Mercurial > pylearn
annotate dataset.py @ 4:f7dcfb5f9d5b
Added test for dataset.
author | bengioy@bengiomac.local |
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date | Sun, 23 Mar 2008 22:14:10 -0400 |
parents | 378b68d5c4ad |
children | 8039918516fe |
rev | line source |
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1 |
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2 |
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3 class DataSet(object): |
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4 """ |
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5 This is a virtual base class or interface for datasets. |
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6 A dataset is basically an iterator over examples. It does not necessarily |
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7 have a fixed length (this is useful for 'streams' which feed on-line learning). |
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8 Datasets with fixed and known length are FiniteDataSet, a subclass of DataSet. |
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9 Examples and datasets have named fields. |
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10 One can obtain a sub-dataset by taking dataset.field or dataset(field1,field2,field3,...). |
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11 Fields are not mutually exclusive, i.e. two fields can overlap in their actual content. |
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12 The content of a field can be of any type, but often will be a numpy tensor. |
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13 """ |
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14 |
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15 def __init__(self): |
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16 pass |
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17 |
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18 def __iter__(self): |
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19 return self |
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20 |
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21 def next(self): |
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22 """Return the next example in the dataset.""" |
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23 raise NotImplementedError |
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24 |
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25 def __getattr__(self,fieldname): |
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26 """Return a sub-dataset containing only the given fieldname as field.""" |
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27 return self(fieldname) |
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28 |
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29 def __call__(self,*fieldnames): |
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30 """Return a sub-dataset containing only the given fieldnames as fields.""" |
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31 raise NotImplementedError |
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32 |
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33 def fieldNames(self): |
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34 """Return the list of field names that are supported by getattr and getFields.""" |
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35 raise NotImplementedError |
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36 |
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37 class FiniteDataSet(DataSet): |
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38 """ |
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39 Virtual interface, a subclass of DataSet for datasets which have a finite, known length. |
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40 Examples are indexed by an integer between 0 and self.length()-1, |
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41 and a subdataset can be obtained by slicing. |
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42 """ |
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43 |
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44 def __init__(self): |
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45 pass |
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46 |
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47 def __len__(self): |
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48 """len(dataset) returns the number of examples in the dataset.""" |
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49 raise NotImplementedError |
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50 |
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51 def __getitem__(self,i): |
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52 """dataset[i] returns the (i+1)-th example of the dataset.""" |
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53 raise NotImplementedError |
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54 |
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55 def __getslice__(self,*slice_args): |
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56 """dataset[i:j] returns the subdataset with examples i,i+1,...,j-1.""" |
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57 raise NotImplementedError |
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58 |
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59 # we may want ArrayDataSet defined in another python file |
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60 |
4 | 61 import numpy |
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62 |
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63 class ArrayDataSet(FiniteDataSet): |
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64 """ |
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65 A fixed-length and fixed-width dataset in which each element is a numpy.array |
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66 or a number, hence the whole dataset corresponds to a numpy.array. Fields |
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67 must correspond to a slice of columns. If the dataset has fields, |
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68 each 'example' is just a one-row ArrayDataSet, otherwise it is a numpy.array. |
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69 Any dataset can also be converted to a numpy.array (losing the notion of fields) |
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70 by the asarray(dataset) call. |
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71 """ |
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72 |
4 | 73 def __init__(self,dataset=None,data=None,fields={}): |
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74 """ |
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75 Construct an ArrayDataSet, either from a DataSet, or from |
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76 a numpy.array plus an optional specification of fields (by |
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77 a dictionary of column slices indexed by field names). |
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78 """ |
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79 self.current_row=-1 # used for view of this dataset as an iterator |
4 | 80 if dataset!=None: |
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81 assert data==None and fields=={} |
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82 # convert dataset to an ArrayDataSet |
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83 raise NotImplementedError |
4 | 84 if data!=None: |
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85 assert dataset==None |
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86 self.data=data |
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87 self.fields=fields |
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88 self.width = data.shape[1] |
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89 for fieldname in fields: |
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90 fieldslice=fields[fieldname] |
4 | 91 # make sure fieldslice.start and fieldslice.step are defined |
92 start=fieldslice.start | |
93 step=fieldslice.step | |
94 if not start: | |
95 start=0 | |
96 if not step: | |
97 step=1 | |
98 if not fieldslice.start or not fieldslice.step: | |
99 fieldslice = slice(start,fieldslice.stop,step) | |
100 # and coherent with the data array | |
101 assert fieldslice.start>=0 and fieldslice.stop<=self.width | |
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102 |
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103 def next(self): |
4 | 104 """ |
105 Return the next example in the dataset. If the dataset has fields, | |
106 the 'example' is just a one-row ArrayDataSet, otherwise it is a numpy.array. | |
107 """ | |
108 if self.fields: | |
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109 self.current_row+=1 |
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110 if self.current_row==len(self.data): |
4 | 111 self.current_row=-1 |
112 raise StopIteration | |
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113 return self[self.current_row] |
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114 else: |
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115 return self.data[self.current_row] |
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116 |
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117 def __getattr__(self,fieldname): |
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118 """Return a sub-dataset containing only the given fieldname as field.""" |
4 | 119 data=self.data[self.fields[fieldname]] |
120 if len(data)==1: | |
121 return data | |
122 else: | |
123 return ArrayDataSet(data=data) | |
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124 |
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125 def __call__(self,*fieldnames): |
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126 """Return a sub-dataset containing only the given fieldnames as fields.""" |
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127 min_col=self.data.shape[1] |
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128 max_col=0 |
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129 for field_slice in self.fields.values(): |
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130 min_col=min(min_col,field_slice.start) |
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131 max_col=max(max_col,field_slice.stop) |
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132 new_fields={} |
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133 for field in self.fields: |
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134 new_fields[field[0]]=slice(field[1].start-min_col,field[1].stop-min_col,field[1].step) |
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135 return ArrayDataSet(data=self.data[:,min_col:max_col],fields=new_fields) |
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136 |
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137 def fieldNames(self): |
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138 """Return the list of field names that are supported by getattr and getFields.""" |
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139 return self.fields.keys() |
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140 |
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141 def __len__(self): |
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142 """len(dataset) returns the number of examples in the dataset.""" |
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143 return len(self.data) |
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144 |
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145 def __getitem__(self,i): |
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146 """ |
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147 dataset[i] returns the (i+1)-th example of the dataset. If the dataset has fields |
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148 then a one-example dataset is returned (to be able to handle example.field accesses). |
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149 """ |
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150 if self.fields: |
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151 if isinstance(i,slice): |
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152 return ArrayDataSet(data=data[slice],fields=self.fields) |
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153 return ArrayDataSet(data=self.data[i:i+1],fields=self.fields) |
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154 else: |
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155 return data[i] |
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156 |
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157 def __getslice__(self,*slice_args): |
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158 """dataset[i:j] returns the subdataset with examples i,i+1,...,j-1.""" |
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159 return ArrayDataSet(data=self.data[slice(slice_args)],fields=self.fields) |
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160 |
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161 def asarray(self): |
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162 if self.fields: |
4 | 163 columns_used = numpy.zeros((self.data.shape[1]),dtype=bool) |
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164 for field_slice in self.fields.values(): |
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165 for c in xrange(field_slice.start,field_slice.stop,field_slice.step): |
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166 columns_used[c]=True |
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167 # try to figure out if we can map all the slices into one slice: |
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168 mappable_to_one_slice = True |
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169 start=0 |
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170 while start<len(columns_used) and not columns_used[start]: |
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171 start+=1 |
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172 stop=len(columns_used) |
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173 while stop>0 and not columns_used[stop-1]: |
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174 stop-=1 |
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175 step=0 |
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176 i=start |
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177 while i<stop: |
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178 j=i+1 |
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179 while not columns_used[j] and j<stop: |
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180 j+=1 |
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181 if step: |
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182 if step!=j-i: |
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183 mappable_to_one_slice = False |
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184 break |
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185 else: |
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186 step = j-i |
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187 if mappable_to_one_slice: |
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188 return data[slice(start,stop,step)] |
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189 # else make contiguous copy |
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190 n_columns = sum(columns_used) |
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191 result = zeros((len(self.data),n_columns)+self.data.shape[2:],self.data.dtype) |
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192 c=0 |
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193 for field_slice in self.fields.values(): |
4 | 194 slice_width=field_slice.stop-field_slice.start/field_slice.step |
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195 # copy the field here |
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196 result[:,slice(c,slice_width)]=self.data[field_slice] |
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197 c+=slice_width |
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198 return result |
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199 return self.data |
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200 |