Mercurial > pylearn
annotate dataset.py @ 39:c682c6e9bf93
Minor edits
author | bengioy@esprit.iro.umontreal.ca |
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date | Thu, 24 Apr 2008 14:46:10 -0400 |
parents | d637ad8f7352 |
children | 88fd1cce08b9 |
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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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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): |
16 | 11 """A virtual base class for datasets. |
12 | |
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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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17 |
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18 We call a DataSet a 'stream' when its length is unbounded (len(dataset)==float("infinity")). |
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19 |
16 | 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 |
16 | 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). |
16 | 25 |
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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56 |
16 | 57 Note: Fields are not mutually exclusive, i.e. two fields can overlap in their actual content. |
58 | |
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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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62 |
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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. |
16 | 68 |
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69 * dataset[i1:i2:s] returns a dataset with the examples i1,i1+s,...i2-s. |
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70 |
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71 * dataset[i] returns an Example. |
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72 |
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73 * dataset[[i1,i2,...in]] returns a dataset with examples i1,i2,...in. |
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74 |
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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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81 |
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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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90 |
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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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95 |
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96 |
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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): |
16 | 132 """Supports the syntax "for i in dataset: ..." |
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133 |
16 | 134 Using this syntax, "i" will be an Example instance (or equivalent) with |
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. |
16 | 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, nor check that fieldnames are valid, nor handle the |
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153 empty fieldnames (meaning 'use all the fields'). |
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154 """ |
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155 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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156 self.dataset=dataset |
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157 self.fieldnames=fieldnames |
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158 self.minibatch_size=minibatch_size |
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159 self.n_batches=n_batches |
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160 self.n_batches_done=0 |
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161 self.next_row=offset |
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162 self.L=len(dataset) |
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163 assert offset+minibatch_size<=self.L |
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164 ds_nbatches = (self.L-offset)/minibatch_size |
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165 if n_batches is not None: |
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166 ds_nbatches = max(n_batches,ds_nbatches) |
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167 if fieldnames: |
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168 assert dataset.hasFields(*fieldnames) |
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169 else: |
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170 fieldnames=dataset.fieldNames() |
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171 self.iterator = dataset.minibatches_nowrap(fieldnames,minibatch_size,ds_nbatches,offset) |
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172 |
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173 def __iter__(self): |
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174 return self |
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175 |
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176 def next_index(self): |
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177 return self.next_row |
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178 |
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179 def next(self): |
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180 if self.n_batches and self.n_batches_done==self.n_batches: |
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181 raise StopIteration |
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182 upper = self.next_row+minibatch_size |
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183 if upper <=self.L: |
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184 minibatch = self.minibatch_iterator.next() |
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185 else: |
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186 if not self.n_batches: |
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187 raise StopIteration |
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188 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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189 # first get the beginning of our minibatch (top of dataset) |
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190 first_part = self.dataset.minibatches_nowrap(fieldnames,self.L-self.next_row,1,self.next_row).next() |
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191 second_part = self.dataset.minibatches_nowrap(fieldnames,upper-self.L,1,0).next() |
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192 minibatch = Example(self.fieldnames, |
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193 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
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194 for name in self.fieldnames]) |
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195 self.next_row=upper |
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196 self.n_batches_done+=1 |
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197 if upper >= L: |
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198 self.next_row -= L |
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199 return minibatch |
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200 |
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201 |
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202 minibatches_fieldnames = None |
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203 minibatches_minibatch_size = 1 |
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204 minibatches_n_batches = None |
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205 def minibatches(self, |
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206 fieldnames = minibatches_fieldnames, |
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207 minibatch_size = minibatches_minibatch_size, |
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208 n_batches = minibatches_n_batches, |
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209 offset = 0): |
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210 """ |
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211 Return an iterator that supports three forms of syntax: |
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212 |
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213 for i in dataset.minibatches(None,**kwargs): ... |
16 | 214 |
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215 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
16 | 216 |
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217 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
16 | 218 |
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219 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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220 tuple, or Example instance. In both cases, i[k] is a list-like container |
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221 of a batch of current examples. In the second case, i[0] is |
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222 list-like container of the f1 field of a batch current examples, i[1] is |
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223 a list-like container of the f2 field, etc. |
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224 |
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225 Using the first syntax, all the fields will be returned in "i". |
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226 Beware that some datasets may not support this syntax, if the number |
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227 of fields is infinite (i.e. field values may be computed "on demand"). |
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228 |
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229 Using the third syntax, i1, i2, i3 will be list-like containers of the |
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230 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
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231 |
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232 The minibatches iterator is expected to return upon each call to next() |
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233 a DataSetFields object, which is a LookupList (indexed by the field names) whose |
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234 elements are iterable over the minibatch examples, and which keeps a pointer to |
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235 a sub-dataset that can be used to iterate over the individual examples |
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236 in the minibatch. Hence a minibatch can be converted back to a regular |
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237 dataset or its fields can be looked at individually (and possibly iterated over). |
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238 |
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239 PARAMETERS |
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240 - fieldnames (list of any type, default None): |
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241 The loop variables i1, i2, i3 (in the example above) should contain the |
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242 f1, f2, and f3 fields of the current batch of examples. If None, the |
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243 derived class can choose a default, e.g. all fields. |
16 | 244 |
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245 - minibatch_size (integer, default 1) |
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246 On every iteration, the variables i1, i2, i3 will have |
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247 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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248 |
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249 - n_batches (integer, default None) |
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250 The iterator will loop exactly this many times, and then stop. If None, |
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251 the derived class can choose a default. If (-1), then the returned |
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252 iterator should support looping indefinitely. |
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253 |
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254 - offset (integer, default 0) |
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255 The iterator will start at example 'offset' in the dataset, rather than the default. |
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256 |
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257 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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258 any other object that supports integer indexing and slicing. |
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259 |
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260 """ |
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261 return MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
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262 |
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263 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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264 """ |
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265 This is the minibatches iterator generator that sub-classes must define. |
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266 It does not need to worry about wrapping around multiple times across the dataset, |
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267 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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268 The next() method of the returned iterator does not even need to worry about |
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269 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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270 before an improper call to minibatches_nowrap's next() is made). |
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271 That next() method can assert that its next row will always be within [0,len(dataset)). |
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272 The iterator returned by minibatches_nowrap does not need to implement |
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273 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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274 """ |
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275 raise AbstractFunction() |
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276 |
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277 def __len__(self): |
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278 """ |
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279 len(dataset) returns the number of examples in the dataset. |
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280 By default, a DataSet is a 'stream', i.e. it has an unbounded (infinite) length. |
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281 Sub-classes which implement finite-length datasets should redefine this method. |
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282 Some methods only make sense for finite-length datasets, and will perform |
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283 assert len(dataset)<DataSet.infinity |
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284 in order to check the finiteness of the dataset. |
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285 """ |
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286 return infinity |
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287 |
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288 def hasFields(self,*fieldnames): |
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289 """ |
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290 Return true if the given field name (or field names, if multiple arguments are |
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291 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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292 of the iterators). |
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293 |
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294 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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295 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
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296 """ |
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297 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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298 |
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299 def fieldNames(self): |
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300 """ |
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301 Return the list of field names that are supported by the iterators, |
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302 and for which hasFields(fieldname) would return True. |
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303 """ |
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304 raise AbstractFunction() |
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305 |
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306 def __call__(self,*fieldnames): |
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307 """ |
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308 Return a dataset that sees only the fields whose name are specified. |
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309 """ |
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310 assert self.hasFields(fieldnames) |
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311 return self.fields(fieldnames).examples() |
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312 |
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313 def fields(self,*fieldnames): |
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314 """ |
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315 Return a DataSetFields object associated with this dataset. |
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316 """ |
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317 return DataSetFields(self,fieldnames) |
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318 |
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319 def __getitem__(self,i): |
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320 """ |
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321 dataset[i] returns the (i+1)-th example of the dataset. |
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322 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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323 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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324 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
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325 |
39 | 326 Note that some stream datasets may be unable to implement random access, i.e. |
327 arbitrary slicing/indexing | |
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328 because they can only iterate through examples one or a minibatch at a time |
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329 and do not actually store or keep past (or future) examples. |
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330 """ |
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331 raise NotImplementedError() |
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332 |
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333 def valuesHStack(self,fieldnames,fieldvalues): |
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334 """ |
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335 Return a value that corresponds to concatenating (horizontally) several field values. |
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336 This can be useful to merge some fields. The implementation of this operation is likely |
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337 to involve a copy of the original values. When the values are numpy arrays, the |
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338 result should be numpy.hstack(values). If it makes sense, this operation should |
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339 work as well when each value corresponds to multiple examples in a minibatch |
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340 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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341 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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342 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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343 pointing to the original values for other data types. |
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344 """ |
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345 all_numpy=True |
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346 for value in fieldvalues: |
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347 if not type(value) is numpy.ndarray: |
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348 all_numpy=False |
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349 if all_numpy: |
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350 return numpy.hstack(fieldvalues) |
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351 # the default implementation of horizontal stacking is to put values in a list |
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352 return fieldvalues |
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353 |
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354 |
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355 def valuesVStack(self,fieldname,values): |
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356 """ |
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357 Return a value that corresponds to concatenating (vertically) several values of the |
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358 same field. This can be important to build a minibatch out of individual examples. This |
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359 is likely to involve a copy of the original values. When the values are numpy arrays, the |
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360 result should be numpy.vstack(values). |
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361 The default is to use numpy.vstack for numpy.ndarray values, and a list |
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362 pointing to the original values for other data types. |
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363 """ |
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364 all_numpy=True |
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365 for value in values: |
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366 if not type(value) is numpy.ndarray: |
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367 all_numpy=False |
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368 if all_numpy: |
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369 return numpy.vstack(values) |
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370 # the default implementation of vertical stacking is to put values in a list |
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371 return values |
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372 |
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373 def __or__(self,other): |
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374 """ |
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375 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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376 fields of the argument datasets. This only works if they all have the same length. |
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377 """ |
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378 return HStackedDataSet(self,other) |
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379 |
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380 def __add__(self,other): |
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381 """ |
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382 dataset1 + dataset2 is a dataset that concatenates the examples from the argument datasets |
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383 (and whose length is the sum of the length of the argument datasets). This only |
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384 works if they all have the same fields. |
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385 """ |
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386 return VStackedDataSet(self,other) |
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387 |
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388 def hstack(datasets): |
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389 """ |
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390 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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391 which is a dataset whose fields list is the concatenation of the fields |
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392 of the individual datasets. |
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393 """ |
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394 assert len(datasets)>0 |
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395 if len(datasets)==1: |
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396 return datasets[0] |
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397 return HStackedDataSet(datasets) |
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398 |
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399 def vstack(datasets): |
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400 """ |
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401 vstack(dataset1,dataset2,...) returns dataset1 + datataset2 + ... |
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402 which is a dataset which iterates first over the examples of dataset1, then |
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403 over those of dataset2, etc. |
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404 """ |
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405 assert len(datasets)>0 |
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406 if len(datasets)==1: |
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407 return datasets[0] |
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408 return VStackedDataSet(datasets) |
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409 |
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410 |
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411 class DataSetFields(LookupList): |
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412 """ |
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413 Although a DataSet iterates over examples (like rows of a matrix), an associated |
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414 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
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415 as a transpose of the associated dataset. |
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416 |
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417 To iterate over fields, one can do |
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418 * for fields in dataset.fields() |
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419 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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420 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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421 and each of these fields is iterable over the examples: |
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422 * for field_examples in dataset.fields(): |
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423 for example_value in field_examples: |
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424 ... |
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425 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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426 such things because the underlying dataset may refuse to access the different fields in |
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427 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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428 The result of fields() is a DataSetFields object, which iterates over fields, |
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429 and whose elements are iterable over examples. A DataSetFields object can |
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430 be turned back into a DataSet with its examples() method: |
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431 dataset2 = dataset1.fields().examples() |
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432 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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433 """ |
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434 def __init__(self,dataset,*fieldnames): |
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435 self.dataset=dataset |
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436 assert dataset.hasFields(*fieldnames) |
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437 LookupList.__init__(self,dataset.fieldNames(), |
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438 dataset.minibatches(fieldnames if len(fieldnames)>0 else self.fieldNames(), |
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439 minibatch_size=len(dataset)).next() |
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440 def examples(self): |
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441 return self.dataset |
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442 |
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443 def __or__(self,other): |
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444 """ |
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445 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
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446 of the list of examples of DataSetFields fields1 and fields2. |
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447 """ |
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448 return (self.examples() + other.examples()).fields() |
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449 |
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450 def __add__(self,other): |
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451 """ |
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452 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
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453 of the fields of DataSetFields fields1 and fields2. |
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454 """ |
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455 return (self.examples() | other.examples()).fields() |
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456 |
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457 |
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458 class MinibatchDataSet(DataSet): |
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459 """ |
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460 Turn a LookupList of same-length fields into an example-iterable dataset. |
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461 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
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462 """ |
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463 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
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464 values_hstack=DataSet().valuesHStack): |
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465 """ |
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466 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
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467 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
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468 semantics as the DataSet methods of the same name (but without the self argument). |
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469 """ |
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470 self.fields=fields_lookuplist |
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471 assert len(fields_lookuplist)>0 |
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472 self.length=len(fields_lookuplist[0]) |
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473 for field in fields_lookuplist[1:]: |
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474 assert self.length==len(field) |
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475 self.values_vstack=values_vstack |
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476 self.values_hstack=values_hstack |
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477 |
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478 def __len__(self): |
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479 return self.length |
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480 |
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481 def __getitem__(self,i): |
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482 return Example(self.fields.keys(),[field[i] for field in self.fields]) |
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Debugged (to the extent of my tests) the new version of dataset
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483 |
29
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Changed apply_function so that it propagates methods of the source.
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484 def fieldNames(self): |
36
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485 return self.fields.keys() |
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486 |
37
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487 def hasFields(self,*fieldnames): |
36
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488 for fieldname in fieldnames: |
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489 if fieldname not in self.fields: |
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490 return False |
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491 return True |
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492 |
37
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Factored the minibatch-writing code into an iterator class inside DataSet
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493 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
36
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494 class Iterator(object): |
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495 def __init__(self,ds): |
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496 self.ds=ds |
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497 self.next_example=offset |
36
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498 assert minibatch_size > 0 |
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499 if offset+minibatch_size > ds.length |
36
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500 raise NotImplementedError() |
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501 def __iter__(self): |
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502 return self |
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503 def next(self): |
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504 upper = next_example+minibatch_size |
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505 assert upper<=self.ds.length |
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Factored the minibatch-writing code into an iterator class inside DataSet
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506 minibatch = Example(self.ds.fields.keys(), |
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Factored the minibatch-writing code into an iterator class inside DataSet
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507 [field[next_example:upper] |
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Factored the minibatch-writing code into an iterator class inside DataSet
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508 for field in self.ds.fields]) |
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509 self.next_example+=minibatch_size |
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510 return DataSetFields(MinibatchDataSet(minibatch),fieldnames) |
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511 |
37
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512 return MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
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513 |
36
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514 def valuesVStack(self,fieldname,fieldvalues): |
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515 return self.values_vstack(fieldname,fieldvalues) |
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516 |
36
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517 def valuesHStack(self,fieldnames,fieldvalues): |
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518 return self.values_hstack(fieldnames,fieldvalues) |
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519 |
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520 class HStackedDataSet(DataSet): |
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521 """ |
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522 A DataSet that wraps several datasets and shows a view that includes all their fields, |
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523 i.e. whose list of fields is the concatenation of their lists of fields. |
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524 |
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525 If a field name is found in more than one of the datasets, then either an error is |
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526 raised or the fields are renamed (either by prefixing the __name__ attribute |
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527 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list). |
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528 |
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529 TODO: automatically detect a chain of stacked datasets due to A | B | C | D ... |
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530 """ |
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531 def __init__(self,datasets,accept_nonunique_names=False): |
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532 DataSet.__init__(self) |
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533 self.datasets=datasets |
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534 self.accept_nonunique_names=accept_nonunique_names |
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535 self.fieldname2dataset={} |
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536 |
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537 def rename_field(fieldname,dataset,i): |
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538 if hasattr(dataset,"__name__"): |
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539 return dataset.__name__ + "." + fieldname |
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540 return fieldname+"."+str(i) |
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541 |
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542 # make sure all datasets have the same length and unique field names |
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543 self.length=None |
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544 names_to_change=[] |
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545 for i in xrange(len(datasets)): |
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546 dataset = datasets[i] |
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547 length=len(dataset) |
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548 if self.length: |
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549 assert self.length==length |
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550 else: |
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551 self.length=length |
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552 for fieldname in dataset.fieldNames(): |
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553 if fieldname in self.fieldname2dataset: # name conflict! |
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554 if accept_nonunique_names: |
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555 fieldname=rename_field(fieldname,dataset,i) |
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556 names2change.append((fieldname,i)) |
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557 else: |
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558 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
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559 self.fieldname2dataset[fieldname]=i |
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560 for fieldname,i in names_to_change: |
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561 del self.fieldname2dataset[fieldname] |
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562 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
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563 |
37
73c4212ba5b3
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564 def hasFields(self,*fieldnames): |
36
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565 for fieldname in fieldnames: |
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566 if not fieldname in self.fieldname2dataset: |
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567 return False |
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568 return True |
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569 |
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570 def fieldNames(self): |
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571 return self.fieldname2dataset.keys() |
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572 |
37
73c4212ba5b3
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573 def minibatches_nowrap(self, |
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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574 fieldnames = minibatches_fieldnames, |
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575 minibatch_size = minibatches_minibatch_size, |
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576 n_batches = minibatches_n_batches, |
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|
577 offset = 0): |
36
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578 |
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579 class Iterator(object): |
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580 def __init__(self,hsds,iterators): |
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581 self.hsds=hsds |
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582 self.iterators=iterators |
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583 def __iter__(self): |
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584 return self |
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585 def next(self): |
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586 # concatenate all the fields of the minibatches |
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587 minibatch = reduce(LookupList.__add__,[iterator.next() for iterator in self.iterators]) |
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588 # and return a DataSetFields whose dataset is the transpose (=examples()) of this minibatch |
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589 return DataSetFields(MinibatchDataSet(minibatch,self.hsds.valuesVStack, |
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590 self.hsds.valuesHStack), |
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591 fieldnames if fieldnames else hsds.fieldNames()) |
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592 |
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593 assert self.hasfields(fieldnames) |
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|
594 # find out which underlying datasets are necessary to service the required fields |
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595 # and construct corresponding minibatch iterators |
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596 if fieldnames: |
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597 datasets=set([]) |
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598 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
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599 for fieldname in fieldnames: |
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600 dataset=self.datasets[self.fieldnames2dataset[fieldname]] |
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601 datasets.add(dataset) |
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602 fields_in_dataset[dataset].append(fieldname) |
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603 datasets=list(datasets) |
37
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Factored the minibatch-writing code into an iterator class inside DataSet
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604 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
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605 for dataset in datasets] |
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606 else: |
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607 datasets=self.datasets |
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608 iterators=[dataset.minibatches(None,minibatch_size,n_batches,offset) for dataset in datasets] |
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609 return Iterator(self,iterators) |
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610 |
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611 |
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612 def valuesVStack(self,fieldname,fieldvalues): |
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613 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
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614 |
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615 def valuesHStack(self,fieldnames,fieldvalues): |
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616 """ |
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617 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
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618 to do the work, hoping that it can cope with the other values (i.e. won't care |
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619 about the incompatible fieldnames). Hence this heuristic will always work if |
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620 all the fieldnames are of the same sub-dataset. |
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621 """ |
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622 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
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623 |
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624 class VStackedDataSet(DataSet): |
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625 """ |
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626 A DataSet that wraps several datasets and shows a view that includes all their examples, |
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627 in the order provided. This clearly assumes that they all have the same field names |
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628 and all (except possibly the last one) are of finite length. |
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629 |
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630 TODO: automatically detect a chain of stacked datasets due to A + B + C + D ... |
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631 """ |
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632 def __init__(self,datasets): |
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633 self.datasets=datasets |
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634 self.length=0 |
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635 self.index2dataset={} |
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636 assert len(datasets)>0 |
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637 fieldnames = datasets[-1].fieldNames() |
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638 self.datasets_start_row=[] |
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639 # We use this map from row index to dataset index for constant-time random access of examples, |
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640 # to avoid having to search for the appropriate dataset each time and slice is asked for. |
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641 for dataset,k in enumerate(datasets[0:-1]): |
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642 L=len(dataset) |
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643 assert L<DataSet.infinity |
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644 for i in xrange(L): |
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645 self.index2dataset[self.length+i]=k |
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646 self.datasets_start_row.append(self.length) |
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647 self.length+=L |
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648 assert dataset.fieldNames()==fieldnames |
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649 self.datasets_start_row.append(self.length) |
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650 self.length+=len(datasets[-1]) |
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651 # If length is very large, we should use a more memory-efficient mechanism |
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652 # that does not store all indices |
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653 if self.length>1000000: |
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654 # 1 million entries would require about 60 meg for the index2dataset map |
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655 # TODO |
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656 print "A more efficient mechanism for index2dataset should be implemented" |
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657 |
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658 def __len__(self): |
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659 return self.length |
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660 |
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661 def fieldNames(self): |
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662 return self.datasets[0].fieldNames() |
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663 |
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664 def hasFields(self,*fieldnames): |
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665 return self.datasets[0].hasFields(*fieldnames) |
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666 |
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667 def locate_row(self,row): |
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668 """Return (dataset_index, row_within_dataset) for global row number""" |
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669 dataset_index = self.index2dataset[row] |
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670 row_within_dataset = self.datasets_start_row[dataset_index] |
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671 return dataset_index, row_within_dataset |
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|
672 |
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673 def minibatches_nowrap(self, |
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674 fieldnames = minibatches_fieldnames, |
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675 minibatch_size = minibatches_minibatch_size, |
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676 n_batches = minibatches_n_batches, |
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|
677 offset = 0): |
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|
678 |
37
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679 class Iterator(object): |
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680 def __init__(self,vsds): |
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681 self.vsds=vsds |
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682 self.next_row=offset |
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683 self.next_dataset_index,self.next_dataset_row=self.vsds.locate_row(offset) |
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684 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
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685 self.next_iterator(vsds.datasets[0],offset,n_batches) |
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|
686 |
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687 def next_iterator(self,dataset,starting_offset,batches_left): |
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688 L=len(dataset) |
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689 ds_nbatches = (L-starting_offset)/minibatch_size |
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690 if batches_left is not None: |
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691 ds_nbatches = max(batches_left,ds_nbatches) |
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692 if minibatch_size>L: |
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693 ds_minibatch_size=L |
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|
694 n_left_in_mb=minibatch_size-L |
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695 ds_nbatches=1 |
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696 else: |
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|
697 n_left_in_mb=0 |
37
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698 return dataset.minibatches(fieldnames,minibatch_size,ds_nbatches,starting_offset), \ |
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699 L-(starting_offset+ds_nbatches*minibatch_size), n_left_in_mb |
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|
700 |
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|
701 def move_to_next_dataset(self): |
38
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702 if self.n_left_at_the_end_of_ds>0: |
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703 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
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704 self.next_iterator(vsds.datasets[self.next_dataset_index], |
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705 self.n_left_at_the_end_of_ds,1) |
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706 else: |
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707 self.next_dataset_index +=1 |
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708 if self.next_dataset_index==len(self.vsds.datasets): |
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|
709 self.next_dataset_index = 0 |
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710 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
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711 self.next_iterator(vsds.datasets[self.next_dataset_index],starting_offset,n_batches) |
37
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712 |
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713 def __iter__(self): |
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714 return self |
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|
715 |
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716 def next(self): |
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717 dataset=self.vsds.datasets[self.next_dataset_index] |
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718 mb = self.next_iterator.next() |
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719 if self.n_left_in_mb: |
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720 extra_mb = [] |
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721 while self.n_left_in_mb>0: |
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722 self.move_to_next_dataset() |
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Factored the minibatch-writing code into an iterator class inside DataSet
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723 extra_mb.append(self.next_iterator.next()) |
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724 mb = Example(names, |
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Factored the minibatch-writing code into an iterator class inside DataSet
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725 [dataset.valuesVStack(name,[mb[name]]+[b[name] for b in extra_mb]) |
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d637ad8f7352
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726 for name in fieldnames]) |
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727 self.next_row+=minibatch_size |
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728 self.next_dataset_row+=minibatch_size |
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729 if self.next_row+minibatch_size>len(dataset): |
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730 self.move_to_next_dataset() |
37
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731 return mb |
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Factored the minibatch-writing code into an iterator class inside DataSet
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732 |
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733 |
23
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734 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
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735 """ |
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736 Wraps an arbitrary DataSet into one for supervised learning tasks by forcing the |
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Working on ApplyFunctionDataSet, added constraint that
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737 user to define a set of fields as the 'input' field and a set of fields |
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Working on ApplyFunctionDataSet, added constraint that
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738 as the 'target' field. Optionally, a single weight_field can also be defined. |
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739 """ |
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740 args = ((input_fields,'input'),(output_fields,'target')) |
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741 if weight_field: args+=(([weight_field],'weight')) |
36
438440ba0627
Rewriting dataset.py completely
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742 return src_dataset.merge_fields(*args) |
23
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Working on ApplyFunctionDataSet, added constraint that
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743 |
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Working on ApplyFunctionDataSet, added constraint that
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744 |
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745 |
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Working on ApplyFunctionDataSet, added constraint that
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746 |