.43841050784053
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86 train -= train_mean
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87 valid -= train_mean
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88 test -= train_mean
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89 train /= train_std
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90 valid /= train_std
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91 test /= train_std
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92 elif name == "harry":
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93 std = 0.69336046033925791#train.std()slow to compute
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94 train /= std
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95 valid /= std
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96 test /= std
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97 elif name == "rita":
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98 v = numpy.asarray(230, dtype=theano.config.floatX)
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99 train /= v
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100 valid /= v
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101 test /= v
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102 else:
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103 raise Exception("This dataset don't have its normalization defined")
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104 if transfer:
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105 transfer = load_filetensor(os.path.join(config.data_root(),"UTLC","filetensor",name+"_transfer.ft"))
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106 return train, valid, test, transfer
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107 else:
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108 return train, valid, test
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109
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110 def load_sparse_dataset(name, normalize=True, transfer=False):
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111 """ Load the train,valid,test data for the dataset `name`
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112 and return it in sparse format.
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113
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114 :param normalize: If True, we normalize the train dataset
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115 before returning it
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116 :param transfer: If True also return the transfer label
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117 """
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118 assert name in ['harry','terry','ule']
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119 trname,vname,tename = [os.path.join(config.data_root(),
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120 'UTLC','sparse',
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121 name+'_'+subset+'.npy')
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122 for subset in ['train','valid','test']]
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123 train = load_sparse(trname)
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124 valid = load_sparse(vname)
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125 test = load_sparse(tename)
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126 if normalize:
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127 if name == "ule":
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128 train = train.astype(theano.config.floatX) / 255
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129 valid = valid.astype(theano.config.floatX) / 255
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130 test = test.astype(theano.config.floatX) / 255
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131 elif name == "harry":
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132 train = train.astype(theano.config.floatX)
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133 valid = valid.astype(theano.config.floatX)
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134 test = test.astype(theano.config.floatX)
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135 std = 0.69336046033925791#train.std()slow to compute
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136 train = (train) / std
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137 valid = (valid) / std
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138 test = (test) / std
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139 elif name == "terry":
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140 train = train.astype(theano.config.floatX)
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141 valid = valid.astype(theano.config.floatX)
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142 test = test.astype(theano.config.floatX)
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143 train = (train) / 300
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144 valid = (valid) / 300
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145 test = (test) / 300
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146 else:
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147 raise Exception("This dataset don't have its normalization defined")
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148 if transfer:
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149 transfer = load_sparse(os.path.join(config.data_root(),"UTLC","sparse",name+"_transfer.npy"))
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150 return train, valid, test, transfer
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151 else:
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152 return train, valid, test
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153
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154 def load_ndarray_label(name):
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155 """ Load the train,valid,test data for the dataset `name`
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156 and return it in ndarray format.
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157
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158 This is only available for the toy dataset ule.
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159 """
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160 assert name in ['ule']
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161 trname,vname,tename = [os.path.join(config.data_root(),
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162 'UTLC','filetensor',
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163 name+'_'+subset+'.ft')
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164 for subset in ['trainl','validl','testl']]
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165 trainl = load_filetensor(trname)
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166 validl = load_filetensor(vname)
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167 testl = load_filetensor(tename)
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168 return trainl, validl, testl
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169
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170 def load_filetensor(fname):
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171 f = None
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172 try:
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173 if not os.path.exists(fname):
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174 fname = fname+'.gz'
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175 assert os.path.exists(fname)
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176 f = gzip.open(fname)
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177 elif fname.endswith('.gz'):
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178 f = gzip.open(fname)
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179 else:
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180 f = open(fname)
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181 d = ft.read(f)
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182 finally:
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183 if f:
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184 f.close()
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185
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186 return d
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187
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188 def load_sparse(fname):
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189 f = None
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190 try:
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191 if not os.path.exists(fname):
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192 fname = fname+'.gz'
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193 assert os.path.exists(fname)
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194 f = gzip.open(fname)
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195 else:
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196 f = open(fname)
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197 d = cPickle.load(f)
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198 finally:
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199 if f:
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200 f.close()
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201 return d
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202
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203 if __name__ == '__main__':
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204 import numpy
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205 import scipy.sparse
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206
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207 # Test loading of transfer data
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208 train, valid, test, transfer = load_ndarray_dataset("ule", normalize=True, transfer=True)
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209 assert train.shape[0]==transfer.shape[0]
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210
1402
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211 for name in ['avicenna','harry','rita','sylvester','ule']:
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212 train, valid, test = load_ndarray_dataset(name, normalize=True)
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213 print name,"dtype, max, min, mean, std"
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diff changeset
214 print train.dtype, train.max(), train.min(), train.mean(), train.std()
1402
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215 assert isinstance(train, numpy.ndarray)
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216 assert isinstance(valid, numpy.ndarray)
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217 assert isinstance(test, numpy.ndarray)
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218 assert train.shape[1]==test.shape[1]==valid.shape[1]
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diff changeset
219
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220 # Test loading of transfer data
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221 train, valid, test, transfer = load_sparse_dataset("ule", normalize=True, transfer=True)
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diff changeset
222 assert train.shape[0]==transfer.shape[0]
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diff changeset
223
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224 for name in ['harry','terry','ule']:
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diff changeset
225 train, valid, test = load_sparse_dataset(name, normalize=True)
1404
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diff changeset
226 nb_elem = numpy.prod(train.shape)
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diff changeset
227 mi = train.data.min()
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diff changeset
228 ma = train.data.max()
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229 mi = min(0, mi)
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diff changeset
230 ma = max(0, ma)
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diff changeset
231 su = train.data.sum()
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diff changeset
232 mean = float(su)/nb_elem
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diff changeset
233 print name,"dtype, max, min, mean, nb non-zero, nb element, %sparse"
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parents: 1402
diff changeset
234 print train.dtype, ma, mi, mean, train.nnz, nb_elem, (nb_elem-float(train.nnz))/nb_elem
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diff changeset
235 print name,"max, min, mean, std (all stats on non-zero element)"
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diff changeset
236 print train.data.max(), train.data.min(), train.data.mean(), train.data.std()
1402
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237 assert scipy.sparse.issparse(train)
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parents:
diff changeset
238 assert scipy.sparse.issparse(valid)
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parents:
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239 assert scipy.sparse.issparse(test)
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parents:
diff changeset
240 assert train.shape[1]==test.shape[1]==valid.shape[1]