annotate dataset.py @ 37:73c4212ba5b3

Factored the minibatch-writing code into an iterator class inside DataSet
author bengioy@esprit.iro.umontreal.ca
date Thu, 24 Apr 2008 12:03:06 -0400
parents 438440ba0627
children d637ad8f7352
rev   line source
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ff4e551490f1 Added LookupList type in lookup_list.py and used it to keep order
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2 from lookup_list import LookupList
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3 Example = LookupList
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4 from misc import *
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5 import copy
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759d17112b23 more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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7 class AbstractFunction (Exception): """Derived class must override this function"""
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8 class NotImplementedYet (NotImplementedError): """Work in progress, this should eventually be implemented"""
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10 class DataSet(object):
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11 """A virtual base class for datasets.
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13 A DataSet can be seen as a generalization of a matrix, meant to be used in conjunction
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14 with learning algorithms (for training and testing them): rows/records are called examples, and
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15 columns/attributes are called fields. The field value for a particular example can be an arbitrary
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16 python object, which depends on the particular dataset.
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18 We call a DataSet a 'stream' when its length is unbounded (len(dataset)==float("infinity")).
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20 A DataSet is a generator of iterators; these iterators can run through the
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21 examples or the fields in a variety of ways. A DataSet need not necessarily have a finite
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22 or known length, so this class can be used to interface to a 'stream' which
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23 feeds on-line learning (however, as noted below, some operations are not
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24 feasible or not recommanded on streams).
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25
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26 To iterate over examples, there are several possibilities:
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27 * for example in dataset([field1, field2,field3, ...]):
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28 * for val1,val2,val3 in dataset([field1, field2,field3]):
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29 * for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N):
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30 * for example in dataset:
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31 Each of these is documented below. All of these iterators are expected
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32 to provide, in addition to the usual 'next()' method, a 'next_index()' method
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33 which returns a non-negative integer pointing to the position of the next
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34 example that will be returned by 'next()' (or of the first example in the
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35 next minibatch returned). This is important because these iterators
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36 can wrap around the dataset in order to do multiple passes through it,
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37 in possibly unregular ways if the minibatch size is not a divisor of the
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38 dataset length.
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39
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40 To iterate over fields, one can do
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41 * for fields in dataset.fields()
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42 * for fields in dataset(field1,field2,...).fields() to select a subset of fields
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43 * for fields in dataset.fields(field1,field2,...) to select a subset of fields
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44 and each of these fields is iterable over the examples:
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45 * for field_examples in dataset.fields():
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46 for example_value in field_examples:
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47 ...
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48 but when the dataset is a stream (unbounded length), it is not recommanded to do
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49 such things because the underlying dataset may refuse to access the different fields in
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50 an unsynchronized ways. Hence the fields() method is illegal for streams, by default.
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51 The result of fields() is a DataSetFields object, which iterates over fields,
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52 and whose elements are iterable over examples. A DataSetFields object can
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53 be turned back into a DataSet with its examples() method:
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54 dataset2 = dataset1.fields().examples()
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55 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1).
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57 Note: Fields are not mutually exclusive, i.e. two fields can overlap in their actual content.
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59 Note: The content of a field can be of any type. Field values can also be 'missing'
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60 (e.g. to handle semi-supervised learning), and in the case of numeric (numpy array)
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61 fields (i.e. an ArrayFieldsDataSet), NaN plays the role of a missing value.
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63 Dataset elements can be indexed and sub-datasets (with a subset
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64 of examples) can be extracted. These operations are not supported
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65 by default in the case of streams.
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66
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67 * dataset[:n] returns a dataset with the n first examples.
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69 * dataset[i1:i2:s] returns a dataset with the examples i1,i1+s,...i2-s.
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71 * dataset[i] returns an Example.
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73 * dataset[[i1,i2,...in]] returns a dataset with examples i1,i2,...in.
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75 Datasets can be concatenated either vertically (increasing the length) or
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76 horizontally (augmenting the set of fields), if they are compatible, using
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77 the following operations (with the same basic semantics as numpy.hstack
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78 and numpy.vstack):
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79
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80 * dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3])
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82 creates a new dataset whose list of fields is the concatenation of the list of
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83 fields of the argument datasets. This only works if they all have the same length.
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84
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85 * dataset1 + dataset2 + dataset3 == dataset.vstack([dataset1,dataset2,dataset3])
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86
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87 creates a new dataset that concatenates the examples from the argument datasets
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88 (and whose length is the sum of the length of the argument datasets). This only
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89 works if they all have the same fields.
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91 According to the same logic, and viewing a DataSetFields object associated to
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92 a DataSet as a kind of transpose of it, fields1 + fields2 concatenates fields of
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93 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their
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94 examples.
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97 A DataSet sub-class should always redefine the following methods:
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98 * __len__ if it is not a stream
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99 * __getitem__ may not be feasible with some streams
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100 * fieldNames
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101 * minibatches_nowrap (called by DataSet.minibatches())
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102 * valuesHStack
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103 * valuesVStack
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104 For efficiency of implementation, a sub-class might also want to redefine
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105 * hasFields
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106 """
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107
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108 infinity = float("infinity")
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109
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110 def __init__(self):
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111 pass
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112
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113 class MinibatchToSingleExampleIterator(object):
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114 """
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115 Converts the result of minibatch iterator with minibatch_size==1 into
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116 single-example values in the result. Therefore the result of
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117 iterating on the dataset itself gives a sequence of single examples
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118 (whereas the result of iterating over minibatches gives in each
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119 Example field an iterable object over the individual examples in
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120 the minibatch).
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121 """
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122 def __init__(self, minibatch_iterator):
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123 self.minibatch_iterator = minibatch_iterator
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124 def __iter__(self): #makes for loop work
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125 return self
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126 def next(self):
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127 return self.minibatch_iterator.next()[0]
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128 def next_index(self):
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129 return self.minibatch_iterator.next_index()
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130
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131 def __iter__(self):
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132 """Supports the syntax "for i in dataset: ..."
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133
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134 Using this syntax, "i" will be an Example instance (or equivalent) with
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135 all the fields of DataSet self. Every field of "i" will give access to
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136 a field of a single example. Fields should be accessible via
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137 i["fielname"] or i[3] (in the order defined by the elements of the
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138 Example returned by this iterator), but the derived class is free
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139 to accept any type of identifier, and add extra functionality to the iterator.
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140
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141 The default implementation calls the minibatches iterator and extracts the first example of each field.
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142 """
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143 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1))
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144
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145
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146 class MinibatchWrapAroundIterator(object):
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147 """
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148 An iterator for minibatches that handles the case where we need to wrap around the
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149 dataset because n_batches*minibatch_size > len(dataset). It is constructed from
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150 a dataset that provides a minibatch iterator that does not need to handle that problem.
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151 This class is a utility for dataset subclass writers, so that they do not have to handle
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152 this issue multiple times.
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153 """
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154 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset):
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155 self.dataset=dataset
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156 self.fieldnames=fieldnames
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157 self.minibatch_size=minibatch_size
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158 self.n_batches=n_batches
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159 self.n_batches_done=0
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160 self.next_row=offset
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161 self.L=len(dataset)
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162 assert offset+minibatch_size<=self.L
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163 ds_nbatches = (self.L-offset)/minibatch_size
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164 if n_batches is not None:
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165 ds_nbatches = max(n_batches,ds_nbatches)
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166 self.iterator = dataset.minibatches_nowrap(fieldnames,minibatch_size,ds_nbatches,offset)
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167
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168 def __iter__(self):
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169 return self
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170
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171 def next_index(self):
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172 return self.next_row
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173
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174 def next(self):
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175 if self.n_batches and self.n_batches_done==self.n_batches:
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176 raise StopIteration
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177 upper = self.next_row+minibatch_size
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178 if upper <=self.L:
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179 minibatch = self.minibatch_iterator.next()
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180 else:
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181 if not self.n_batches:
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182 raise StopIteration
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183 # we must concatenate (vstack) the bottom and top parts of our minibatch
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184 # first get the beginning of our minibatch (top of dataset)
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185 first_part = self.dataset.minibatches_nowrap(fieldnames,self.L-self.next_row,1,self.next_row).next()
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186 second_part = self.dataset.minibatches_nowrap(fieldnames,upper-self.L,1,0).next()
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187 minibatch = Example(self.fieldnames,
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188 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]])
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189 for name in self.fieldnames])
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190 self.next_row=upper
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191 self.n_batches_done+=1
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192 if upper >= L:
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193 self.next_row -= L
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194 return minibatch
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195
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196
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197 minibatches_fieldnames = None
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198 minibatches_minibatch_size = 1
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199 minibatches_n_batches = None
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200 def minibatches(self,
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201 fieldnames = minibatches_fieldnames,
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202 minibatch_size = minibatches_minibatch_size,
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203 n_batches = minibatches_n_batches,
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204 offset = 0):
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205 """
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206 Return an iterator that supports three forms of syntax:
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207
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208 for i in dataset.minibatches(None,**kwargs): ...
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209
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210 for i in dataset.minibatches([f1, f2, f3],**kwargs): ...
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211
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212 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ...
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213
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214 Using the first two syntaxes, "i" will be an indexable object, such as a list,
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215 tuple, or Example instance. In both cases, i[k] is a list-like container
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216 of a batch of current examples. In the second case, i[0] is
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217 list-like container of the f1 field of a batch current examples, i[1] is
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218 a list-like container of the f2 field, etc.
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219
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220 Using the first syntax, all the fields will be returned in "i".
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221 Beware that some datasets may not support this syntax, if the number
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222 of fields is infinite (i.e. field values may be computed "on demand").
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223
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224 Using the third syntax, i1, i2, i3 will be list-like containers of the
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225 f1, f2, and f3 fields of a batch of examples on each loop iteration.
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226
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227 The minibatches iterator is expected to return upon each call to next()
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228 a DataSetFields object, which is a LookupList (indexed by the field names) whose
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229 elements are iterable over the minibatch examples, and which keeps a pointer to
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230 a sub-dataset that can be used to iterate over the individual examples
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231 in the minibatch. Hence a minibatch can be converted back to a regular
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232 dataset or its fields can be looked at individually (and possibly iterated over).
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233
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234 PARAMETERS
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235 - fieldnames (list of any type, default None):
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236 The loop variables i1, i2, i3 (in the example above) should contain the
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237 f1, f2, and f3 fields of the current batch of examples. If None, the
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238 derived class can choose a default, e.g. all fields.
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239
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240 - minibatch_size (integer, default 1)
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241 On every iteration, the variables i1, i2, i3 will have
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242 exactly minibatch_size elements. e.g. len(i1) == minibatch_size
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243
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244 - n_batches (integer, default None)
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245 The iterator will loop exactly this many times, and then stop. If None,
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246 the derived class can choose a default. If (-1), then the returned
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247 iterator should support looping indefinitely.
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248
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249 - offset (integer, default 0)
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250 The iterator will start at example 'offset' in the dataset, rather than the default.
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251
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252 Note: A list-like container is something like a tuple, list, numpy.ndarray or
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253 any other object that supports integer indexing and slicing.
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254
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255 """
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256 return MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset)
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257
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258 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset):
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259 """
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260 This is the minibatches iterator generator that sub-classes must define.
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261 It does not need to worry about wrapping around multiple times across the dataset,
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262 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called.
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263 The next() method of the returned iterator does not even need to worry about
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264 the termination condition (as StopIteration will be raised by DataSet.minibatches
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265 before an improper call to minibatches_nowrap's next() is made).
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266 That next() method can assert that its next row will always be within [0,len(dataset)).
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267 The iterator returned by minibatches_nowrap does not need to implement
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268 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator.
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269 """
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270 raise AbstractFunction()
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271
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272 def __len__(self):
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273 """
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274 len(dataset) returns the number of examples in the dataset.
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275 By default, a DataSet is a 'stream', i.e. it has an unbounded (infinite) length.
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276 Sub-classes which implement finite-length datasets should redefine this method.
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277 Some methods only make sense for finite-length datasets, and will perform
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278 assert len(dataset)<DataSet.infinity
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279 in order to check the finiteness of the dataset.
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280 """
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281 return infinity
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282
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283 def hasFields(self,*fieldnames):
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284 """
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285 Return true if the given field name (or field names, if multiple arguments are
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286 given) is recognized by the DataSet (i.e. can be used as a field name in one
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287 of the iterators).
29
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288
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289 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames()
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290 method. Many datasets may store their field names in a dictionary, which would allow more efficiency.
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291 """
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292 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0
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293
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294 def fieldNames(self):
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295 """
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296 Return the list of field names that are supported by the iterators,
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297 and for which hasFields(fieldname) would return True.
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298 """
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299 raise AbstractFunction()
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300
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301 def __call__(self,*fieldnames):
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302 """
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303 Return a dataset that sees only the fields whose name are specified.
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304 """
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305 assert self.hasFields(fieldnames)
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306 return self.fields(fieldnames).examples()
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307
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308 def fields(self,*fieldnames):
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309 """
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310 Return a DataSetFields object associated with this dataset.
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311 """
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312 return DataSetFields(self,fieldnames)
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313
2
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314 def __getitem__(self,i):
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315 """
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316 dataset[i] returns the (i+1)-th example of the dataset.
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317 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1.
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318 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2.
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319 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in.
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2cd82666b9a7 Added statscollector and started writing dataset and learner.
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320
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321 Note that some stream datasets may be unable to implement slicing/indexing
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322 because they can only iterate through examples one or a minibatch at a time
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323 and do not actually store or keep past (or future) examples.
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324 """
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325 raise NotImplementedError()
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326
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327 def valuesHStack(self,fieldnames,fieldvalues):
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328 """
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329 Return a value that corresponds to concatenating (horizontally) several field values.
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330 This can be useful to merge some fields. The implementation of this operation is likely
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331 to involve a copy of the original values. When the values are numpy arrays, the
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332 result should be numpy.hstack(values). If it makes sense, this operation should
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333 work as well when each value corresponds to multiple examples in a minibatch
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334 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix,
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335 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values).
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336 The default is to use numpy.hstack for numpy.ndarray values, and a list
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337 pointing to the original values for other data types.
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338 """
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339 all_numpy=True
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340 for value in fieldvalues:
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341 if not type(value) is numpy.ndarray:
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342 all_numpy=False
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343 if all_numpy:
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344 return numpy.hstack(fieldvalues)
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345 # the default implementation of horizontal stacking is to put values in a list
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346 return fieldvalues
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347
672fe4b23032 Fixed dataset errors so that _test_dataset.py works again.
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348
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349 def valuesVStack(self,fieldname,values):
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350 """
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351 Return a value that corresponds to concatenating (vertically) several values of the
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352 same field. This can be important to build a minibatch out of individual examples. This
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353 is likely to involve a copy of the original values. When the values are numpy arrays, the
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354 result should be numpy.vstack(values).
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355 The default is to use numpy.vstack for numpy.ndarray values, and a list
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356 pointing to the original values for other data types.
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357 """
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358 all_numpy=True
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359 for value in values:
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360 if not type(value) is numpy.ndarray:
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361 all_numpy=False
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362 if all_numpy:
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363 return numpy.vstack(values)
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364 # the default implementation of vertical stacking is to put values in a list
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365 return values
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366
36
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367 def __or__(self,other):
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368 """
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369 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of
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370 fields of the argument datasets. This only works if they all have the same length.
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371 """
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372 return HStackedDataSet(self,other)
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378b68d5c4ad Added first (untested) version of ArrayDataSet
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373
36
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374 def __add__(self,other):
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375 """
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376 dataset1 + dataset2 is a dataset that concatenates the examples from the argument datasets
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377 (and whose length is the sum of the length of the argument datasets). This only
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378 works if they all have the same fields.
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379 """
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380 return VStackedDataSet(self,other)
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381
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382 def hstack(datasets):
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383 """
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384 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ...
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385 which is a dataset whose fields list is the concatenation of the fields
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386 of the individual datasets.
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387 """
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diff changeset
388 assert len(datasets)>0
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389 if len(datasets)==1:
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390 return datasets[0]
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391 return HStackedDataSet(datasets)
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759d17112b23 more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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diff changeset
392
36
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393 def vstack(datasets):
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394 """
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395 vstack(dataset1,dataset2,...) returns dataset1 + datataset2 + ...
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396 which is a dataset which iterates first over the examples of dataset1, then
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397 over those of dataset2, etc.
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398 """
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diff changeset
399 assert len(datasets)>0
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400 if len(datasets)==1:
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401 return datasets[0]
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402 return VStackedDataSet(datasets)
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diff changeset
403
759d17112b23 more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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parents: 16 12
diff changeset
404
36
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diff changeset
405 class DataSetFields(LookupList):
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parents: 29
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406 """
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diff changeset
407 Although a DataSet iterates over examples (like rows of a matrix), an associated
438440ba0627 Rewriting dataset.py completely
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408 DataSetFields iterates over fields (like columns of a matrix), and can be understood
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
409 as a transpose of the associated dataset.
17
759d17112b23 more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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parents: 16 12
diff changeset
410
36
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parents: 29
diff changeset
411 To iterate over fields, one can do
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diff changeset
412 * for fields in dataset.fields()
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diff changeset
413 * for fields in dataset(field1,field2,...).fields() to select a subset of fields
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diff changeset
414 * for fields in dataset.fields(field1,field2,...) to select a subset of fields
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parents: 29
diff changeset
415 and each of these fields is iterable over the examples:
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diff changeset
416 * for field_examples in dataset.fields():
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diff changeset
417 for example_value in field_examples:
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418 ...
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419 but when the dataset is a stream (unbounded length), it is not recommanded to do
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diff changeset
420 such things because the underlying dataset may refuse to access the different fields in
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diff changeset
421 an unsynchronized ways. Hence the fields() method is illegal for streams, by default.
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diff changeset
422 The result of fields() is a DataSetFields object, which iterates over fields,
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parents: 29
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423 and whose elements are iterable over examples. A DataSetFields object can
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parents: 29
diff changeset
424 be turned back into a DataSet with its examples() method:
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diff changeset
425 dataset2 = dataset1.fields().examples()
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diff changeset
426 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1).
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parents: 29
diff changeset
427 """
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diff changeset
428 def __init__(self,dataset,*fieldnames):
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429 self.dataset=dataset
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
diff changeset
430 assert dataset.hasFields(*fieldnames)
36
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431 LookupList.__init__(self,dataset.fieldNames(),
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
diff changeset
432 dataset.minibatches(fieldnames if len(fieldnames)>0 else self.fieldNames(),
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
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433 minibatch_size=len(dataset)).next()
36
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434 def examples(self):
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435 return self.dataset
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parents: 29
diff changeset
436
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diff changeset
437 def __or__(self,other):
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438 """
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439 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation
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440 of the list of examples of DataSetFields fields1 and fields2.
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441 """
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442 return (self.examples() + other.examples()).fields()
17
759d17112b23 more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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parents: 16 12
diff changeset
443
36
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444 def __add__(self,other):
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759d17112b23 more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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parents: 16 12
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445 """
36
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446 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation
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447 of the fields of DataSetFields fields1 and fields2.
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448 """
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449 return (self.examples() | other.examples()).fields()
17
759d17112b23 more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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parents: 16 12
diff changeset
450
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
451
36
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452 class MinibatchDataSet(DataSet):
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453 """
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diff changeset
454 Turn a LookupList of same-length fields into an example-iterable dataset.
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diff changeset
455 Each element of the lookup-list should be an iterable and sliceable, all of the same length.
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parents: 29
diff changeset
456 """
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diff changeset
457 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack,
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458 values_hstack=DataSet().valuesHStack):
17
759d17112b23 more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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parents: 16 12
diff changeset
459 """
36
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diff changeset
460 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues)
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diff changeset
461 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same
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diff changeset
462 semantics as the DataSet methods of the same name (but without the self argument).
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diff changeset
463 """
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diff changeset
464 self.fields=fields_lookuplist
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parents: 29
diff changeset
465 assert len(fields_lookuplist)>0
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466 self.length=len(fields_lookuplist[0])
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467 for field in fields_lookuplist[1:]:
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468 assert self.length==len(field)
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469 self.values_vstack=values_vstack
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470 self.values_hstack=values_hstack
3
378b68d5c4ad Added first (untested) version of ArrayDataSet
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parents: 2
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471
378b68d5c4ad Added first (untested) version of ArrayDataSet
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diff changeset
472 def __len__(self):
36
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473 return self.length
28
541a273bc89f Removed __array__ method from dataset, whose
bengioy@grenat.iro.umontreal.ca
parents: 26
diff changeset
474
36
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475 def __getitem__(self,i):
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476 return Example(self.fields.keys(),[field[i] for field in 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
477
29
46c5c90019c2 Changed apply_function so that it propagates methods of the source.
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parents: 28
diff changeset
478 def fieldNames(self):
36
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479 return self.fields.keys()
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diff changeset
480
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
diff changeset
481 def hasFields(self,*fieldnames):
36
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482 for fieldname in fieldnames:
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diff changeset
483 if fieldname not in self.fields:
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484 return False
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diff changeset
485 return True
20
266c68cb6136 Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents: 19
diff changeset
486
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
487 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset):
36
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diff changeset
488 class Iterator(object):
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diff changeset
489 def __init__(self,ds):
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490 self.ds=ds
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
diff changeset
491 self.next_example=offset
36
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diff changeset
492 assert minibatch_size > 0
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
diff changeset
493 if offset+minibatch_size > ds.length
36
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diff changeset
494 raise NotImplementedError()
20
266c68cb6136 Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents: 19
diff changeset
495 def __iter__(self):
266c68cb6136 Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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parents: 19
diff changeset
496 return self
266c68cb6136 Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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diff changeset
497 def next(self):
36
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diff changeset
498 upper = next_example+minibatch_size
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
499 assert upper<=self.ds.length
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
500 minibatch = Example(self.ds.fields.keys(),
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
diff changeset
501 [field[next_example:upper]
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
502 for field in self.ds.fields])
36
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diff changeset
503 self.next_example+=minibatch_size
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504 return DataSetFields(MinibatchDataSet(minibatch),fieldnames)
20
266c68cb6136 Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents: 19
diff changeset
505
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
506 return MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset)
20
266c68cb6136 Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents: 19
diff changeset
507
36
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diff changeset
508 def valuesVStack(self,fieldname,fieldvalues):
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diff changeset
509 return self.values_vstack(fieldname,fieldvalues)
20
266c68cb6136 Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents: 19
diff changeset
510
36
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diff changeset
511 def valuesHStack(self,fieldnames,fieldvalues):
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512 return self.values_hstack(fieldnames,fieldvalues)
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diff changeset
513
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diff changeset
514 class HStackedDataSet(DataSet):
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parents: 29
diff changeset
515 """
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parents: 29
diff changeset
516 A DataSet that wraps several datasets and shows a view that includes all their fields,
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
517 i.e. whose list of fields is the concatenation of their lists of fields.
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parents: 29
diff changeset
518
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
519 If a field name is found in more than one of the datasets, then either an error is
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
520 raised or the fields are renamed (either by prefixing the __name__ attribute
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parents: 29
diff changeset
521 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list).
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parents: 29
diff changeset
522
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
523 TODO: automatically detect a chain of stacked datasets due to A | B | C | D ...
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
524 """
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parents: 29
diff changeset
525 def __init__(self,datasets,accept_nonunique_names=False):
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diff changeset
526 DataSet.__init__(self)
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diff changeset
527 self.datasets=datasets
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diff changeset
528 self.accept_nonunique_names=accept_nonunique_names
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diff changeset
529 self.fieldname2dataset={}
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parents: 29
diff changeset
530
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diff changeset
531 def rename_field(fieldname,dataset,i):
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parents: 29
diff changeset
532 if hasattr(dataset,"__name__"):
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parents: 29
diff changeset
533 return dataset.__name__ + "." + fieldname
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parents: 29
diff changeset
534 return fieldname+"."+str(i)
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parents: 29
diff changeset
535
438440ba0627 Rewriting dataset.py completely
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diff changeset
536 # make sure all datasets have the same length and unique field names
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
537 self.length=None
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parents: 29
diff changeset
538 names_to_change=[]
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bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
539 for i in xrange(len(datasets)):
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parents: 29
diff changeset
540 dataset = datasets[i]
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parents: 29
diff changeset
541 length=len(dataset)
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parents: 29
diff changeset
542 if self.length:
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parents: 29
diff changeset
543 assert self.length==length
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
544 else:
438440ba0627 Rewriting dataset.py completely
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diff changeset
545 self.length=length
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parents: 29
diff changeset
546 for fieldname in dataset.fieldNames():
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bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
547 if fieldname in self.fieldname2dataset: # name conflict!
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parents: 29
diff changeset
548 if accept_nonunique_names:
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parents: 29
diff changeset
549 fieldname=rename_field(fieldname,dataset,i)
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bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
550 names2change.append((fieldname,i))
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
551 else:
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
552 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname)
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
553 self.fieldname2dataset[fieldname]=i
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
554 for fieldname,i in names_to_change:
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
555 del self.fieldname2dataset[fieldname]
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
556 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
557
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
558 def hasFields(self,*fieldnames):
36
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
559 for fieldname in fieldnames:
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
560 if not fieldname in self.fieldname2dataset:
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
561 return False
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
562 return True
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
563
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
564 def fieldNames(self):
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
565 return self.fieldname2dataset.keys()
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
566
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
567 def minibatches_nowrap(self,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
568 fieldnames = minibatches_fieldnames,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
569 minibatch_size = minibatches_minibatch_size,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
570 n_batches = minibatches_n_batches,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
571 offset = 0):
36
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bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
572
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
573 class Iterator(object):
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
574 def __init__(self,hsds,iterators):
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parents: 29
diff changeset
575 self.hsds=hsds
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
576 self.iterators=iterators
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
577 def __iter__(self):
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parents: 29
diff changeset
578 return self
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bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
579 def next(self):
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parents: 29
diff changeset
580 # concatenate all the fields of the minibatches
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
581 minibatch = reduce(LookupList.__add__,[iterator.next() for iterator in self.iterators])
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
582 # and return a DataSetFields whose dataset is the transpose (=examples()) of this minibatch
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
583 return DataSetFields(MinibatchDataSet(minibatch,self.hsds.valuesVStack,
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
584 self.hsds.valuesHStack),
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
585 fieldnames if fieldnames else hsds.fieldNames())
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
586
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
587 assert self.hasfields(fieldnames)
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
588 # find out which underlying datasets are necessary to service the required fields
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
589 # and construct corresponding minibatch iterators
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
590 if fieldnames:
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
591 datasets=set([])
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
592 fields_in_dataset=dict([(dataset,[]) for dataset in datasets])
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
593 for fieldname in fieldnames:
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
594 dataset=self.datasets[self.fieldnames2dataset[fieldname]]
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
595 datasets.add(dataset)
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
596 fields_in_dataset[dataset].append(fieldname)
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
597 datasets=list(datasets)
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
598 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset)
36
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
599 for dataset in datasets]
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
600 else:
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
601 datasets=self.datasets
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
602 iterators=[dataset.minibatches(None,minibatch_size,n_batches,offset) for dataset in datasets]
36
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
603 return Iterator(self,iterators)
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
604
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
605
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
606 def valuesVStack(self,fieldname,fieldvalues):
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
607 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues)
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
608
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
609 def valuesHStack(self,fieldnames,fieldvalues):
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
610 """
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
611 We will use the sub-dataset associated with the first fieldname in the fieldnames list
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
612 to do the work, hoping that it can cope with the other values (i.e. won't care
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
613 about the incompatible fieldnames). Hence this heuristic will always work if
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
614 all the fieldnames are of the same sub-dataset.
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
615 """
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
616 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues)
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
617
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
618 class VStackedDataSet(DataSet):
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
619 """
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
620 A DataSet that wraps several datasets and shows a view that includes all their examples,
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
621 in the order provided. This clearly assumes that they all have the same field names
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
622 and all (except possibly the last one) are of finite length.
438440ba0627 Rewriting dataset.py completely
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parents: 29
diff changeset
623
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
624 TODO: automatically detect a chain of stacked datasets due to A + B + C + D ...
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
625 """
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
626 def __init__(self,datasets):
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bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
627 self.datasets=datasets
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
628 self.length=0
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
629 self.index2dataset={}
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
630 assert len(datasets)>0
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
631 fieldnames = datasets[-1].fieldNames()
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
632 # We use this map from row index to dataset index for constant-time random access of examples,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
633 # to avoid having to search for the appropriate dataset each time and slice is asked for.
36
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
634 for dataset,k in enumerate(datasets[0:-1]):
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
635 L=len(dataset)
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
636 assert L<DataSet.infinity
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
637 for i in xrange(L):
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
638 self.index2dataset[self.length+i]=k
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
639 self.length+=L
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
640 assert dataset.fieldNames()==fieldnames
36
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
641 self.last_start=self.length
438440ba0627 Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents: 29
diff changeset
642 self.length+=len(datasets[-1])
37
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
643 # If length is very large, we should use a more memory-efficient mechanism
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
644 # that does not store all indices
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
645 if self.length>1000000:
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
646 # 1 million entries would require about 60 meg for the index2dataset map
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
647 # TODO
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
648 print "A more efficient mechanism for index2dataset should be implemented"
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
649
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
650 def __len__(self):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
651 return self.length
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
652
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
653 def fieldNames(self):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
654 return self.datasets[0].fieldNames()
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
655
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
656 def hasFields(self,*fieldnames):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
657 return self.datasets[0].hasFields(*fieldnames)
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
658
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
659 def minibatches_nowrap(self,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
660 fieldnames = minibatches_fieldnames,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
661 minibatch_size = minibatches_minibatch_size,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
662 n_batches = minibatches_n_batches,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
663 offset = 0):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
664 class Iterator(object):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
665 def __init__(self,vsds):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
666 self.vsds=vsds
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
667 self.next_row=offset
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
668 self.next_dataset_index=0
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
669 self.next_dataset_row=0
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
670 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
671 self.next_iterator(vsds.datasets[0],offset,n_batches)
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
672
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
673 def next_iterator(self,dataset,starting_offset,batches_left):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
674 L=len(dataset)
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
675 ds_nbatches = (L-starting_offset)/minibatch_size
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
676 if batches_left is not None:
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
677 ds_nbatches = max(batches_left,ds_nbatches)
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
678 if minibatch_size>L:
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
679 ds_minibatch_size=L
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
680 n_left_in_mb=minibatch_size-L
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
681 else: n_left_in_mb=0
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
682 return dataset.minibatches(fieldnames,minibatch_size,ds_nbatches,starting_offset), \
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents: 36
diff changeset
683 L-(starting_offset+ds_nbatches*minibatch_size), n_left_in_mb
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
684
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
685 def move_to_next_dataset(self):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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686 self.next_dataset_index +=1
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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687 if self.next_dataset_index==len(self.vsds.datasets):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
688 self.next_dataset_index = 0
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
689 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
690 self.next_iterator(vsds.datasets[self.next_dataset_index],starting_offset,n_batches)
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
691
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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692 def __iter__(self):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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693 return self
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
694
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
695 def next(self):
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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696 dataset=self.vsds.datasets[self.next_dataset_index]
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
697 mb = self.next_iterator.next()
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
698 if self.n_left_in_mb:
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
699 names=self.vsds.fieldNames()
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
700 extra_mb = []
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
701 while self.n_left_in_mb>0:
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
702 self.move_to_next_dataset()
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
703 extra_mb.append(self.next_iterator.next())
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
704 mb = Example(names,
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
705 [dataset.valuesVStack(name,[mb[name]]+[b[name] for b in extra_mb])
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
706 for name in names])
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
707 self.next_row+=minibatch_size
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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diff changeset
708 return mb
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
diff changeset
709
73c4212ba5b3 Factored the minibatch-writing code into an iterator class inside DataSet
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parents: 36
diff changeset
710
23
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
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diff changeset
711 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None):
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
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diff changeset
712 """
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
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diff changeset
713 Wraps an arbitrary DataSet into one for supervised learning tasks by forcing the
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
bengioy@esprit.iro.umontreal.ca
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diff changeset
714 user to define a set of fields as the 'input' field and a set of fields
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
bengioy@esprit.iro.umontreal.ca
parents: 22
diff changeset
715 as the 'target' field. Optionally, a single weight_field can also be defined.
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
bengioy@esprit.iro.umontreal.ca
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diff changeset
716 """
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
bengioy@esprit.iro.umontreal.ca
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diff changeset
717 args = ((input_fields,'input'),(output_fields,'target'))
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
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diff changeset
718 if weight_field: args+=(([weight_field],'weight'))
36
438440ba0627 Rewriting dataset.py completely
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diff changeset
719 return src_dataset.merge_fields(*args)
23
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
bengioy@esprit.iro.umontreal.ca
parents: 22
diff changeset
720
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
bengioy@esprit.iro.umontreal.ca
parents: 22
diff changeset
721
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
bengioy@esprit.iro.umontreal.ca
parents: 22
diff changeset
722
526e192b0699 Working on ApplyFunctionDataSet, added constraint that
bengioy@esprit.iro.umontreal.ca
parents: 22
diff changeset
723