Mercurial > pylearn
annotate dataset.py @ 43:e92244f30116
Corrected iterator logic errors
| author | bengioy@grenat.iro.umontreal.ca |
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| date | Mon, 28 Apr 2008 11:41:28 -0400 |
| parents | 9b68774fcc6b |
| children | 5a85fda9b19b |
| rev | line source |
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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 unique_elements_list_intersection |
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5 from string import join |
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6 from sys import maxint |
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7 |
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more comments, looping ArrayDataSet iterator, bugfixes to lookup_list, more tests
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8 class AbstractFunction (Exception): """Derived class must override this function""" |
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9 class NotImplementedYet (NotImplementedError): """Work in progress, this should eventually be implemented""" |
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10 #class UnboundedDataSet (Exception): """Trying to obtain length of unbounded dataset (a stream)""" |
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12 class DataSet(object): |
| 16 | 13 """A virtual base class for datasets. |
| 14 | |
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15 A DataSet can be seen as a generalization of a matrix, meant to be used in conjunction |
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16 with learning algorithms (for training and testing them): rows/records are called examples, and |
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17 columns/attributes are called fields. The field value for a particular example can be an arbitrary |
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18 python object, which depends on the particular dataset. |
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19 |
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20 We call a DataSet a 'stream' when its length is unbounded (otherwise its __len__ method |
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21 should raise an UnboundedDataSet exception). |
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22 |
| 16 | 23 A DataSet is a generator of iterators; these iterators can run through the |
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24 examples or the fields in a variety of ways. A DataSet need not necessarily have a finite |
| 16 | 25 or known length, so this class can be used to interface to a 'stream' which |
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26 feeds on-line learning (however, as noted below, some operations are not |
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27 feasible or not recommanded on streams). |
| 16 | 28 |
| 29 To iterate over examples, there are several possibilities: | |
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30 * for example in dataset([field1, field2,field3, ...]): |
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31 * for val1,val2,val3 in dataset([field1, field2,field3]): |
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32 * for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N): |
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33 * for mini1,mini2,mini3 in dataset.minibatches([field1, field2, ...],minibatch_size=N): |
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34 * for example in dataset: |
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35 Each of these is documented below. All of these iterators are expected |
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36 to provide, in addition to the usual 'next()' method, a 'next_index()' method |
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37 which returns a non-negative integer pointing to the position of the next |
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38 example that will be returned by 'next()' (or of the first example in the |
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39 next minibatch returned). This is important because these iterators |
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40 can wrap around the dataset in order to do multiple passes through it, |
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41 in possibly unregular ways if the minibatch size is not a divisor of the |
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42 dataset length. |
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43 |
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44 To iterate over fields, one can do |
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45 * for fields in dataset.fields() |
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46 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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47 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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48 and each of these fields is iterable over the examples: |
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49 * for field_examples in dataset.fields(): |
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50 for example_value in field_examples: |
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51 ... |
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52 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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53 such things because the underlying dataset may refuse to access the different fields in |
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54 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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55 The result of fields() is a DataSetFields object, which iterates over fields, |
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56 and whose elements are iterable over examples. A DataSetFields object can |
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57 be turned back into a DataSet with its examples() method: |
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58 dataset2 = dataset1.fields().examples() |
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59 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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60 |
| 16 | 61 Note: Fields are not mutually exclusive, i.e. two fields can overlap in their actual content. |
| 62 | |
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63 Note: The content of a field can be of any type. Field values can also be 'missing' |
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64 (e.g. to handle semi-supervised learning), and in the case of numeric (numpy array) |
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65 fields (i.e. an ArrayFieldsDataSet), NaN plays the role of a missing value. |
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66 |
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67 Dataset elements can be indexed and sub-datasets (with a subset |
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68 of examples) can be extracted. These operations are not supported |
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69 by default in the case of streams. |
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70 |
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71 * dataset[:n] returns a dataset with the n first examples. |
| 16 | 72 |
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73 * dataset[i1:i2:s] returns a dataset with the examples i1,i1+s,...i2-s. |
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74 |
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75 * dataset[i] returns an Example. |
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76 |
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77 * dataset[[i1,i2,...in]] returns a dataset with examples i1,i2,...in. |
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78 |
| 41 | 79 * dataset['key'] returns a property associated with the given 'key' string. |
| 80 If 'key' is a fieldname, then the VStacked field values (iterable over | |
| 81 field values) for that field is returned. Other keys may be supported | |
| 82 by different dataset subclasses. The following key names are should be supported: | |
| 83 - 'description': a textual description or name for the dataset | |
| 84 - '<fieldname>.type': a type name or value for a given <fieldname> | |
| 85 | |
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86 Datasets can be concatenated either vertically (increasing the length) or |
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87 horizontally (augmenting the set of fields), if they are compatible, using |
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88 the following operations (with the same basic semantics as numpy.hstack |
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89 and numpy.vstack): |
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90 |
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91 * dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3]) |
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92 |
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93 creates a new dataset whose list of fields is the concatenation of the list of |
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94 fields of the argument datasets. This only works if they all have the same length. |
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95 |
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96 * dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3]) |
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97 |
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98 creates a new dataset that concatenates the examples from the argument datasets |
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99 (and whose length is the sum of the length of the argument datasets). This only |
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100 works if they all have the same fields. |
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101 |
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102 According to the same logic, and viewing a DataSetFields object associated to |
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103 a DataSet as a kind of transpose of it, fields1 + fields2 concatenates fields of |
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104 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their |
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105 examples. |
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106 |
| 41 | 107 A dataset can hold arbitrary key-value pairs that may be used to access meta-data |
| 108 or other properties of the dataset or associated with the dataset or the result | |
| 109 of a computation stored in a dataset. These can be accessed through the [key] syntax | |
| 110 when key is a string (or more specifically, neither an integer, a slice, nor a list). | |
| 111 | |
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112 A DataSet sub-class should always redefine the following methods: |
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113 * __len__ if it is not a stream |
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114 * fieldNames |
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115 * minibatches_nowrap (called by DataSet.minibatches()) |
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116 * valuesHStack |
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117 * valuesVStack |
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118 For efficiency of implementation, a sub-class might also want to redefine |
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119 * hasFields |
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120 * __getitem__ may not be feasible with some streams |
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121 * __iter__ |
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122 """ |
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123 |
| 41 | 124 def __init__(self,description=None,field_types=None): |
| 125 if description is None: | |
| 126 # by default return "<DataSetType>(<SuperClass1>,<SuperClass2>,...)" | |
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127 description = type(self).__name__ + " ( " + join([x.__name__ for x in type(self).__bases__]) + " )" |
| 41 | 128 self.description=description |
| 129 self.field_types=field_types | |
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130 |
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131 class MinibatchToSingleExampleIterator(object): |
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132 """ |
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133 Converts the result of minibatch iterator with minibatch_size==1 into |
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134 single-example values in the result. Therefore the result of |
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135 iterating on the dataset itself gives a sequence of single examples |
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136 (whereas the result of iterating over minibatches gives in each |
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137 Example field an iterable object over the individual examples in |
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138 the minibatch). |
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139 """ |
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140 def __init__(self, minibatch_iterator): |
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141 self.minibatch_iterator = minibatch_iterator |
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142 def __iter__(self): #makes for loop work |
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143 return self |
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144 def next(self): |
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145 size1_minibatch = self.minibatch_iterator.next() |
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146 return Example(size1_minibatch.keys(),[value[0] for value in size1_minibatch.values()]) |
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147 |
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148 def next_index(self): |
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149 return self.minibatch_iterator.next_index() |
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150 |
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151 def __iter__(self): |
| 16 | 152 """Supports the syntax "for i in dataset: ..." |
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153 |
| 16 | 154 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 155 all the fields of DataSet self. Every field of "i" will give access to | |
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156 a field of a single example. Fields should be accessible via |
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157 i["fielname"] or i[3] (in the order defined by the elements of the |
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158 Example returned by this iterator), but the derived class is free |
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159 to accept any type of identifier, and add extra functionality to the iterator. |
| 16 | 160 |
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161 The default implementation calls the minibatches iterator and extracts the first example of each field. |
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162 """ |
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163 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) |
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164 |
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165 |
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166 class MinibatchWrapAroundIterator(object): |
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167 """ |
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168 An iterator for minibatches that handles the case where we need to wrap around the |
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169 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
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170 a dataset that provides a minibatch iterator that does not need to handle that problem. |
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171 This class is a utility for dataset subclass writers, so that they do not have to handle |
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172 this issue multiple times, nor check that fieldnames are valid, nor handle the |
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173 empty fieldnames (meaning 'use all the fields'). |
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174 """ |
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175 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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176 self.dataset=dataset |
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177 self.fieldnames=fieldnames |
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178 self.minibatch_size=minibatch_size |
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179 self.n_batches=n_batches |
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180 self.n_batches_done=0 |
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181 self.next_row=offset |
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182 self.L=len(dataset) |
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183 assert offset+minibatch_size<=self.L |
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184 ds_nbatches = (self.L-offset)/minibatch_size |
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185 if n_batches is not None: |
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186 ds_nbatches = max(n_batches,ds_nbatches) |
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187 if fieldnames: |
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188 assert dataset.hasFields(*fieldnames) |
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189 else: |
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190 fieldnames=dataset.fieldNames() |
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191 self.iterator = dataset.minibatches_nowrap(fieldnames,minibatch_size,ds_nbatches,offset) |
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192 |
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193 def __iter__(self): |
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194 return self |
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195 |
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196 def next_index(self): |
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197 return self.next_row |
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198 |
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199 def next(self): |
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200 if self.n_batches and self.n_batches_done==self.n_batches: |
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201 raise StopIteration |
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202 upper = self.next_row+self.minibatch_size |
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203 if upper <=self.L: |
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204 minibatch = self.iterator.next() |
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205 else: |
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206 if not self.n_batches: |
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207 raise StopIteration |
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208 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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209 # first get the beginning of our minibatch (top of dataset) |
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210 first_part = self.dataset.minibatches_nowrap(fieldnames,self.L-self.next_row,1,self.next_row).next() |
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211 second_part = self.dataset.minibatches_nowrap(fieldnames,upper-self.L,1,0).next() |
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212 minibatch = Example(self.fieldnames, |
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213 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
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214 for name in self.fieldnames]) |
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215 self.next_row=upper |
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216 self.n_batches_done+=1 |
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217 if upper >= self.L and self.n_batches: |
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218 self.next_row -= self.L |
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219 return minibatch |
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220 |
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221 |
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222 minibatches_fieldnames = None |
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223 minibatches_minibatch_size = 1 |
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224 minibatches_n_batches = None |
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225 def minibatches(self, |
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226 fieldnames = minibatches_fieldnames, |
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227 minibatch_size = minibatches_minibatch_size, |
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228 n_batches = minibatches_n_batches, |
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229 offset = 0): |
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230 """ |
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231 Return an iterator that supports three forms of syntax: |
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232 |
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233 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 234 |
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235 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 236 |
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237 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 238 |
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239 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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240 tuple, or Example instance. In both cases, i[k] is a list-like container |
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241 of a batch of current examples. In the second case, i[0] is |
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242 list-like container of the f1 field of a batch current examples, i[1] is |
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243 a list-like container of the f2 field, etc. |
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244 |
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245 Using the first syntax, all the fields will be returned in "i". |
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246 Using the third syntax, i1, i2, i3 will be list-like containers of the |
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247 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
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248 |
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249 The minibatches iterator is expected to return upon each call to next() |
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250 a DataSetFields object, which is a LookupList (indexed by the field names) whose |
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251 elements are iterable over the minibatch examples, and which keeps a pointer to |
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252 a sub-dataset that can be used to iterate over the individual examples |
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253 in the minibatch. Hence a minibatch can be converted back to a regular |
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254 dataset or its fields can be looked at individually (and possibly iterated over). |
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255 |
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256 PARAMETERS |
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257 - fieldnames (list of any type, default None): |
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258 The loop variables i1, i2, i3 (in the example above) should contain the |
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259 f1, f2, and f3 fields of the current batch of examples. If None, the |
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260 derived class can choose a default, e.g. all fields. |
| 16 | 261 |
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262 - minibatch_size (integer, default 1) |
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263 On every iteration, the variables i1, i2, i3 will have |
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264 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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265 |
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266 - n_batches (integer, default None) |
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267 The iterator will loop exactly this many times, and then stop. If None, |
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268 the derived class can choose a default. If (-1), then the returned |
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269 iterator should support looping indefinitely. |
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270 |
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271 - offset (integer, default 0) |
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272 The iterator will start at example 'offset' in the dataset, rather than the default. |
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273 |
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274 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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275 any other object that supports integer indexing and slicing. |
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276 |
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277 """ |
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278 return DataSet.MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
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279 |
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280 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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281 """ |
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282 This is the minibatches iterator generator that sub-classes must define. |
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283 It does not need to worry about wrapping around multiple times across the dataset, |
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284 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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285 The next() method of the returned iterator does not even need to worry about |
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286 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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287 before an improper call to minibatches_nowrap's next() is made). |
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288 That next() method can assert that its next row will always be within [0,len(dataset)). |
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289 The iterator returned by minibatches_nowrap does not need to implement |
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290 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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291 """ |
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292 raise AbstractFunction() |
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293 |
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294 def __len__(self): |
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295 """ |
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296 len(dataset) returns the number of examples in the dataset. |
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297 By default, a DataSet is a 'stream', i.e. it has an unbounded length (raises UnboundedDataSet). |
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298 Sub-classes which implement finite-length datasets should redefine this method. |
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299 Some methods only make sense for finite-length datasets. |
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300 """ |
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301 raise UnboundedDataSet() |
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302 |
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303 def hasFields(self,*fieldnames): |
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304 """ |
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305 Return true if the given field name (or field names, if multiple arguments are |
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306 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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307 of the iterators). |
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308 |
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309 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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310 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
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311 """ |
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312 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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313 |
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314 def fieldNames(self): |
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315 """ |
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316 Return the list of field names that are supported by the iterators, |
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317 and for which hasFields(fieldname) would return True. |
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318 """ |
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319 raise AbstractFunction() |
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320 |
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321 def __call__(self,*fieldnames): |
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322 """ |
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323 Return a dataset that sees only the fields whose name are specified. |
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324 """ |
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325 assert self.hasFields(*fieldnames) |
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326 return self.fields(*fieldnames).examples() |
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327 |
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328 def fields(self,*fieldnames): |
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329 """ |
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330 Return a DataSetFields object associated with this dataset. |
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331 """ |
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332 return DataSetFields(self,*fieldnames) |
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333 |
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334 def __getitem__(self,i): |
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335 """ |
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336 dataset[i] returns the (i+1)-th example of the dataset. |
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337 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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338 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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339 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
| 41 | 340 dataset['key'] returns a property associated with the given 'key' string. |
| 341 If 'key' is a fieldname, then the VStacked field values (iterable over | |
| 342 field values) for that field is returned. Other keys may be supported | |
| 343 by different dataset subclasses. The following key names are encouraged: | |
| 344 - 'description': a textual description or name for the dataset | |
| 345 - '<fieldname>.type': a type name or value for a given <fieldname> | |
|
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346 |
| 39 | 347 Note that some stream datasets may be unable to implement random access, i.e. |
| 348 arbitrary slicing/indexing | |
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349 because they can only iterate through examples one or a minibatch at a time |
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350 and do not actually store or keep past (or future) examples. |
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351 |
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352 The default implementation of getitem uses the minibatches iterator |
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353 to obtain one example, one slice, or a list of examples. It may not |
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354 always be the most efficient way to obtain the result, especially if |
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355 the data are actually stored in a memory array. |
|
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356 """ |
| 41 | 357 # check for an index |
|
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358 if type(i) is int: |
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359 return DataSet.MinibatchToSingleExampleIterator( |
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360 self.minibatches(minibatch_size=1,n_batches=1,offset=i)).next() |
| 41 | 361 rows=None |
| 362 # or a slice | |
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363 if type(i) is slice: |
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364 if not i.start: i.start=0 |
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365 if not i.step: i.step=1 |
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366 if i.step is 1: |
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367 return self.minibatches(minibatch_size=i.stop-i.start,n_batches=1,offset=i.start).next().examples() |
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368 rows = range(i.start,i.stop,i.step) |
| 41 | 369 # or a list of indices |
| 370 elif type(i) is list: | |
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371 rows = i |
| 41 | 372 if rows is not None: |
| 373 fields_values = zip(*[self[row] for row in rows]) | |
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374 return DataSet.MinibatchDataSet( |
| 41 | 375 Example(self.fieldNames(),[ self.valuesVStack(fieldname,field_values) |
| 376 for fieldname,field_values | |
| 377 in zip(self.fieldNames(),fields_values)])) | |
| 378 # else check for a fieldname | |
| 379 if self.hasFields(i): | |
| 380 return self.minibatches(fieldnames=[i],minibatch_size=len(self),n_batches=1,offset=0).next()[0] | |
| 381 # else we are trying to access a property of the dataset | |
| 382 assert i in self.__dict__ # else it means we are trying to access a non-existing property | |
| 383 return self.__dict__[i] | |
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384 |
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385 def valuesHStack(self,fieldnames,fieldvalues): |
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386 """ |
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387 Return a value that corresponds to concatenating (horizontally) several field values. |
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388 This can be useful to merge some fields. The implementation of this operation is likely |
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389 to involve a copy of the original values. When the values are numpy arrays, the |
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390 result should be numpy.hstack(values). If it makes sense, this operation should |
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391 work as well when each value corresponds to multiple examples in a minibatch |
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392 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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393 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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394 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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395 pointing to the original values for other data types. |
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396 """ |
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397 all_numpy=True |
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398 for value in fieldvalues: |
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399 if not type(value) is numpy.ndarray: |
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400 all_numpy=False |
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401 if all_numpy: |
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402 return numpy.hstack(fieldvalues) |
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403 # the default implementation of horizontal stacking is to put values in a list |
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404 return fieldvalues |
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405 |
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406 |
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407 def valuesVStack(self,fieldname,values): |
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408 """ |
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409 Return a value that corresponds to concatenating (vertically) several values of the |
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410 same field. This can be important to build a minibatch out of individual examples. This |
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411 is likely to involve a copy of the original values. When the values are numpy arrays, the |
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412 result should be numpy.vstack(values). |
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413 The default is to use numpy.vstack for numpy.ndarray values, and a list |
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414 pointing to the original values for other data types. |
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415 """ |
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416 all_numpy=True |
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417 for value in values: |
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418 if not type(value) is numpy.ndarray: |
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419 all_numpy=False |
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420 if all_numpy: |
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421 return numpy.vstack(values) |
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422 # the default implementation of vertical stacking is to put values in a list |
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423 return values |
|
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424 |
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425 def __or__(self,other): |
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426 """ |
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427 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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428 fields of the argument datasets. This only works if they all have the same length. |
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429 """ |
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430 return HStackedDataSet(self,other) |
|
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431 |
|
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432 def __and__(self,other): |
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433 """ |
|
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434 dataset1 & dataset2 is a dataset that concatenates the examples from the argument datasets |
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435 (and whose length is the sum of the length of the argument datasets). This only |
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436 works if they all have the same fields. |
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437 """ |
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438 return VStackedDataSet(self,other) |
|
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439 |
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440 def hstack(datasets): |
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441 """ |
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442 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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443 which is a dataset whose fields list is the concatenation of the fields |
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444 of the individual datasets. |
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445 """ |
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446 assert len(datasets)>0 |
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447 if len(datasets)==1: |
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448 return datasets[0] |
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449 return HStackedDataSet(datasets) |
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450 |
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451 def vstack(datasets): |
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452 """ |
|
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453 vstack(dataset1,dataset2,...) returns dataset1 & datataset2 & ... |
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454 which is a dataset which iterates first over the examples of dataset1, then |
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455 over those of dataset2, etc. |
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456 """ |
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457 assert len(datasets)>0 |
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458 if len(datasets)==1: |
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459 return datasets[0] |
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460 return VStackedDataSet(datasets) |
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461 |
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462 class FieldsSubsetDataSet(DataSet): |
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463 """ |
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464 A sub-class of DataSet that selects a subset of the fields. |
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465 """ |
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466 def __init__(self,src,fieldnames): |
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467 self.src=src |
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468 self.fieldnames=fieldnames |
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469 assert src.hasFields(*fieldnames) |
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470 self.valuesHStack = src.valuesHStack |
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471 self.valuesVStack = src.valuesVStack |
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472 |
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473 def __len__(self): return len(self.src) |
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474 |
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475 def fieldNames(self): |
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476 return self.fieldnames |
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477 |
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478 def __iter__(self): |
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479 class Iterator(object): |
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480 def __init__(self,ds): |
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481 self.ds=ds |
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482 self.src_iter=ds.src.__iter__() |
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483 def __iter__(self): return self |
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484 def next(self): |
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485 example = self.src_iter.next() |
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486 return Example(self.ds.fieldnames, |
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487 [example[field] for field in self.ds.fieldnames]) |
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488 return Iterator(self) |
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489 |
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490 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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491 assert self.hasFields(*fieldnames) |
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492 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
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493 def __getitem__(self,i): |
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494 return FieldsSubsetDataSet(self.src[i],self.fieldnames) |
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495 |
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496 |
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497 class DataSetFields(LookupList): |
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498 """ |
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499 Although a DataSet iterates over examples (like rows of a matrix), an associated |
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500 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
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501 as a transpose of the associated dataset. |
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502 |
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503 To iterate over fields, one can do |
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504 * for fields in dataset.fields() |
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505 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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506 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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507 and each of these fields is iterable over the examples: |
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508 * for field_examples in dataset.fields(): |
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509 for example_value in field_examples: |
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510 ... |
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511 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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512 such things because the underlying dataset may refuse to access the different fields in |
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513 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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514 The result of fields() is a DataSetFields object, which iterates over fields, |
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515 and whose elements are iterable over examples. A DataSetFields object can |
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516 be turned back into a DataSet with its examples() method: |
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517 dataset2 = dataset1.fields().examples() |
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518 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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519 |
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520 DataSetFields can be concatenated vertically or horizontally. To be consistent with |
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521 the syntax used for DataSets, the | concatenates the fields and the & concatenates |
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522 the examples. |
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523 """ |
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524 def __init__(self,dataset,*fieldnames): |
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525 if not fieldnames: |
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526 fieldnames=dataset.fieldNames() |
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527 elif fieldnames is not dataset.fieldNames(): |
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528 dataset = FieldsSubsetDataSet(dataset,fieldnames) |
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529 assert dataset.hasFields(*fieldnames) |
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530 self.dataset=dataset |
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531 minibatch_iterator = dataset.minibatches(fieldnames, |
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532 minibatch_size=len(dataset), |
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533 n_batches=1) |
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534 minibatch=minibatch_iterator.next() |
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535 LookupList.__init__(self,fieldnames,minibatch) |
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536 |
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537 def examples(self): |
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538 return self.dataset |
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539 |
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540 def __or__(self,other): |
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541 """ |
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542 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
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543 of the list of examples of DataSetFields fields1 and fields2. |
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544 """ |
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545 return (self.examples() + other.examples()).fields() |
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|
546 |
|
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547 def __and__(self,other): |
|
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548 """ |
|
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549 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
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550 of the fields of DataSetFields fields1 and fields2. |
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551 """ |
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552 return (self.examples() | other.examples()).fields() |
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553 |
|
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554 |
|
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555 class MinibatchDataSet(DataSet): |
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556 """ |
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557 Turn a LookupList of same-length fields into an example-iterable dataset. |
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558 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
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559 """ |
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560 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
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561 values_hstack=DataSet().valuesHStack): |
|
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|
562 """ |
|
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563 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
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564 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
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565 semantics as the DataSet methods of the same name (but without the self argument). |
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566 """ |
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567 self.fields=fields_lookuplist |
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568 assert len(fields_lookuplist)>0 |
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569 self.length=len(fields_lookuplist[0]) |
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570 for field in fields_lookuplist[1:]: |
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571 assert self.length==len(field) |
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572 self.values_vstack=values_vstack |
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573 self.values_hstack=values_hstack |
|
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574 |
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575 def __len__(self): |
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576 return self.length |
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577 |
|
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578 def __getitem__(self,i): |
|
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579 return DataSetFields(MinibatchDataSet( |
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580 Example(self.fields.keys(),[field[i] for field in self.fields])),self.fields) |
|
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|
581 |
|
29
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582 def fieldNames(self): |
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583 return self.fields.keys() |
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584 |
|
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585 def hasFields(self,*fieldnames): |
|
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586 for fieldname in fieldnames: |
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587 if fieldname not in self.fields: |
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588 return False |
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589 return True |
|
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590 |
|
37
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591 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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592 class Iterator(object): |
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593 def __init__(self,ds): |
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594 self.ds=ds |
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595 self.next_example=offset |
|
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596 assert minibatch_size > 0 |
| 41 | 597 if offset+minibatch_size > ds.length: |
|
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598 raise NotImplementedError() |
|
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599 def __iter__(self): |
|
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600 return self |
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601 def next(self): |
|
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602 upper = next_example+minibatch_size |
|
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603 assert upper<=self.ds.length |
|
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604 minibatch = Example(self.ds.fields.keys(), |
|
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|
605 [field[next_example:upper] |
|
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606 for field in self.ds.fields]) |
|
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607 self.next_example+=minibatch_size |
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608 return DataSetFields(MinibatchDataSet(minibatch),fieldnames) |
|
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|
609 |
|
40
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610 return Iterator(self) |
|
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611 |
|
36
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612 def valuesVStack(self,fieldname,fieldvalues): |
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613 return self.values_vstack(fieldname,fieldvalues) |
|
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614 |
|
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615 def valuesHStack(self,fieldnames,fieldvalues): |
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616 return self.values_hstack(fieldnames,fieldvalues) |
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617 |
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618 class HStackedDataSet(DataSet): |
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619 """ |
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620 A DataSet that wraps several datasets and shows a view that includes all their fields, |
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621 i.e. whose list of fields is the concatenation of their lists of fields. |
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622 |
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623 If a field name is found in more than one of the datasets, then either an error is |
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624 raised or the fields are renamed (either by prefixing the __name__ attribute |
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625 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list). |
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626 |
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627 TODO: automatically detect a chain of stacked datasets due to A | B | C | D ... |
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628 """ |
| 41 | 629 def __init__(self,datasets,accept_nonunique_names=False,description=None,field_types=None): |
| 630 DataSet.__init__(self,description,field_types) | |
|
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631 self.datasets=datasets |
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632 self.accept_nonunique_names=accept_nonunique_names |
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633 self.fieldname2dataset={} |
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634 |
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635 def rename_field(fieldname,dataset,i): |
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636 if hasattr(dataset,"__name__"): |
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637 return dataset.__name__ + "." + fieldname |
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638 return fieldname+"."+str(i) |
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639 |
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640 # make sure all datasets have the same length and unique field names |
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641 self.length=None |
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642 names_to_change=[] |
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643 for i in xrange(len(datasets)): |
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644 dataset = datasets[i] |
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645 length=len(dataset) |
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646 if self.length: |
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647 assert self.length==length |
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648 else: |
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649 self.length=length |
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650 for fieldname in dataset.fieldNames(): |
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651 if fieldname in self.fieldname2dataset: # name conflict! |
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652 if accept_nonunique_names: |
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653 fieldname=rename_field(fieldname,dataset,i) |
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654 names2change.append((fieldname,i)) |
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655 else: |
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656 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
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657 self.fieldname2dataset[fieldname]=i |
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658 for fieldname,i in names_to_change: |
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659 del self.fieldname2dataset[fieldname] |
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660 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
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661 |
|
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662 def hasFields(self,*fieldnames): |
|
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663 for fieldname in fieldnames: |
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664 if not fieldname in self.fieldname2dataset: |
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665 return False |
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666 return True |
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667 |
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668 def fieldNames(self): |
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669 return self.fieldname2dataset.keys() |
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670 |
| 41 | 671 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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672 |
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673 class Iterator(object): |
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674 def __init__(self,hsds,iterators): |
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675 self.hsds=hsds |
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676 self.iterators=iterators |
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677 def __iter__(self): |
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678 return self |
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679 def next(self): |
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|
680 # concatenate all the fields of the minibatches |
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681 minibatch = reduce(LookupList.__add__,[iterator.next() for iterator in self.iterators]) |
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682 # and return a DataSetFields whose dataset is the transpose (=examples()) of this minibatch |
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683 return DataSetFields(MinibatchDataSet(minibatch,self.hsds.valuesVStack, |
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684 self.hsds.valuesHStack), |
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685 fieldnames if fieldnames else hsds.fieldNames()) |
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686 |
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687 assert self.hasfields(fieldnames) |
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688 # find out which underlying datasets are necessary to service the required fields |
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689 # and construct corresponding minibatch iterators |
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690 if fieldnames: |
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691 datasets=set([]) |
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692 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
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693 for fieldname in fieldnames: |
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694 dataset=self.datasets[self.fieldnames2dataset[fieldname]] |
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695 datasets.add(dataset) |
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696 fields_in_dataset[dataset].append(fieldname) |
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697 datasets=list(datasets) |
|
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698 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
|
36
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699 for dataset in datasets] |
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700 else: |
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701 datasets=self.datasets |
|
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702 iterators=[dataset.minibatches(None,minibatch_size,n_batches,offset) for dataset in datasets] |
|
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703 return Iterator(self,iterators) |
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|
704 |
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705 |
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706 def valuesVStack(self,fieldname,fieldvalues): |
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707 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
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708 |
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709 def valuesHStack(self,fieldnames,fieldvalues): |
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710 """ |
|
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711 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
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712 to do the work, hoping that it can cope with the other values (i.e. won't care |
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713 about the incompatible fieldnames). Hence this heuristic will always work if |
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714 all the fieldnames are of the same sub-dataset. |
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715 """ |
|
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716 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
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717 |
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718 class VStackedDataSet(DataSet): |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
719 """ |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
720 A DataSet that wraps several datasets and shows a view that includes all their examples, |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
721 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
|
722 and all (except possibly the last one) are of finite length. |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
723 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
724 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
|
725 """ |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
726 def __init__(self,datasets): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
727 self.datasets=datasets |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
728 self.length=0 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
729 self.index2dataset={} |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
730 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
|
731 fieldnames = datasets[-1].fieldNames() |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
732 self.datasets_start_row=[] |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
733 # 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
|
734 # 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
|
735 for dataset,k in enumerate(datasets[0:-1]): |
|
40
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
736 try: |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
737 L=len(dataset) |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
738 except UnboundedDataSet: |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
739 print "All VStacked datasets (except possibly the last) must be bounded (have a length)." |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
740 assert False |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
741 for i in xrange(L): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
742 self.index2dataset[self.length+i]=k |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
743 self.datasets_start_row.append(self.length) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
744 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
|
745 assert dataset.fieldNames()==fieldnames |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
746 self.datasets_start_row.append(self.length) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
747 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
|
748 # 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
|
749 # 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
|
750 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
|
751 # 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
|
752 # TODO |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
753 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
|
754 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
755 def __len__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
756 return self.length |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
757 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
758 def fieldNames(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
759 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
|
760 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
761 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
|
762 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
|
763 |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
764 def locate_row(self,row): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
765 """Return (dataset_index, row_within_dataset) for global row number""" |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
766 dataset_index = self.index2dataset[row] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
767 row_within_dataset = self.datasets_start_row[dataset_index] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
768 return dataset_index, row_within_dataset |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
769 |
| 41 | 770 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
771 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
772 class Iterator(object): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
773 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
|
774 self.vsds=vsds |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
775 self.next_row=offset |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
776 self.next_dataset_index,self.next_dataset_row=self.vsds.locate_row(offset) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
777 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
|
778 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
|
779 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
780 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
|
781 L=len(dataset) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
782 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
|
783 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
|
784 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
|
785 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
|
786 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
|
787 n_left_in_mb=minibatch_size-L |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
788 ds_nbatches=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
789 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
790 n_left_in_mb=0 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
791 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
|
792 L-(starting_offset+ds_nbatches*minibatch_size), 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
|
793 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
794 def move_to_next_dataset(self): |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
795 if self.n_left_at_the_end_of_ds>0: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
796 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
797 self.next_iterator(vsds.datasets[self.next_dataset_index], |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
798 self.n_left_at_the_end_of_ds,1) |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
799 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
800 self.next_dataset_index +=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
801 if self.next_dataset_index==len(self.vsds.datasets): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
802 self.next_dataset_index = 0 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
803 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
804 self.next_iterator(vsds.datasets[self.next_dataset_index],starting_offset,n_batches) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
805 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
806 def __iter__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
807 return self |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
808 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
809 def next(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
810 dataset=self.vsds.datasets[self.next_dataset_index] |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
811 mb = self.next_iterator.next() |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
812 if 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
|
813 extra_mb = [] |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
814 while self.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
|
815 self.move_to_next_dataset() |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
816 extra_mb.append(self.next_iterator.next()) |
|
40
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
817 examples = Example(names, |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
818 [dataset.valuesVStack(name, |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
819 [mb[name]]+[b[name] for b in extra_mb]) |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
820 for name in fieldnames]) |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
821 mb = DataSetFields(MinibatchDataSet(examples),fieldnames) |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
822 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
823 self.next_row+=minibatch_size |
|
38
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824 self.next_dataset_row+=minibatch_size |
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825 if self.next_row+minibatch_size>len(dataset): |
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826 self.move_to_next_dataset() |
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827 return |
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828 |
| 41 | 829 class ArrayFieldsDataSet(DataSet): |
| 830 """ | |
| 831 Virtual super-class of datasets whose field values are numpy array, | |
| 832 thus defining valuesHStack and valuesVStack for sub-classes. | |
| 833 """ | |
| 834 def __init__(self,description=None,field_types=None): | |
| 835 DataSet.__init__(self,description,field_types) | |
| 836 def valuesHStack(self,fieldnames,fieldvalues): | |
| 837 """Concatenate field values horizontally, e.g. two vectors | |
| 838 become a longer vector, two matrices become a wider matrix, etc.""" | |
| 839 return numpy.hstack(fieldvalues) | |
| 840 def valuesVStack(self,fieldname,values): | |
| 841 """Concatenate field values vertically, e.g. two vectors | |
| 842 become a two-row matrix, two matrices become a longer matrix, etc.""" | |
| 843 return numpy.vstack(values) | |
| 844 | |
| 845 class ArrayDataSet(ArrayFieldsDataSet): | |
| 846 """ | |
| 847 An ArrayDataSet stores the fields as groups of columns in a numpy tensor, | |
| 848 whose first axis iterates over examples, second axis determines fields. | |
| 849 If the underlying array is N-dimensional (has N axes), then the field | |
| 850 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | |
| 851 """ | |
| 852 | |
| 853 """ | |
| 854 Construct an ArrayDataSet from the underlying numpy array (data) and | |
|
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855 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified |
| 41 | 856 using the standard arguments for indexing/slicing: integer for a column index, |
| 857 slice for an interval of columns (with possible stride), or iterable of column indices. | |
| 858 """ | |
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859 def __init__(self, data_array, fields_columns): |
| 41 | 860 self.data=data_array |
|
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861 self.fields_columns=fields_columns |
| 41 | 862 |
| 863 # check consistency and complete slices definitions | |
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864 for fieldname, fieldcolumns in self.fields_columns.items(): |
| 41 | 865 if type(fieldcolumns) is int: |
| 866 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1] | |
| 867 elif type(fieldcolumns) is slice: | |
| 868 start,step=None,None | |
| 869 if not fieldcolumns.start: | |
| 870 start=0 | |
| 871 if not fieldcolumns.step: | |
| 872 step=1 | |
| 873 if start or step: | |
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874 self.fields_columns[fieldname]=slice(start,fieldcolumns.stop,step) |
| 41 | 875 elif hasattr(fieldcolumns,"__iter__"): # something like a list |
| 876 for i in fieldcolumns: | |
| 877 assert i>=0 and i<data_array.shape[1] | |
| 878 | |
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879 def fieldNames(self): |
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880 return self.fields_columns.keys() |
| 41 | 881 |
|
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882 def __len__(self): |
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883 return len(self.data) |
| 41 | 884 |
|
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885 #def __getitem__(self,i): |
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886 # """More efficient implementation than the default""" |
| 41 | 887 |
|
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888 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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889 class Iterator(LookupList): # store the result in the lookup-list values |
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890 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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891 if fieldnames is None: fieldnames = dataset.fieldNames() |
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892 LookupList.__init__(self,fieldnames,[0]*len(fieldnames)) |
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893 self.dataset=dataset |
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894 self.minibatch_size=minibatch_size |
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895 assert offset>=0 and offset<len(dataset.data) |
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896 assert offset+minibatch_size<=len(dataset.data) |
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897 self.current=offset |
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898 def __iter__(self): |
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899 return self |
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900 def next(self): |
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901 sub_data = self.dataset.data[self.current:self.current+self.minibatch_size] |
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902 self._values = [sub_data[:,self.dataset.fields_columns[f]] for f in self._names] |
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903 self.current+=self.minibatch_size |
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904 return self |
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905 |
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906 return Iterator(self,fieldnames,minibatch_size,n_batches,offset) |
| 41 | 907 |
|
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908 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
|
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909 """ |
|
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910 Wraps an arbitrary DataSet into one for supervised learning tasks by forcing the |
|
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911 user to define a set of fields as the 'input' field and a set of fields |
|
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912 as the 'target' field. Optionally, a single weight_field can also be defined. |
|
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913 """ |
|
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914 args = ((input_fields,'input'),(output_fields,'target')) |
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915 if weight_field: args+=(([weight_field],'weight')) |
|
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916 return src_dataset.merge_fields(*args) |
|
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917 |
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918 |
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919 |
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920 |