Mercurial > pylearn
annotate dataset.py @ 66:dde1fb1b63ba
fixed test and removed print
| author | Frederic Bastien <bastienf@iro.umontreal.ca> |
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| date | Fri, 02 May 2008 11:24:17 -0400 |
| parents | d48eba49a2f4 |
| children | 2b6656b2ef52 40476a7746e8 |
| 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 import numpy |
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9 class AbstractFunction (Exception): """Derived class must override this function""" |
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10 class NotImplementedYet (NotImplementedError): """Work in progress, this should eventually be implemented""" |
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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 return sys.maxint). |
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| 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): |
| 50 | 33 * for mini1,mini2,mini3 in dataset.minibatches([field1, field2, field3], minibatch_size=N): |
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34 * for example in dataset: |
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35 print example['x'] |
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36 * for x,y,z in dataset: |
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37 Each of these is documented below. All of these iterators are expected |
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38 to provide, in addition to the usual 'next()' method, a 'next_index()' method |
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39 which returns a non-negative integer pointing to the position of the next |
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40 example that will be returned by 'next()' (or of the first example in the |
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41 next minibatch returned). This is important because these iterators |
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42 can wrap around the dataset in order to do multiple passes through it, |
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43 in possibly unregular ways if the minibatch size is not a divisor of the |
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44 dataset length. |
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45 |
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46 To iterate over fields, one can do |
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47 * for field in dataset.fields(): |
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48 for field_value in field: # iterate over the values associated to that field for all the dataset examples |
| 50 | 49 * for field in dataset(field1,field2,...).fields() to select a subset of fields |
| 50 * for field in dataset.fields(field1,field2,...) to select a subset of fields | |
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51 and each of these fields is iterable over the examples: |
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52 * for field_examples in dataset.fields(): |
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53 for example_value in field_examples: |
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54 ... |
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55 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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56 such things because the underlying dataset may refuse to access the different fields in |
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57 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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58 The result of fields() is a DataSetFields object, which iterates over fields, |
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59 and whose elements are iterable over examples. A DataSetFields object can |
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60 be turned back into a DataSet with its examples() method: |
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61 dataset2 = dataset1.fields().examples() |
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62 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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63 |
| 16 | 64 Note: Fields are not mutually exclusive, i.e. two fields can overlap in their actual content. |
| 65 | |
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66 Note: The content of a field can be of any type. Field values can also be 'missing' |
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67 (e.g. to handle semi-supervised learning), and in the case of numeric (numpy array) |
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68 fields (i.e. an ArrayFieldsDataSet), NaN plays the role of a missing value. |
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69 What about non-numeric values? None. |
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70 |
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71 Dataset elements can be indexed and sub-datasets (with a subset |
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72 of examples) can be extracted. These operations are not supported |
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73 by default in the case of streams. |
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74 |
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75 * dataset[:n] returns a dataset with the n first examples. |
| 16 | 76 |
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77 * dataset[i1:i2:s] returns a dataset with the examples i1,i1+s,...i2-s. |
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78 |
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79 * dataset[i] returns an Example. |
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80 |
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81 * dataset[[i1,i2,...in]] returns a dataset with examples i1,i2,...in. |
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82 |
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83 * dataset[fieldname] an iterable over the values of the field fieldname across |
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84 the dataset (the iterable is obtained by default by calling valuesVStack |
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85 over the values for individual examples). |
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86 |
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87 * dataset.<property> returns the value of a property associated with |
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88 the name <property>. The following properties should be supported: |
| 41 | 89 - 'description': a textual description or name for the dataset |
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90 - 'fieldtypes': a list of types (one per field) |
| 41 | 91 |
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92 Datasets can be concatenated either vertically (increasing the length) or |
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93 horizontally (augmenting the set of fields), if they are compatible, using |
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94 the following operations (with the same basic semantics as numpy.hstack |
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95 and numpy.vstack): |
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96 |
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97 * dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3]) |
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98 |
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99 creates a new dataset whose list of fields is the concatenation of the list of |
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100 fields of the argument datasets. This only works if they all have the same length. |
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101 |
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102 * dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3]) |
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103 |
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104 creates a new dataset that concatenates the examples from the argument datasets |
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105 (and whose length is the sum of the length of the argument datasets). This only |
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106 works if they all have the same fields. |
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107 |
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108 According to the same logic, and viewing a DataSetFields object associated to |
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109 a DataSet as a kind of transpose of it, fields1 & fields2 concatenates fields of |
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110 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their |
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111 examples. |
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112 |
| 41 | 113 A dataset can hold arbitrary key-value pairs that may be used to access meta-data |
| 114 or other properties of the dataset or associated with the dataset or the result | |
| 115 of a computation stored in a dataset. These can be accessed through the [key] syntax | |
| 116 when key is a string (or more specifically, neither an integer, a slice, nor a list). | |
| 117 | |
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118 A DataSet sub-class should always redefine the following methods: |
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119 * __len__ if it is not a stream |
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120 * fieldNames |
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121 * minibatches_nowrap (called by DataSet.minibatches()) |
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122 * valuesHStack |
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123 * valuesVStack |
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124 For efficiency of implementation, a sub-class might also want to redefine |
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125 * hasFields |
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126 * __getitem__ may not be feasible with some streams |
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127 * __iter__ |
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128 """ |
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129 |
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130 def __init__(self,description=None,fieldtypes=None): |
| 41 | 131 if description is None: |
| 132 # by default return "<DataSetType>(<SuperClass1>,<SuperClass2>,...)" | |
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133 description = type(self).__name__ + " ( " + join([x.__name__ for x in type(self).__bases__]) + " )" |
| 41 | 134 self.description=description |
| 60 | 135 self.fieldtypes=fieldtypes |
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136 |
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137 class MinibatchToSingleExampleIterator(object): |
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138 """ |
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139 Converts the result of minibatch iterator with minibatch_size==1 into |
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140 single-example values in the result. Therefore the result of |
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141 iterating on the dataset itself gives a sequence of single examples |
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142 (whereas the result of iterating over minibatches gives in each |
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143 Example field an iterable object over the individual examples in |
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144 the minibatch). |
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145 """ |
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146 def __init__(self, minibatch_iterator): |
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147 self.minibatch_iterator = minibatch_iterator |
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148 self.minibatch = None |
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149 def __iter__(self): #makes for loop work |
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150 return self |
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151 def next(self): |
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152 size1_minibatch = self.minibatch_iterator.next() |
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153 if not self.minibatch: |
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154 self.minibatch = Example(size1_minibatch.keys(),[value[0] for value in size1_minibatch.values()]) |
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155 else: |
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156 self.minibatch._values = [value[0] for value in size1_minibatch.values()] |
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157 return self.minibatch |
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158 |
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159 def next_index(self): |
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160 return self.minibatch_iterator.next_index() |
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161 |
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162 def __iter__(self): |
| 16 | 163 """Supports the syntax "for i in dataset: ..." |
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164 |
| 16 | 165 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 166 all the fields of DataSet self. Every field of "i" will give access to | |
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167 a field of a single example. Fields should be accessible via |
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168 i["fielname"] or i[3] (in the order defined by the elements of the |
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169 Example returned by this iterator), but the derived class is free |
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170 to accept any type of identifier, and add extra functionality to the iterator. |
| 16 | 171 |
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172 The default implementation calls the minibatches iterator and extracts the first example of each field. |
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173 """ |
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174 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) |
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175 |
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176 |
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177 class MinibatchWrapAroundIterator(object): |
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178 """ |
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179 An iterator for minibatches that handles the case where we need to wrap around the |
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180 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
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181 a dataset that provides a minibatch iterator that does not need to handle that problem. |
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182 This class is a utility for dataset subclass writers, so that they do not have to handle |
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183 this issue multiple times, nor check that fieldnames are valid, nor handle the |
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184 empty fieldnames (meaning 'use all the fields'). |
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185 """ |
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186 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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187 self.dataset=dataset |
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188 self.fieldnames=fieldnames |
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189 self.minibatch_size=minibatch_size |
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190 self.n_batches=n_batches |
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191 self.n_batches_done=0 |
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192 self.next_row=offset |
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193 self.L=len(dataset) |
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194 assert offset+minibatch_size<=self.L |
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195 ds_nbatches = (self.L-offset)/minibatch_size |
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196 if n_batches is not None: |
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197 ds_nbatches = max(n_batches,ds_nbatches) |
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198 if fieldnames: |
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199 assert dataset.hasFields(*fieldnames) |
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200 else: |
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201 fieldnames=dataset.fieldNames() |
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202 self.iterator = dataset.minibatches_nowrap(fieldnames,minibatch_size,ds_nbatches,offset) |
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203 |
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204 def __iter__(self): |
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205 return self |
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206 |
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207 def next_index(self): |
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208 return self.next_row |
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209 |
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210 def next(self): |
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211 if self.n_batches and self.n_batches_done==self.n_batches: |
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212 raise StopIteration |
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213 upper = self.next_row+self.minibatch_size |
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214 if upper <=self.L: |
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215 minibatch = self.iterator.next() |
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216 else: |
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217 if not self.n_batches: |
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218 raise StopIteration |
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219 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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220 # first get the beginning of our minibatch (top of dataset) |
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221 first_part = self.dataset.minibatches_nowrap(fieldnames,self.L-self.next_row,1,self.next_row).next() |
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222 second_part = self.dataset.minibatches_nowrap(fieldnames,upper-self.L,1,0).next() |
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223 minibatch = Example(self.fieldnames, |
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224 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
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225 for name in self.fieldnames]) |
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226 self.next_row=upper |
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227 self.n_batches_done+=1 |
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228 if upper >= self.L and self.n_batches: |
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229 self.next_row -= self.L |
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230 return minibatch |
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231 |
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232 |
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233 minibatches_fieldnames = None |
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234 minibatches_minibatch_size = 1 |
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235 minibatches_n_batches = None |
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236 def minibatches(self, |
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237 fieldnames = minibatches_fieldnames, |
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238 minibatch_size = minibatches_minibatch_size, |
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239 n_batches = minibatches_n_batches, |
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240 offset = 0): |
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241 """ |
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242 Return an iterator that supports three forms of syntax: |
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243 |
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244 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 245 |
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246 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 247 |
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248 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 249 |
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250 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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251 tuple, or Example instance. In both cases, i[k] is a list-like container |
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252 of a batch of current examples. In the second case, i[0] is |
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253 list-like container of the f1 field of a batch current examples, i[1] is |
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254 a list-like container of the f2 field, etc. |
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255 |
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256 Using the first syntax, all the fields will be returned in "i". |
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257 Using the third syntax, i1, i2, i3 will be list-like containers of the |
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258 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
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259 |
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260 The minibatches iterator is expected to return upon each call to next() |
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261 a DataSetFields object, which is a LookupList (indexed by the field names) whose |
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262 elements are iterable over the minibatch examples, and which keeps a pointer to |
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263 a sub-dataset that can be used to iterate over the individual examples |
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264 in the minibatch. Hence a minibatch can be converted back to a regular |
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265 dataset or its fields can be looked at individually (and possibly iterated over). |
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266 |
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267 PARAMETERS |
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268 - fieldnames (list of any type, default None): |
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269 The loop variables i1, i2, i3 (in the example above) should contain the |
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270 f1, f2, and f3 fields of the current batch of examples. If None, the |
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271 derived class can choose a default, e.g. all fields. |
| 16 | 272 |
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273 - minibatch_size (integer, default 1) |
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274 On every iteration, the variables i1, i2, i3 will have |
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275 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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276 |
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277 - n_batches (integer, default None) |
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278 The iterator will loop exactly this many times, and then stop. If None, |
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279 the derived class can choose a default. If (-1), then the returned |
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280 iterator should support looping indefinitely. |
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281 |
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282 - offset (integer, default 0) |
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283 The iterator will start at example 'offset' in the dataset, rather than the default. |
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284 |
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285 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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286 any other object that supports integer indexing and slicing. |
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287 |
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288 """ |
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289 return DataSet.MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
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290 |
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291 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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292 """ |
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293 This is the minibatches iterator generator that sub-classes must define. |
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294 It does not need to worry about wrapping around multiple times across the dataset, |
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295 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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296 The next() method of the returned iterator does not even need to worry about |
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297 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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298 before an improper call to minibatches_nowrap's next() is made). |
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299 That next() method can assert that its next row will always be within [0,len(dataset)). |
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300 The iterator returned by minibatches_nowrap does not need to implement |
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301 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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302 """ |
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303 raise AbstractFunction() |
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304 |
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305 def __len__(self): |
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306 """ |
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307 len(dataset) returns the number of examples in the dataset. |
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308 By default, a DataSet is a 'stream', i.e. it has an unbounded length (sys.maxint). |
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309 Sub-classes which implement finite-length datasets should redefine this method. |
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310 Some methods only make sense for finite-length datasets. |
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311 """ |
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312 return sys.maxint |
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313 |
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314 def is_unbounded(self): |
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315 """ |
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316 Tests whether a dataset is unbounded (e.g. a stream). |
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317 """ |
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318 return len(self)==sys.maxint |
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319 |
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320 def hasFields(self,*fieldnames): |
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321 """ |
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322 Return true if the given field name (or field names, if multiple arguments are |
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323 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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324 of the iterators). |
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325 |
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326 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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327 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
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328 """ |
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329 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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330 |
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331 def fieldNames(self): |
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332 """ |
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333 Return the list of field names that are supported by the iterators, |
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334 and for which hasFields(fieldname) would return True. |
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335 """ |
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336 raise AbstractFunction() |
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337 |
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338 def __call__(self,*fieldnames): |
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339 """ |
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340 Return a dataset that sees only the fields whose name are specified. |
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341 """ |
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342 assert self.hasFields(*fieldnames) |
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343 return self.fields(*fieldnames).examples() |
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344 |
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345 def fields(self,*fieldnames): |
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346 """ |
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347 Return a DataSetFields object associated with this dataset. |
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348 """ |
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349 return DataSetFields(self,*fieldnames) |
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350 |
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351 def __getitem__(self,i): |
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352 """ |
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353 dataset[i] returns the (i+1)-th example of the dataset. |
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354 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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355 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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356 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
| 41 | 357 dataset['key'] returns a property associated with the given 'key' string. |
| 358 If 'key' is a fieldname, then the VStacked field values (iterable over | |
| 359 field values) for that field is returned. Other keys may be supported | |
| 360 by different dataset subclasses. The following key names are encouraged: | |
| 361 - 'description': a textual description or name for the dataset | |
| 362 - '<fieldname>.type': a type name or value for a given <fieldname> | |
|
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363 |
| 39 | 364 Note that some stream datasets may be unable to implement random access, i.e. |
| 365 arbitrary slicing/indexing | |
|
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366 because they can only iterate through examples one or a minibatch at a time |
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367 and do not actually store or keep past (or future) examples. |
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368 |
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369 The default implementation of getitem uses the minibatches iterator |
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370 to obtain one example, one slice, or a list of examples. It may not |
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371 always be the most efficient way to obtain the result, especially if |
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372 the data are actually stored in a memory array. |
|
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373 """ |
| 41 | 374 # check for an index |
|
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375 if type(i) is int: |
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376 return DataSet.MinibatchToSingleExampleIterator( |
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377 self.minibatches(minibatch_size=1,n_batches=1,offset=i)).next() |
| 41 | 378 rows=None |
| 379 # or a slice | |
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380 if type(i) is slice: |
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381 if not i.start: i.start=0 |
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382 if not i.step: i.step=1 |
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383 if i.step is 1: |
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384 return self.minibatches(minibatch_size=i.stop-i.start,n_batches=1,offset=i.start).next().examples() |
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385 rows = range(i.start,i.stop,i.step) |
| 41 | 386 # or a list of indices |
| 387 elif type(i) is list: | |
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388 rows = i |
| 41 | 389 if rows is not None: |
|
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390 examples = [self[row] for row in rows] |
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391 fields_values = zip(*examples) |
|
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392 return MinibatchDataSet( |
| 41 | 393 Example(self.fieldNames(),[ self.valuesVStack(fieldname,field_values) |
| 394 for fieldname,field_values | |
| 395 in zip(self.fieldNames(),fields_values)])) | |
| 396 # else check for a fieldname | |
| 397 if self.hasFields(i): | |
| 398 return self.minibatches(fieldnames=[i],minibatch_size=len(self),n_batches=1,offset=0).next()[0] | |
| 399 # else we are trying to access a property of the dataset | |
| 400 assert i in self.__dict__ # else it means we are trying to access a non-existing property | |
| 401 return self.__dict__[i] | |
|
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402 |
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403 def valuesHStack(self,fieldnames,fieldvalues): |
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404 """ |
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405 Return a value that corresponds to concatenating (horizontally) several field values. |
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406 This can be useful to merge some fields. The implementation of this operation is likely |
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407 to involve a copy of the original values. When the values are numpy arrays, the |
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408 result should be numpy.hstack(values). If it makes sense, this operation should |
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409 work as well when each value corresponds to multiple examples in a minibatch |
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410 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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411 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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412 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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413 pointing to the original values for other data types. |
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414 """ |
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415 all_numpy=True |
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416 for value in fieldvalues: |
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417 if not type(value) is numpy.ndarray: |
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418 all_numpy=False |
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419 if all_numpy: |
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420 return numpy.hstack(fieldvalues) |
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421 # the default implementation of horizontal stacking is to put values in a list |
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422 return fieldvalues |
|
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423 |
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424 |
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425 def valuesVStack(self,fieldname,values): |
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426 """ |
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427 Return a value that corresponds to concatenating (vertically) several values of the |
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428 same field. This can be important to build a minibatch out of individual examples. This |
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429 is likely to involve a copy of the original values. When the values are numpy arrays, the |
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430 result should be numpy.vstack(values). |
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431 The default is to use numpy.vstack for numpy.ndarray values, and a list |
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432 pointing to the original values for other data types. |
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433 """ |
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434 all_numpy=True |
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435 for value in values: |
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436 if not type(value) is numpy.ndarray: |
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437 all_numpy=False |
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438 if all_numpy: |
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439 return numpy.vstack(values) |
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440 # the default implementation of vertical stacking is to put values in a list |
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441 return values |
|
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442 |
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443 def __or__(self,other): |
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444 """ |
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445 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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446 fields of the argument datasets. This only works if they all have the same length. |
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447 """ |
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448 return HStackedDataSet(self,other) |
|
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449 |
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450 def __and__(self,other): |
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451 """ |
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452 dataset1 & dataset2 is a dataset that concatenates the examples from the argument datasets |
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453 (and whose length is the sum of the length of the argument datasets). This only |
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454 works if they all have the same fields. |
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455 """ |
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456 return VStackedDataSet(self,other) |
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457 |
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458 def hstack(datasets): |
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459 """ |
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460 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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461 which is a dataset whose fields list is the concatenation of the fields |
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462 of the individual datasets. |
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463 """ |
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464 assert len(datasets)>0 |
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465 if len(datasets)==1: |
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466 return datasets[0] |
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467 return HStackedDataSet(datasets) |
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468 |
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469 def vstack(datasets): |
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470 """ |
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471 vstack(dataset1,dataset2,...) returns dataset1 & datataset2 & ... |
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472 which is a dataset which iterates first over the examples of dataset1, then |
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473 over those of dataset2, etc. |
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474 """ |
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475 assert len(datasets)>0 |
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476 if len(datasets)==1: |
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477 return datasets[0] |
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478 return VStackedDataSet(datasets) |
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479 |
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480 class FieldsSubsetDataSet(DataSet): |
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481 """ |
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482 A sub-class of DataSet that selects a subset of the fields. |
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483 """ |
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484 def __init__(self,src,fieldnames): |
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485 self.src=src |
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486 self.fieldnames=fieldnames |
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487 assert src.hasFields(*fieldnames) |
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488 self.valuesHStack = src.valuesHStack |
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489 self.valuesVStack = src.valuesVStack |
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490 |
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491 def __len__(self): return len(self.src) |
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492 |
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493 def fieldNames(self): |
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494 return self.fieldnames |
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495 |
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496 def __iter__(self): |
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497 class FieldsSubsetIterator(object): |
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498 def __init__(self,ds): |
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499 self.ds=ds |
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500 self.src_iter=ds.src.__iter__() |
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501 self.example=None |
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502 def __iter__(self): return self |
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503 def next(self): |
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504 complete_example = self.src_iter.next() |
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505 if self.example: |
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506 self.example._values=[complete_example[field] |
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507 for field in self.ds.fieldnames] |
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508 else: |
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509 self.example=Example(self.ds.fieldnames, |
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510 [complete_example[field] for field in self.ds.fieldnames]) |
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511 return self.example |
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512 return FieldsSubsetIterator(self) |
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513 |
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514 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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515 assert self.hasFields(*fieldnames) |
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516 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
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517 def __getitem__(self,i): |
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518 return FieldsSubsetDataSet(self.src[i],self.fieldnames) |
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519 |
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520 |
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521 class DataSetFields(LookupList): |
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522 """ |
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523 Although a DataSet iterates over examples (like rows of a matrix), an associated |
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524 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
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525 as a transpose of the associated dataset. |
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526 |
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527 To iterate over fields, one can do |
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528 * for fields in dataset.fields() |
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529 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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530 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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531 and each of these fields is iterable over the examples: |
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532 * for field_examples in dataset.fields(): |
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533 for example_value in field_examples: |
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534 ... |
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535 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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536 such things because the underlying dataset may refuse to access the different fields in |
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537 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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538 The result of fields() is a DataSetFields object, which iterates over fields, |
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539 and whose elements are iterable over examples. A DataSetFields object can |
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540 be turned back into a DataSet with its examples() method: |
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541 dataset2 = dataset1.fields().examples() |
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542 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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543 |
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544 DataSetFields can be concatenated vertically or horizontally. To be consistent with |
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545 the syntax used for DataSets, the | concatenates the fields and the & concatenates |
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546 the examples. |
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547 """ |
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548 def __init__(self,dataset,*fieldnames): |
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549 original_dataset=dataset |
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550 if not fieldnames: |
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551 fieldnames=dataset.fieldNames() |
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552 elif not fieldnames==dataset.fieldNames(): |
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553 dataset = FieldsSubsetDataSet(dataset,fieldnames) |
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554 assert dataset.hasFields(*fieldnames) |
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555 self.dataset=dataset |
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556 |
|
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557 if isinstance(dataset,MinibatchDataSet): |
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558 LookupList.__init__(self,fieldnames,list(dataset._fields)) |
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559 elif isinstance(original_dataset,MinibatchDataSet): |
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560 LookupList.__init__(self,fieldnames, |
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561 [original_dataset._fields[field] |
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562 for field in fieldnames]) |
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563 else: |
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564 minibatch_iterator = dataset.minibatches(fieldnames, |
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565 minibatch_size=len(dataset), |
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566 n_batches=1) |
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567 minibatch=minibatch_iterator.next() |
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568 LookupList.__init__(self,fieldnames,minibatch) |
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569 |
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570 def examples(self): |
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571 return self.dataset |
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572 |
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573 def __or__(self,other): |
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574 """ |
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575 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
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576 of the list of examples of DataSetFields fields1 and fields2. |
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577 """ |
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578 return (self.examples() + other.examples()).fields() |
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579 |
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580 def __and__(self,other): |
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581 """ |
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582 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
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583 of the fields of DataSetFields fields1 and fields2. |
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584 """ |
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585 return (self.examples() | other.examples()).fields() |
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|
586 |
|
37
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587 |
|
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588 class MinibatchDataSet(DataSet): |
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589 """ |
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590 Turn a LookupList of same-length fields into an example-iterable dataset. |
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591 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
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592 """ |
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593 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
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594 values_hstack=DataSet().valuesHStack): |
|
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595 """ |
|
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596 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
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597 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
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598 semantics as the DataSet methods of the same name (but without the self argument). |
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599 """ |
|
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600 self._fields=fields_lookuplist |
|
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601 assert len(fields_lookuplist)>0 |
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602 self.length=len(fields_lookuplist[0]) |
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603 for field in fields_lookuplist[1:]: |
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604 assert self.length==len(field) |
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605 self.values_vstack=values_vstack |
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606 self.values_hstack=values_hstack |
|
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607 |
|
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608 def __len__(self): |
|
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609 return self.length |
|
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610 |
|
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611 def __getitem__(self,i): |
|
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612 if type(i) in (int,slice,list): |
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613 return DataSetFields(MinibatchDataSet( |
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614 Example(self._fields.keys(),[field[i] for field in self._fields])),self._fields) |
|
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615 if self.hasFields(i): |
|
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616 return self._fields[i] |
|
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617 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
|
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618 return self.__dict__[i] |
|
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619 |
|
29
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620 def fieldNames(self): |
|
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621 return self._fields.keys() |
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622 |
|
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623 def hasFields(self,*fieldnames): |
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624 for fieldname in fieldnames: |
|
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625 if fieldname not in self._fields.keys(): |
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626 return False |
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627 return True |
|
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628 |
|
37
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629 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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630 class Iterator(object): |
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631 def __init__(self,ds): |
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632 self.ds=ds |
|
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633 self.next_example=offset |
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634 assert minibatch_size > 0 |
| 41 | 635 if offset+minibatch_size > ds.length: |
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636 raise NotImplementedError() |
|
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637 def __iter__(self): |
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638 return self |
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639 def next(self): |
|
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|
640 upper = self.next_example+minibatch_size |
|
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641 assert upper<=self.ds.length |
|
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642 minibatch = Example(self.ds._fields.keys(), |
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643 [field[self.next_example:upper] |
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644 for field in self.ds._fields]) |
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645 self.next_example+=minibatch_size |
| 62 | 646 return DataSetFields(MinibatchDataSet(minibatch),*fieldnames) |
|
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|
647 |
|
40
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
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648 return Iterator(self) |
|
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649 |
|
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650 def valuesVStack(self,fieldname,fieldvalues): |
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651 return self.values_vstack(fieldname,fieldvalues) |
|
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|
652 |
|
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653 def valuesHStack(self,fieldnames,fieldvalues): |
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654 return self.values_hstack(fieldnames,fieldvalues) |
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655 |
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656 class HStackedDataSet(DataSet): |
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657 """ |
|
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658 A DataSet that wraps several datasets and shows a view that includes all their fields, |
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659 i.e. whose list of fields is the concatenation of their lists of fields. |
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660 |
|
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661 If a field name is found in more than one of the datasets, then either an error is |
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662 raised or the fields are renamed (either by prefixing the __name__ attribute |
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|
663 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list). |
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664 |
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665 TODO: automatically detect a chain of stacked datasets due to A | B | C | D ... |
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666 """ |
| 41 | 667 def __init__(self,datasets,accept_nonunique_names=False,description=None,field_types=None): |
| 668 DataSet.__init__(self,description,field_types) | |
|
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669 self.datasets=datasets |
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670 self.accept_nonunique_names=accept_nonunique_names |
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671 self.fieldname2dataset={} |
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672 |
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673 def rename_field(fieldname,dataset,i): |
|
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674 if hasattr(dataset,"__name__"): |
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675 return dataset.__name__ + "." + fieldname |
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676 return fieldname+"."+str(i) |
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|
677 |
|
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678 # make sure all datasets have the same length and unique field names |
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679 self.length=None |
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|
680 names_to_change=[] |
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681 for i in xrange(len(datasets)): |
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682 dataset = datasets[i] |
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683 length=len(dataset) |
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684 if self.length: |
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685 assert self.length==length |
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686 else: |
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687 self.length=length |
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688 for fieldname in dataset.fieldNames(): |
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689 if fieldname in self.fieldname2dataset: # name conflict! |
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690 if accept_nonunique_names: |
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691 fieldname=rename_field(fieldname,dataset,i) |
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692 names2change.append((fieldname,i)) |
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693 else: |
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|
694 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
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695 self.fieldname2dataset[fieldname]=i |
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696 for fieldname,i in names_to_change: |
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697 del self.fieldname2dataset[fieldname] |
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698 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
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|
699 |
|
37
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700 def hasFields(self,*fieldnames): |
|
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701 for fieldname in fieldnames: |
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702 if not fieldname in self.fieldname2dataset: |
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703 return False |
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704 return True |
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705 |
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706 def fieldNames(self): |
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707 return self.fieldname2dataset.keys() |
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708 |
| 41 | 709 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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|
710 |
|
44
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|
711 class HStackedIterator(object): |
|
36
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|
712 def __init__(self,hsds,iterators): |
|
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|
713 self.hsds=hsds |
|
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|
714 self.iterators=iterators |
|
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|
715 def __iter__(self): |
|
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|
716 return self |
|
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|
717 def next(self): |
|
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diff
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|
718 # concatenate all the fields of the minibatches |
|
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diff
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|
719 minibatch = reduce(LookupList.__add__,[iterator.next() for iterator in self.iterators]) |
|
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diff
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|
720 # and return a DataSetFields whose dataset is the transpose (=examples()) of this minibatch |
|
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721 return DataSetFields(MinibatchDataSet(minibatch,self.hsds.valuesVStack, |
|
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diff
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|
722 self.hsds.valuesHStack), |
|
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diff
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|
723 fieldnames if fieldnames else hsds.fieldNames()) |
|
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|
724 |
|
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|
725 assert self.hasfields(fieldnames) |
|
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diff
changeset
|
726 # find out which underlying datasets are necessary to service the required fields |
|
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|
727 # and construct corresponding minibatch iterators |
|
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changeset
|
728 if fieldnames: |
|
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changeset
|
729 datasets=set([]) |
|
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|
730 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
|
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Rewriting dataset.py completely
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diff
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|
731 for fieldname in fieldnames: |
|
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|
732 dataset=self.datasets[self.fieldnames2dataset[fieldname]] |
|
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|
733 datasets.add(dataset) |
|
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Rewriting dataset.py completely
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diff
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|
734 fields_in_dataset[dataset].append(fieldname) |
|
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diff
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|
735 datasets=list(datasets) |
|
37
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Factored the minibatch-writing code into an iterator class inside DataSet
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|
736 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
|
36
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|
737 for dataset in datasets] |
|
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|
738 else: |
|
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|
739 datasets=self.datasets |
|
37
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Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
740 iterators=[dataset.minibatches(None,minibatch_size,n_batches,offset) for dataset in datasets] |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
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diff
changeset
|
741 return HStackedIterator(self,iterators) |
|
36
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|
742 |
|
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|
743 |
|
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|
744 def valuesVStack(self,fieldname,fieldvalues): |
|
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|
745 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
|
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|
746 |
|
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diff
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|
747 def valuesHStack(self,fieldnames,fieldvalues): |
|
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diff
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|
748 """ |
|
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Rewriting dataset.py completely
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diff
changeset
|
749 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
|
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diff
changeset
|
750 to do the work, hoping that it can cope with the other values (i.e. won't care |
|
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parents:
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diff
changeset
|
751 about the incompatible fieldnames). Hence this heuristic will always work if |
|
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Rewriting dataset.py completely
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29
diff
changeset
|
752 all the fieldnames are of the same sub-dataset. |
|
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diff
changeset
|
753 """ |
|
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29
diff
changeset
|
754 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
|
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parents:
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diff
changeset
|
755 |
|
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diff
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|
756 class VStackedDataSet(DataSet): |
|
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diff
changeset
|
757 """ |
|
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29
diff
changeset
|
758 A DataSet that wraps several datasets and shows a view that includes all their examples, |
|
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Rewriting dataset.py completely
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parents:
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diff
changeset
|
759 in the order provided. This clearly assumes that they all have the same field names |
|
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Rewriting dataset.py completely
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diff
changeset
|
760 and all (except possibly the last one) are of finite length. |
|
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diff
changeset
|
761 |
|
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diff
changeset
|
762 TODO: automatically detect a chain of stacked datasets due to A + B + C + D ... |
|
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diff
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|
763 """ |
|
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diff
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|
764 def __init__(self,datasets): |
|
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diff
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|
765 self.datasets=datasets |
|
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|
766 self.length=0 |
|
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diff
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|
767 self.index2dataset={} |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
768 assert len(datasets)>0 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
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diff
changeset
|
769 fieldnames = datasets[-1].fieldNames() |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
770 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
|
771 # 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
|
772 # to avoid having to search for the appropriate dataset each time and slice is asked for. |
|
36
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diff
changeset
|
773 for dataset,k in enumerate(datasets[0:-1]): |
|
48
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
774 assert dataset.is_unbounded() # All VStacked datasets (except possibly the last) must be bounded (have a length). |
|
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
775 L=len(dataset) |
|
36
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diff
changeset
|
776 for i in xrange(L): |
|
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diff
changeset
|
777 self.index2dataset[self.length+i]=k |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
778 self.datasets_start_row.append(self.length) |
|
36
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diff
changeset
|
779 self.length+=L |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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36
diff
changeset
|
780 assert dataset.fieldNames()==fieldnames |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
781 self.datasets_start_row.append(self.length) |
|
36
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diff
changeset
|
782 self.length+=len(datasets[-1]) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
783 # 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
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parents:
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diff
changeset
|
784 # 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
|
785 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
|
786 # 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
|
787 # TODO |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
788 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
|
789 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
790 def __len__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
791 return self.length |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
792 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
793 def fieldNames(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
794 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
|
795 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
796 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
|
797 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
|
798 |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
799 def locate_row(self,row): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
800 """Return (dataset_index, row_within_dataset) for global row number""" |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
801 dataset_index = self.index2dataset[row] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
802 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
|
803 return dataset_index, row_within_dataset |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
804 |
| 41 | 805 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
806 |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
807 class VStackedIterator(object): |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
808 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
|
809 self.vsds=vsds |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
810 self.next_row=offset |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
811 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
|
812 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
|
813 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
|
814 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
815 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
|
816 L=len(dataset) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
817 ds_nbatches = (L-starting_offset)/minibatch_size |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
818 if batches_left is not None: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
819 ds_nbatches = max(batches_left,ds_nbatches) |
|
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Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
820 if minibatch_size>L: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
821 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
|
822 n_left_in_mb=minibatch_size-L |
|
38
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diff
changeset
|
823 ds_nbatches=1 |
|
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Finished first untested version of VStackedDataset
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diff
changeset
|
824 else: |
|
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diff
changeset
|
825 n_left_in_mb=0 |
|
37
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diff
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|
826 return dataset.minibatches(fieldnames,minibatch_size,ds_nbatches,starting_offset), \ |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
827 L-(starting_offset+ds_nbatches*minibatch_size), n_left_in_mb |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
828 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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36
diff
changeset
|
829 def move_to_next_dataset(self): |
|
38
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Finished first untested version of VStackedDataset
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diff
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|
830 if self.n_left_at_the_end_of_ds>0: |
|
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diff
changeset
|
831 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
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diff
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|
832 self.next_iterator(vsds.datasets[self.next_dataset_index], |
|
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diff
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|
833 self.n_left_at_the_end_of_ds,1) |
|
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diff
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|
834 else: |
|
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Finished first untested version of VStackedDataset
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diff
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|
835 self.next_dataset_index +=1 |
|
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Finished first untested version of VStackedDataset
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parents:
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diff
changeset
|
836 if self.next_dataset_index==len(self.vsds.datasets): |
|
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diff
changeset
|
837 self.next_dataset_index = 0 |
|
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diff
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|
838 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
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diff
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|
839 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
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diff
changeset
|
840 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
841 def __iter__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
842 return self |
|
73c4212ba5b3
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parents:
36
diff
changeset
|
843 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
844 def next(self): |
|
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
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diff
changeset
|
845 dataset=self.vsds.datasets[self.next_dataset_index] |
|
73c4212ba5b3
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diff
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|
846 mb = self.next_iterator.next() |
|
73c4212ba5b3
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diff
changeset
|
847 if self.n_left_in_mb: |
|
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
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diff
changeset
|
848 extra_mb = [] |
|
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
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diff
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|
849 while self.n_left_in_mb>0: |
|
73c4212ba5b3
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36
diff
changeset
|
850 self.move_to_next_dataset() |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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diff
changeset
|
851 extra_mb.append(self.next_iterator.next()) |
|
40
88fd1cce08b9
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parents:
39
diff
changeset
|
852 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
|
853 [dataset.valuesVStack(name, |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
854 [mb[name]]+[b[name] for b in extra_mb]) |
|
88fd1cce08b9
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bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
855 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
|
856 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
|
857 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
858 self.next_row+=minibatch_size |
|
38
d637ad8f7352
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parents:
37
diff
changeset
|
859 self.next_dataset_row+=minibatch_size |
|
d637ad8f7352
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parents:
37
diff
changeset
|
860 if self.next_row+minibatch_size>len(dataset): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
861 self.move_to_next_dataset() |
|
44
5a85fda9b19b
Fixed some more iterator bugs
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43
diff
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|
862 return examples |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
863 return VStackedIterator(self) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
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|
864 |
| 41 | 865 class ArrayFieldsDataSet(DataSet): |
| 866 """ | |
| 867 Virtual super-class of datasets whose field values are numpy array, | |
| 868 thus defining valuesHStack and valuesVStack for sub-classes. | |
| 869 """ | |
| 870 def __init__(self,description=None,field_types=None): | |
| 871 DataSet.__init__(self,description,field_types) | |
| 872 def valuesHStack(self,fieldnames,fieldvalues): | |
| 873 """Concatenate field values horizontally, e.g. two vectors | |
| 874 become a longer vector, two matrices become a wider matrix, etc.""" | |
| 875 return numpy.hstack(fieldvalues) | |
| 876 def valuesVStack(self,fieldname,values): | |
| 877 """Concatenate field values vertically, e.g. two vectors | |
| 878 become a two-row matrix, two matrices become a longer matrix, etc.""" | |
| 879 return numpy.vstack(values) | |
| 880 | |
| 881 class ArrayDataSet(ArrayFieldsDataSet): | |
| 882 """ | |
| 883 An ArrayDataSet stores the fields as groups of columns in a numpy tensor, | |
| 884 whose first axis iterates over examples, second axis determines fields. | |
| 885 If the underlying array is N-dimensional (has N axes), then the field | |
| 886 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | |
| 887 """ | |
| 888 | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
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|
889 def __init__(self, data_array, fields_columns): |
|
55
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
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diff
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|
890 """ |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
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|
891 Construct an ArrayDataSet from the underlying numpy array (data) and |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
892 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
893 using the standard arguments for indexing/slicing: integer for a column index, |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
894 slice for an interval of columns (with possible stride), or iterable of column indices. |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
895 """ |
| 41 | 896 self.data=data_array |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
897 self.fields_columns=fields_columns |
| 41 | 898 |
| 899 # check consistency and complete slices definitions | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
900 for fieldname, fieldcolumns in self.fields_columns.items(): |
| 41 | 901 if type(fieldcolumns) is int: |
| 902 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1] | |
| 903 elif type(fieldcolumns) is slice: | |
| 904 start,step=None,None | |
| 905 if not fieldcolumns.start: | |
| 906 start=0 | |
| 907 if not fieldcolumns.step: | |
| 908 step=1 | |
| 909 if start or step: | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
910 self.fields_columns[fieldname]=slice(start,fieldcolumns.stop,step) |
| 41 | 911 elif hasattr(fieldcolumns,"__iter__"): # something like a list |
| 912 for i in fieldcolumns: | |
| 913 assert i>=0 and i<data_array.shape[1] | |
| 914 | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
915 def fieldNames(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
916 return self.fields_columns.keys() |
| 41 | 917 |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
918 def __len__(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
919 return len(self.data) |
| 41 | 920 |
|
55
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Efficient implementation of getitem for ArrayDataSet
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diff
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|
921 def __getitem__(self,i): |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
922 """More efficient implementation than the default __getitem__""" |
|
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Efficient implementation of getitem for ArrayDataSet
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diff
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|
923 fieldnames=self.fields_columns.keys() |
|
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Efficient implementation of getitem for ArrayDataSet
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diff
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|
924 if type(i) is int: |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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48
diff
changeset
|
925 return Example(fieldnames, |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
926 [self.data[i,self.fields_columns[f]] for f in fieldnames]) |
|
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Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
927 if type(i) in (slice,list): |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
928 return MinibatchDataSet(Example(fieldnames, |
|
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Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
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|
929 [self.data[i,self.fields_columns[f]] for f in fieldnames])) |
|
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Efficient implementation of getitem for ArrayDataSet
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diff
changeset
|
930 # else check for a fieldname |
|
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Efficient implementation of getitem for ArrayDataSet
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diff
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|
931 if self.hasFields(i): |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
932 return Example([i],[self.data[self.fields_columns[i],:]]) |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
933 # else we are trying to access a property of the dataset |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
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|
934 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
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diff
changeset
|
935 return self.__dict__[i] |
|
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Efficient implementation of getitem for ArrayDataSet
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diff
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|
936 |
| 41 | 937 |
|
42
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Testing basic functionality and removing obvious bugs
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41
diff
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|
938 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
44
5a85fda9b19b
Fixed some more iterator bugs
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diff
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|
939 class ArrayDataSetIterator(object): |
|
42
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Testing basic functionality and removing obvious bugs
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parents:
41
diff
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|
940 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
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diff
changeset
|
941 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
44
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diff
changeset
|
942 # store the resulting minibatch in a lookup-list of values |
|
5a85fda9b19b
Fixed some more iterator bugs
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diff
changeset
|
943 self.minibatch = LookupList(fieldnames,[0]*len(fieldnames)) |
|
42
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Testing basic functionality and removing obvious bugs
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diff
changeset
|
944 self.dataset=dataset |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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diff
changeset
|
945 self.minibatch_size=minibatch_size |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
946 assert offset>=0 and offset<len(dataset.data) |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
947 assert offset+minibatch_size<=len(dataset.data) |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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diff
changeset
|
948 self.current=offset |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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diff
changeset
|
949 def __iter__(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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diff
changeset
|
950 return self |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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41
diff
changeset
|
951 def next(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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diff
changeset
|
952 sub_data = self.dataset.data[self.current:self.current+self.minibatch_size] |
|
44
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Fixed some more iterator bugs
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diff
changeset
|
953 self.minibatch._values = [sub_data[:,self.dataset.fields_columns[f]] for f in self.minibatch._names] |
|
43
e92244f30116
Corrected iterator logic errors
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42
diff
changeset
|
954 self.current+=self.minibatch_size |
|
44
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diff
changeset
|
955 return self.minibatch |
|
42
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Testing basic functionality and removing obvious bugs
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41
diff
changeset
|
956 |
|
44
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Fixed some more iterator bugs
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43
diff
changeset
|
957 return ArrayDataSetIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
57
1aabd2e2bb5f
Added empty classes with doc: CachedDataSet and ApplyFunctionDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
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56
diff
changeset
|
958 |
|
1aabd2e2bb5f
Added empty classes with doc: CachedDataSet and ApplyFunctionDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
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56
diff
changeset
|
959 |
|
1aabd2e2bb5f
Added empty classes with doc: CachedDataSet and ApplyFunctionDataSet
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56
diff
changeset
|
960 class CachedDataSet(DataSet): |
|
1aabd2e2bb5f
Added empty classes with doc: CachedDataSet and ApplyFunctionDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
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56
diff
changeset
|
961 """ |
|
1aabd2e2bb5f
Added empty classes with doc: CachedDataSet and ApplyFunctionDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
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56
diff
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|
962 Wrap a dataset whose values are computationally expensive to obtain |
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963 (e.g. because they involve some computation, or disk access), |
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964 so that repeated accesses to the same example are done cheaply, |
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965 by caching every example value that has been accessed at least once. |
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966 |
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967 Optionally, for finite-length dataset, all the values can be computed |
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968 (and cached) upon construction of the CachedDataSet, rather at the |
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969 first access. |
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970 """ |
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971 |
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972 class ApplyFunctionDataSet(DataSet): |
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973 """ |
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974 A dataset that contains as fields the results of applying a given function |
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975 example-wise or minibatch-wise to all the fields of an input dataset. |
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976 The output of the function should be an iterable (e.g. a list or a LookupList) |
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977 over the resulting values. In minibatch mode, the function is expected |
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978 to work on minibatches (takes a minibatch in input and returns a minibatch |
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979 in output). |
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980 |
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981 The function is applied each time an example or a minibatch is accessed. |
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982 To avoid re-doing computation, wrap this dataset inside a CachedDataSet. |
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983 """ |
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984 |
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985 |
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986 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
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987 """ |
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988 Wraps an arbitrary DataSet into one for supervised learning tasks by forcing the |
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989 user to define a set of fields as the 'input' field and a set of fields |
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990 as the 'target' field. Optionally, a single weight_field can also be defined. |
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991 """ |
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992 args = ((input_fields,'input'),(output_fields,'target')) |
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993 if weight_field: args+=(([weight_field],'weight')) |
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994 return src_dataset.merge_fields(*args) |
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995 |
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996 |
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997 |
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998 |