Mercurial > pylearn
annotate dataset.py @ 78:3499918faa9d
In the middle of designing TLearner
| author | bengioy@bengiomac.local |
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| date | Mon, 05 May 2008 09:35:30 -0400 |
| parents | 1e2bb5bad636 |
| children | 158653a9bc7c |
| 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): |
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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 |
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49 - for field in dataset(field1,field2,...).fields() to select a subset of fields |
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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) |
| 78 | 91 A DataSet may have other attributes that it makes visible to other objects. These are |
| 92 used to store information that is not example-wise but global to the dataset. | |
| 93 The list of names of these attributes is given by the attribute_names() method. | |
| 41 | 94 |
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95 Datasets can be concatenated either vertically (increasing the length) or |
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96 horizontally (augmenting the set of fields), if they are compatible, using |
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97 the following operations (with the same basic semantics as numpy.hstack |
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98 and numpy.vstack): |
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99 |
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100 - dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3]) |
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101 |
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102 creates a new dataset whose list of fields is the concatenation of the list of |
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103 fields of the argument datasets. This only works if they all have the same length. |
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104 |
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105 - dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3]) |
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106 |
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107 creates a new dataset that concatenates the examples from the argument datasets |
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108 (and whose length is the sum of the length of the argument datasets). This only |
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109 works if they all have the same fields. |
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110 |
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111 According to the same logic, and viewing a DataSetFields object associated to |
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112 a DataSet as a kind of transpose of it, fields1 & fields2 concatenates fields of |
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113 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their |
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114 examples. |
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115 |
| 41 | 116 A dataset can hold arbitrary key-value pairs that may be used to access meta-data |
| 117 or other properties of the dataset or associated with the dataset or the result | |
| 118 of a computation stored in a dataset. These can be accessed through the [key] syntax | |
| 119 when key is a string (or more specifically, neither an integer, a slice, nor a list). | |
| 78 | 120 |
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121 A DataSet sub-class should always redefine the following methods: |
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122 - __len__ if it is not a stream |
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123 - fieldNames |
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124 - minibatches_nowrap (called by DataSet.minibatches()) |
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125 - valuesHStack |
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126 - valuesVStack |
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127 For efficiency of implementation, a sub-class might also want to redefine |
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128 - hasFields |
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129 - __getitem__ may not be feasible with some streams |
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130 - __iter__ |
| 78 | 131 A sub-class should also append attributes to self._attribute_names |
| 132 (the default value returned by attributeNames()). | |
| 133 By convention, attributes not in attributeNames() should have a name | |
| 134 starting with an underscore. | |
| 135 @todo enforce/test that convention! | |
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136 """ |
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137 |
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138 numpy_vstack = lambda fieldname,values: return numpy.vstack(values) |
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139 numpy_hstack = lambda fieldnames,values: return numpy.hstack(values) |
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140 |
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141 def __init__(self,description=None,fieldtypes=None): |
| 41 | 142 if description is None: |
| 143 # by default return "<DataSetType>(<SuperClass1>,<SuperClass2>,...)" | |
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144 description = type(self).__name__ + " ( " + join([x.__name__ for x in type(self).__bases__]) + " )" |
| 41 | 145 self.description=description |
| 60 | 146 self.fieldtypes=fieldtypes |
| 78 | 147 self._attribute_names = ["description"] |
| 148 if fieldtypes: | |
| 149 self._attribute_names.append("fieldtypes") | |
| 150 | |
| 151 def attributeNames(self): return self._attribute_names | |
| 152 | |
| 153 def setAttributes(self,attribute_names,attribute_values): | |
| 154 for name,value in zip(attribute_names,attribute_values): | |
| 155 self.__setattr__(name,value) | |
|
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156 |
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157 class MinibatchToSingleExampleIterator(object): |
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158 """ |
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159 Converts the result of minibatch iterator with minibatch_size==1 into |
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160 single-example values in the result. Therefore the result of |
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161 iterating on the dataset itself gives a sequence of single examples |
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162 (whereas the result of iterating over minibatches gives in each |
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163 Example field an iterable object over the individual examples in |
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164 the minibatch). |
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165 """ |
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166 def __init__(self, minibatch_iterator): |
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167 self.minibatch_iterator = minibatch_iterator |
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168 self.minibatch = None |
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169 def __iter__(self): #makes for loop work |
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170 return self |
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171 def next(self): |
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172 size1_minibatch = self.minibatch_iterator.next() |
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173 if not self.minibatch: |
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174 self.minibatch = Example(size1_minibatch.keys(),[value[0] for value in size1_minibatch.values()]) |
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175 else: |
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176 self.minibatch._values = [value[0] for value in size1_minibatch.values()] |
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177 return self.minibatch |
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178 |
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179 def next_index(self): |
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180 return self.minibatch_iterator.next_index() |
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181 |
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182 def __iter__(self): |
| 16 | 183 """Supports the syntax "for i in dataset: ..." |
|
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184 |
| 16 | 185 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 186 all the fields of DataSet self. Every field of "i" will give access to | |
|
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187 a field of a single example. Fields should be accessible via |
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188 i["fielname"] or i[3] (in the order defined by the elements of the |
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189 Example returned by this iterator), but the derived class is free |
|
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190 to accept any type of identifier, and add extra functionality to the iterator. |
| 16 | 191 |
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192 The default implementation calls the minibatches iterator and extracts the first example of each field. |
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193 """ |
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194 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) |
|
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195 |
|
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196 |
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197 class MinibatchWrapAroundIterator(object): |
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198 """ |
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199 An iterator for minibatches that handles the case where we need to wrap around the |
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200 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
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201 a dataset that provides a minibatch iterator that does not need to handle that problem. |
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202 This class is a utility for dataset subclass writers, so that they do not have to handle |
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203 this issue multiple times, nor check that fieldnames are valid, nor handle the |
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204 empty fieldnames (meaning 'use all the fields'). |
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205 """ |
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206 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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207 self.dataset=dataset |
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208 self.fieldnames=fieldnames |
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209 self.minibatch_size=minibatch_size |
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210 self.n_batches=n_batches |
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211 self.n_batches_done=0 |
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212 self.next_row=offset |
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213 self.L=len(dataset) |
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214 assert offset+minibatch_size<=self.L |
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215 ds_nbatches = (self.L-offset)/minibatch_size |
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216 if n_batches is not None: |
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217 ds_nbatches = max(n_batches,ds_nbatches) |
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218 if fieldnames: |
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219 assert dataset.hasFields(*fieldnames) |
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220 else: |
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221 fieldnames=dataset.fieldNames() |
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222 self.iterator = dataset.minibatches_nowrap(fieldnames,minibatch_size,ds_nbatches,offset) |
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223 |
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224 def __iter__(self): |
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225 return self |
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226 |
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227 def next_index(self): |
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228 return self.next_row |
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229 |
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230 def next(self): |
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231 if self.n_batches and self.n_batches_done==self.n_batches: |
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232 raise StopIteration |
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233 upper = self.next_row+self.minibatch_size |
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234 if upper <=self.L: |
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235 minibatch = self.iterator.next() |
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236 else: |
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237 if not self.n_batches: |
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238 raise StopIteration |
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239 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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240 # first get the beginning of our minibatch (top of dataset) |
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241 first_part = self.dataset.minibatches_nowrap(fieldnames,self.L-self.next_row,1,self.next_row).next() |
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242 second_part = self.dataset.minibatches_nowrap(fieldnames,upper-self.L,1,0).next() |
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243 minibatch = Example(self.fieldnames, |
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244 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
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245 for name in self.fieldnames]) |
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246 self.next_row=upper |
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247 self.n_batches_done+=1 |
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248 if upper >= self.L and self.n_batches: |
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249 self.next_row -= self.L |
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250 return DataSetFields(MinibatchDataSet(minibatch,self.dataset.valuesVStack, |
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251 self.dataset.valuesHStack), |
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252 minibatch.keys()) |
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253 |
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254 |
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255 minibatches_fieldnames = None |
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256 minibatches_minibatch_size = 1 |
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257 minibatches_n_batches = None |
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258 def minibatches(self, |
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259 fieldnames = minibatches_fieldnames, |
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260 minibatch_size = minibatches_minibatch_size, |
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261 n_batches = minibatches_n_batches, |
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262 offset = 0): |
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263 """ |
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264 Return an iterator that supports three forms of syntax: |
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265 |
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266 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 267 |
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268 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 269 |
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270 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 271 |
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272 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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273 tuple, or Example instance. In both cases, i[k] is a list-like container |
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274 of a batch of current examples. In the second case, i[0] is |
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275 list-like container of the f1 field of a batch current examples, i[1] is |
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276 a list-like container of the f2 field, etc. |
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277 |
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278 Using the first syntax, all the fields will be returned in "i". |
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279 Using the third syntax, i1, i2, i3 will be list-like containers of the |
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280 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
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281 |
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282 The minibatches iterator is expected to return upon each call to next() |
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283 a DataSetFields object, which is a LookupList (indexed by the field names) whose |
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284 elements are iterable over the minibatch examples, and which keeps a pointer to |
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285 a sub-dataset that can be used to iterate over the individual examples |
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286 in the minibatch. Hence a minibatch can be converted back to a regular |
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287 dataset or its fields can be looked at individually (and possibly iterated over). |
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288 |
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289 PARAMETERS |
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290 - fieldnames (list of any type, default None): |
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291 The loop variables i1, i2, i3 (in the example above) should contain the |
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292 f1, f2, and f3 fields of the current batch of examples. If None, the |
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293 derived class can choose a default, e.g. all fields. |
| 16 | 294 |
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295 - minibatch_size (integer, default 1) |
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296 On every iteration, the variables i1, i2, i3 will have |
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297 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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298 |
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299 - n_batches (integer, default None) |
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300 The iterator will loop exactly this many times, and then stop. If None, |
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301 the derived class can choose a default. If (-1), then the returned |
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302 iterator should support looping indefinitely. |
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303 |
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304 - offset (integer, default 0) |
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305 The iterator will start at example 'offset' in the dataset, rather than the default. |
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306 |
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307 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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308 any other object that supports integer indexing and slicing. |
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309 |
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310 """ |
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311 return DataSet.MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
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312 |
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313 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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314 """ |
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315 This is the minibatches iterator generator that sub-classes must define. |
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316 It does not need to worry about wrapping around multiple times across the dataset, |
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317 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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318 The next() method of the returned iterator does not even need to worry about |
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319 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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320 before an improper call to minibatches_nowrap's next() is made). |
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321 That next() method can assert that its next row will always be within [0,len(dataset)). |
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322 The iterator returned by minibatches_nowrap does not need to implement |
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323 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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324 """ |
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325 raise AbstractFunction() |
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326 |
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327 def __len__(self): |
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328 """ |
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329 len(dataset) returns the number of examples in the dataset. |
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330 By default, a DataSet is a 'stream', i.e. it has an unbounded length (sys.maxint). |
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331 Sub-classes which implement finite-length datasets should redefine this method. |
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332 Some methods only make sense for finite-length datasets. |
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333 """ |
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334 return sys.maxint |
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335 |
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336 def is_unbounded(self): |
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337 """ |
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338 Tests whether a dataset is unbounded (e.g. a stream). |
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339 """ |
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340 return len(self)==sys.maxint |
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341 |
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342 def hasFields(self,*fieldnames): |
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343 """ |
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344 Return true if the given field name (or field names, if multiple arguments are |
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345 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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346 of the iterators). |
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347 |
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348 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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349 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
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350 """ |
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351 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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352 |
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353 def fieldNames(self): |
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354 """ |
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355 Return the list of field names that are supported by the iterators, |
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356 and for which hasFields(fieldname) would return True. |
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357 """ |
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358 raise AbstractFunction() |
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359 |
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360 def __call__(self,*fieldnames): |
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361 """ |
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362 Return a dataset that sees only the fields whose name are specified. |
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363 """ |
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364 assert self.hasFields(*fieldnames) |
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365 return self.fields(*fieldnames).examples() |
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366 |
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367 def fields(self,*fieldnames): |
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368 """ |
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369 Return a DataSetFields object associated with this dataset. |
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370 """ |
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371 return DataSetFields(self,fieldnames) |
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372 |
|
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373 def __getitem__(self,i): |
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374 """ |
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375 dataset[i] returns the (i+1)-th example of the dataset. |
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376 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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377 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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378 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
| 41 | 379 dataset['key'] returns a property associated with the given 'key' string. |
| 380 If 'key' is a fieldname, then the VStacked field values (iterable over | |
| 381 field values) for that field is returned. Other keys may be supported | |
| 382 by different dataset subclasses. The following key names are encouraged: | |
| 383 - 'description': a textual description or name for the dataset | |
| 384 - '<fieldname>.type': a type name or value for a given <fieldname> | |
|
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385 |
| 39 | 386 Note that some stream datasets may be unable to implement random access, i.e. |
| 387 arbitrary slicing/indexing | |
|
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388 because they can only iterate through examples one or a minibatch at a time |
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389 and do not actually store or keep past (or future) examples. |
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390 |
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391 The default implementation of getitem uses the minibatches iterator |
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392 to obtain one example, one slice, or a list of examples. It may not |
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393 always be the most efficient way to obtain the result, especially if |
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394 the data are actually stored in a memory array. |
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395 """ |
| 41 | 396 # check for an index |
|
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397 if type(i) is int: |
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398 return DataSet.MinibatchToSingleExampleIterator( |
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399 self.minibatches(minibatch_size=1,n_batches=1,offset=i)).next() |
| 41 | 400 rows=None |
| 401 # or a slice | |
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402 if type(i) is slice: |
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403 if not i.start: i.start=0 |
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404 if not i.step: i.step=1 |
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405 if i.step is 1: |
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406 return self.minibatches(minibatch_size=i.stop-i.start,n_batches=1,offset=i.start).next().examples() |
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407 rows = range(i.start,i.stop,i.step) |
| 41 | 408 # or a list of indices |
| 409 elif type(i) is list: | |
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410 rows = i |
| 41 | 411 if rows is not None: |
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412 examples = [self[row] for row in rows] |
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413 fields_values = zip(*examples) |
|
45
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414 return MinibatchDataSet( |
| 41 | 415 Example(self.fieldNames(),[ self.valuesVStack(fieldname,field_values) |
| 416 for fieldname,field_values | |
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417 in zip(self.fieldNames(),fields_values)]), |
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418 self.valuesVStack,self.valuesHStack) |
| 41 | 419 # else check for a fieldname |
| 420 if self.hasFields(i): | |
| 421 return self.minibatches(fieldnames=[i],minibatch_size=len(self),n_batches=1,offset=0).next()[0] | |
| 422 # else we are trying to access a property of the dataset | |
| 423 assert i in self.__dict__ # else it means we are trying to access a non-existing property | |
| 424 return self.__dict__[i] | |
|
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425 |
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426 def valuesHStack(self,fieldnames,fieldvalues): |
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427 """ |
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428 Return a value that corresponds to concatenating (horizontally) several field values. |
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429 This can be useful to merge some fields. The implementation of this operation is likely |
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430 to involve a copy of the original values. When the values are numpy arrays, the |
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431 result should be numpy.hstack(values). If it makes sense, this operation should |
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432 work as well when each value corresponds to multiple examples in a minibatch |
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433 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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434 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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435 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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436 pointing to the original values for other data types. |
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437 """ |
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438 all_numpy=True |
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439 for value in fieldvalues: |
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440 if not type(value) is numpy.ndarray: |
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441 all_numpy=False |
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442 if all_numpy: |
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443 return numpy.hstack(fieldvalues) |
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444 # the default implementation of horizontal stacking is to put values in a list |
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445 return fieldvalues |
|
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446 |
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447 |
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448 def valuesVStack(self,fieldname,values): |
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449 """ |
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450 Return a value that corresponds to concatenating (vertically) several values of the |
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451 same field. This can be important to build a minibatch out of individual examples. This |
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452 is likely to involve a copy of the original values. When the values are numpy arrays, the |
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453 result should be numpy.vstack(values). |
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454 The default is to use numpy.vstack for numpy.ndarray values, and a list |
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455 pointing to the original values for other data types. |
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456 """ |
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457 all_numpy=True |
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458 for value in values: |
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459 if not type(value) is numpy.ndarray: |
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460 all_numpy=False |
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461 if all_numpy: |
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462 return numpy.vstack(values) |
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463 # the default implementation of vertical stacking is to put values in a list |
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464 return values |
|
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465 |
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466 def __or__(self,other): |
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467 """ |
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468 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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469 fields of the argument datasets. This only works if they all have the same length. |
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470 """ |
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471 return HStackedDataSet(self,other) |
|
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472 |
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473 def __and__(self,other): |
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474 """ |
|
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475 dataset1 & dataset2 is a dataset that concatenates the examples from the argument datasets |
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476 (and whose length is the sum of the length of the argument datasets). This only |
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477 works if they all have the same fields. |
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478 """ |
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479 return VStackedDataSet(self,other) |
|
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480 |
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481 def hstack(datasets): |
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482 """ |
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483 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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484 which is a dataset whose fields list is the concatenation of the fields |
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485 of the individual datasets. |
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486 """ |
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487 assert len(datasets)>0 |
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488 if len(datasets)==1: |
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489 return datasets[0] |
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490 return HStackedDataSet(datasets) |
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491 |
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492 def vstack(datasets): |
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493 """ |
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494 vstack(dataset1,dataset2,...) returns dataset1 & datataset2 & ... |
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495 which is a dataset which iterates first over the examples of dataset1, then |
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496 over those of dataset2, etc. |
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497 """ |
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498 assert len(datasets)>0 |
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499 if len(datasets)==1: |
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500 return datasets[0] |
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501 return VStackedDataSet(datasets) |
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502 |
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503 class FieldsSubsetDataSet(DataSet): |
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504 """ |
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505 A sub-class of DataSet that selects a subset of the fields. |
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506 """ |
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507 def __init__(self,src,fieldnames): |
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508 self.src=src |
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509 self.fieldnames=fieldnames |
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510 assert src.hasFields(*fieldnames) |
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511 self.valuesHStack = src.valuesHStack |
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512 self.valuesVStack = src.valuesVStack |
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513 |
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514 def __len__(self): return len(self.src) |
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515 |
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516 def fieldNames(self): |
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517 return self.fieldnames |
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518 |
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519 def __iter__(self): |
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520 class FieldsSubsetIterator(object): |
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521 def __init__(self,ds): |
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522 self.ds=ds |
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523 self.src_iter=ds.src.__iter__() |
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524 self.example=None |
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525 def __iter__(self): return self |
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526 def next(self): |
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527 complete_example = self.src_iter.next() |
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528 if self.example: |
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529 self.example._values=[complete_example[field] |
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530 for field in self.ds.fieldnames] |
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531 else: |
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532 self.example=Example(self.ds.fieldnames, |
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533 [complete_example[field] for field in self.ds.fieldnames]) |
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534 return self.example |
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535 return FieldsSubsetIterator(self) |
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536 |
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537 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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538 assert self.hasFields(*fieldnames) |
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539 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
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540 def __getitem__(self,i): |
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541 return FieldsSubsetDataSet(self.src[i],self.fieldnames) |
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542 |
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543 |
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544 class DataSetFields(LookupList): |
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545 """ |
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546 Although a DataSet iterates over examples (like rows of a matrix), an associated |
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547 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
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548 as a transpose of the associated dataset. |
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549 |
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550 To iterate over fields, one can do |
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551 * for fields in dataset.fields() |
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552 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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553 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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554 and each of these fields is iterable over the examples: |
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555 * for field_examples in dataset.fields(): |
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556 for example_value in field_examples: |
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557 ... |
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558 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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559 such things because the underlying dataset may refuse to access the different fields in |
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560 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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561 The result of fields() is a DataSetFields object, which iterates over fields, |
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562 and whose elements are iterable over examples. A DataSetFields object can |
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563 be turned back into a DataSet with its examples() method: |
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564 dataset2 = dataset1.fields().examples() |
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565 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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566 |
|
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567 DataSetFields can be concatenated vertically or horizontally. To be consistent with |
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568 the syntax used for DataSets, the | concatenates the fields and the & concatenates |
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569 the examples. |
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570 """ |
|
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571 def __init__(self,dataset,fieldnames): |
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572 original_dataset=dataset |
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573 if not fieldnames: |
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574 fieldnames=dataset.fieldNames() |
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575 elif not fieldnames==dataset.fieldNames(): |
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576 dataset = FieldsSubsetDataSet(dataset,fieldnames) |
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577 assert dataset.hasFields(*fieldnames) |
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578 self.dataset=dataset |
|
66
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579 |
|
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580 if isinstance(dataset,MinibatchDataSet): |
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581 LookupList.__init__(self,fieldnames,list(dataset._fields)) |
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582 elif isinstance(original_dataset,MinibatchDataSet): |
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583 LookupList.__init__(self,fieldnames, |
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|
584 [original_dataset._fields[field] |
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585 for field in fieldnames]) |
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586 else: |
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587 minibatch_iterator = dataset.minibatches(fieldnames, |
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588 minibatch_size=len(dataset), |
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589 n_batches=1) |
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|
590 minibatch=minibatch_iterator.next() |
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|
591 LookupList.__init__(self,fieldnames,minibatch) |
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592 |
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593 def examples(self): |
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594 return self.dataset |
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595 |
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596 def __or__(self,other): |
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597 """ |
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598 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
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599 of the list of examples of DataSetFields fields1 and fields2. |
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600 """ |
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601 return (self.examples() + other.examples()).fields() |
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602 |
|
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603 def __and__(self,other): |
|
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|
604 """ |
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605 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
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606 of the fields of DataSetFields fields1 and fields2. |
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607 """ |
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608 return (self.examples() | other.examples()).fields() |
|
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|
609 |
|
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610 |
|
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611 class MinibatchDataSet(DataSet): |
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612 """ |
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613 Turn a LookupList of same-length fields into an example-iterable dataset. |
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614 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
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615 """ |
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616 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
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617 values_hstack=DataSet().valuesHStack): |
|
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618 """ |
|
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619 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
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620 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
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621 semantics as the DataSet methods of the same name (but without the self argument). |
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622 """ |
|
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623 self._fields=fields_lookuplist |
|
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624 assert len(fields_lookuplist)>0 |
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625 self.length=len(fields_lookuplist[0]) |
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626 for field in fields_lookuplist[1:]: |
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627 assert self.length==len(field) |
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628 self.values_vstack=values_vstack |
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629 self.values_hstack=values_hstack |
|
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630 |
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631 def __len__(self): |
|
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632 return self.length |
|
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633 |
|
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634 def __getitem__(self,i): |
|
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635 if type(i) is int: |
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636 return Example(self._fields.keys(),[field[i] for field in self._fields]) |
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637 if type(i) in (slice,list): |
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638 return MinibatchDataSet(Example(self._fields.keys(), |
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639 [field[i] for field in self._fields]), |
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640 self.valuesVStack,self.valuesHStack) |
|
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641 if self.hasFields(i): |
|
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642 return self._fields[i] |
|
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643 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
|
48
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644 return self.__dict__[i] |
|
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645 |
|
29
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646 def fieldNames(self): |
|
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647 return self._fields.keys() |
|
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648 |
|
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649 def hasFields(self,*fieldnames): |
|
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650 for fieldname in fieldnames: |
|
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651 if fieldname not in self._fields.keys(): |
|
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652 return False |
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653 return True |
|
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|
654 |
|
37
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655 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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656 class Iterator(object): |
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657 def __init__(self,ds): |
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658 self.ds=ds |
|
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659 self.next_example=offset |
|
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660 assert minibatch_size > 0 |
| 41 | 661 if offset+minibatch_size > ds.length: |
|
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662 raise NotImplementedError() |
|
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663 def __iter__(self): |
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664 return self |
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665 def next(self): |
|
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666 upper = self.next_example+minibatch_size |
|
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667 assert upper<=self.ds.length |
|
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|
668 minibatch = Example(self.ds._fields.keys(), |
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669 [field[self.next_example:upper] |
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670 for field in self.ds._fields]) |
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671 self.next_example+=minibatch_size |
|
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672 return minibatch |
|
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|
673 |
|
40
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
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|
674 return Iterator(self) |
|
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|
675 |
|
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676 def valuesVStack(self,fieldname,fieldvalues): |
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677 return self.values_vstack(fieldname,fieldvalues) |
|
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|
678 |
|
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679 def valuesHStack(self,fieldnames,fieldvalues): |
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680 return self.values_hstack(fieldnames,fieldvalues) |
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|
681 |
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682 class HStackedDataSet(DataSet): |
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683 """ |
|
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|
684 A DataSet that wraps several datasets and shows a view that includes all their fields, |
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685 i.e. whose list of fields is the concatenation of their lists of fields. |
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|
686 |
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687 If a field name is found in more than one of the datasets, then either an error is |
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688 raised or the fields are renamed (either by prefixing the __name__ attribute |
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689 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list). |
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|
690 |
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691 TODO: automatically detect a chain of stacked datasets due to A | B | C | D ... |
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692 """ |
| 41 | 693 def __init__(self,datasets,accept_nonunique_names=False,description=None,field_types=None): |
| 694 DataSet.__init__(self,description,field_types) | |
|
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695 self.datasets=datasets |
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696 self.accept_nonunique_names=accept_nonunique_names |
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697 self.fieldname2dataset={} |
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|
698 |
|
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699 def rename_field(fieldname,dataset,i): |
|
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700 if hasattr(dataset,"__name__"): |
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701 return dataset.__name__ + "." + fieldname |
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702 return fieldname+"."+str(i) |
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|
703 |
|
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704 # make sure all datasets have the same length and unique field names |
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705 self.length=None |
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706 names_to_change=[] |
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707 for i in xrange(len(datasets)): |
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708 dataset = datasets[i] |
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709 length=len(dataset) |
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710 if self.length: |
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711 assert self.length==length |
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712 else: |
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713 self.length=length |
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714 for fieldname in dataset.fieldNames(): |
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715 if fieldname in self.fieldname2dataset: # name conflict! |
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716 if accept_nonunique_names: |
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717 fieldname=rename_field(fieldname,dataset,i) |
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718 names2change.append((fieldname,i)) |
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719 else: |
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720 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
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721 self.fieldname2dataset[fieldname]=i |
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722 for fieldname,i in names_to_change: |
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parents:
29
diff
changeset
|
723 del self.fieldname2dataset[fieldname] |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
724 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
725 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
726 def hasFields(self,*fieldnames): |
|
36
438440ba0627
Rewriting dataset.py completely
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parents:
29
diff
changeset
|
727 for fieldname in fieldnames: |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
728 if not fieldname in self.fieldname2dataset: |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
729 return False |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
730 return True |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
731 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
732 def fieldNames(self): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
733 return self.fieldname2dataset.keys() |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
734 |
| 41 | 735 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
736 |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
737 class HStackedIterator(object): |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
738 def __init__(self,hsds,iterators): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
739 self.hsds=hsds |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
740 self.iterators=iterators |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
741 def __iter__(self): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
742 return self |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
743 def next(self): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
744 # concatenate all the fields of the minibatches |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
bengioy@bengiomac.local
parents:
72
diff
changeset
|
745 return reduce(LookupList.__add__,[iterator.next() for iterator in self.iterators]) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
746 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
747 assert self.hasfields(fieldnames) |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
748 # find out which underlying datasets are necessary to service the required fields |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
749 # and construct corresponding minibatch iterators |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
750 if fieldnames: |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
751 datasets=set([]) |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
752 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
753 for fieldname in fieldnames: |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
754 dataset=self.datasets[self.fieldnames2dataset[fieldname]] |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
755 datasets.add(dataset) |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
756 fields_in_dataset[dataset].append(fieldname) |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
757 datasets=list(datasets) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
758 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
759 for dataset in datasets] |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
760 else: |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
761 datasets=self.datasets |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
762 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
parents:
43
diff
changeset
|
763 return HStackedIterator(self,iterators) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
764 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
765 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
766 def valuesVStack(self,fieldname,fieldvalues): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
767 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
768 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
769 def valuesHStack(self,fieldnames,fieldvalues): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
770 """ |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
771 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
772 to do the work, hoping that it can cope with the other values (i.e. won't care |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
773 about the incompatible fieldnames). Hence this heuristic will always work if |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
774 all the fieldnames are of the same sub-dataset. |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
775 """ |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
776 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
777 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
778 class VStackedDataSet(DataSet): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
779 """ |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
780 A DataSet that wraps several datasets and shows a view that includes all their examples, |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
781 in the order provided. This clearly assumes that they all have the same field names |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
782 and all (except possibly the last one) are of finite length. |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
783 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
784 TODO: automatically detect a chain of stacked datasets due to A + B + C + D ... |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
785 """ |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
786 def __init__(self,datasets): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
787 self.datasets=datasets |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
788 self.length=0 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
789 self.index2dataset={} |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
790 assert len(datasets)>0 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
791 fieldnames = datasets[-1].fieldNames() |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
792 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
|
793 # 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
|
794 # to avoid having to search for the appropriate dataset each time and slice is asked for. |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
795 for dataset,k in enumerate(datasets[0:-1]): |
|
48
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
796 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
|
797 L=len(dataset) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
798 for i in xrange(L): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
799 self.index2dataset[self.length+i]=k |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
800 self.datasets_start_row.append(self.length) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
801 self.length+=L |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
802 assert dataset.fieldNames()==fieldnames |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
803 self.datasets_start_row.append(self.length) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
804 self.length+=len(datasets[-1]) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
805 # If length is very large, we should use a more memory-efficient mechanism |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
806 # 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
|
807 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
|
808 # 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
|
809 # TODO |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
810 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
|
811 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
812 def __len__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
813 return self.length |
|
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 fieldNames(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
816 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
|
817 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
818 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
|
819 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
|
820 |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
821 def locate_row(self,row): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
822 """Return (dataset_index, row_within_dataset) for global row number""" |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
823 dataset_index = self.index2dataset[row] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
824 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
|
825 return dataset_index, row_within_dataset |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
826 |
| 41 | 827 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
|
828 |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
829 class VStackedIterator(object): |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
830 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
|
831 self.vsds=vsds |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
832 self.next_row=offset |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
833 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
|
834 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
|
835 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
|
836 |
|
73c4212ba5b3
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diff
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|
837 def next_iterator(self,dataset,starting_offset,batches_left): |
|
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Factored the minibatch-writing code into an iterator class inside DataSet
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|
838 L=len(dataset) |
|
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diff
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|
839 ds_nbatches = (L-starting_offset)/minibatch_size |
|
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diff
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|
840 if batches_left is not None: |
|
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|
841 ds_nbatches = max(batches_left,ds_nbatches) |
|
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|
842 if minibatch_size>L: |
|
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Factored the minibatch-writing code into an iterator class inside DataSet
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|
843 ds_minibatch_size=L |
|
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Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
844 n_left_in_mb=minibatch_size-L |
|
38
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|
845 ds_nbatches=1 |
|
d637ad8f7352
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diff
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|
846 else: |
|
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|
847 n_left_in_mb=0 |
|
37
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diff
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|
848 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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|
849 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
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|
850 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
851 def move_to_next_dataset(self): |
|
38
d637ad8f7352
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37
diff
changeset
|
852 if self.n_left_at_the_end_of_ds>0: |
|
d637ad8f7352
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diff
changeset
|
853 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
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parents:
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diff
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|
854 self.next_iterator(vsds.datasets[self.next_dataset_index], |
|
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diff
changeset
|
855 self.n_left_at_the_end_of_ds,1) |
|
d637ad8f7352
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37
diff
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|
856 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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diff
changeset
|
857 self.next_dataset_index +=1 |
|
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parents:
37
diff
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|
858 if self.next_dataset_index==len(self.vsds.datasets): |
|
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Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
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37
diff
changeset
|
859 self.next_dataset_index = 0 |
|
d637ad8f7352
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|
860 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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|
861 self.next_iterator(vsds.datasets[self.next_dataset_index],starting_offset,n_batches) |
|
37
73c4212ba5b3
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diff
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|
862 |
|
73c4212ba5b3
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diff
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|
863 def __iter__(self): |
|
73c4212ba5b3
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|
864 return self |
|
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diff
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|
865 |
|
73c4212ba5b3
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diff
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|
866 def next(self): |
|
73c4212ba5b3
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diff
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|
867 dataset=self.vsds.datasets[self.next_dataset_index] |
|
73c4212ba5b3
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diff
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|
868 mb = self.next_iterator.next() |
|
73c4212ba5b3
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diff
changeset
|
869 if self.n_left_in_mb: |
|
73c4212ba5b3
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diff
changeset
|
870 extra_mb = [] |
|
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Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
871 while self.n_left_in_mb>0: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
872 self.move_to_next_dataset() |
|
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Factored the minibatch-writing code into an iterator class inside DataSet
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36
diff
changeset
|
873 extra_mb.append(self.next_iterator.next()) |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
bengioy@bengiomac.local
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diff
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|
874 mb = Example(fieldnames, |
|
40
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
875 [dataset.valuesVStack(name, |
|
88fd1cce08b9
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bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
876 [mb[name]]+[b[name] for b in extra_mb]) |
|
88fd1cce08b9
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bengioy@esprit.iro.umontreal.ca
parents:
39
diff
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|
877 for name in fieldnames]) |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
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39
diff
changeset
|
878 |
|
37
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
879 self.next_row+=minibatch_size |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
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37
diff
changeset
|
880 self.next_dataset_row+=minibatch_size |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
881 if self.next_row+minibatch_size>len(dataset): |
|
d637ad8f7352
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bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
882 self.move_to_next_dataset() |
|
44
5a85fda9b19b
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diff
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|
883 return examples |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
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diff
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|
884 return VStackedIterator(self) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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36
diff
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|
885 |
| 41 | 886 class ArrayFieldsDataSet(DataSet): |
| 887 """ | |
| 888 Virtual super-class of datasets whose field values are numpy array, | |
| 889 thus defining valuesHStack and valuesVStack for sub-classes. | |
| 890 """ | |
| 891 def __init__(self,description=None,field_types=None): | |
| 892 DataSet.__init__(self,description,field_types) | |
| 893 def valuesHStack(self,fieldnames,fieldvalues): | |
| 894 """Concatenate field values horizontally, e.g. two vectors | |
| 895 become a longer vector, two matrices become a wider matrix, etc.""" | |
| 896 return numpy.hstack(fieldvalues) | |
| 897 def valuesVStack(self,fieldname,values): | |
| 898 """Concatenate field values vertically, e.g. two vectors | |
| 899 become a two-row matrix, two matrices become a longer matrix, etc.""" | |
| 900 return numpy.vstack(values) | |
| 901 | |
| 902 class ArrayDataSet(ArrayFieldsDataSet): | |
| 903 """ | |
| 904 An ArrayDataSet stores the fields as groups of columns in a numpy tensor, | |
| 905 whose first axis iterates over examples, second axis determines fields. | |
| 906 If the underlying array is N-dimensional (has N axes), then the field | |
| 907 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | |
| 908 """ | |
| 909 | |
|
42
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Testing basic functionality and removing obvious bugs
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41
diff
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|
910 def __init__(self, data_array, fields_columns): |
|
55
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parents:
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diff
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|
911 """ |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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diff
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|
912 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
|
913 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified |
|
66619ce44497
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
914 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:
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diff
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|
915 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
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|
916 """ |
| 41 | 917 self.data=data_array |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
918 self.fields_columns=fields_columns |
| 41 | 919 |
| 920 # check consistency and complete slices definitions | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
921 for fieldname, fieldcolumns in self.fields_columns.items(): |
| 41 | 922 if type(fieldcolumns) is int: |
| 923 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1] | |
| 924 elif type(fieldcolumns) is slice: | |
| 925 start,step=None,None | |
| 926 if not fieldcolumns.start: | |
| 927 start=0 | |
| 928 if not fieldcolumns.step: | |
| 929 step=1 | |
| 930 if start or step: | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
931 self.fields_columns[fieldname]=slice(start,fieldcolumns.stop,step) |
| 41 | 932 elif hasattr(fieldcolumns,"__iter__"): # something like a list |
| 933 for i in fieldcolumns: | |
| 934 assert i>=0 and i<data_array.shape[1] | |
| 935 | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
936 def fieldNames(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
937 return self.fields_columns.keys() |
| 41 | 938 |
|
42
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Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
939 def __len__(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
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diff
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|
940 return len(self.data) |
| 41 | 941 |
|
55
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
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diff
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|
942 def __getitem__(self,i): |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
943 """More efficient implementation than the default __getitem__""" |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
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diff
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|
944 fieldnames=self.fields_columns.keys() |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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diff
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|
945 if type(i) is int: |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
946 return Example(fieldnames, |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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parents:
48
diff
changeset
|
947 [self.data[i,self.fields_columns[f]] for f in fieldnames]) |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
948 if type(i) in (slice,list): |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
949 return MinibatchDataSet(Example(fieldnames, |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
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72
diff
changeset
|
950 [self.data[i,self.fields_columns[f]] for f in fieldnames]), |
|
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
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|
951 self.valuesVStack,self.valuesHStack) |
|
55
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Efficient implementation of getitem for ArrayDataSet
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parents:
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diff
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|
952 # else check for a fieldname |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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48
diff
changeset
|
953 if self.hasFields(i): |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
954 return Example([i],[self.data[self.fields_columns[i],:]]) |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff
changeset
|
955 # else we are trying to access a property of the dataset |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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diff
changeset
|
956 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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diff
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|
957 return self.__dict__[i] |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
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diff
changeset
|
958 |
| 41 | 959 |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
960 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
|
961 class ArrayDataSetIterator(object): |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
962 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
963 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
44
5a85fda9b19b
Fixed some more iterator bugs
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parents:
43
diff
changeset
|
964 # store the resulting minibatch in a lookup-list of values |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
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diff
changeset
|
965 self.minibatch = LookupList(fieldnames,[0]*len(fieldnames)) |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
966 self.dataset=dataset |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
967 self.minibatch_size=minibatch_size |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
968 assert offset>=0 and offset<len(dataset.data) |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
969 assert offset+minibatch_size<=len(dataset.data) |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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diff
changeset
|
970 self.current=offset |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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diff
changeset
|
971 def __iter__(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
972 return self |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
973 def next(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
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parents:
41
diff
changeset
|
974 sub_data = self.dataset.data[self.current:self.current+self.minibatch_size] |
|
44
5a85fda9b19b
Fixed some more iterator bugs
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43
diff
changeset
|
975 self.minibatch._values = [sub_data[:,self.dataset.fields_columns[f]] for f in self.minibatch._names] |
|
43
e92244f30116
Corrected iterator logic errors
bengioy@grenat.iro.umontreal.ca
parents:
42
diff
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|
976 self.current+=self.minibatch_size |
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977 return self.minibatch |
|
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978 |
|
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979 return ArrayDataSetIterator(self,fieldnames,minibatch_size,n_batches,offset) |
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980 |
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|
981 |
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982 class CachedDataSet(DataSet): |
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983 """ |
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984 Wrap a dataset whose values are computationally expensive to obtain |
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985 (e.g. because they involve some computation, or disk access), |
|
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986 so that repeated accesses to the same example are done cheaply, |
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|
987 by caching every example value that has been accessed at least once. |
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988 |
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989 Optionally, for finite-length dataset, all the values can be computed |
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990 (and cached) upon construction of the CachedDataSet, rather at the |
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991 first access. |
|
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992 |
|
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993 @todo when cache_all_upon_construction create mini-batches that are as |
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994 large as possible but not so large as to fill up memory. |
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995 |
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996 @todo add disk-buffering capability, so that when the cache becomes too |
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997 big for memory, we cache things on disk, trying to keep in memory only |
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998 the record most likely to be accessed next. |
|
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999 """ |
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1000 def __init__(self,source_dataset,cache_all_upon_construction=False): |
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1001 self.source_dataset=source_dataset |
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1002 self.cache_all_upon_construction=cache_all_upon_construction |
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1003 if cache_all_upon_construction: |
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1004 # this potentially brings all the source examples |
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1005 # into memory at once, which may be too much |
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1006 # the work could possibly be done by minibatches |
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1007 # that are as large as possible but no more than what memory allows. |
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1008 self.cached_examples = zip(*source_dataset.minibatches(minibatch_size=len(source_dataset)).__iter__().next()) |
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1009 else: |
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1010 self.cached_examples = [] |
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1011 |
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1012 self.fieldNames = source_dataset.fieldNames |
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1013 self.hasFields = source_dataset.hasFields |
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1014 self.valuesHStack = source_dataset.valuesHStack |
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1015 self.valuesVStack = source_dataset.valuesVStack |
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|
1016 |
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1017 def __len__(self): |
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1018 return len(self.source_dataset) |
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1019 |
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1020 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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|
1021 class CacheIterator(object): |
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1022 def __init__(self,dataset): |
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1023 self.dataset=dataset |
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1024 self.current=offset |
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1025 def __iter__(self): return self |
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|
1026 def next(self): |
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1027 upper = self.current+minibatch_size |
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1028 cache_len = len(self.dataset.cached_examples) |
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1029 if upper>=cache_len: # whole minibatch is not already in cache |
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|
1030 # cache everything from current length to upper |
|
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|
1031 for example in self.dataset.source_dataset[cache_len:upper]: |
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|
1032 self.dataset.cached_examples.append(example) |
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1033 all_fields_minibatch = Example(self.dataset.fieldNames(), |
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|
1034 self.dataset.cached_examples[self.current:self.current+minibatch_size]) |
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|
1035 if self.dataset.fieldNames()==fieldnames: |
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|
1036 return all_fields_minibatch |
|
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|
1037 return Example(fieldnames,[all_fields_minibatch[name] for name in fieldnames]) |
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|
1038 return CacheIterator(self) |
|
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|
1039 |
|
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|
1040 |
|
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|
1041 class ApplyFunctionDataSet(DataSet): |
|
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|
1042 """ |
|
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|
1043 A dataset that contains as fields the results of applying a given function |
|
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|
1044 example-wise or minibatch-wise to all the fields of an input dataset. |
|
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|
1045 The output of the function should be an iterable (e.g. a list or a LookupList) |
|
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|
1046 over the resulting values. |
|
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|
1047 |
|
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|
1048 In minibatch mode, the function is expected to work on minibatches (takes |
|
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|
1049 a minibatch in input and returns a minibatch in output). More precisely, |
|
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|
1050 it means that each element of the input or output list should be iterable |
|
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|
1051 and indexable over the individual example values (typically these |
|
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|
1052 elements will be numpy arrays). All of the elements in the input and |
|
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|
1053 output lists should have the same length, which is the length of the |
|
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|
1054 minibatch. |
|
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|
1055 |
|
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diff
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|
1056 The function is applied each time an example or a minibatch is accessed. |
|
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|
1057 To avoid re-doing computation, wrap this dataset inside a CachedDataSet. |
|
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|
1058 |
|
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|
1059 If the values_{h,v}stack functions are not provided, then |
|
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1060 the input_dataset.values{H,V}Stack functions are used by default. |
|
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|
1061 """ |
|
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|
1062 def __init__(self,input_dataset,function,output_names,minibatch_mode=True, |
|
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1063 values_hstack=None,values_vstack=None, |
|
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1064 description=None,fieldtypes=None): |
|
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|
1065 """ |
|
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|
1066 Constructor takes an input dataset that has as many fields as the function |
|
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|
1067 expects as inputs. The resulting dataset has as many fields as the function |
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1068 produces as outputs, and that should correspond to the number of output names |
|
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|
1069 (provided in a list). |
|
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|
1070 |
|
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|
1071 Note that the expected semantics of the function differs in minibatch mode |
|
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|
1072 (it takes minibatches of inputs and produces minibatches of outputs, as |
|
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|
1073 documented in the class comment). |
|
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|
1074 """ |
|
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|
1075 self.input_dataset=input_dataset |
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|
1076 self.function=function |
|
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|
1077 self.output_names=output_names |
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1078 self.minibatch_mode=minibatch_mode |
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|
1079 DataSet.__init__(description,fieldtypes) |
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|
1080 self.valuesHStack = values_hstack if values_hstack else input_dataset.valuesHStack |
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1081 self.valuesVStack = values_vstack if values_vstack else input_dataset.valuesVStack |
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|
1082 |
|
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|
1083 def __len__(self): |
|
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1084 return len(self.input_dataset) |
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1085 |
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1086 def fieldnames(self): |
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1087 return self.output_names |
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1088 |
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1089 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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1090 class ApplyFunctionIterator(object): |
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1091 def __init__(self,output_dataset): |
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1092 self.input_dataset=output_dataset.input_dataset |
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1093 self.output_dataset=output_dataset |
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1094 self.input_iterator=input_dataset.minibatches(minibatch_size=minibatch_size, |
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1095 n_batches=n_batches,offset=offset).__iter__() |
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1096 |
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1097 def __iter__(self): return self |
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1098 |
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1099 def next(self): |
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1100 function_inputs = self.input_iterator.next() |
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1101 all_output_names = self.output_dataset.output_names |
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1102 if self.output_dataset.minibatch_mode: |
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1103 function_outputs = self.output_dataset.function(function_inputs) |
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1104 else: |
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1105 input_examples = zip(*function_inputs) |
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1106 output_examples = [self.output_dataset.function(input_example) |
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1107 for input_example in input_examples] |
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1108 function_outputs = [self.output_dataset.valuesVStack(name,values) |
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1109 for name,values in zip(all_output_names, |
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1110 zip(*output_examples))] |
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1111 all_outputs = Example(all_output_names,function_outputs) |
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1112 if fieldnames==all_output_names: |
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1113 return all_outputs |
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1114 return Example(fieldnames,[all_outputs[name] for name in fieldnames]) |
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1115 |
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1116 return ApplyFunctionIterator(self.input_dataset,self) |
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1117 |
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1118 def __iter__(self): # only implemented for increased efficiency |
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1119 class ApplyFunctionSingleExampleIterator(object): |
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1120 def __init__(self,output_dataset): |
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1121 self.current=0 |
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1122 self.output_dataset=output_dataset |
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1123 self.input_iterator=output_dataset.input_dataset.__iter__() |
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1124 def __iter__(self): return self |
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1125 def next(self): |
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1126 function_inputs = self.input_iterator.next() |
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1127 if self.output_dataset.minibatch_mode: |
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1128 function_outputs = [output[0] for output in self.output_dataset.function(function_inputs)] |
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1129 else: |
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1130 function_outputs = self.output_dataset.function(function_inputs) |
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1131 return Example(self.output_dataset.output_names,function_outputs) |
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1132 return ApplyFunctionSingleExampleIterator(self) |
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1133 |
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1134 |
|
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1135 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
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1136 """ |
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1137 Wraps an arbitrary DataSet into one for supervised learning tasks by forcing the |
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1138 user to define a set of fields as the 'input' field and a set of fields |
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1139 as the 'target' field. Optionally, a single weight_field can also be defined. |
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1140 """ |
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1141 args = ((input_fields,'input'),(output_fields,'target')) |
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1142 if weight_field: args+=(([weight_field],'weight')) |
|
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1143 return src_dataset.merge_fields(*args) |
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1144 |
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1145 |
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1146 |
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1147 |