Mercurial > pylearn
annotate dataset.py @ 188:f01ac276c6fb
added __contains__ to Dataset, added parent constructor call to ArrayDataSet
| author | James Bergstra <bergstrj@iro.umontreal.ca> |
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| date | Wed, 14 May 2008 14:49:08 -0400 |
| parents | 895b4b60f5e8 |
| children | cb6b945acf5a |
| 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, copy |
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8 |
| 166 | 9 from exceptions import * |
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11 class AttributesHolder(object): |
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12 def __init__(self): pass |
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13 |
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14 def attributeNames(self): |
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15 raise AbstractFunction() |
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16 |
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17 def setAttributes(self,attribute_names,attribute_values,make_copies=False): |
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18 """ |
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19 Allow the attribute_values to not be a list (but a single value) if the attribute_names is of length 1. |
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20 """ |
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21 if len(attribute_names)==1 and not (isinstance(attribute_values,list) or isinstance(attribute_values,tuple) ): |
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22 attribute_values = [attribute_values] |
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23 if make_copies: |
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24 for name,value in zip(attribute_names,attribute_values): |
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25 self.__setattr__(name,copy.deepcopy(value)) |
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26 else: |
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27 for name,value in zip(attribute_names,attribute_values): |
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28 self.__setattr__(name,value) |
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29 |
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30 |
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31 class DataSet(AttributesHolder): |
| 16 | 32 """A virtual base class for datasets. |
| 33 | |
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34 A DataSet can be seen as a generalization of a matrix, meant to be used in conjunction |
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35 with learning algorithms (for training and testing them): rows/records are called examples, and |
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36 columns/attributes are called fields. The field value for a particular example can be an arbitrary |
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37 python object, which depends on the particular dataset. |
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38 |
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39 We call a DataSet a 'stream' when its length is unbounded (otherwise its __len__ method |
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40 should return sys.maxint). |
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| 16 | 42 A DataSet is a generator of iterators; these iterators can run through the |
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43 examples or the fields in a variety of ways. A DataSet need not necessarily have a finite |
| 16 | 44 or known length, so this class can be used to interface to a 'stream' which |
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45 feeds on-line learning (however, as noted below, some operations are not |
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46 feasible or not recommanded on streams). |
| 16 | 47 |
| 48 To iterate over examples, there are several possibilities: | |
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49 - for example in dataset: |
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50 - for val1,val2,... in dataset: |
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51 - for example in dataset(field1, field2,field3, ...): |
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52 - for val1,val2,val3 in dataset(field1, field2,field3): |
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53 - for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N): |
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54 - for mini1,mini2,mini3 in dataset.minibatches([field1, field2, field3], minibatch_size=N): |
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55 Each of these is documented below. All of these iterators are expected |
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56 to provide, in addition to the usual 'next()' method, a 'next_index()' method |
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57 which returns a non-negative integer pointing to the position of the next |
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58 example that will be returned by 'next()' (or of the first example in the |
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59 next minibatch returned). This is important because these iterators |
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60 can wrap around the dataset in order to do multiple passes through it, |
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61 in possibly unregular ways if the minibatch size is not a divisor of the |
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62 dataset length. |
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63 |
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64 To iterate over fields, one can do |
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65 - for field in dataset.fields(): |
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66 for field_value in field: # iterate over the values associated to that field for all the dataset examples |
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67 - for field in dataset(field1,field2,...).fields() to select a subset of fields |
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68 - for field in dataset.fields(field1,field2,...) to select a subset of fields |
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69 and each of these fields is iterable over the examples: |
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70 - for field_examples in dataset.fields(): |
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71 for example_value in field_examples: |
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72 ... |
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73 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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74 such things because the underlying dataset may refuse to access the different fields in |
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75 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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76 The result of fields() is a L{DataSetFields} object, which iterates over fields, |
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77 and whose elements are iterable over examples. A DataSetFields object can |
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78 be turned back into a DataSet with its examples() method:: |
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79 dataset2 = dataset1.fields().examples() |
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80 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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81 |
| 16 | 82 Note: Fields are not mutually exclusive, i.e. two fields can overlap in their actual content. |
| 83 | |
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84 Note: The content of a field can be of any type. Field values can also be 'missing' |
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85 (e.g. to handle semi-supervised learning), and in the case of numeric (numpy array) |
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86 fields (i.e. an ArrayFieldsDataSet), NaN plays the role of a missing value. |
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87 What about non-numeric values? None. |
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88 |
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89 Dataset elements can be indexed and sub-datasets (with a subset |
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90 of examples) can be extracted. These operations are not supported |
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91 by default in the case of streams. |
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92 |
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93 - dataset[:n] returns a dataset with the n first examples. |
| 16 | 94 |
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95 - dataset[i1:i2:s] returns a dataset with the examples i1,i1+s,...i2-s. |
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96 |
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97 - dataset[i] returns an Example. |
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98 |
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99 - dataset[[i1,i2,...in]] returns a dataset with examples i1,i2,...in. |
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100 |
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101 - dataset[fieldname] an iterable over the values of the field fieldname across |
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102 the dataset (the iterable is obtained by default by calling valuesVStack |
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103 over the values for individual examples). |
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104 |
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105 - dataset.<property> returns the value of a property associated with |
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106 the name <property>. The following properties should be supported: |
| 41 | 107 - 'description': a textual description or name for the dataset |
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108 - 'fieldtypes': a list of types (one per field) |
| 78 | 109 A DataSet may have other attributes that it makes visible to other objects. These are |
| 110 used to store information that is not example-wise but global to the dataset. | |
| 111 The list of names of these attributes is given by the attribute_names() method. | |
| 41 | 112 |
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113 Datasets can be concatenated either vertically (increasing the length) or |
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114 horizontally (augmenting the set of fields), if they are compatible, using |
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115 the following operations (with the same basic semantics as numpy.hstack |
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116 and numpy.vstack): |
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117 |
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118 - dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3]) |
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119 |
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120 creates a new dataset whose list of fields is the concatenation of the list of |
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121 fields of the argument datasets. This only works if they all have the same length. |
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122 |
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123 - dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3]) |
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124 |
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125 creates a new dataset that concatenates the examples from the argument datasets |
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126 (and whose length is the sum of the length of the argument datasets). This only |
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127 works if they all have the same fields. |
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128 |
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129 According to the same logic, and viewing a DataSetFields object associated to |
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130 a DataSet as a kind of transpose of it, fields1 & fields2 concatenates fields of |
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131 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their |
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132 examples. |
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133 |
| 41 | 134 A dataset can hold arbitrary key-value pairs that may be used to access meta-data |
| 135 or other properties of the dataset or associated with the dataset or the result | |
| 136 of a computation stored in a dataset. These can be accessed through the [key] syntax | |
| 137 when key is a string (or more specifically, neither an integer, a slice, nor a list). | |
| 78 | 138 |
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139 A DataSet sub-class should always redefine the following methods: |
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140 - __len__ if it is not a stream |
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141 - fieldNames |
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142 - minibatches_nowrap (called by DataSet.minibatches()) |
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143 - valuesHStack |
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144 - valuesVStack |
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145 For efficiency of implementation, a sub-class might also want to redefine |
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146 - hasFields |
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147 - __getitem__ may not be feasible with some streams |
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148 - __iter__ |
| 78 | 149 A sub-class should also append attributes to self._attribute_names |
| 150 (the default value returned by attributeNames()). | |
| 151 By convention, attributes not in attributeNames() should have a name | |
| 152 starting with an underscore. | |
| 153 @todo enforce/test that convention! | |
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154 """ |
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155 |
| 83 | 156 numpy_vstack = lambda fieldname,values: numpy.vstack(values) |
| 157 numpy_hstack = lambda fieldnames,values: numpy.hstack(values) | |
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158 |
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159 def __init__(self,description=None,fieldtypes=None): |
| 41 | 160 if description is None: |
| 161 # by default return "<DataSetType>(<SuperClass1>,<SuperClass2>,...)" | |
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162 description = type(self).__name__ + " ( " + join([x.__name__ for x in type(self).__bases__]) + " )" |
| 41 | 163 self.description=description |
| 60 | 164 self.fieldtypes=fieldtypes |
| 78 | 165 self._attribute_names = ["description"] |
| 166 if fieldtypes: | |
| 167 self._attribute_names.append("fieldtypes") | |
| 168 | |
| 169 def attributeNames(self): return self._attribute_names | |
| 170 | |
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171 class MinibatchToSingleExampleIterator(object): |
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172 """ |
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173 Converts the result of minibatch iterator with minibatch_size==1 into |
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174 single-example values in the result. Therefore the result of |
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175 iterating on the dataset itself gives a sequence of single examples |
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176 (whereas the result of iterating over minibatches gives in each |
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177 Example field an iterable object over the individual examples in |
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178 the minibatch). |
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179 """ |
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180 def __init__(self, minibatch_iterator): |
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181 self.minibatch_iterator = minibatch_iterator |
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182 self.minibatch = None |
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183 def __iter__(self): #makes for loop work |
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184 return self |
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185 def next(self): |
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186 size1_minibatch = self.minibatch_iterator.next() |
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187 if not self.minibatch: |
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188 self.minibatch = Example(size1_minibatch.keys(),[value[0] for value in size1_minibatch.values()]) |
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189 else: |
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190 self.minibatch._values = [value[0] for value in size1_minibatch.values()] |
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191 return self.minibatch |
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192 |
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193 def next_index(self): |
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194 return self.minibatch_iterator.next_index() |
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195 |
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196 def __iter__(self): |
| 16 | 197 """Supports the syntax "for i in dataset: ..." |
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198 |
| 16 | 199 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 200 all the fields of DataSet self. Every field of "i" will give access to | |
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201 a field of a single example. Fields should be accessible via |
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202 i["fielname"] or i[3] (in the order defined by the elements of the |
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203 Example returned by this iterator), but the derived class is free |
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204 to accept any type of identifier, and add extra functionality to the iterator. |
| 16 | 205 |
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206 The default implementation calls the minibatches iterator and extracts the first example of each field. |
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207 """ |
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208 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) |
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209 |
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210 def __contains__(self, fieldname): |
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211 return (fieldname in self.fieldNames()) \ |
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212 or (fieldname in self.attributeNames()) |
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213 |
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214 class MinibatchWrapAroundIterator(object): |
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215 """ |
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216 An iterator for minibatches that handles the case where we need to wrap around the |
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217 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
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218 a dataset that provides a minibatch iterator that does not need to handle that problem. |
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219 This class is a utility for dataset subclass writers, so that they do not have to handle |
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220 this issue multiple times, nor check that fieldnames are valid, nor handle the |
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221 empty fieldnames (meaning 'use all the fields'). |
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222 """ |
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223 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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224 self.dataset=dataset |
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225 self.fieldnames=fieldnames |
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226 self.minibatch_size=minibatch_size |
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227 self.n_batches=n_batches |
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228 self.n_batches_done=0 |
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229 self.next_row=offset |
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230 self.offset=offset |
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231 self.L=len(dataset) |
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232 assert offset+minibatch_size<=self.L |
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233 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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234 if n_batches is not None: |
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235 ds_nbatches = min(n_batches,ds_nbatches) |
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236 if fieldnames: |
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237 assert dataset.hasFields(*fieldnames) |
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238 else: |
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239 self.fieldnames=dataset.fieldNames() |
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240 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, |
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241 ds_nbatches,self.next_row) |
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242 |
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243 def __iter__(self): |
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244 return self |
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245 |
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246 def next_index(self): |
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247 return self.next_row |
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248 |
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249 def next(self): |
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250 if self.n_batches and self.n_batches_done==self.n_batches: |
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251 raise StopIteration |
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252 elif not self.n_batches and self.next_row ==self.L: |
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253 raise StopIteration |
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254 upper = self.next_row+self.minibatch_size |
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255 if upper <=self.L: |
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256 minibatch = self.iterator.next() |
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257 else: |
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258 if not self.n_batches: |
|
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259 upper=min(upper, self.L) |
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260 # if their is not a fixed number of batch, we continue to the end of the dataset. |
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261 # this can create a minibatch that is smaller then the minibatch_size |
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262 assert (self.L-self.next_row)<=self.minibatch_size |
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263 minibatch = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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264 else: |
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265 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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266 # first get the beginning of our minibatch (top of dataset) |
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267 first_part = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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268 second_part = self.dataset.minibatches_nowrap(self.fieldnames,upper-self.L,1,0).next() |
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269 minibatch = Example(self.fieldnames, |
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270 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
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271 for name in self.fieldnames]) |
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272 self.next_row=upper |
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273 self.n_batches_done+=1 |
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274 if upper >= self.L and self.n_batches: |
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275 self.next_row -= self.L |
|
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276 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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277 if self.n_batches is not None: |
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278 ds_nbatches = min(self.n_batches,ds_nbatches) |
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279 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, |
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280 ds_nbatches,self.next_row) |
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281 return DataSetFields(MinibatchDataSet(minibatch,self.dataset.valuesVStack, |
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282 self.dataset.valuesHStack), |
|
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283 minibatch.keys()) |
|
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284 |
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285 |
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286 minibatches_fieldnames = None |
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287 minibatches_minibatch_size = 1 |
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288 minibatches_n_batches = None |
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289 def minibatches(self, |
|
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290 fieldnames = minibatches_fieldnames, |
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291 minibatch_size = minibatches_minibatch_size, |
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292 n_batches = minibatches_n_batches, |
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293 offset = 0): |
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294 """ |
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295 Return an iterator that supports three forms of syntax: |
|
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296 |
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297 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 298 |
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299 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 300 |
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301 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 302 |
|
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303 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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304 tuple, or Example instance. In both cases, i[k] is a list-like container |
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305 of a batch of current examples. In the second case, i[0] is |
|
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306 list-like container of the f1 field of a batch current examples, i[1] is |
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307 a list-like container of the f2 field, etc. |
|
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308 |
|
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309 Using the first syntax, all the fields will be returned in "i". |
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310 Using the third syntax, i1, i2, i3 will be list-like containers of the |
|
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311 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
|
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312 |
|
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313 The minibatches iterator is expected to return upon each call to next() |
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314 a DataSetFields object, which is a LookupList (indexed by the field names) whose |
| 80 | 315 elements are iterable and indexable over the minibatch examples, and which keeps a pointer to |
|
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316 a sub-dataset that can be used to iterate over the individual examples |
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317 in the minibatch. Hence a minibatch can be converted back to a regular |
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318 dataset or its fields can be looked at individually (and possibly iterated over). |
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319 |
|
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320 PARAMETERS |
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321 - fieldnames (list of any type, default None): |
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322 The loop variables i1, i2, i3 (in the example above) should contain the |
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323 f1, f2, and f3 fields of the current batch of examples. If None, the |
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324 derived class can choose a default, e.g. all fields. |
| 16 | 325 |
|
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326 - minibatch_size (integer, default 1) |
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327 On every iteration, the variables i1, i2, i3 will have |
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328 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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329 |
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330 - n_batches (integer, default None) |
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331 The iterator will loop exactly this many times, and then stop. If None, |
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332 the derived class can choose a default. If (-1), then the returned |
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333 iterator should support looping indefinitely. |
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334 |
|
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335 - offset (integer, default 0) |
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336 The iterator will start at example 'offset' in the dataset, rather than the default. |
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337 |
|
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338 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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339 any other object that supports integer indexing and slicing. |
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340 |
|
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341 """ |
|
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342 return DataSet.MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
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343 |
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344 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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345 """ |
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346 This is the minibatches iterator generator that sub-classes must define. |
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347 It does not need to worry about wrapping around multiple times across the dataset, |
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348 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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349 The next() method of the returned iterator does not even need to worry about |
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350 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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351 before an improper call to minibatches_nowrap's next() is made). |
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352 That next() method can assert that its next row will always be within [0,len(dataset)). |
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353 The iterator returned by minibatches_nowrap does not need to implement |
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354 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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355 """ |
|
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356 raise AbstractFunction() |
|
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357 |
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358 def __len__(self): |
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359 """ |
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360 len(dataset) returns the number of examples in the dataset. |
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361 By default, a DataSet is a 'stream', i.e. it has an unbounded length (sys.maxint). |
|
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362 Sub-classes which implement finite-length datasets should redefine this method. |
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363 Some methods only make sense for finite-length datasets. |
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364 """ |
| 123 | 365 return maxint |
|
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366 |
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367 def is_unbounded(self): |
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368 """ |
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369 Tests whether a dataset is unbounded (e.g. a stream). |
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370 """ |
| 123 | 371 return len(self)==maxint |
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372 |
|
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373 def hasFields(self,*fieldnames): |
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374 """ |
|
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375 Return true if the given field name (or field names, if multiple arguments are |
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376 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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377 of the iterators). |
|
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378 |
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379 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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380 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
|
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381 """ |
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382 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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383 |
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384 def fieldNames(self): |
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385 """ |
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386 Return the list of field names that are supported by the iterators, |
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387 and for which hasFields(fieldname) would return True. |
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388 """ |
|
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389 raise AbstractFunction() |
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390 |
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391 def __call__(self,*fieldnames): |
|
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392 """ |
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393 Return a dataset that sees only the fields whose name are specified. |
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394 """ |
|
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395 assert self.hasFields(*fieldnames) |
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396 return self.fields(*fieldnames).examples() |
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397 |
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398 def fields(self,*fieldnames): |
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399 """ |
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400 Return a DataSetFields object associated with this dataset. |
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401 """ |
|
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402 return DataSetFields(self,fieldnames) |
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403 |
|
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404 def __getitem__(self,i): |
|
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405 """ |
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406 dataset[i] returns the (i+1)-th example of the dataset. |
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407 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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408 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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409 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
| 41 | 410 dataset['key'] returns a property associated with the given 'key' string. |
| 411 If 'key' is a fieldname, then the VStacked field values (iterable over | |
| 412 field values) for that field is returned. Other keys may be supported | |
| 413 by different dataset subclasses. The following key names are encouraged: | |
| 414 - 'description': a textual description or name for the dataset | |
| 415 - '<fieldname>.type': a type name or value for a given <fieldname> | |
|
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416 |
| 39 | 417 Note that some stream datasets may be unable to implement random access, i.e. |
| 418 arbitrary slicing/indexing | |
|
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419 because they can only iterate through examples one or a minibatch at a time |
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420 and do not actually store or keep past (or future) examples. |
|
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421 |
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422 The default implementation of getitem uses the minibatches iterator |
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423 to obtain one example, one slice, or a list of examples. It may not |
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424 always be the most efficient way to obtain the result, especially if |
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425 the data are actually stored in a memory array. |
|
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426 """ |
| 41 | 427 # check for an index |
|
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428 if type(i) is int: |
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429 return DataSet.MinibatchToSingleExampleIterator( |
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430 self.minibatches(minibatch_size=1,n_batches=1,offset=i)).next() |
| 41 | 431 rows=None |
| 432 # or a slice | |
|
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433 if type(i) is slice: |
|
135
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434 if not i.start: i=slice(0,i.stop,i.step) |
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435 if not i.step: i=slice(i.start,i.stop,1) |
|
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436 if i.step is 1: |
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437 return self.minibatches(minibatch_size=i.stop-i.start,n_batches=1,offset=i.start).next().examples() |
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438 rows = range(i.start,i.stop,i.step) |
| 41 | 439 # or a list of indices |
| 440 elif type(i) is list: | |
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441 rows = i |
| 41 | 442 if rows is not None: |
|
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443 examples = [self[row] for row in rows] |
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444 fields_values = zip(*examples) |
|
45
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445 return MinibatchDataSet( |
| 41 | 446 Example(self.fieldNames(),[ self.valuesVStack(fieldname,field_values) |
| 447 for fieldname,field_values | |
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448 in zip(self.fieldNames(),fields_values)]), |
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449 self.valuesVStack,self.valuesHStack) |
| 41 | 450 # else check for a fieldname |
| 451 if self.hasFields(i): | |
| 452 return self.minibatches(fieldnames=[i],minibatch_size=len(self),n_batches=1,offset=0).next()[0] | |
| 453 # else we are trying to access a property of the dataset | |
| 454 assert i in self.__dict__ # else it means we are trying to access a non-existing property | |
| 455 return self.__dict__[i] | |
|
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456 |
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457 def valuesHStack(self,fieldnames,fieldvalues): |
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458 """ |
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459 Return a value that corresponds to concatenating (horizontally) several field values. |
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460 This can be useful to merge some fields. The implementation of this operation is likely |
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461 to involve a copy of the original values. When the values are numpy arrays, the |
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462 result should be numpy.hstack(values). If it makes sense, this operation should |
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463 work as well when each value corresponds to multiple examples in a minibatch |
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464 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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465 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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466 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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467 pointing to the original values for other data types. |
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468 """ |
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469 all_numpy=True |
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470 for value in fieldvalues: |
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471 if not type(value) is numpy.ndarray: |
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472 all_numpy=False |
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473 if all_numpy: |
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474 return numpy.hstack(fieldvalues) |
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475 # the default implementation of horizontal stacking is to put values in a list |
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476 return fieldvalues |
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477 |
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478 |
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479 def valuesVStack(self,fieldname,values): |
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480 """ |
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481 Return a value that corresponds to concatenating (vertically) several values of the |
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482 same field. This can be important to build a minibatch out of individual examples. This |
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483 is likely to involve a copy of the original values. When the values are numpy arrays, the |
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484 result should be numpy.vstack(values). |
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485 The default is to use numpy.vstack for numpy.ndarray values, and a list |
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486 pointing to the original values for other data types. |
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487 """ |
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488 all_numpy=True |
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489 for value in values: |
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490 if not type(value) is numpy.ndarray: |
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491 all_numpy=False |
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492 if all_numpy: |
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493 return numpy.vstack(values) |
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494 # the default implementation of vertical stacking is to put values in a list |
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495 return values |
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496 |
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497 def __or__(self,other): |
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498 """ |
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499 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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500 fields of the argument datasets. This only works if they all have the same length. |
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501 """ |
|
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502 return HStackedDataSet([self,other]) |
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503 |
|
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504 def __and__(self,other): |
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505 """ |
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506 dataset1 & dataset2 is a dataset that concatenates the examples from the argument datasets |
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507 (and whose length is the sum of the length of the argument datasets). This only |
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508 works if they all have the same fields. |
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509 """ |
|
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510 return VStackedDataSet([self,other]) |
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511 |
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512 def hstack(datasets): |
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513 """ |
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514 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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515 which is a dataset whose fields list is the concatenation of the fields |
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516 of the individual datasets. |
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517 """ |
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518 assert len(datasets)>0 |
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519 if len(datasets)==1: |
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520 return datasets[0] |
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521 return HStackedDataSet(datasets) |
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522 |
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523 def vstack(datasets): |
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524 """ |
|
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525 vstack(dataset1,dataset2,...) returns dataset1 & datataset2 & ... |
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526 which is a dataset which iterates first over the examples of dataset1, then |
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527 over those of dataset2, etc. |
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528 """ |
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529 assert len(datasets)>0 |
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530 if len(datasets)==1: |
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531 return datasets[0] |
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532 return VStackedDataSet(datasets) |
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533 |
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534 class FieldsSubsetDataSet(DataSet): |
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535 """ |
| 167 | 536 A sub-class of L{DataSet} that selects a subset of the fields. |
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537 """ |
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538 def __init__(self,src,fieldnames): |
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539 self.src=src |
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540 self.fieldnames=fieldnames |
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541 assert src.hasFields(*fieldnames) |
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542 self.valuesHStack = src.valuesHStack |
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543 self.valuesVStack = src.valuesVStack |
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544 |
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545 def __len__(self): return len(self.src) |
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546 |
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547 def fieldNames(self): |
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548 return self.fieldnames |
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549 |
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550 def __iter__(self): |
|
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551 class FieldsSubsetIterator(object): |
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552 def __init__(self,ds): |
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553 self.ds=ds |
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554 self.src_iter=ds.src.__iter__() |
|
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555 self.example=None |
|
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556 def __iter__(self): return self |
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557 def next(self): |
|
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558 complete_example = self.src_iter.next() |
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559 if self.example: |
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560 self.example._values=[complete_example[field] |
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561 for field in self.ds.fieldnames] |
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562 else: |
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563 self.example=Example(self.ds.fieldnames, |
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564 [complete_example[field] for field in self.ds.fieldnames]) |
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565 return self.example |
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566 return FieldsSubsetIterator(self) |
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567 |
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568 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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569 assert self.hasFields(*fieldnames) |
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570 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
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571 def __getitem__(self,i): |
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572 return FieldsSubsetDataSet(self.src[i],self.fieldnames) |
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573 |
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574 |
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575 class DataSetFields(LookupList): |
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576 """ |
| 167 | 577 Although a L{DataSet} iterates over examples (like rows of a matrix), an associated |
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578 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
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579 as a transpose of the associated dataset. |
|
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580 |
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581 To iterate over fields, one can do |
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582 * for fields in dataset.fields() |
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583 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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584 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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585 and each of these fields is iterable over the examples: |
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586 * for field_examples in dataset.fields(): |
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587 for example_value in field_examples: |
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588 ... |
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589 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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590 such things because the underlying dataset may refuse to access the different fields in |
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591 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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592 The result of fields() is a DataSetFields object, which iterates over fields, |
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593 and whose elements are iterable over examples. A DataSetFields object can |
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594 be turned back into a DataSet with its examples() method: |
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595 dataset2 = dataset1.fields().examples() |
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596 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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597 |
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598 DataSetFields can be concatenated vertically or horizontally. To be consistent with |
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599 the syntax used for DataSets, the | concatenates the fields and the & concatenates |
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600 the examples. |
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601 """ |
|
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602 def __init__(self,dataset,fieldnames): |
|
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603 original_dataset=dataset |
|
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604 if not fieldnames: |
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605 fieldnames=dataset.fieldNames() |
|
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606 elif not fieldnames==dataset.fieldNames(): |
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607 dataset = FieldsSubsetDataSet(dataset,fieldnames) |
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608 assert dataset.hasFields(*fieldnames) |
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609 self.dataset=dataset |
|
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610 |
|
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611 if isinstance(dataset,MinibatchDataSet): |
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612 LookupList.__init__(self,fieldnames,list(dataset._fields)) |
|
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613 elif isinstance(original_dataset,MinibatchDataSet): |
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614 LookupList.__init__(self,fieldnames, |
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615 [original_dataset._fields[field] |
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616 for field in fieldnames]) |
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617 else: |
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618 minibatch_iterator = dataset.minibatches(fieldnames, |
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619 minibatch_size=len(dataset), |
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620 n_batches=1) |
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621 minibatch=minibatch_iterator.next() |
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622 LookupList.__init__(self,fieldnames,minibatch) |
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623 |
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624 def examples(self): |
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625 return self.dataset |
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626 |
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627 def __or__(self,other): |
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628 """ |
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629 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
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630 of the list of examples of DataSetFields fields1 and fields2. |
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631 """ |
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632 return (self.examples() + other.examples()).fields() |
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633 |
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634 def __and__(self,other): |
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635 """ |
|
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636 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
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637 of the fields of DataSetFields fields1 and fields2. |
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638 """ |
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639 return (self.examples() | other.examples()).fields() |
|
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640 |
|
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641 |
|
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642 class MinibatchDataSet(DataSet): |
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643 """ |
| 167 | 644 Turn a L{LookupList} of same-length (iterable) fields into an example-iterable dataset. |
|
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645 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
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646 """ |
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647 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
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648 values_hstack=DataSet().valuesHStack): |
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649 """ |
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650 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
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651 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
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652 semantics as the DataSet methods of the same name (but without the self argument). |
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653 """ |
|
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654 self._fields=fields_lookuplist |
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655 assert len(fields_lookuplist)>0 |
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656 self.length=len(fields_lookuplist[0]) |
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657 for field in fields_lookuplist[1:]: |
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658 assert self.length==len(field) |
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659 self.values_vstack=values_vstack |
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660 self.values_hstack=values_hstack |
|
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661 |
|
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662 def __len__(self): |
|
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663 return self.length |
|
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664 |
|
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665 def __getitem__(self,i): |
| 80 | 666 if type(i) in (slice,list): |
|
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667 return DataSetFields(MinibatchDataSet( |
| 80 | 668 Example(self._fields.keys(),[field[i] for field in self._fields])),self.fieldNames()) |
| 669 if type(i) is int: | |
| 85 | 670 return Example(self._fields.keys(),[field[i] for field in self._fields]) |
|
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671 if self.hasFields(i): |
|
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672 return self._fields[i] |
|
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Efficient implementation of getitem for ArrayDataSet
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673 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
|
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674 return self.__dict__[i] |
|
11
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Debugged (to the extent of my tests) the new version of dataset
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|
675 |
|
29
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Changed apply_function so that it propagates methods of the source.
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|
676 def fieldNames(self): |
|
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677 return self._fields.keys() |
|
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678 |
|
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|
679 def hasFields(self,*fieldnames): |
|
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680 for fieldname in fieldnames: |
|
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681 if fieldname not in self._fields.keys(): |
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682 return False |
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683 return True |
|
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|
684 |
|
37
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|
685 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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686 class Iterator(object): |
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687 def __init__(self,ds): |
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688 self.ds=ds |
|
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689 self.next_example=offset |
|
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690 assert minibatch_size > 0 |
| 41 | 691 if offset+minibatch_size > ds.length: |
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692 raise NotImplementedError() |
|
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693 def __iter__(self): |
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694 return self |
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695 def next(self): |
|
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696 upper = self.next_example+minibatch_size |
|
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697 assert upper<=self.ds.length |
|
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698 minibatch = Example(self.ds._fields.keys(), |
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699 [field[self.next_example:upper] |
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700 for field in self.ds._fields]) |
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701 self.next_example+=minibatch_size |
|
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702 return minibatch |
|
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|
703 |
|
40
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704 return Iterator(self) |
|
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|
705 |
|
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706 def valuesVStack(self,fieldname,fieldvalues): |
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707 return self.values_vstack(fieldname,fieldvalues) |
|
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|
708 |
|
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709 def valuesHStack(self,fieldnames,fieldvalues): |
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710 return self.values_hstack(fieldnames,fieldvalues) |
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|
711 |
|
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diff
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|
712 class HStackedDataSet(DataSet): |
|
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|
713 """ |
| 167 | 714 A L{DataSet} that wraps several datasets and shows a view that includes all their fields, |
|
36
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|
715 i.e. whose list of fields is the concatenation of their lists of fields. |
|
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|
716 |
|
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|
717 If a field name is found in more than one of the datasets, then either an error is |
|
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|
718 raised or the fields are renamed (either by prefixing the __name__ attribute |
|
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diff
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|
719 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list). |
|
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720 |
| 167 | 721 @todo: automatically detect a chain of stacked datasets due to A | B | C | D ... |
|
36
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|
722 """ |
| 41 | 723 def __init__(self,datasets,accept_nonunique_names=False,description=None,field_types=None): |
| 724 DataSet.__init__(self,description,field_types) | |
|
36
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725 self.datasets=datasets |
|
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|
726 self.accept_nonunique_names=accept_nonunique_names |
|
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727 self.fieldname2dataset={} |
|
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|
728 |
|
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|
729 def rename_field(fieldname,dataset,i): |
|
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|
730 if hasattr(dataset,"__name__"): |
|
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731 return dataset.__name__ + "." + fieldname |
|
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|
732 return fieldname+"."+str(i) |
|
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|
733 |
|
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|
734 # make sure all datasets have the same length and unique field names |
|
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735 self.length=None |
|
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|
736 names_to_change=[] |
|
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|
737 for i in xrange(len(datasets)): |
|
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738 dataset = datasets[i] |
|
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739 length=len(dataset) |
|
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740 if self.length: |
|
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741 assert self.length==length |
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742 else: |
|
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|
743 self.length=length |
|
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|
744 for fieldname in dataset.fieldNames(): |
|
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745 if fieldname in self.fieldname2dataset: # name conflict! |
|
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746 if accept_nonunique_names: |
|
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|
747 fieldname=rename_field(fieldname,dataset,i) |
|
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748 names2change.append((fieldname,i)) |
|
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749 else: |
|
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|
750 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
|
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|
751 self.fieldname2dataset[fieldname]=i |
|
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|
752 for fieldname,i in names_to_change: |
|
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diff
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|
753 del self.fieldname2dataset[fieldname] |
|
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754 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
|
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|
755 |
|
37
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|
756 def hasFields(self,*fieldnames): |
|
36
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757 for fieldname in fieldnames: |
|
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|
758 if not fieldname in self.fieldname2dataset: |
|
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|
759 return False |
|
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|
760 return True |
|
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|
761 |
|
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|
762 def fieldNames(self): |
|
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|
763 return self.fieldname2dataset.keys() |
|
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|
764 |
| 41 | 765 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
36
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|
766 |
|
44
5a85fda9b19b
Fixed some more iterator bugs
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diff
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|
767 class HStackedIterator(object): |
|
36
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|
768 def __init__(self,hsds,iterators): |
|
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|
769 self.hsds=hsds |
|
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|
770 self.iterators=iterators |
|
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|
771 def __iter__(self): |
|
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|
772 return self |
|
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|
773 def next(self): |
|
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|
774 # concatenate all the fields of the minibatches |
| 140 | 775 l=LookupList() |
| 776 for iter in self.iterators: | |
| 777 l.append_lookuplist(iter.next()) | |
| 778 return l | |
|
36
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|
779 |
| 125 | 780 assert self.hasFields(*fieldnames) |
|
36
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|
781 # find out which underlying datasets are necessary to service the required fields |
|
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|
782 # and construct corresponding minibatch iterators |
| 140 | 783 if fieldnames and fieldnames!=self.fieldNames(): |
|
36
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|
784 datasets=set([]) |
|
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|
785 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
|
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|
786 for fieldname in fieldnames: |
| 136 | 787 dataset=self.datasets[self.fieldname2dataset[fieldname]] |
|
36
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|
788 datasets.add(dataset) |
|
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|
789 fields_in_dataset[dataset].append(fieldname) |
|
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|
790 datasets=list(datasets) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
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|
791 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
|
36
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diff
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|
792 for dataset in datasets] |
|
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|
793 else: |
|
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diff
changeset
|
794 datasets=self.datasets |
|
37
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Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
795 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
|
796 return HStackedIterator(self,iterators) |
|
36
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diff
changeset
|
797 |
|
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diff
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|
798 |
|
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|
799 def valuesVStack(self,fieldname,fieldvalues): |
|
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|
800 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
|
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|
801 |
|
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|
802 def valuesHStack(self,fieldnames,fieldvalues): |
|
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|
803 """ |
|
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|
804 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
|
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diff
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|
805 to do the work, hoping that it can cope with the other values (i.e. won't care |
|
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diff
changeset
|
806 about the incompatible fieldnames). Hence this heuristic will always work if |
|
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changeset
|
807 all the fieldnames are of the same sub-dataset. |
|
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diff
changeset
|
808 """ |
|
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diff
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|
809 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
|
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diff
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|
810 |
|
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|
811 class VStackedDataSet(DataSet): |
|
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|
812 """ |
| 167 | 813 A L{DataSet} that wraps several datasets and shows a view that includes all their examples, |
|
36
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diff
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|
814 in the order provided. This clearly assumes that they all have the same field names |
|
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diff
changeset
|
815 and all (except possibly the last one) are of finite length. |
|
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|
816 |
| 167 | 817 @todo: automatically detect a chain of stacked datasets due to A + B + C + D ... |
|
36
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diff
changeset
|
818 """ |
|
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diff
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|
819 def __init__(self,datasets): |
|
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|
820 self.datasets=datasets |
|
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|
821 self.length=0 |
|
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diff
changeset
|
822 self.index2dataset={} |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
823 assert len(datasets)>0 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
824 fieldnames = datasets[-1].fieldNames() |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
825 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
|
826 # 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
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diff
changeset
|
827 # to avoid having to search for the appropriate dataset each time and slice is asked for. |
|
36
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diff
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|
828 for dataset,k in enumerate(datasets[0:-1]): |
|
48
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
829 assert dataset.is_unbounded() # All VStacked datasets (except possibly the last) must be bounded (have a length). |
|
b6730f9a336d
Fixing MinibatchDataSet getitem
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parents:
46
diff
changeset
|
830 L=len(dataset) |
|
36
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diff
changeset
|
831 for i in xrange(L): |
|
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diff
changeset
|
832 self.index2dataset[self.length+i]=k |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
833 self.datasets_start_row.append(self.length) |
|
36
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diff
changeset
|
834 self.length+=L |
|
37
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Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
835 assert dataset.fieldNames()==fieldnames |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
836 self.datasets_start_row.append(self.length) |
|
36
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diff
changeset
|
837 self.length+=len(datasets[-1]) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
838 # 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
|
839 # 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
|
840 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
|
841 # 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
|
842 # TODO |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
843 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
|
844 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
845 def __len__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
846 return self.length |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
847 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
848 def fieldNames(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
849 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
|
850 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
851 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
|
852 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
|
853 |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
854 def locate_row(self,row): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
855 """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
|
856 dataset_index = self.index2dataset[row] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
857 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
|
858 return dataset_index, row_within_dataset |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
859 |
| 41 | 860 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
|
861 |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
862 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
|
863 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
|
864 self.vsds=vsds |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
865 self.next_row=offset |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
866 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
|
867 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
|
868 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
|
869 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
870 def next_iterator(self,dataset,starting_offset,batches_left): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
871 L=len(dataset) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
872 ds_nbatches = (L-starting_offset)/minibatch_size |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
873 if batches_left is not None: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
874 ds_nbatches = max(batches_left,ds_nbatches) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
875 if minibatch_size>L: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
876 ds_minibatch_size=L |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
877 n_left_in_mb=minibatch_size-L |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
878 ds_nbatches=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
879 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
880 n_left_in_mb=0 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
881 return dataset.minibatches(fieldnames,minibatch_size,ds_nbatches,starting_offset), \ |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
882 L-(starting_offset+ds_nbatches*minibatch_size), n_left_in_mb |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
883 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
884 def move_to_next_dataset(self): |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
885 if self.n_left_at_the_end_of_ds>0: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
886 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
887 self.next_iterator(vsds.datasets[self.next_dataset_index], |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
888 self.n_left_at_the_end_of_ds,1) |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
889 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
890 self.next_dataset_index +=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
891 if self.next_dataset_index==len(self.vsds.datasets): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
892 self.next_dataset_index = 0 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
893 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
894 self.next_iterator(vsds.datasets[self.next_dataset_index],starting_offset,n_batches) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
895 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
896 def __iter__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
897 return self |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
898 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
899 def next(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
900 dataset=self.vsds.datasets[self.next_dataset_index] |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
901 mb = self.next_iterator.next() |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
902 if self.n_left_in_mb: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
903 extra_mb = [] |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
904 while self.n_left_in_mb>0: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
905 self.move_to_next_dataset() |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
906 extra_mb.append(self.next_iterator.next()) |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
bengioy@bengiomac.local
parents:
72
diff
changeset
|
907 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
|
908 [dataset.valuesVStack(name, |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
909 [mb[name]]+[b[name] for b in extra_mb]) |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
910 for name in fieldnames]) |
|
88fd1cce08b9
replaced infinity for length by raise UnboundedDataSet and use & instead of + to concatenate datasets
bengioy@esprit.iro.umontreal.ca
parents:
39
diff
changeset
|
911 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
912 self.next_row+=minibatch_size |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
913 self.next_dataset_row+=minibatch_size |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
914 if self.next_row+minibatch_size>len(dataset): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
915 self.move_to_next_dataset() |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
916 return examples |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
917 return VStackedIterator(self) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
918 |
| 41 | 919 class ArrayFieldsDataSet(DataSet): |
| 920 """ | |
| 921 Virtual super-class of datasets whose field values are numpy array, | |
| 922 thus defining valuesHStack and valuesVStack for sub-classes. | |
| 923 """ | |
| 924 def __init__(self,description=None,field_types=None): | |
| 925 DataSet.__init__(self,description,field_types) | |
| 926 def valuesHStack(self,fieldnames,fieldvalues): | |
| 927 """Concatenate field values horizontally, e.g. two vectors | |
| 928 become a longer vector, two matrices become a wider matrix, etc.""" | |
| 929 return numpy.hstack(fieldvalues) | |
| 930 def valuesVStack(self,fieldname,values): | |
| 931 """Concatenate field values vertically, e.g. two vectors | |
| 932 become a two-row matrix, two matrices become a longer matrix, etc.""" | |
| 933 return numpy.vstack(values) | |
| 934 | |
| 935 class ArrayDataSet(ArrayFieldsDataSet): | |
| 936 """ | |
| 937 An ArrayDataSet stores the fields as groups of columns in a numpy tensor, | |
| 938 whose first axis iterates over examples, second axis determines fields. | |
| 939 If the underlying array is N-dimensional (has N axes), then the field | |
| 940 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | |
| 941 """ | |
| 942 | |
|
188
f01ac276c6fb
added __contains__ to Dataset, added parent constructor call to ArrayDataSet
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
171
diff
changeset
|
943 def __init__(self, data_array, fields_columns, **kwargs): |
|
55
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
944 """ |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
945 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
|
946 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
947 using the standard arguments for indexing/slicing: integer for a column index, |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
948 slice for an interval of columns (with possible stride), or iterable of column indices. |
|
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
949 """ |
|
188
f01ac276c6fb
added __contains__ to Dataset, added parent constructor call to ArrayDataSet
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
171
diff
changeset
|
950 ArrayFieldsDataSet.__init__(self, **kwargs) |
| 41 | 951 self.data=data_array |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
952 self.fields_columns=fields_columns |
| 41 | 953 |
| 954 # check consistency and complete slices definitions | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
955 for fieldname, fieldcolumns in self.fields_columns.items(): |
| 41 | 956 if type(fieldcolumns) is int: |
| 957 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1] | |
|
99
a8da709eb6a9
in ArrayDataSet.__init__ if a columns is an index, we change it to be a list that containt only this index. This way, we remove the special case where the columns is an index for all subsequent call.
Frederic Bastien <bastienf@iro.umontreal.ca>
parents:
98
diff
changeset
|
958 self.fields_columns[fieldname]=[fieldcolumns] |
| 41 | 959 elif type(fieldcolumns) is slice: |
| 960 start,step=None,None | |
| 961 if not fieldcolumns.start: | |
| 962 start=0 | |
| 963 if not fieldcolumns.step: | |
| 964 step=1 | |
| 965 if start or step: | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
966 self.fields_columns[fieldname]=slice(start,fieldcolumns.stop,step) |
| 41 | 967 elif hasattr(fieldcolumns,"__iter__"): # something like a list |
| 968 for i in fieldcolumns: | |
| 969 assert i>=0 and i<data_array.shape[1] | |
| 970 | |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
971 def fieldNames(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
972 return self.fields_columns.keys() |
| 41 | 973 |
|
42
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
974 def __len__(self): |
|
9b68774fcc6b
Testing basic functionality and removing obvious bugs
bengioy@grenat.iro.umontreal.ca
parents:
41
diff
changeset
|
975 return len(self.data) |
| 41 | 976 |
| 80 | 977 def __getitem__(self,key): |
|
55
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|
978 """More efficient implementation than the default __getitem__""" |
|
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|
979 fieldnames=self.fields_columns.keys() |
| 80 | 980 if type(key) is int: |
|
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|
981 return Example(fieldnames, |
| 80 | 982 [self.data[key,self.fields_columns[f]] for f in fieldnames]) |
| 983 if type(key) is slice: | |
|
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|
984 return MinibatchDataSet(Example(fieldnames, |
| 80 | 985 [self.data[key,self.fields_columns[f]] for f in fieldnames])) |
| 986 if type(key) is list: | |
| 987 for i in range(len(key)): | |
| 988 if self.hasFields(key[i]): | |
| 989 key[i]=self.fields_columns[key[i]] | |
| 990 return MinibatchDataSet(Example(fieldnames, | |
|
88
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|
991 #we must separate differently for list as numpy |
| 128 | 992 # doesn't support self.data[[i1,...],[i2,...]] |
|
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|
993 # when their is more then two i1 and i2 |
|
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|
994 [self.data[key,:][:,self.fields_columns[f]] |
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|
995 if isinstance(self.fields_columns[f],list) else |
| 128 | 996 self.data[key,self.fields_columns[f]] for f in fieldnames]), |
| 997 | |
|
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|
998 self.valuesVStack,self.valuesHStack) |
| 80 | 999 |
|
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|
1000 # else check for a fieldname |
| 80 | 1001 if self.hasFields(key): |
|
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1002 return self.data[:,self.fields_columns[key]] |
|
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|
1003 # else we are trying to access a property of the dataset |
| 80 | 1004 assert key in self.__dict__ # else it means we are trying to access a non-existing property |
| 1005 return self.__dict__[key] | |
|
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|
1006 |
| 41 | 1007 |
|
42
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diff
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|
1008 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
44
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Fixed some more iterator bugs
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diff
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|
1009 class ArrayDataSetIterator(object): |
|
42
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|
1010 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
|
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|
1011 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
44
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|
1012 # store the resulting minibatch in a lookup-list of values |
|
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|
1013 self.minibatch = LookupList(fieldnames,[0]*len(fieldnames)) |
|
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|
1014 self.dataset=dataset |
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|
1015 self.minibatch_size=minibatch_size |
|
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|
1016 assert offset>=0 and offset<len(dataset.data) |
|
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|
1017 assert offset+minibatch_size<=len(dataset.data) |
|
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|
1018 self.current=offset |
|
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diff
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|
1019 def __iter__(self): |
|
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|
1020 return self |
|
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|
1021 def next(self): |
|
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diff
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|
1022 sub_data = self.dataset.data[self.current:self.current+self.minibatch_size] |
|
44
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diff
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|
1023 self.minibatch._values = [sub_data[:,self.dataset.fields_columns[f]] for f in self.minibatch._names] |
|
43
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|
1024 self.current+=self.minibatch_size |
|
44
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|
1025 return self.minibatch |
|
42
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diff
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|
1026 |
|
44
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diff
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|
1027 return ArrayDataSetIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
57
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diff
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|
1028 |
|
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diff
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|
1029 |
|
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parents:
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diff
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|
1030 class CachedDataSet(DataSet): |
|
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diff
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|
1031 """ |
| 167 | 1032 Wrap a L{DataSet} whose values are computationally expensive to obtain |
|
57
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|
1033 (e.g. because they involve some computation, or disk access), |
|
1aabd2e2bb5f
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diff
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|
1034 so that repeated accesses to the same example are done cheaply, |
|
1aabd2e2bb5f
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diff
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|
1035 by caching every example value that has been accessed at least once. |
|
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|
1036 |
|
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diff
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|
1037 Optionally, for finite-length dataset, all the values can be computed |
|
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diff
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|
1038 (and cached) upon construction of the CachedDataSet, rather at the |
|
1aabd2e2bb5f
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diff
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|
1039 first access. |
|
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|
1040 |
| 167 | 1041 @todo: when cache_all_upon_construction create mini-batches that are as |
|
77
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|
1042 large as possible but not so large as to fill up memory. |
|
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|
1043 |
| 167 | 1044 @todo: add disk-buffering capability, so that when the cache becomes too |
|
73
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|
1045 big for memory, we cache things on disk, trying to keep in memory only |
|
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|
1046 the record most likely to be accessed next. |
|
57
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diff
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|
1047 """ |
|
73
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|
1048 def __init__(self,source_dataset,cache_all_upon_construction=False): |
|
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|
1049 self.source_dataset=source_dataset |
|
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|
1050 self.cache_all_upon_construction=cache_all_upon_construction |
|
152
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Fixes in CacheDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
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diff
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|
1051 self.cached_examples = [] |
|
73
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|
1052 if cache_all_upon_construction: |
|
77
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|
1053 # this potentially brings all the source examples |
|
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|
1054 # into memory at once, which may be too much |
|
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|
1055 # the work could possibly be done by minibatches |
|
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|
1056 # that are as large as possible but no more than what memory allows. |
|
146
8173e196e291
Trying to make CacheDataSet work
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diff
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|
1057 fields_values = source_dataset.minibatches(minibatch_size=len(source_dataset)).__iter__().next() |
|
152
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Fixes in CacheDataSet
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diff
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|
1058 assert all([len(self)==len(field_values) for field_values in fields_values]) |
|
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Fixes in CacheDataSet
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151
diff
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|
1059 for example in fields_values.examples(): |
|
171
895b4b60f5e8
bugfix. Otherwise the example was writed over and not a new one was returned
Frederic Bastien <bastienf@iro.umontreal.ca>
parents:
167
diff
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|
1060 self.cached_examples.append(copy.copy(example)) |
|
57
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diff
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|
1061 |
|
73
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|
1062 self.fieldNames = source_dataset.fieldNames |
|
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|
1063 self.hasFields = source_dataset.hasFields |
|
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|
1064 self.valuesHStack = source_dataset.valuesHStack |
|
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|
1065 self.valuesVStack = source_dataset.valuesVStack |
|
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|
1066 |
|
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|
1067 def __len__(self): |
|
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|
1068 return len(self.source_dataset) |
|
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|
1069 |
|
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|
1070 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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|
1071 class CacheIterator(object): |
|
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|
1072 def __init__(self,dataset): |
|
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|
1073 self.dataset=dataset |
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|
1074 self.current=offset |
|
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|
1075 def __iter__(self): return self |
|
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|
1076 def next(self): |
|
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|
1077 upper = self.current+minibatch_size |
|
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|
1078 cache_len = len(self.dataset.cached_examples) |
|
135
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Fixed bugs in dataset to make test_mlp.py work
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|
1079 if upper>cache_len: # whole minibatch is not already in cache |
|
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|
1080 # cache everything from current length to upper |
|
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|
1081 for example in self.dataset.source_dataset[cache_len:upper]: |
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|
1082 self.dataset.cached_examples.append(example) |
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|
1083 all_fields_minibatch = Example(self.dataset.fieldNames(), |
|
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|
1084 zip(*self.dataset.cached_examples[self.current:self.current+minibatch_size])) |
|
163
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diff
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|
1085 self.current+=minibatch_size |
|
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|
1086 if self.dataset.fieldNames()==fieldnames: |
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|
1087 return all_fields_minibatch |
|
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|
1088 return Example(fieldnames,[all_fields_minibatch[name] for name in fieldnames]) |
|
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|
1089 return CacheIterator(self) |
|
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|
1090 |
| 153 | 1091 def __getitem__(self,i): |
| 1092 if type(i)==int and len(self.cached_examples)>i: | |
| 1093 return self.cached_examples[i] | |
| 1094 else: | |
| 1095 return DataSet.__getitem__(self,i) | |
|
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|
1096 |
|
57
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diff
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|
1097 class ApplyFunctionDataSet(DataSet): |
|
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diff
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|
1098 """ |
| 167 | 1099 A L{DataSet} that contains as fields the results of applying a |
| 1100 given function example-wise or minibatch-wise to all the fields of | |
| 1101 an input dataset. The output of the function should be an iterable | |
| 1102 (e.g. a list or a LookupList) over the resulting values. | |
|
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|
1103 |
| 167 | 1104 In minibatch mode, the function is expected to work on minibatches |
| 1105 (takes a minibatch in input and returns a minibatch in output). More | |
| 1106 precisely, it means that each element of the input or output list | |
| 1107 should be iterable and indexable over the individual example values | |
| 1108 (typically these elements will be numpy arrays). All of the elements | |
| 1109 in the input and output lists should have the same length, which is | |
| 1110 the length of the minibatch. | |
|
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1111 |
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1112 The function is applied each time an example or a minibatch is accessed. |
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1113 To avoid re-doing computation, wrap this dataset inside a CachedDataSet. |
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1114 |
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1115 If the values_{h,v}stack functions are not provided, then |
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1116 the input_dataset.values{H,V}Stack functions are used by default. |
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1117 """ |
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1118 def __init__(self,input_dataset,function,output_names,minibatch_mode=True, |
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1119 values_hstack=None,values_vstack=None, |
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1120 description=None,fieldtypes=None): |
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1121 """ |
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1122 Constructor takes an input dataset that has as many fields as the function |
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1123 expects as inputs. The resulting dataset has as many fields as the function |
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1124 produces as outputs, and that should correspond to the number of output names |
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1125 (provided in a list). |
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1126 |
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1127 Note that the expected semantics of the function differs in minibatch mode |
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1128 (it takes minibatches of inputs and produces minibatches of outputs, as |
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1129 documented in the class comment). |
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1130 """ |
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1131 self.input_dataset=input_dataset |
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1132 self.function=function |
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1133 self.output_names=output_names |
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1134 self.minibatch_mode=minibatch_mode |
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1135 DataSet.__init__(self,description,fieldtypes) |
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1136 self.valuesHStack = values_hstack if values_hstack else input_dataset.valuesHStack |
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1137 self.valuesVStack = values_vstack if values_vstack else input_dataset.valuesVStack |
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1138 |
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1139 def __len__(self): |
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1140 return len(self.input_dataset) |
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1141 |
|
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1142 def fieldNames(self): |
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1143 return self.output_names |
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1144 |
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1145 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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1146 class ApplyFunctionIterator(object): |
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1147 def __init__(self,output_dataset): |
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1148 self.input_dataset=output_dataset.input_dataset |
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1149 self.output_dataset=output_dataset |
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1150 self.input_iterator=self.input_dataset.minibatches(minibatch_size=minibatch_size, |
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1151 n_batches=n_batches,offset=offset).__iter__() |
|
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1152 |
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1153 def __iter__(self): return self |
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1154 |
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1155 def next(self): |
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1156 function_inputs = self.input_iterator.next() |
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1157 all_output_names = self.output_dataset.output_names |
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1158 if self.output_dataset.minibatch_mode: |
|
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1159 function_outputs = self.output_dataset.function(*function_inputs) |
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1160 else: |
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1161 input_examples = zip(*function_inputs) |
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1162 output_examples = [self.output_dataset.function(input_example) |
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1163 for input_example in input_examples] |
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1164 function_outputs = [self.output_dataset.valuesVStack(name,values) |
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1165 for name,values in zip(all_output_names, |
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1166 zip(*output_examples))] |
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1167 all_outputs = Example(all_output_names,function_outputs) |
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1168 if fieldnames==all_output_names: |
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1169 return all_outputs |
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1170 return Example(fieldnames,[all_outputs[name] for name in fieldnames]) |
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1171 |
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1172 return ApplyFunctionIterator(self) |
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1173 |
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1174 def __iter__(self): # only implemented for increased efficiency |
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1175 class ApplyFunctionSingleExampleIterator(object): |
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1176 def __init__(self,output_dataset): |
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1177 self.current=0 |
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1178 self.output_dataset=output_dataset |
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1179 self.input_iterator=output_dataset.input_dataset.__iter__() |
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1180 def __iter__(self): return self |
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1181 def next(self): |
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1182 function_inputs = self.input_iterator.next() |
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1183 if self.output_dataset.minibatch_mode: |
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1184 function_outputs = [output[0] for output in self.output_dataset.function(function_inputs)] |
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1185 else: |
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1186 function_outputs = self.output_dataset.function(function_inputs) |
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1187 return Example(self.output_dataset.output_names,function_outputs) |
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1188 return ApplyFunctionSingleExampleIterator(self) |
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1189 |
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1190 |
|
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1191 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
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1192 """ |
| 167 | 1193 Wraps an arbitrary L{DataSet} into one for supervised learning tasks |
| 1194 by forcing the user to define a set of fields as the 'input' field | |
| 1195 and a set of fields as the 'target' field. Optionally, a single | |
| 1196 weight_field can also be defined. | |
|
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1197 """ |
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1198 args = ((input_fields,'input'),(output_fields,'target')) |
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1199 if weight_field: args+=(([weight_field],'weight')) |
|
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1200 return src_dataset.merge_fields(*args) |
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1201 |
|
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1202 |
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|
1203 |
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1204 |