Mercurial > pylearn
annotate dataset.py @ 34:1b152f46ad0c
consolidated code
| author | bergstrj@iro.umontreal.ca |
|---|---|
| date | Thu, 17 Apr 2008 12:49:48 -0400 |
| parents | 46c5c90019c2 |
| children | 438440ba0627 |
| rev | line source |
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1 |
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2 from lookup_list import LookupList |
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3 Example = LookupList |
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4 import copy |
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5 |
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6 class AbstractFunction (Exception): """Derived class must override this function""" |
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7 |
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8 class DataSet(object): |
| 16 | 9 """A virtual base class for datasets. |
| 10 | |
| 11 A DataSet is a generator of iterators; these iterators can run through the | |
| 12 examples in a variety of ways. A DataSet need not necessarily have a finite | |
| 13 or known length, so this class can be used to interface to a 'stream' which | |
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14 feeds on-line learning. |
| 16 | 15 |
| 16 To iterate over examples, there are several possibilities: | |
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17 - for example in dataset.zip([field1, field2,field3, ...]) |
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18 - for val1,val2,val3 in dataset.zip([field1, field2,field3]) |
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19 - for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N) |
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20 - for example in dataset |
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21 Each of these is documented below. All of these iterators are expected |
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22 to provide, in addition to the usual 'next()' method, a 'next_index()' method |
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23 which returns a non-negative integer pointing to the position of the next |
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24 example that will be returned by 'next()' (or of the first example in the |
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25 next minibatch returned). This is important because these iterators |
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26 can wrap around the dataset in order to do multiple passes through it, |
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27 in possibly unregular ways if the minibatch size is not a divisor of the |
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28 dataset length. |
| 16 | 29 |
| 30 Note: Fields are not mutually exclusive, i.e. two fields can overlap in their actual content. | |
| 31 | |
| 32 Note: The content of a field can be of any type. | |
| 33 | |
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34 Note: A dataset can recognize a potentially infinite number of field names (i.e. the field |
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35 values can be computed on-demand, when particular field names are used in one of the |
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36 iterators). |
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37 |
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38 Datasets of finite length should be sub-classes of FiniteLengthDataSet. |
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39 |
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40 Datasets whose elements can be indexed and whose sub-datasets (with a subset |
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41 of examples) can be extracted should be sub-classes of |
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42 SliceableDataSet. |
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43 |
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44 Datasets with a finite number of fields should be sub-classes of |
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45 FiniteWidthDataSet. |
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46 """ |
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47 |
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48 def __init__(self): |
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49 pass |
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50 |
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51 class Iterator(LookupList): |
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52 def __init__(self, ll): |
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53 LookupList.__init__(self, ll.keys(), ll.values()) |
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54 self.ll = ll |
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55 def __iter__(self): #makes for loop work |
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56 return self |
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57 def next(self): |
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58 self.ll.next() |
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59 self._values = [v[0] for v in self.ll._values] |
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60 return self |
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61 def next_index(self): |
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62 return self.ll.next_index() |
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63 |
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64 def __iter__(self): |
| 16 | 65 """Supports the syntax "for i in dataset: ..." |
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66 |
| 16 | 67 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 68 all the fields of DataSet self. Every field of "i" will give access to | |
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69 a field of a single example. Fields should be accessible via |
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70 i["fielname"] or i[3] (in the order defined by the elements of the |
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71 Example returned by this iterator), but the derived class is free |
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72 to accept any type of identifier, and add extra functionality to the iterator. |
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73 """ |
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74 return DataSet.Iterator(self.minibatches(None, minibatch_size = 1)) |
| 16 | 75 |
| 76 def zip(self, *fieldnames): | |
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77 """ |
| 16 | 78 Supports two forms of syntax: |
| 79 | |
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80 for i in dataset.zip([f1, f2, f3]): ... |
| 16 | 81 |
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82 for i1, i2, i3 in dataset.zip([f1, f2, f3]): ... |
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83 |
| 16 | 84 Using the first syntax, "i" will be an indexable object, such as a list, |
| 85 tuple, or Example instance, such that on every iteration, i[0] is the f1 | |
| 86 field of the current example, i[1] is the f2 field, and so on. | |
| 87 | |
| 88 Using the second syntax, i1, i2, i3 will contain the the contents of the | |
| 89 f1, f2, and f3 fields of a single example on each loop iteration. | |
| 90 | |
| 91 The derived class may accept fieldname arguments of any type. | |
| 92 | |
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93 """ |
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94 return DataSet.Iterator(self.minibatches(fieldnames, minibatch_size = 1)) |
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95 |
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96 minibatches_fieldnames = None |
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97 minibatches_minibatch_size = 1 |
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98 minibatches_n_batches = None |
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99 def minibatches(self, |
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100 fieldnames = minibatches_fieldnames, |
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101 minibatch_size = minibatches_minibatch_size, |
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102 n_batches = minibatches_n_batches): |
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103 """ |
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104 Supports three forms of syntax: |
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105 |
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106 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 107 |
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108 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 109 |
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110 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 111 |
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112 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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113 tuple, or Example instance. In both cases, i[k] is a list-like container |
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114 of a batch of current examples. In the second case, i[0] is |
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115 list-like container of the f1 field of a batch current examples, i[1] is |
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116 a list-like container of the f2 field, etc. |
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117 |
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118 Using the first syntax, all the fields will be returned in "i". |
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119 Beware that some datasets may not support this syntax, if the number |
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120 of fields is infinite (i.e. field values may be computed "on demand"). |
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121 |
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122 Using the third syntax, i1, i2, i3 will be list-like containers of the |
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123 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
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124 |
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125 PARAMETERS |
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126 - fieldnames (list of any type, default None): |
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127 The loop variables i1, i2, i3 (in the example above) should contain the |
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128 f1, f2, and f3 fields of the current batch of examples. If None, the |
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129 derived class can choose a default, e.g. all fields. |
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131 - minibatch_size (integer, default 1) |
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132 On every iteration, the variables i1, i2, i3 will have |
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133 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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134 |
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135 - n_batches (integer, default None) |
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136 The iterator will loop exactly this many times, and then stop. If None, |
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137 the derived class can choose a default. If (-1), then the returned |
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138 iterator should support looping indefinitely. |
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139 |
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140 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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141 any other object that supports integer indexing and slicing. |
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142 |
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143 """ |
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144 raise AbstractFunction() |
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145 |
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146 def hasFields(self,*fieldnames): |
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147 """ |
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148 Return true if the given field name (or field names, if multiple arguments are |
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149 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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150 of the iterators). |
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151 """ |
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152 raise AbstractFunction() |
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153 |
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154 |
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155 def merge_fields(self,*specifications): |
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156 """ |
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157 Return a new dataset that maps old fields (of self) to new fields (of the returned |
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158 dataset). The minimal syntax that should be supported is the following: |
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159 new_field_specifications = [new_field_spec1, new_field_spec2, ...] |
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160 new_field_spec = ([old_field1, old_field2, ...], new_field) |
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161 In general both old_field and new_field should be strings, but some datasets may also |
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162 support additional indexing schemes within each field (e.g. column slice |
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163 of a matrix-like field). |
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164 """ |
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165 raise AbstractFunction() |
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166 |
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167 def merge_field_values(self,*field_value_pairs): |
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168 """ |
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169 Return the value that corresponds to merging the values of several fields, |
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170 given as arguments (field_name, field_value) pairs with self.hasField(field_name). |
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171 This may be used by implementations of merge_fields. |
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172 Raise a ValueError if the operation is not possible. |
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173 """ |
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174 fieldnames,fieldvalues = zip(*field_value_pairs) |
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175 raise ValueError("Unable to merge values of these fields:"+repr(fieldnames)) |
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176 |
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177 def examples2minibatch(self,examples): |
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178 """ |
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179 Combine a list of Examples into a minibatch. A minibatch is an Example whose fields |
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180 are iterable over the examples of the minibatch. |
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181 """ |
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182 raise AbstractFunction() |
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183 |
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184 def rename(self,rename_dict): |
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185 """ |
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186 Changes a dataset into one that renames fields, using a dictionnary that maps old field |
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187 names to new field names. The only fields visible by the returned dataset are those |
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188 whose names are keys of the rename_dict. |
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189 """ |
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190 self_class = self.__class__ |
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191 class SelfRenamingDataSet(RenamingDataSet,self_class): |
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192 pass |
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193 self.__class__ = SelfRenamingDataSet |
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194 # set the rename_dict and src fields |
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195 SelfRenamingDataSet.__init__(self,self,rename_dict) |
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196 return self |
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197 |
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198 def apply_function(self,function, input_fields, output_fields, copy_inputs=True, accept_minibatches=True, cache=True): |
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199 """ |
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200 Changes a dataset into one that contains as fields the results of applying |
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201 the given function (example-wise) to the specified input_fields. The |
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202 function should return a sequence whose elements will be stored in |
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203 fields whose names are given in the output_fields list. If copy_inputs |
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204 is True then the resulting dataset will also contain the fields of self. |
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205 If accept_minibatches, then the function may be called |
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206 with minibatches as arguments (what is returned by the minibatches |
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207 iterator). In any case, the computations may be delayed until the examples |
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208 of the resulting dataset are requested. If cache is True, then |
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209 once the output fields for some examples have been computed, then |
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210 are cached (to avoid recomputation if the same examples are again |
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211 requested). |
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212 """ |
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213 self_class = self.__class__ |
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214 class SelfApplyFunctionDataSet(ApplyFunctionDataSet,self_class): |
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215 pass |
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216 self.__class__ = SelfApplyFunctionDataSet |
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217 # set the required additional fields |
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218 ApplyFunctionDataSet.__init__(self,self,function, input_fields, output_fields, copy_inputs, accept_minibatches, cache) |
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219 return self |
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220 |
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221 |
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222 class FiniteLengthDataSet(DataSet): |
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223 """ |
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224 Virtual interface for datasets that have a finite length (number of examples), |
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225 and thus recognize a len(dataset) call. |
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226 """ |
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227 def __init__(self): |
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228 DataSet.__init__(self) |
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229 |
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230 def __len__(self): |
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231 """len(dataset) returns the number of examples in the dataset.""" |
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232 raise AbstractFunction() |
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233 |
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234 def __call__(self,fieldname_or_fieldnames): |
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235 """ |
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236 Extract one or more fields. This may be an expensive operation when the |
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237 dataset is large. It is not the recommanded way to access individual values |
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238 (use the iterators instead). If the argument is a string fieldname, then the result |
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239 is a sequence (iterable object) of values for that field, for the whole dataset. If the |
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240 argument is a list of field names, then the result is a 'batch', i.e., an Example with keys |
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241 corresponding to the given field names and values being iterable objects over the |
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242 individual example values. |
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243 """ |
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244 if type(fieldname_or_fieldnames) is string: |
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245 minibatch = self.minibatches([fieldname_or_fieldnames],len(self)).next() |
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246 return minibatch[fieldname_or_fieldnames] |
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247 return self.minibatches(fieldname_or_fieldnames,len(self)).next() |
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248 |
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249 class SliceableDataSet(DataSet): |
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250 """ |
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251 Virtual interface, a subclass of DataSet for datasets which are sliceable |
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252 and whose individual elements can be accessed, generally respecting the |
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253 python semantics for [spec], where spec is either a non-negative integer |
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254 (for selecting one example), a python slice(start,stop,step) for selecting a regular |
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255 sub-dataset comprising examples start,start+step,start+2*step,...,n (with n<stop), or a |
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256 sequence (e.g. a list) of integers [i1,i2,...,in] for selecting |
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257 an arbitrary subset of examples. This is useful for obtaining |
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258 sub-datasets, e.g. for splitting a dataset into training and test sets. |
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259 """ |
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260 def __init__(self): |
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261 DataSet.__init__(self) |
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262 |
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263 def minibatches(self, |
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264 fieldnames = DataSet.minibatches_fieldnames, |
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265 minibatch_size = DataSet.minibatches_minibatch_size, |
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266 n_batches = DataSet.minibatches_n_batches): |
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267 """ |
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268 If the n_batches is empty, we want to see all the examples possible |
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269 for the given minibatch_size (possibly missing a few at the end of the dataset). |
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270 """ |
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271 # substitute the defaults: |
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272 if n_batches is None: n_batches = len(self) / minibatch_size |
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273 return DataSet.Iterator(self, fieldnames, minibatch_size, n_batches) |
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274 |
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275 def __getitem__(self,i): |
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276 """ |
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277 dataset[i] returns the (i+1)-th example of the dataset. |
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278 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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279 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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280 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
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281 """ |
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282 raise AbstractFunction() |
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283 |
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284 def __getslice__(self,*slice_args): |
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285 """ |
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286 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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287 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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288 """ |
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289 raise AbstractFunction() |
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290 |
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291 |
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292 class FiniteWidthDataSet(DataSet): |
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293 """ |
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294 Virtual interface for datasets that have a finite width (number of fields), |
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295 and thus return a list of fieldNames. |
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296 """ |
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297 def __init__(self): |
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298 DataSet.__init__(self) |
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299 |
|
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300 def hasFields(self,*fields): |
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301 has_fields=True |
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302 fieldnames = self.fieldNames() |
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303 for name in fields: |
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304 if name not in fieldnames: |
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305 has_fields=False |
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306 return has_fields |
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307 |
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308 def fieldNames(self): |
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309 """Return the list of field names that are supported by the iterators, |
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310 and for which hasFields(fieldname) would return True.""" |
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311 raise AbstractFunction() |
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312 |
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313 |
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314 class RenamingDataSet(FiniteWidthDataSet): |
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315 """A DataSet that wraps another one, and makes it look like the field names |
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316 are different |
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317 |
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318 Renaming is done by a dictionary that maps new names to the old ones used in |
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319 self.src. |
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320 """ |
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321 def __init__(self, src, rename_dct): |
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322 DataSet.__init__(self) |
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323 self.src = src |
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324 self.rename_dct = copy.copy(rename_dct) |
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325 |
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326 def fieldNames(self): |
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327 return self.rename_dct.keys() |
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328 |
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329 def minibatches(self, |
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330 fieldnames = DataSet.minibatches_fieldnames, |
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331 minibatch_size = DataSet.minibatches_minibatch_size, |
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332 n_batches = DataSet.minibatches_n_batches): |
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333 dct = self.rename_dct |
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334 new_fieldnames = [dct.get(f, f) for f in fieldnames] |
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335 return self.src.minibatches(new_fieldnames, minibatches_size, n_batches) |
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336 |
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337 |
|
3
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338 # we may want ArrayDataSet defined in another python file |
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339 |
| 4 | 340 import numpy |
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341 |
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342 def as_array_dataset(dataset): |
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343 # Generally datasets can be efficient by making data fields overlap, but |
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344 # this function doesn't know which fields overlap. So, it should check if |
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345 # dataset supports an as_array_dataset member function, and return that if |
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346 # possible. |
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347 if hasattr(dataset, 'as_array_dataset'): |
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348 return dataset.as_array_dataset() |
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349 |
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350 raise NotImplementedError |
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351 |
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352 # Make ONE big minibatch with all the examples, to separate the fields. |
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353 n_examples = len(dataset) |
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354 batch = dataset.minibatches( minibatch_size = len(dataset)).next() |
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355 |
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356 # Each field of the underlying dataset must be convertible to a numpy array of the same type |
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357 # currently just double, but should use the smallest compatible dtype |
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358 n_fields = len(batch) |
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359 fieldnames = batch.fields.keys() |
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360 total_width = 0 |
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361 type = None |
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362 fields = LookupList() |
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363 for i in xrange(n_fields): |
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364 field = array(batch[i]) |
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365 assert field.shape[0]==n_examples |
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366 width = field.shape[1] |
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367 start=total_width |
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368 total_width += width |
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369 fields[fieldnames[i]]=slice(start,total_width,1) |
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370 # many complicated things remain to be done: |
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371 # - find common dtype |
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372 # - decide what to do with extra dimensions if not the same in all fields |
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373 # - try to see if we can avoid the copy? |
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374 |
|
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375 class ArrayDataSet(FiniteLengthDataSet,FiniteWidthDataSet,SliceableDataSet): |
|
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376 """ |
|
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377 An ArrayDataSet behaves like a numpy array but adds the notion of named fields |
|
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378 from DataSet (and the ability to view the values of multiple fields as an 'Example'). |
|
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379 It is a fixed-length and fixed-width dataset |
|
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380 in which each element is a fixed dimension numpy array or a number, hence the whole |
|
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381 dataset corresponds to a numpy array. Fields |
|
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382 must correspond to a slice of array columns or to a list of column numbers. |
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383 If the dataset has fields, |
|
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384 each 'example' is just a one-row ArrayDataSet, otherwise it is a numpy array. |
|
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385 Any dataset can also be converted to a numpy array (losing the notion of fields |
|
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386 by the numpy.array(dataset) call. |
|
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387 """ |
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388 |
| 19 | 389 class Iterator(LookupList): |
|
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390 """An iterator over a finite dataset that implements wrap-around""" |
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391 def __init__(self, dataset, fieldnames, minibatch_size, next_max): |
|
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392 if fieldnames is None: fieldnames = dataset.fieldNames() |
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393 LookupList.__init__(self, fieldnames, [0]*len(fieldnames)) |
|
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394 self.dataset=dataset |
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395 self.minibatch_size=minibatch_size |
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396 self.next_count = 0 |
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397 self.next_max = next_max |
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398 self.current = -self.minibatch_size |
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399 assert minibatch_size > 0 |
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400 if minibatch_size >= len(dataset): |
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401 raise NotImplementedError() |
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402 |
| 19 | 403 def __iter__(self): #makes for loop work |
|
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404 return self |
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405 |
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406 @staticmethod |
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407 def matcat(a, b): |
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408 a0, a1 = a.shape |
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409 b0, b1 = b.shape |
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410 assert a1 == b1 |
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411 assert a.dtype is b.dtype |
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412 rval = numpy.empty( (a0 + b0, a1), dtype=a.dtype) |
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413 rval[:a0,:] = a |
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414 rval[a0:,:] = b |
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415 return rval |
|
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416 |
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417 def next_index(self): |
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418 n_rows = self.dataset.data.shape[0] |
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419 next_i = self.current+self.minibatch_size |
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420 if next_i >= n_rows: |
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421 next_i -= n_rows |
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422 return next_i |
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423 |
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424 def next(self): |
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425 |
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426 #check for end-of-loop |
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427 self.next_count += 1 |
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428 if self.next_count == self.next_max: |
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429 raise StopIteration |
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430 |
|
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431 #determine the first and last elements of the minibatch slice we'll return |
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432 n_rows = self.dataset.data.shape[0] |
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433 self.current = self.next_index() |
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434 upper = self.current + self.minibatch_size |
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435 |
| 19 | 436 data = self.dataset.data |
| 437 | |
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438 if upper <= n_rows: |
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439 #this is the easy case, we only need once slice |
| 19 | 440 dataview = data[self.current:upper] |
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441 else: |
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442 # the minibatch wraps around the end of the dataset |
| 19 | 443 dataview = data[self.current:] |
|
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444 upper -= n_rows |
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445 assert upper > 0 |
| 19 | 446 dataview = self.matcat(dataview, data[:upper]) |
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447 |
| 19 | 448 self._values = [dataview[:, self.dataset.fields[f]]\ |
| 449 for f in self._names] | |
| 450 return self | |
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451 |
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452 |
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453 def __init__(self, data, fields=None): |
|
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454 """ |
|
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455 There are two ways to construct an ArrayDataSet: (1) from an |
|
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456 existing dataset (which may result in a copy of the data in a numpy array), |
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457 or (2) from a numpy.array (the data argument), along with an optional description |
|
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458 of the fields (a LookupList of column slices (or column lists) indexed by field names). |
|
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459 """ |
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460 self.data=data |
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461 self.fields=fields |
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462 rows, cols = data.shape |
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463 |
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464 if fields: |
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
465 for fieldname,fieldslice in fields.items(): |
|
28
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Removed __array__ method from dataset, whose
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26
diff
changeset
|
466 assert type(fieldslice) is int or isinstance(fieldslice,slice) or hasattr(fieldslice,"__iter__") |
|
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Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
467 if hasattr(fieldslice,"__iter__"): # is a sequence |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
468 for i in fieldslice: |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
469 assert type(i) is int |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
470 elif isinstance(fieldslice,slice): |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
471 # make sure fieldslice.start and fieldslice.step are defined |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
472 start=fieldslice.start |
|
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Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
473 step=fieldslice.step |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
474 if not start: |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
475 start=0 |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
476 if not step: |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
477 step=1 |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
478 if not fieldslice.start or not fieldslice.step: |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
479 fields[fieldname] = fieldslice = slice(start,fieldslice.stop,step) |
|
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Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
480 # and coherent with the data array |
|
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Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
481 assert fieldslice.start >= 0 and fieldslice.stop <= cols |
|
17
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
482 |
|
759d17112b23
more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
483 def minibatches(self, |
|
759d17112b23
more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
bergstrj@iro.umontreal.ca
diff
changeset
|
484 fieldnames = DataSet.minibatches_fieldnames, |
|
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
485 minibatch_size = DataSet.minibatches_minibatch_size, |
|
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
486 n_batches = DataSet.minibatches_n_batches): |
|
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
487 """ |
|
22
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
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diff
changeset
|
488 If the fieldnames list is None, it means that we want to see ALL the fields. |
|
17
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
489 |
|
22
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Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
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parents:
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diff
changeset
|
490 If the n_batches is None, we want to see all the examples possible |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
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|
491 for the given minibatch_size (possibly missing some near the end). |
|
17
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diff
changeset
|
492 """ |
|
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diff
changeset
|
493 # substitute the defaults: |
|
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diff
changeset
|
494 if n_batches is None: n_batches = len(self) / minibatch_size |
|
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
495 return ArrayDataSet.Iterator(self, fieldnames, minibatch_size, n_batches) |
|
3
378b68d5c4ad
Added first (untested) version of ArrayDataSet
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parents:
2
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|
496 |
|
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|
497 def fieldNames(self): |
|
22
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|
498 """Return the list of field names that are supported by getattr and hasField.""" |
|
3
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|
499 return self.fields.keys() |
|
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Added first (untested) version of ArrayDataSet
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parents:
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|
500 |
|
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parents:
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diff
changeset
|
501 def __len__(self): |
|
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parents:
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|
502 """len(dataset) returns the number of examples in the dataset.""" |
|
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parents:
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diff
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|
503 return len(self.data) |
|
1
2cd82666b9a7
Added statscollector and started writing dataset and learner.
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parents:
0
diff
changeset
|
504 |
|
3
378b68d5c4ad
Added first (untested) version of ArrayDataSet
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parents:
2
diff
changeset
|
505 def __getitem__(self,i): |
|
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|
506 """ |
|
28
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diff
changeset
|
507 dataset[i] returns the (i+1)-th Example of the dataset. |
|
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Removed __array__ method from dataset, whose
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diff
changeset
|
508 If there are no fields the result is just a numpy array (for the i-th row of the dataset data matrix). |
|
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Removed __array__ method from dataset, whose
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diff
changeset
|
509 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
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diff
changeset
|
510 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
|
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Removed __array__ method from dataset, whose
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diff
changeset
|
511 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
|
3
378b68d5c4ad
Added first (untested) version of ArrayDataSet
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parents:
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diff
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|
512 """ |
|
378b68d5c4ad
Added first (untested) version of ArrayDataSet
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parents:
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diff
changeset
|
513 if self.fields: |
|
11
be128b9127c8
Debugged (to the extent of my tests) the new version of dataset
bengioy@esprit.iro.umontreal.ca
parents:
9
diff
changeset
|
514 fieldnames,fieldslices=zip(*self.fields.items()) |
|
12
ff4e551490f1
Added LookupList type in lookup_list.py and used it to keep order
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parents:
11
diff
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|
515 return Example(self.fields.keys(),[self.data[i,fieldslice] for fieldslice in self.fields.values()]) |
|
3
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Added first (untested) version of ArrayDataSet
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parents:
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diff
changeset
|
516 else: |
|
7
6f8f338686db
Moved iterating counter into a FiniteDataSetIterator to allow embedded iterations and multiple threads iterating at the same time on a dataset.
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diff
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|
517 return self.data[i] |
|
3
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diff
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|
518 |
|
17
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changeset
|
519 def __getslice__(self,*args): |
|
28
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Removed __array__ method from dataset, whose
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diff
changeset
|
520 """ |
|
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Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
521 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
522 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
523 """ |
|
17
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff
changeset
|
524 return ArrayDataSet(self.data.__getslice__(*args), fields=self.fields) |
|
3
378b68d5c4ad
Added first (untested) version of ArrayDataSet
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parents:
2
diff
changeset
|
525 |
|
28
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
526 def indices_of_unique_columns_used(self): |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
527 """ |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
528 Return the unique indices of the columns actually used by the fields, and a boolean |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
529 that signals (if True) that used columns overlap. If they do then the |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
530 indices are not repeated in the result. |
| 15 | 531 """ |
|
7
6f8f338686db
Moved iterating counter into a FiniteDataSetIterator to allow embedded iterations and multiple threads iterating at the same time on a dataset.
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parents:
6
diff
changeset
|
532 columns_used = numpy.zeros((self.data.shape[1]),dtype=bool) |
|
28
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Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
533 overlapping_columns = False |
|
7
6f8f338686db
Moved iterating counter into a FiniteDataSetIterator to allow embedded iterations and multiple threads iterating at the same time on a dataset.
bengioy@bengiomac.local
parents:
6
diff
changeset
|
534 for field_slice in self.fields.values(): |
|
28
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
535 if sum(columns_used[field_slice])>0: overlapping_columns=True |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
536 columns_used[field_slice]=True |
|
541a273bc89f
Removed __array__ method from dataset, whose
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parents:
26
diff
changeset
|
537 return [i for i,used in enumerate(columns_used) if used],overlapping_columns |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
538 |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
539 def slice_of_unique_columns_used(self): |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
540 """ |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
541 Return None if the indices_of_unique_columns_used do not form a slice. If they do, |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
542 return that slice. It means that the columns used can be extracted |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
543 from the data array without making a copy. If the fields overlap |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
544 but their unique columns used form a slice, still return that slice. |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
545 """ |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
546 columns_used,overlapping_columns = self.indices_of_columns_used() |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
547 mappable_to_one_slice = True |
|
22
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
548 if not overlapping_fields: |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
549 start=0 |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
550 while start<len(columns_used) and not columns_used[start]: |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
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parents:
20
diff
changeset
|
551 start+=1 |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
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parents:
20
diff
changeset
|
552 stop=len(columns_used) |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
553 while stop>0 and not columns_used[stop-1]: |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
554 stop-=1 |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
555 step=0 |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
556 i=start |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
557 while i<stop: |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
558 j=i+1 |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
559 while j<stop and not columns_used[j]: |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
560 j+=1 |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
561 if step: |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
562 if step!=j-i: |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
563 mappable_to_one_slice = False |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
564 break |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
565 else: |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
566 step = j-i |
|
b6b36f65664f
Created virtual sub-classes of DataSet: {Finite{Length,Width},Sliceable}DataSet,
bengioy@esprit.iro.umontreal.ca
parents:
20
diff
changeset
|
567 i=j |
|
28
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
568 return slice(start,stop,step) |
|
541a273bc89f
Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents:
26
diff
changeset
|
569 |
|
29
46c5c90019c2
Changed apply_function so that it propagates methods of the source.
bengioy@grenat.iro.umontreal.ca
parents:
28
diff
changeset
|
570 class ApplyFunctionDataSet(FiniteWidthDataSet): |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
571 """ |
|
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
572 A dataset that contains as fields the results of applying |
|
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573 a given function (example-wise) to specified input_fields of a source |
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574 dataset. The function should return a sequence whose elements will be stored in |
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575 fields whose names are given in the output_fields list. If copy_inputs |
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|
576 is True then the resulting dataset will also contain the fields of the source. |
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577 dataset. If accept_minibatches, then the function expects |
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578 minibatches as arguments (what is returned by the minibatches |
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579 iterator). In any case, the computations may be delayed until the examples |
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580 of self are requested. If cache is True, then |
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581 once the output fields for some examples have been computed, then |
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582 are cached (to avoid recomputation if the same examples are again requested). |
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583 """ |
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584 def __init__(src,function, input_fields, output_fields, copy_inputs=True, accept_minibatches=True, cache=True, compute_now=False): |
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585 DataSet.__init__(self) |
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586 self.src=src |
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587 self.function=function |
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588 assert src.hasFields(input_fields) |
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589 self.input_fields=input_fields |
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590 self.output_fields=output_fields |
|
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591 assert not (copy_inputs and compute_now and not hasattr(src,'fieldNames')) |
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592 self.copy_inputs=copy_inputs |
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593 self.accept_minibatches=accept_minibatches |
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594 self.cache=cache |
|
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595 self.compute_now=compute_now |
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596 if compute_now: |
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597 assert hasattr(src,'__len__') and len(src)>=0 |
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598 fieldnames = output_fields |
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599 if copy_inputs: fieldnames = src.fieldNames() + output_fields |
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600 if accept_minibatches: |
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601 # make a single minibatch with all the inputs |
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602 inputs = src.minibatches(input_fields,len(src)).next() |
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603 # and apply the function to it, and transpose into a list of examples (field values, actually) |
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604 self.cached_examples = zip(*Example(output_fields,function(*inputs))) |
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605 else: |
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606 # compute a list with one tuple per example, with the function outputs |
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607 self.cached_examples = [ function(input) for input in src.zip(input_fields) ] |
|
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608 elif cache: |
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609 # maybe a fixed-size array kind of structure would be more efficient than a list |
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610 # in the case where src is FiniteDataSet. -YB |
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611 self.cached_examples = [] |
|
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612 |
|
29
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613 def fieldNames(self): |
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614 if self.copy_inputs: |
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615 return self.output_fields + self.src.fieldNames() |
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616 return self.output_fields |
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617 |
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618 def minibatches(self, |
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619 fieldnames = DataSet.minibatches_fieldnames, |
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620 minibatch_size = DataSet.minibatches_minibatch_size, |
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621 n_batches = DataSet.minibatches_n_batches): |
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622 |
|
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623 class Iterator(LookupList): |
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624 |
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625 def __init__(self,dataset): |
|
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626 if fieldnames is None: |
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627 assert hasattr(dataset,"fieldNames") |
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628 fieldnames = dataset.fieldNames() |
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629 self.example_index=0 |
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630 LookupList.__init__(self, fieldnames, [0]*len(fieldnames)) |
|
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631 self.dataset=dataset |
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632 self.src_iterator=self.src.minibatches(list(set.union(set(fieldnames),set(dataset.input_fields))), |
|
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633 minibatch_size,n_batches) |
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634 self.fieldnames_not_in_input = [] |
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635 if self.copy_inputs: |
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636 self.fieldnames_not_in_input = filter(lambda x: not x in dataset.input_fields, fieldnames) |
|
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637 |
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638 def __iter__(self): |
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639 return self |
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|
640 |
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641 def next_index(self): |
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642 return self.src_iterator.next_index() |
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643 |
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644 def next(self): |
|
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645 example_index = self.src_iterator.next_index() |
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646 src_examples = self.src_iterator.next() |
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647 if self.dataset.copy_inputs: |
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648 function_inputs = [src_examples[field_name] for field_name in self.dataset.input_fields] |
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649 else: |
|
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650 function_inputs = src_examples |
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651 if self.dataset.cached_examples: |
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652 cache_len=len(self.cached_examples) |
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653 if example_index<cache_len+minibatch_size: |
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654 outputs_list = self.cached_examples[example_index:example_index+minibatch_size] |
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655 # convert the minibatch list of examples |
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656 # into a list of fields each of which iterate over the minibatch |
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657 outputs = zip(*outputs_list) |
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658 else: |
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659 outputs = self.dataset.function(*function_inputs) |
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|
660 if self.dataset.cache: |
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661 # convert the list of fields, each of which can iterate over the minibatch |
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662 # into a list of examples in the minibatch (each of which is a list of field values) |
|
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663 outputs_list = zip(*outputs) |
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664 # copy the outputs_list into the cache |
|
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665 for i in xrange(cache_len,example_index): |
|
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666 self.cached_examples.append(None) |
|
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667 self.cached_examples += outputs_list |
|
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|
668 else: |
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669 outputs = self.dataset.function(*function_inputs) |
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670 |
|
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671 return Example(self.fieldnames_not_in_input+self.dataset.output_fields, |
|
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672 [src_examples[field_name] for field_name in self.fieldnames_not_in_input]+outputs) |
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|
673 |
|
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674 |
|
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|
675 for fieldname in fieldnames: |
|
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676 assert fieldname in self.output_fields or self.src.hasFields(fieldname) |
|
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677 return Iterator(self) |
|
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|
678 |
|
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|
679 |
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680 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
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681 """ |
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682 Wraps an arbitrary DataSet into one for supervised learning tasks by forcing the |
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683 user to define a set of fields as the 'input' field and a set of fields |
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684 as the 'target' field. Optionally, a single weight_field can also be defined. |
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685 """ |
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686 args = ((input_fields,'input'),(output_fields,'target')) |
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687 if weight_field: args+=(([weight_field],'weight')) |
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688 return src_dataset.rename(*args) |
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689 |
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690 |
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691 |
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692 |