Mercurial > ift6266

annotate datasets/ftfile.py @ 209:d982dfa583df

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author fsavard
date Fri, 05 Mar 2010 18:08:34 -0500
parents f0f47b045cbf
children 1faae5079522
rev   line source
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1 from pylearn.io.filetensor import _read_header, _prod
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2 import numpy, theano
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3 from dataset import DataSet
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4 from dsetiter import DataIterator
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5 from itertools import izip, imap
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6
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7 class FTFile(object):
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8 def __init__(self, fname, scale=1, dtype=None):
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9 r"""
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10 Tests:
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11 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft')
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12 """
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13 self.file = open(fname, 'rb')
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14 self.magic_t, self.elsize, _, self.dim, _ = _read_header(self.file, False)
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15 self.size = self.dim[0]
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16 self.scale = scale
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17 self.dtype = dtype
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18
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19 def skip(self, num):
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20 r"""
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21 Skips `num` items in the file.
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22
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23 If `num` is negative, skips size-num.
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24
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25 Tests:
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26 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft')
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27 >>> f.size
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28 58646
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29 >>> f.elsize
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30 4
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31 >>> f.file.tell()
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32 20
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33 >>> f.skip(1000)
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34 >>> f.file.tell()
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35 4020
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36 >>> f.size
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37 57646
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38 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft')
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39 >>> f.size
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40 58646
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41 >>> f.file.tell()
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42 20
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43 >>> f.skip(-1000)
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44 >>> f.file.tell()
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45 230604
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46 >>> f.size
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47 1000
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48 """
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49 if num < 0:
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50 num += self.size
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51 if num < 0:
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52 raise ValueError('Skipping past the start of the file')
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53 if num >= self.size:
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54 self.size = 0
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55 else:
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56 self.size -= num
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57 f_start = self.file.tell()
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58 self.file.seek(f_start + (self.elsize * _prod(self.dim[1:]) * num))
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59
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60 def read(self, num):
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61 r"""
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62 Reads `num` elements from the file and return the result as a
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63 numpy matrix. Last read is truncated.
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64
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65 Tests:
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66 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_labels.ft')
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67 >>> f.read(1)
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68 array([6], dtype=int32)
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69 >>> f.read(10)
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70 array([7, 4, 7, 5, 6, 4, 8, 0, 9, 6], dtype=int32)
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71 >>> f.skip(58630)
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72 >>> f.read(10)
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73 array([9, 2, 4, 2, 8], dtype=int32)
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74 >>> f.read(10)
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75 array([], dtype=int32)
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76 >>> f = FTFile('/data/lisa/data/nist/by_class/digits/digits_test_data.ft')
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77 >>> f.read(1)
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78 array([[0, 0, 0, ..., 0, 0, 0]], dtype=uint8)
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79 """
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80 if num > self.size:
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81 num = self.size
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82 self.dim[0] = num
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83 self.size -= num
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84 res = numpy.fromfile(self.file, dtype=self.magic_t, count=_prod(self.dim)).reshape(self.dim)
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85 if self.dtype is not None:
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86 res = res.astype(self.dtype)
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87 if self.scale != 1:
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88 res /= self.scale
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89 return res
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90
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91 class FTSource(object):
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92 def __init__(self, file, skip=0, size=None, dtype=None, scale=1):
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93 r"""
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94 Create a data source from a possible subset of a .ft file.
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95
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96 Parameters:
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97 `file` (string) -- the filename
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98 `skip` (int, optional) -- amount of examples to skip from
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99 the start of the file. If
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100 negative, skips filesize - skip.
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101 `size` (int, optional) -- truncates number of examples
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102 read (after skipping). If
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103 negative truncates to
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104 filesize - size
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105 (also after skipping).
180
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106 `dtype` (dtype, optional) -- convert the data to this
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107 dtype after reading.
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108 `scale` (number, optional) -- scale (that is divide) the
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109 data by this number (after
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110 dtype conversion, if any).
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111
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112 Tests:
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113 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft')
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114 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=1000)
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115 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=10)
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116 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=100, size=120)
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117 """
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118 self.file = file
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119 self.skip = skip
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120 self.size = size
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121 self.dtype = dtype
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122 self.scale = scale
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123
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124 def open(self):
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125 r"""
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126 Returns an FTFile that corresponds to this dataset.
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127
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128 Tests:
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129 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft')
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130 >>> f = s.open()
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131 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=1)
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132 >>> len(s.open().read(2))
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133 1
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134 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=57646)
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135 >>> s.open().size
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136 1000
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137 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', skip=57646, size=1)
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138 >>> s.open().size
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139 1
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140 >>> s = FTSource('/data/lisa/data/nist/by_class/digits/digits_test_data.ft', size=-10)
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141 >>> s.open().size
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142 58636
163
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143 """
180
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144 f = FTFile(self.file, scale=self.scale, dtype=self.dtype)
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145 if self.skip != 0:
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146 f.skip(self.skip)
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147 if self.size is not None and self.size < f.size:
173
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148 if self.size < 0:
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149 f.size += self.size
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150 else:
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151 f.size = self.size
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152 return f
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153
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154 class FTData(object):
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155 r"""
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156 This is a list of FTSources.
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157 """
180
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158 def __init__(self, datafiles, labelfiles, skip=0, size=None,
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159 inscale=1, indtype=None, outscale=1, outdtype=None):
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160 self.inputs = [FTSource(f, skip, size, scale=inscale, dtype=indtype)
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161 for f in datafiles]
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162 self.outputs = [FTSource(f, skip, size, scale=outscale, dtype=outdtype)
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163 for f in labelfiles]
163
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164
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165 def open_inputs(self):
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166 return [f.open() for f in self.inputs]
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167
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168 def open_outputs(self):
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169 return [f.open() for f in self.outputs]
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170
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171
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172 class FTDataSet(DataSet):
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173 def __init__(self, train_data, train_lbl, test_data, test_lbl, valid_data=None, valid_lbl=None, indtype=None, outdtype=None, inscale=1, outscale=1):
163
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174 r"""
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175 Defines a DataSet from a bunch of files.
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176
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177 Parameters:
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178 `train_data` -- list of train data files
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179 `train_label` -- list of train label files (same length as `train_data`)
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180 `test_data`, `test_labels` -- same thing as train, but for
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181 test. The number of files
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182 can differ from train.
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183 `valid_data`, `valid_labels` -- same thing again for validation.
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184 (optional)
180
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185 `indtype`, `outdtype`, -- see FTSource.__init__()
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186 `inscale`, `outscale` (optional)
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187
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188
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189 If `valid_data` and `valid_labels` are not supplied then a sample
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190 approximately equal in size to the test set is taken from the train
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191 set.
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192 """
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193 if valid_data is None:
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194 total_valid_size = sum(FTFile(td).size for td in test_data)
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195 valid_size = total_valid_size/len(train_data)
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196 self._train = FTData(train_data, train_lbl, size=-valid_size)
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197 self._valid = FTData(train_data, train_lbl, skip=-valid_size)
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198 else:
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199 self._train = FTData(train_data, train_lbl)
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200 self._valid = FTData(valid_data, valid_lbl)
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201 self._test = FTData(test_data, test_lbl)
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202
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203 def _return_it(self, batchsize, bufsize, ftdata):
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204 return izip(DataIterator(ftdata.open_inputs(), batchsize, bufsize),
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205 DataIterator(ftdata.open_outputs(), batchsize, bufsize))