Mercurial > pylearn
annotate dataset.py @ 284:8e923cb2e8fc
renamed file
| author | Frederic Bastien <bastienf@iro.umontreal.ca> |
|---|---|
| date | Fri, 06 Jun 2008 13:52:37 -0400 |
| parents | ed70580f2324 |
| children | 9b533cc7874a |
| 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 def getAttributes(self,attribute_names=None, return_copy=False): |
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31 """ |
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32 Return all (if attribute_names=None, in the order of attributeNames()) or a specified subset of attributes. |
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33 """ |
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34 if attribute_names is None: |
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35 attribute_names = self.attributeNames() |
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36 if return_copy: |
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37 return [copy.copy(self.__getattribute__(name)) for name in attribute_names] |
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38 else: |
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39 return [self.__getattribute__(name) for name in attribute_names] |
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40 |
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41 |
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42 class DataSet(AttributesHolder): |
| 16 | 43 """A virtual base class for datasets. |
| 44 | |
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45 A DataSet can be seen as a generalization of a matrix, meant to be used in conjunction |
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46 with learning algorithms (for training and testing them): rows/records are called examples, and |
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47 columns/attributes are called fields. The field value for a particular example can be an arbitrary |
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48 python object, which depends on the particular dataset. |
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49 |
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50 We call a DataSet a 'stream' when its length is unbounded (in which case its __len__ method |
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51 should return sys.maxint). |
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| 16 | 53 A DataSet is a generator of iterators; these iterators can run through the |
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54 examples or the fields in a variety of ways. A DataSet need not necessarily have a finite |
| 16 | 55 or known length, so this class can be used to interface to a 'stream' which |
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56 feeds on-line learning (however, as noted below, some operations are not |
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57 feasible or not recommended on streams). |
| 16 | 58 |
| 59 To iterate over examples, there are several possibilities: | |
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60 - for example in dataset: |
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61 - for val1,val2,... in dataset: |
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62 - for example in dataset(field1, field2,field3, ...): |
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63 - for val1,val2,val3 in dataset(field1, field2,field3): |
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64 - for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N): |
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65 - for mini1,mini2,mini3 in dataset.minibatches([field1, field2, field3], minibatch_size=N): |
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66 Each of these is documented below. All of these iterators are expected |
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67 to provide, in addition to the usual 'next()' method, a 'next_index()' method |
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68 which returns a non-negative integer pointing to the position of the next |
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69 example that will be returned by 'next()' (or of the first example in the |
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70 next minibatch returned). This is important because these iterators |
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71 can wrap around the dataset in order to do multiple passes through it, |
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72 in possibly unregular ways if the minibatch size is not a divisor of the |
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73 dataset length. |
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74 |
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75 To iterate over fields, one can do |
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76 - for field in dataset.fields(): |
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77 for field_value in field: # iterate over the values associated to that field for all the dataset examples |
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78 - for field in dataset(field1,field2,...).fields() to select a subset of fields |
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79 - for field in dataset.fields(field1,field2,...) to select a subset of fields |
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80 and each of these fields is iterable over the examples: |
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81 - for field_examples in dataset.fields(): |
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82 for example_value in field_examples: |
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83 ... |
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84 but when the dataset is a stream (unbounded length), it is not recommended to do |
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85 such things because the underlying dataset may refuse to access the different fields in |
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86 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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87 The result of fields() is a L{DataSetFields} object, which iterates over fields, |
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88 and whose elements are iterable over examples. A DataSetFields object can |
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89 be turned back into a DataSet with its examples() method:: |
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90 dataset2 = dataset1.fields().examples() |
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91 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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92 |
| 16 | 93 Note: Fields are not mutually exclusive, i.e. two fields can overlap in their actual content. |
| 94 | |
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95 Note: The content of a field can be of any type. Field values can also be 'missing' |
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96 (e.g. to handle semi-supervised learning), and in the case of numeric (numpy array) |
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97 fields (i.e. an ArrayFieldsDataSet), NaN plays the role of a missing value. |
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98 What about non-numeric values? None. |
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99 |
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100 Dataset elements can be indexed and sub-datasets (with a subset |
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101 of examples) can be extracted. These operations are not supported |
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102 by default in the case of streams. |
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103 |
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104 - dataset[:n] returns a dataset with the n first examples. |
| 16 | 105 |
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106 - dataset[i1:i2:s] returns a dataset with the examples i1,i1+s,...i2-s. |
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107 |
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108 - dataset[i] returns an Example. |
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109 |
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110 - dataset[[i1,i2,...in]] returns a dataset with examples i1,i2,...in. |
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111 |
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112 - dataset.<property> returns the value of a property associated with |
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113 the name <property>. The following properties should be supported: |
| 41 | 114 - 'description': a textual description or name for the dataset |
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115 - 'fieldtypes': a list of types (one per field) |
| 78 | 116 A DataSet may have other attributes that it makes visible to other objects. These are |
| 117 used to store information that is not example-wise but global to the dataset. | |
| 118 The list of names of these attributes is given by the attribute_names() method. | |
| 41 | 119 |
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120 Datasets can be concatenated either vertically (increasing the length) or |
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121 horizontally (augmenting the set of fields), if they are compatible, using |
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122 the following operations (with the same basic semantics as numpy.hstack |
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123 and numpy.vstack): |
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124 |
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125 - dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3]) |
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126 |
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127 creates a new dataset whose list of fields is the concatenation of the list of |
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128 fields of the argument datasets. This only works if they all have the same length. |
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129 |
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130 - dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3]) |
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131 |
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132 creates a new dataset that concatenates the examples from the argument datasets |
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133 (and whose length is the sum of the length of the argument datasets). This only |
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134 works if they all have the same fields. |
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135 |
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136 According to the same logic, and viewing a DataSetFields object associated to |
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137 a DataSet as a kind of transpose of it, fields1 & fields2 concatenates fields of |
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138 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their |
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139 examples. |
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140 |
| 41 | 141 A dataset can hold arbitrary key-value pairs that may be used to access meta-data |
| 142 or other properties of the dataset or associated with the dataset or the result | |
| 143 of a computation stored in a dataset. These can be accessed through the [key] syntax | |
| 144 when key is a string (or more specifically, neither an integer, a slice, nor a list). | |
| 78 | 145 |
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146 A DataSet sub-class should always redefine the following methods: |
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147 - __len__ if it is not a stream |
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148 - fieldNames |
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149 - minibatches_nowrap (called by DataSet.minibatches()) |
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150 For efficiency of implementation, a sub-class might also want to redefine |
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151 - valuesHStack |
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152 - valuesVStack |
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153 - hasFields |
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154 - __getitem__ may not be feasible with some streams |
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155 - __iter__ |
| 78 | 156 A sub-class should also append attributes to self._attribute_names |
| 157 (the default value returned by attributeNames()). | |
| 158 By convention, attributes not in attributeNames() should have a name | |
| 159 starting with an underscore. | |
| 160 @todo enforce/test that convention! | |
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161 """ |
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162 |
| 83 | 163 numpy_vstack = lambda fieldname,values: numpy.vstack(values) |
| 164 numpy_hstack = lambda fieldnames,values: numpy.hstack(values) | |
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165 |
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166 def __init__(self,description=None,fieldtypes=None): |
| 41 | 167 if description is None: |
| 168 # by default return "<DataSetType>(<SuperClass1>,<SuperClass2>,...)" | |
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169 description = type(self).__name__ + " ( " + join([x.__name__ for x in type(self).__bases__]) + " )" |
| 41 | 170 self.description=description |
| 60 | 171 self.fieldtypes=fieldtypes |
| 78 | 172 self._attribute_names = ["description"] |
| 173 if fieldtypes: | |
| 174 self._attribute_names.append("fieldtypes") | |
| 175 | |
| 176 def attributeNames(self): return self._attribute_names | |
| 177 | |
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178 class MinibatchToSingleExampleIterator(object): |
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179 """ |
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180 Converts the result of minibatch iterator with minibatch_size==1 into |
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181 single-example values in the result. Therefore the result of |
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182 iterating on the dataset itself gives a sequence of single examples |
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183 (whereas the result of iterating over minibatches gives in each |
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184 Example field an iterable object over the individual examples in |
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185 the minibatch). |
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186 """ |
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187 def __init__(self, minibatch_iterator): |
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188 self.minibatch_iterator = minibatch_iterator |
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189 self.minibatch = None |
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190 def __iter__(self): #makes for loop work |
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191 return self |
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192 def next(self): |
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193 size1_minibatch = self.minibatch_iterator.next() |
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194 if not self.minibatch: |
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195 self.minibatch = Example(size1_minibatch.keys(),[value[0] for value in size1_minibatch.values()]) |
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196 else: |
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197 self.minibatch._values = [value[0] for value in size1_minibatch.values()] |
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198 return self.minibatch |
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199 |
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200 def next_index(self): |
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201 return self.minibatch_iterator.next_index() |
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202 |
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203 def __iter__(self): |
| 16 | 204 """Supports the syntax "for i in dataset: ..." |
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205 |
| 16 | 206 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 207 all the fields of DataSet self. Every field of "i" will give access to | |
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208 a field of a single example. Fields should be accessible via |
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209 i["fielname"] or i[3] (in the order defined by the elements of the |
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210 Example returned by this iterator), but the derived class is free |
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211 to accept any type of identifier, and add extra functionality to the iterator. |
| 16 | 212 |
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213 The default implementation calls the minibatches iterator and extracts the first example of each field. |
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214 """ |
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215 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) |
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216 |
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217 def __contains__(self, fieldname): |
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218 return (fieldname in self.fieldNames()) \ |
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219 or (fieldname in self.attributeNames()) |
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220 |
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221 class MinibatchWrapAroundIterator(object): |
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222 """ |
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223 An iterator for minibatches that handles the case where we need to wrap around the |
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224 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
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225 a dataset that provides a minibatch iterator that does not need to handle that problem. |
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226 This class is a utility for dataset subclass writers, so that they do not have to handle |
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227 this issue multiple times, nor check that fieldnames are valid, nor handle the |
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228 empty fieldnames (meaning 'use all the fields'). |
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229 """ |
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230 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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231 self.dataset=dataset |
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232 self.fieldnames=fieldnames |
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233 self.minibatch_size=minibatch_size |
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234 self.n_batches=n_batches |
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235 self.n_batches_done=0 |
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236 self.next_row=offset |
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237 self.offset=offset |
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238 self.L=len(dataset) |
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239 assert offset+minibatch_size<=self.L |
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240 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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241 if n_batches is not None: |
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242 ds_nbatches = min(n_batches,ds_nbatches) |
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243 if fieldnames: |
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244 assert dataset.hasFields(*fieldnames) |
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245 else: |
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246 self.fieldnames=dataset.fieldNames() |
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247 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, |
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248 ds_nbatches,self.next_row) |
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249 |
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250 def __iter__(self): |
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251 return self |
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252 |
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253 def next_index(self): |
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254 return self.next_row |
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255 |
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256 def next(self): |
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257 if self.n_batches and self.n_batches_done==self.n_batches: |
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258 raise StopIteration |
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259 elif not self.n_batches and self.next_row ==self.L: |
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260 raise StopIteration |
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261 upper = self.next_row+self.minibatch_size |
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262 if upper <=self.L: |
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263 minibatch = self.iterator.next() |
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264 else: |
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265 if not self.n_batches: |
|
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266 upper=min(upper, self.L) |
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267 # if their is not a fixed number of batch, we continue to the end of the dataset. |
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268 # this can create a minibatch that is smaller then the minibatch_size |
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269 assert (self.L-self.next_row)<=self.minibatch_size |
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270 minibatch = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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271 else: |
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272 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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273 # first get the beginning of our minibatch (top of dataset) |
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274 first_part = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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275 second_part = self.dataset.minibatches_nowrap(self.fieldnames,upper-self.L,1,0).next() |
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276 minibatch = Example(self.fieldnames, |
|
268
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277 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
|
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278 for name in self.fieldnames]) |
|
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279 self.next_row=upper |
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280 self.n_batches_done+=1 |
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281 if upper >= self.L and self.n_batches: |
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282 self.next_row -= self.L |
|
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283 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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284 if self.n_batches is not None: |
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285 ds_nbatches = min(self.n_batches,ds_nbatches) |
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286 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, |
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287 ds_nbatches,self.next_row) |
|
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288 return DataSetFields(MinibatchDataSet(minibatch,self.dataset.valuesVStack, |
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289 self.dataset.valuesHStack), |
|
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290 minibatch.keys()) |
|
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291 |
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292 |
|
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293 minibatches_fieldnames = None |
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294 minibatches_minibatch_size = 1 |
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295 minibatches_n_batches = None |
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296 def minibatches(self, |
|
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297 fieldnames = minibatches_fieldnames, |
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298 minibatch_size = minibatches_minibatch_size, |
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299 n_batches = minibatches_n_batches, |
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300 offset = 0): |
|
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301 """ |
|
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302 Return an iterator that supports three forms of syntax: |
|
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303 |
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304 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 305 |
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306 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 307 |
|
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308 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 309 |
|
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310 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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311 tuple, or Example instance. In both cases, i[k] is a list-like container |
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312 of a batch of current examples. In the second case, i[0] is |
|
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313 list-like container of the f1 field of a batch current examples, i[1] is |
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314 a list-like container of the f2 field, etc. |
|
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315 |
|
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316 Using the first syntax, all the fields will be returned in "i". |
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317 Using the third syntax, i1, i2, i3 will be list-like containers of the |
|
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318 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
|
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319 |
|
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320 The minibatches iterator is expected to return upon each call to next() |
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321 a DataSetFields object, which is a LookupList (indexed by the field names) whose |
| 80 | 322 elements are iterable and indexable over the minibatch examples, and which keeps a pointer to |
|
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323 a sub-dataset that can be used to iterate over the individual examples |
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324 in the minibatch. Hence a minibatch can be converted back to a regular |
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325 dataset or its fields can be looked at individually (and possibly iterated over). |
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326 |
|
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327 PARAMETERS |
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328 - fieldnames (list of any type, default None): |
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329 The loop variables i1, i2, i3 (in the example above) should contain the |
|
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330 f1, f2, and f3 fields of the current batch of examples. If None, the |
|
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331 derived class can choose a default, e.g. all fields. |
| 16 | 332 |
|
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333 - minibatch_size (integer, default 1) |
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334 On every iteration, the variables i1, i2, i3 will have |
|
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|
335 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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336 |
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337 - n_batches (integer, default None) |
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338 The iterator will loop exactly this many times, and then stop. If None, |
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339 the derived class can choose a default. If (-1), then the returned |
|
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340 iterator should support looping indefinitely. |
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341 |
|
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342 - offset (integer, default 0) |
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343 The iterator will start at example 'offset' in the dataset, rather than the default. |
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344 |
|
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345 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
|
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346 any other object that supports integer indexing and slicing. |
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347 |
|
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348 """ |
|
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349 return DataSet.MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
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350 |
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351 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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352 """ |
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353 This is the minibatches iterator generator that sub-classes must define. |
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354 It does not need to worry about wrapping around multiple times across the dataset, |
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355 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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356 The next() method of the returned iterator does not even need to worry about |
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357 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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358 before an improper call to minibatches_nowrap's next() is made). |
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359 That next() method can assert that its next row will always be within [0,len(dataset)). |
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360 The iterator returned by minibatches_nowrap does not need to implement |
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361 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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362 """ |
|
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363 raise AbstractFunction() |
|
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364 |
|
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365 def __len__(self): |
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366 """ |
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367 len(dataset) returns the number of examples in the dataset. |
|
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368 By default, a DataSet is a 'stream', i.e. it has an unbounded length (sys.maxint). |
|
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369 Sub-classes which implement finite-length datasets should redefine this method. |
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370 Some methods only make sense for finite-length datasets. |
|
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371 """ |
| 123 | 372 return maxint |
|
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373 |
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374 def is_unbounded(self): |
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375 """ |
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376 Tests whether a dataset is unbounded (e.g. a stream). |
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377 """ |
| 123 | 378 return len(self)==maxint |
|
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379 |
|
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380 def hasFields(self,*fieldnames): |
|
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381 """ |
|
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382 Return true if the given field name (or field names, if multiple arguments are |
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383 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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384 of the iterators). |
|
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385 |
|
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386 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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387 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
|
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388 """ |
|
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389 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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390 |
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391 def fieldNames(self): |
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392 """ |
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393 Return the list of field names that are supported by the iterators, |
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394 and for which hasFields(fieldname) would return True. |
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395 """ |
|
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396 raise AbstractFunction() |
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397 |
|
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398 def __call__(self,*fieldnames): |
|
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399 """ |
|
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400 Return a dataset that sees only the fields whose name are specified. |
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401 """ |
|
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402 assert self.hasFields(*fieldnames) |
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403 return self.fields(*fieldnames).examples() |
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404 |
|
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405 def fields(self,*fieldnames): |
|
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406 """ |
|
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407 Return a DataSetFields object associated with this dataset. |
|
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408 """ |
|
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409 return DataSetFields(self,fieldnames) |
|
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410 |
|
269
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411 def getitem_key(self, fieldname): |
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412 """A not-so-well thought-out place to put code that used to be in |
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413 getitem. |
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414 """ |
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415 #removing as per discussion June 4. --JSB |
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416 |
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417 i = fieldname |
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418 # else check for a fieldname |
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419 if self.hasFields(i): |
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420 return self.minibatches(fieldnames=[i],minibatch_size=len(self),n_batches=1,offset=0).next()[0] |
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421 # else we are trying to access a property of the dataset |
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422 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
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423 return self.__dict__[i] |
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424 |
|
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425 def __getitem__(self,i): |
|
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426 """ |
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427 dataset[i] returns the (i+1)-th example of the dataset. |
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428 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
|
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429 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
|
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430 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
| 41 | 431 dataset['key'] returns a property associated with the given 'key' string. |
| 432 If 'key' is a fieldname, then the VStacked field values (iterable over | |
| 433 field values) for that field is returned. Other keys may be supported | |
| 434 by different dataset subclasses. The following key names are encouraged: | |
| 435 - 'description': a textual description or name for the dataset | |
| 436 - '<fieldname>.type': a type name or value for a given <fieldname> | |
|
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437 |
| 39 | 438 Note that some stream datasets may be unable to implement random access, i.e. |
| 439 arbitrary slicing/indexing | |
|
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440 because they can only iterate through examples one or a minibatch at a time |
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441 and do not actually store or keep past (or future) examples. |
|
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442 |
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443 The default implementation of getitem uses the minibatches iterator |
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444 to obtain one example, one slice, or a list of examples. It may not |
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445 always be the most efficient way to obtain the result, especially if |
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446 the data are actually stored in a memory array. |
|
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447 """ |
| 41 | 448 # check for an index |
|
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449 if type(i) is int: |
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450 return DataSet.MinibatchToSingleExampleIterator( |
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451 self.minibatches(minibatch_size=1,n_batches=1,offset=i)).next() |
| 41 | 452 rows=None |
| 453 # or a slice | |
|
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454 if type(i) is slice: |
|
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455 #print 'i=',i |
|
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456 if not i.start: i=slice(0,i.stop,i.step) |
|
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457 if not i.stop: i=slice(i.start,len(self),i.step) |
|
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458 if not i.step: i=slice(i.start,i.stop,1) |
|
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459 if i.step is 1: |
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460 return self.minibatches(minibatch_size=i.stop-i.start,n_batches=1,offset=i.start).next().examples() |
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461 rows = range(i.start,i.stop,i.step) |
| 41 | 462 # or a list of indices |
| 463 elif type(i) is list: | |
|
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464 rows = i |
| 41 | 465 if rows is not None: |
|
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466 examples = [self[row] for row in rows] |
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467 fields_values = zip(*examples) |
|
45
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468 return MinibatchDataSet( |
| 41 | 469 Example(self.fieldNames(),[ self.valuesVStack(fieldname,field_values) |
| 470 for fieldname,field_values | |
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471 in zip(self.fieldNames(),fields_values)]), |
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472 self.valuesVStack,self.valuesHStack) |
|
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473 raise TypeError(i, type(i)) |
|
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474 |
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475 def valuesHStack(self,fieldnames,fieldvalues): |
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476 """ |
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477 Return a value that corresponds to concatenating (horizontally) several field values. |
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478 This can be useful to merge some fields. The implementation of this operation is likely |
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479 to involve a copy of the original values. When the values are numpy arrays, the |
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480 result should be numpy.hstack(values). If it makes sense, this operation should |
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481 work as well when each value corresponds to multiple examples in a minibatch |
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482 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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483 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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484 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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485 pointing to the original values for other data types. |
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486 """ |
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487 all_numpy=True |
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488 for value in fieldvalues: |
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489 if not type(value) is numpy.ndarray: |
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490 all_numpy=False |
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491 if all_numpy: |
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492 return numpy.hstack(fieldvalues) |
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493 # the default implementation of horizontal stacking is to put values in a list |
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494 return fieldvalues |
|
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495 |
|
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496 |
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497 def valuesVStack(self,fieldname,values): |
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498 """ |
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499 Return a value that corresponds to concatenating (vertically) several values of the |
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500 same field. This can be important to build a minibatch out of individual examples. This |
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501 is likely to involve a copy of the original values. When the values are numpy arrays, the |
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502 result should be numpy.vstack(values). |
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503 The default is to use numpy.vstack for numpy.ndarray values, and a list |
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504 pointing to the original values for other data types. |
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505 """ |
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506 all_numpy=True |
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507 for value in values: |
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508 if not type(value) is numpy.ndarray: |
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509 all_numpy=False |
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510 if all_numpy: |
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511 return numpy.vstack(values) |
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512 # the default implementation of vertical stacking is to put values in a list |
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513 return values |
|
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514 |
|
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515 def __or__(self,other): |
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516 """ |
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517 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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518 fields of the argument datasets. This only works if they all have the same length. |
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519 """ |
|
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520 return HStackedDataSet([self,other]) |
|
3
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|
521 |
|
40
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522 def __and__(self,other): |
|
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523 """ |
|
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524 dataset1 & dataset2 is a dataset that concatenates the examples from the argument datasets |
|
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525 (and whose length is the sum of the length of the argument datasets). This only |
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526 works if they all have the same fields. |
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527 """ |
|
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528 return VStackedDataSet([self,other]) |
|
23
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529 |
|
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530 def hstack(datasets): |
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531 """ |
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532 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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533 which is a dataset whose fields list is the concatenation of the fields |
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534 of the individual datasets. |
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535 """ |
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536 assert len(datasets)>0 |
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537 if len(datasets)==1: |
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538 return datasets[0] |
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539 return HStackedDataSet(datasets) |
|
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540 |
|
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541 def vstack(datasets): |
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542 """ |
|
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543 vstack(dataset1,dataset2,...) returns dataset1 & datataset2 & ... |
|
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544 which is a dataset which iterates first over the examples of dataset1, then |
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545 over those of dataset2, etc. |
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546 """ |
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547 assert len(datasets)>0 |
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548 if len(datasets)==1: |
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549 return datasets[0] |
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550 return VStackedDataSet(datasets) |
|
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|
551 |
|
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|
552 class FieldsSubsetDataSet(DataSet): |
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553 """ |
| 167 | 554 A sub-class of L{DataSet} that selects a subset of the fields. |
|
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555 """ |
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556 def __init__(self,src,fieldnames): |
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557 self.src=src |
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558 self.fieldnames=fieldnames |
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559 assert src.hasFields(*fieldnames) |
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560 self.valuesHStack = src.valuesHStack |
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561 self.valuesVStack = src.valuesVStack |
|
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562 |
|
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563 def __len__(self): return len(self.src) |
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564 |
|
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565 def fieldNames(self): |
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|
566 return self.fieldnames |
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567 |
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|
568 def __iter__(self): |
|
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|
569 class FieldsSubsetIterator(object): |
|
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570 def __init__(self,ds): |
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|
571 self.ds=ds |
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572 self.src_iter=ds.src.__iter__() |
|
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|
573 self.example=None |
|
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574 def __iter__(self): return self |
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575 def next(self): |
|
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|
576 complete_example = self.src_iter.next() |
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577 if self.example: |
|
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578 self.example._values=[complete_example[field] |
|
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|
579 for field in self.ds.fieldnames] |
|
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|
580 else: |
|
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|
581 self.example=Example(self.ds.fieldnames, |
|
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parents:
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diff
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|
582 [complete_example[field] for field in self.ds.fieldnames]) |
|
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|
583 return self.example |
|
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584 return FieldsSubsetIterator(self) |
|
42
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|
585 |
|
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changeset
|
586 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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|
587 assert self.hasFields(*fieldnames) |
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changeset
|
588 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
|
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589 def __getitem__(self,i): |
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590 return FieldsSubsetDataSet(self.src[i],self.fieldnames) |
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591 |
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592 |
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593 class DataSetFields(LookupList): |
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594 """ |
| 167 | 595 Although a L{DataSet} iterates over examples (like rows of a matrix), an associated |
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596 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
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597 as a transpose of the associated dataset. |
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598 |
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599 To iterate over fields, one can do |
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600 * for fields in dataset.fields() |
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601 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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602 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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603 and each of these fields is iterable over the examples: |
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604 * for field_examples in dataset.fields(): |
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605 for example_value in field_examples: |
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606 ... |
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607 but when the dataset is a stream (unbounded length), it is not recommended to do |
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608 such things because the underlying dataset may refuse to access the different fields in |
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609 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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610 The result of fields() is a DataSetFields object, which iterates over fields, |
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611 and whose elements are iterable over examples. A DataSetFields object can |
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612 be turned back into a DataSet with its examples() method: |
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613 dataset2 = dataset1.fields().examples() |
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614 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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615 |
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616 DataSetFields can be concatenated vertically or horizontally. To be consistent with |
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617 the syntax used for DataSets, the | concatenates the fields and the & concatenates |
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618 the examples. |
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619 """ |
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620 def __init__(self,dataset,fieldnames): |
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621 original_dataset=dataset |
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622 if not fieldnames: |
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623 fieldnames=dataset.fieldNames() |
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624 elif not list(fieldnames)==list(dataset.fieldNames()): |
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625 #we must cast to list, othersize('x','y')!=['x','y'] |
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626 dataset = FieldsSubsetDataSet(dataset,fieldnames) |
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627 assert dataset.hasFields(*fieldnames) |
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628 self.dataset=dataset |
|
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629 |
|
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630 if isinstance(dataset,MinibatchDataSet): |
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631 LookupList.__init__(self,fieldnames,list(dataset._fields)) |
|
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632 elif isinstance(original_dataset,MinibatchDataSet): |
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633 LookupList.__init__(self,fieldnames, |
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634 [original_dataset._fields[field] |
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635 for field in fieldnames]) |
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636 else: |
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637 minibatch_iterator = dataset.minibatches(fieldnames, |
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638 minibatch_size=len(dataset), |
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639 n_batches=1) |
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640 minibatch=minibatch_iterator.next() |
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641 LookupList.__init__(self,fieldnames,minibatch) |
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642 |
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643 def examples(self): |
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644 return self.dataset |
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645 |
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646 def __or__(self,other): |
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647 """ |
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648 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
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649 of the list of examples of DataSetFields fields1 and fields2. |
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650 """ |
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651 return (self.examples() + other.examples()).fields() |
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652 |
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653 def __and__(self,other): |
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654 """ |
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655 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
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656 of the fields of DataSetFields fields1 and fields2. |
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657 """ |
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658 return (self.examples() | other.examples()).fields() |
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659 |
|
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660 |
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661 class MinibatchDataSet(DataSet): |
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662 """ |
| 167 | 663 Turn a L{LookupList} of same-length (iterable) fields into an example-iterable dataset. |
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664 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
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665 """ |
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666 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
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667 values_hstack=DataSet().valuesHStack): |
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668 """ |
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669 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
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670 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
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671 semantics as the DataSet methods of the same name (but without the self argument). |
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672 """ |
|
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673 |
|
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674 self._fields=fields_lookuplist |
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675 assert len(fields_lookuplist)>0 |
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676 self.length=len(fields_lookuplist[0]) |
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677 for field in fields_lookuplist[1:]: |
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678 if self.length != len(field) : |
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679 print 'self.length = ',self.length |
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680 print 'len(field) = ', len(field) |
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681 print 'self._fields.keys() = ', self._fields.keys() |
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682 print 'field=',field |
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683 assert self.length==len(field) |
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684 self.values_vstack=values_vstack |
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685 self.values_hstack=values_hstack |
|
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686 |
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687 def __len__(self): |
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688 return self.length |
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689 |
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690 def __getitem__(self,i): |
| 80 | 691 if type(i) in (slice,list): |
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692 return DataSetFields(MinibatchDataSet( |
| 80 | 693 Example(self._fields.keys(),[field[i] for field in self._fields])),self.fieldNames()) |
| 694 if type(i) is int: | |
| 85 | 695 return Example(self._fields.keys(),[field[i] for field in self._fields]) |
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696 if self.hasFields(i): |
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697 return self._fields[i] |
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698 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
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699 return self.__dict__[i] |
|
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700 |
|
29
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701 def fieldNames(self): |
|
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702 return self._fields.keys() |
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703 |
|
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bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
704 def hasFields(self,*fieldnames): |
|
36
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diff
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|
705 for fieldname in fieldnames: |
|
61
a8b70a9117ad
bugfix: in MinibatchDataSet renamed the class variable fields to _fields as parent class have a function called field.
Frederic Bastien <bastienf@iro.umontreal.ca>
parents:
60
diff
changeset
|
706 if fieldname not in self._fields.keys(): |
|
36
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diff
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|
707 return False |
|
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diff
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|
708 return True |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
709 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
710 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
223
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should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
711 #@TODO bug somewhere here, fieldnames doesnt seem to be well handled |
|
36
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diff
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|
712 class Iterator(object): |
|
223
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diff
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|
713 def __init__(self,ds,fieldnames): |
|
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218
diff
changeset
|
714 # tbm: added two next lines to handle fieldnames |
|
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diff
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|
715 if fieldnames is None: fieldnames = ds._fields.keys() |
|
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diff
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|
716 self.fieldnames = fieldnames |
|
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|
717 |
|
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diff
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|
718 self.ds=ds |
|
37
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Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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diff
changeset
|
719 self.next_example=offset |
|
36
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|
720 assert minibatch_size > 0 |
| 41 | 721 if offset+minibatch_size > ds.length: |
|
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diff
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|
722 raise NotImplementedError() |
|
20
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Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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parents:
19
diff
changeset
|
723 def __iter__(self): |
|
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
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|
724 return self |
|
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
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|
725 def next(self): |
|
61
a8b70a9117ad
bugfix: in MinibatchDataSet renamed the class variable fields to _fields as parent class have a function called field.
Frederic Bastien <bastienf@iro.umontreal.ca>
parents:
60
diff
changeset
|
726 upper = self.next_example+minibatch_size |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
727 assert upper<=self.ds.length |
|
223
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should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
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diff
changeset
|
728 #minibatch = Example(self.ds._fields.keys(), |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
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diff
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|
729 # [field[self.next_example:upper] |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
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diff
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|
730 # for field in self.ds._fields]) |
|
517364d48ae0
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parents:
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diff
changeset
|
731 # tbm: modif to use fieldnames |
|
517364d48ae0
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diff
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|
732 values = [] |
|
517364d48ae0
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218
diff
changeset
|
733 for f in self.fieldnames : |
|
517364d48ae0
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Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
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diff
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|
734 #print 'we have field',f,'in fieldnames' |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
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diff
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|
735 values.append( self.ds._fields[f][self.next_example:upper] ) |
|
517364d48ae0
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diff
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|
736 minibatch = Example(self.fieldnames,values) |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
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parents:
218
diff
changeset
|
737 #print minibatch |
|
36
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diff
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|
738 self.next_example+=minibatch_size |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
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72
diff
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|
739 return minibatch |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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diff
changeset
|
740 |
|
223
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diff
changeset
|
741 # tbm: added fieldnames to handle subset of fieldnames |
|
517364d48ae0
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218
diff
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|
742 return Iterator(self,fieldnames) |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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parents:
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diff
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|
743 |
|
36
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diff
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|
744 def valuesVStack(self,fieldname,fieldvalues): |
|
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diff
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|
745 return self.values_vstack(fieldname,fieldvalues) |
|
20
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19
diff
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|
746 |
|
36
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diff
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|
747 def valuesHStack(self,fieldnames,fieldvalues): |
|
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|
748 return self.values_hstack(fieldnames,fieldvalues) |
|
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diff
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|
749 |
|
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diff
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|
750 class HStackedDataSet(DataSet): |
|
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|
751 """ |
| 167 | 752 A L{DataSet} that wraps several datasets and shows a view that includes all their fields, |
|
36
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|
753 i.e. whose list of fields is the concatenation of their lists of fields. |
|
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|
754 |
|
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|
755 If a field name is found in more than one of the datasets, then either an error is |
|
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diff
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|
756 raised or the fields are renamed (either by prefixing the __name__ attribute |
|
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diff
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|
757 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list). |
|
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|
758 |
| 167 | 759 @todo: automatically detect a chain of stacked datasets due to A | B | C | D ... |
|
36
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|
760 """ |
| 41 | 761 def __init__(self,datasets,accept_nonunique_names=False,description=None,field_types=None): |
| 762 DataSet.__init__(self,description,field_types) | |
|
36
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|
763 self.datasets=datasets |
|
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|
764 self.accept_nonunique_names=accept_nonunique_names |
|
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|
765 self.fieldname2dataset={} |
|
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|
766 |
|
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|
767 def rename_field(fieldname,dataset,i): |
|
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|
768 if hasattr(dataset,"__name__"): |
|
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|
769 return dataset.__name__ + "." + fieldname |
|
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diff
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|
770 return fieldname+"."+str(i) |
|
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diff
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|
771 |
|
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|
772 # make sure all datasets have the same length and unique field names |
|
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|
773 self.length=None |
|
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|
774 names_to_change=[] |
|
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|
775 for i in xrange(len(datasets)): |
|
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|
776 dataset = datasets[i] |
|
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|
777 length=len(dataset) |
|
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|
778 if self.length: |
|
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|
779 assert self.length==length |
|
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diff
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|
780 else: |
|
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|
781 self.length=length |
|
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|
782 for fieldname in dataset.fieldNames(): |
|
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|
783 if fieldname in self.fieldname2dataset: # name conflict! |
|
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|
784 if accept_nonunique_names: |
|
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|
785 fieldname=rename_field(fieldname,dataset,i) |
|
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|
786 names2change.append((fieldname,i)) |
|
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|
787 else: |
|
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|
788 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
|
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|
789 self.fieldname2dataset[fieldname]=i |
|
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|
790 for fieldname,i in names_to_change: |
|
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|
791 del self.fieldname2dataset[fieldname] |
|
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|
792 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
|
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|
793 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
794 def hasFields(self,*fieldnames): |
|
36
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|
795 for fieldname in fieldnames: |
|
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|
796 if not fieldname in self.fieldname2dataset: |
|
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|
797 return False |
|
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|
798 return True |
|
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|
799 |
|
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|
800 def fieldNames(self): |
|
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|
801 return self.fieldname2dataset.keys() |
|
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|
802 |
| 41 | 803 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
36
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diff
changeset
|
804 |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
805 class HStackedIterator(object): |
|
36
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|
806 def __init__(self,hsds,iterators): |
|
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|
807 self.hsds=hsds |
|
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|
808 self.iterators=iterators |
|
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|
809 def __iter__(self): |
|
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|
810 return self |
|
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|
811 def next(self): |
|
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diff
changeset
|
812 # concatenate all the fields of the minibatches |
| 140 | 813 l=LookupList() |
| 814 for iter in self.iterators: | |
| 815 l.append_lookuplist(iter.next()) | |
| 816 return l | |
|
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|
817 |
| 125 | 818 assert self.hasFields(*fieldnames) |
|
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changeset
|
819 # find out which underlying datasets are necessary to service the required fields |
|
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|
820 # and construct corresponding minibatch iterators |
| 140 | 821 if fieldnames and fieldnames!=self.fieldNames(): |
|
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|
822 datasets=set([]) |
|
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|
823 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
|
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|
824 for fieldname in fieldnames: |
| 136 | 825 dataset=self.datasets[self.fieldname2dataset[fieldname]] |
|
36
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
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29
diff
changeset
|
826 datasets.add(dataset) |
|
438440ba0627
Rewriting dataset.py completely
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parents:
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diff
changeset
|
827 fields_in_dataset[dataset].append(fieldname) |
|
438440ba0627
Rewriting dataset.py completely
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parents:
29
diff
changeset
|
828 datasets=list(datasets) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
829 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
830 for dataset in datasets] |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
831 else: |
|
438440ba0627
Rewriting dataset.py completely
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parents:
29
diff
changeset
|
832 datasets=self.datasets |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
833 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
|
834 return HStackedIterator(self,iterators) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
835 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
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diff
changeset
|
836 |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
837 def valuesVStack(self,fieldname,fieldvalues): |
|
438440ba0627
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bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
838 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
|
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|
839 |
|
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diff
changeset
|
840 def valuesHStack(self,fieldnames,fieldvalues): |
|
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diff
changeset
|
841 """ |
|
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parents:
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diff
changeset
|
842 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
|
438440ba0627
Rewriting dataset.py completely
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29
diff
changeset
|
843 to do the work, hoping that it can cope with the other values (i.e. won't care |
|
438440ba0627
Rewriting dataset.py completely
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parents:
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diff
changeset
|
844 about the incompatible fieldnames). Hence this heuristic will always work if |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
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diff
changeset
|
845 all the fieldnames are of the same sub-dataset. |
|
438440ba0627
Rewriting dataset.py completely
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diff
changeset
|
846 """ |
|
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parents:
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diff
changeset
|
847 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
|
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Rewriting dataset.py completely
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diff
changeset
|
848 |
|
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Rewriting dataset.py completely
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diff
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|
849 class VStackedDataSet(DataSet): |
|
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|
850 """ |
| 167 | 851 A L{DataSet} that wraps several datasets and shows a view that includes all their examples, |
|
36
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Rewriting dataset.py completely
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diff
changeset
|
852 in the order provided. This clearly assumes that they all have the same field names |
|
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Rewriting dataset.py completely
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parents:
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diff
changeset
|
853 and all (except possibly the last one) are of finite length. |
|
438440ba0627
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diff
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|
854 |
| 167 | 855 @todo: automatically detect a chain of stacked datasets due to A + B + C + D ... |
|
36
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|
856 """ |
|
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Rewriting dataset.py completely
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diff
changeset
|
857 def __init__(self,datasets): |
|
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diff
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|
858 self.datasets=datasets |
|
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diff
changeset
|
859 self.length=0 |
|
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parents:
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diff
changeset
|
860 self.index2dataset={} |
|
37
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
861 assert len(datasets)>0 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
862 fieldnames = datasets[-1].fieldNames() |
|
38
d637ad8f7352
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parents:
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diff
changeset
|
863 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
|
864 # We use this map from row index to dataset index for constant-time random access of examples, |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
865 # to avoid having to search for the appropriate dataset each time and slice is asked for. |
|
36
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parents:
29
diff
changeset
|
866 for dataset,k in enumerate(datasets[0:-1]): |
|
48
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
867 assert dataset.is_unbounded() # All VStacked datasets (except possibly the last) must be bounded (have a length). |
|
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
868 L=len(dataset) |
|
36
438440ba0627
Rewriting dataset.py completely
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diff
changeset
|
869 for i in xrange(L): |
|
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Rewriting dataset.py completely
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parents:
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diff
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|
870 self.index2dataset[self.length+i]=k |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
871 self.datasets_start_row.append(self.length) |
|
36
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diff
changeset
|
872 self.length+=L |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
873 assert dataset.fieldNames()==fieldnames |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
874 self.datasets_start_row.append(self.length) |
|
36
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diff
changeset
|
875 self.length+=len(datasets[-1]) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
876 # If length is very large, we should use a more memory-efficient mechanism |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
877 # 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
|
878 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
|
879 # 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
|
880 # TODO |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
881 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
|
882 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
883 def __len__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
884 return self.length |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
885 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
886 def fieldNames(self): |
|
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
887 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
|
888 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
889 def hasFields(self,*fieldnames): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
890 return self.datasets[0].hasFields(*fieldnames) |
|
73c4212ba5b3
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parents:
36
diff
changeset
|
891 |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
892 def locate_row(self,row): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
893 """Return (dataset_index, row_within_dataset) for global row number""" |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
894 dataset_index = self.index2dataset[row] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
895 row_within_dataset = self.datasets_start_row[dataset_index] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
896 return dataset_index, row_within_dataset |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
897 |
| 41 | 898 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
|
899 |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
900 class VStackedIterator(object): |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
901 def __init__(self,vsds): |
|
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
902 self.vsds=vsds |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
903 self.next_row=offset |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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diff
changeset
|
904 self.next_dataset_index,self.next_dataset_row=self.vsds.locate_row(offset) |
|
37
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bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
905 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
|
906 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
|
907 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
908 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
|
909 L=len(dataset) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
910 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
|
911 if batches_left is not None: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
912 ds_nbatches = max(batches_left,ds_nbatches) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
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diff
changeset
|
913 if minibatch_size>L: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
914 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
|
915 n_left_in_mb=minibatch_size-L |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
916 ds_nbatches=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
917 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
918 n_left_in_mb=0 |
|
37
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
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diff
changeset
|
919 return dataset.minibatches(fieldnames,minibatch_size,ds_nbatches,starting_offset), \ |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
920 L-(starting_offset+ds_nbatches*minibatch_size), n_left_in_mb |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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36
diff
changeset
|
921 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
922 def move_to_next_dataset(self): |
|
38
d637ad8f7352
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parents:
37
diff
changeset
|
923 if self.n_left_at_the_end_of_ds>0: |
|
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Finished first untested version of VStackedDataset
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37
diff
changeset
|
924 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
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parents:
37
diff
changeset
|
925 self.next_iterator(vsds.datasets[self.next_dataset_index], |
|
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Finished first untested version of VStackedDataset
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diff
changeset
|
926 self.n_left_at_the_end_of_ds,1) |
|
d637ad8f7352
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37
diff
changeset
|
927 else: |
|
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Finished first untested version of VStackedDataset
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diff
changeset
|
928 self.next_dataset_index +=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
929 if self.next_dataset_index==len(self.vsds.datasets): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
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diff
changeset
|
930 self.next_dataset_index = 0 |
|
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Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
931 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
932 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
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36
diff
changeset
|
933 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
934 def __iter__(self): |
|
73c4212ba5b3
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diff
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|
935 return self |
|
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diff
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|
936 |
|
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diff
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|
937 def next(self): |
|
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diff
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|
938 dataset=self.vsds.datasets[self.next_dataset_index] |
|
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diff
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|
939 mb = self.next_iterator.next() |
|
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diff
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|
940 if self.n_left_in_mb: |
|
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diff
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|
941 extra_mb = [] |
|
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diff
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|
942 while self.n_left_in_mb>0: |
|
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diff
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|
943 self.move_to_next_dataset() |
|
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diff
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|
944 extra_mb.append(self.next_iterator.next()) |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
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diff
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|
945 mb = Example(fieldnames, |
|
40
88fd1cce08b9
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diff
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|
946 [dataset.valuesVStack(name, |
|
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diff
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|
947 [mb[name]]+[b[name] for b in extra_mb]) |
|
88fd1cce08b9
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diff
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|
948 for name in fieldnames]) |
|
88fd1cce08b9
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diff
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|
949 |
|
37
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diff
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|
950 self.next_row+=minibatch_size |
|
38
d637ad8f7352
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diff
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|
951 self.next_dataset_row+=minibatch_size |
|
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diff
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|
952 if self.next_row+minibatch_size>len(dataset): |
|
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diff
changeset
|
953 self.move_to_next_dataset() |
|
44
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diff
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|
954 return examples |
|
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diff
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|
955 return VStackedIterator(self) |
|
37
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diff
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|
956 |
| 41 | 957 class ArrayFieldsDataSet(DataSet): |
| 958 """ | |
| 959 Virtual super-class of datasets whose field values are numpy array, | |
| 960 thus defining valuesHStack and valuesVStack for sub-classes. | |
| 961 """ | |
|
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|
962 def __init__(self,description=None,field_types=None): |
|
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|
963 DataSet.__init__(self,description,field_types) |
|
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|
964 def valuesHStack(self,fieldnames,fieldvalues): |
| 41 | 965 """Concatenate field values horizontally, e.g. two vectors |
| 966 become a longer vector, two matrices become a wider matrix, etc.""" | |
| 967 return numpy.hstack(fieldvalues) | |
|
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|
968 def valuesVStack(self,fieldname,values): |
| 41 | 969 """Concatenate field values vertically, e.g. two vectors |
| 970 become a two-row matrix, two matrices become a longer matrix, etc.""" | |
| 971 return numpy.vstack(values) | |
| 972 | |
| 973 class ArrayDataSet(ArrayFieldsDataSet): | |
| 974 """ | |
| 975 An ArrayDataSet stores the fields as groups of columns in a numpy tensor, | |
| 976 whose first axis iterates over examples, second axis determines fields. | |
| 977 If the underlying array is N-dimensional (has N axes), then the field | |
| 978 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | |
| 979 """ | |
| 980 | |
|
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diff
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|
981 def __init__(self, data_array, fields_columns, **kwargs): |
|
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|
982 """ |
|
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|
983 Construct an ArrayDataSet from the underlying numpy array (data) and |
|
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|
984 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified |
|
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diff
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|
985 using the standard arguments for indexing/slicing: integer for a column index, |
|
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diff
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|
986 slice for an interval of columns (with possible stride), or iterable of column indices. |
|
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|
987 """ |
|
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|
988 ArrayFieldsDataSet.__init__(self, **kwargs) |
| 41 | 989 self.data=data_array |
|
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diff
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|
990 self.fields_columns=fields_columns |
| 41 | 991 |
| 992 # check consistency and complete slices definitions | |
|
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diff
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|
993 for fieldname, fieldcolumns in self.fields_columns.items(): |
| 41 | 994 if type(fieldcolumns) is int: |
| 995 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1] | |
|
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diff
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|
996 if 1: |
|
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|
997 #I changed this because it didn't make sense to me, |
|
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|
998 # and it made it more difficult to write my learner. |
|
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|
999 # If it breaks stuff, let's talk about it. |
|
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|
1000 # - James 22/05/2008 |
|
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|
1001 self.fields_columns[fieldname]=[fieldcolumns] |
|
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|
1002 else: |
|
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|
1003 self.fields_columns[fieldname]=fieldcolumns |
| 41 | 1004 elif type(fieldcolumns) is slice: |
| 1005 start,step=None,None | |
| 1006 if not fieldcolumns.start: | |
| 1007 start=0 | |
| 1008 if not fieldcolumns.step: | |
| 1009 step=1 | |
| 1010 if start or step: | |
|
42
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Testing basic functionality and removing obvious bugs
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diff
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|
1011 self.fields_columns[fieldname]=slice(start,fieldcolumns.stop,step) |
| 41 | 1012 elif hasattr(fieldcolumns,"__iter__"): # something like a list |
| 1013 for i in fieldcolumns: | |
| 1014 assert i>=0 and i<data_array.shape[1] | |
| 1015 | |
|
42
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diff
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|
1016 def fieldNames(self): |
|
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diff
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|
1017 return self.fields_columns.keys() |
| 41 | 1018 |
|
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diff
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|
1019 def __len__(self): |
|
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diff
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|
1020 return len(self.data) |
| 41 | 1021 |
| 80 | 1022 def __getitem__(self,key): |
|
55
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diff
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|
1023 """More efficient implementation than the default __getitem__""" |
|
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diff
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|
1024 fieldnames=self.fields_columns.keys() |
|
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|
1025 values=self.fields_columns.values() |
| 80 | 1026 if type(key) is int: |
|
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|
1027 return Example(fieldnames, |
|
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|
1028 [self.data[key,col] for col in values]) |
| 80 | 1029 if type(key) is slice: |
|
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diff
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|
1030 return MinibatchDataSet(Example(fieldnames, |
|
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|
1031 [self.data[key,col] for col in values])) |
| 80 | 1032 if type(key) is list: |
| 1033 for i in range(len(key)): | |
| 1034 if self.hasFields(key[i]): | |
| 1035 key[i]=self.fields_columns[key[i]] | |
| 1036 return MinibatchDataSet(Example(fieldnames, | |
|
88
6749d18e11c8
bugfix as numpy numpy don't support self.data[[i1,...],[i2,...]] when their is more then two i1 and i2
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85
diff
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|
1037 #we must separate differently for list as numpy |
| 128 | 1038 # doesn't support self.data[[i1,...],[i2,...]] |
|
88
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diff
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|
1039 # when their is more then two i1 and i2 |
|
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|
1040 [self.data[key,:][:,col] |
|
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|
1041 if isinstance(col,list) else |
|
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|
1042 self.data[key,col] for col in values]), |
|
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|
1043 |
| 128 | 1044 |
|
73
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|
1045 self.valuesVStack,self.valuesHStack) |
| 80 | 1046 |
|
55
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|
1047 # else check for a fieldname |
| 80 | 1048 if self.hasFields(key): |
|
105
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diff
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|
1049 return self.data[:,self.fields_columns[key]] |
|
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|
1050 # else we are trying to access a property of the dataset |
| 80 | 1051 assert key in self.__dict__ # else it means we are trying to access a non-existing property |
| 1052 return self.__dict__[key] | |
|
55
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|
1053 |
|
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|
1054 def __iter__(self): |
|
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|
1055 class ArrayDataSetIteratorIter(object): |
|
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|
1056 def __init__(self,dataset,fieldnames): |
|
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|
1057 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
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|
1058 # store the resulting minibatch in a lookup-list of values |
|
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|
1059 self.minibatch = LookupList(fieldnames,[0]*len(fieldnames)) |
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|
1060 self.dataset=dataset |
|
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|
1061 self.current=0 |
|
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|
1062 self.columns = [self.dataset.fields_columns[f] |
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|
1063 for f in self.minibatch._names] |
|
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|
1064 self.l = self.dataset.data.shape[0] |
|
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|
1065 def __iter__(self): |
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|
1066 return self |
|
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|
1067 def next(self): |
|
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|
1068 #@todo: we suppose that we need to stop only when minibatch_size == 1. |
|
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|
1069 # Otherwise, MinibatchWrapAroundIterator do it. |
|
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|
1070 if self.current>=self.l: |
|
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|
1071 raise StopIteration |
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|
1072 sub_data = self.dataset.data[self.current] |
|
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|
1073 self.minibatch._values = [sub_data[c] for c in self.columns] |
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|
1074 |
|
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|
1075 self.current+=1 |
|
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|
1076 return self.minibatch |
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|
1077 |
|
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|
1078 return ArrayDataSetIteratorIter(self,self.fieldNames()) |
|
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|
1079 |
|
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|
1080 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
44
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|
1081 class ArrayDataSetIterator(object): |
|
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|
1082 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
|
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|
1083 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
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|
1084 # store the resulting minibatch in a lookup-list of values |
|
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1085 self.minibatch = LookupList(fieldnames,[0]*len(fieldnames)) |
|
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|
1086 self.dataset=dataset |
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|
1087 self.minibatch_size=minibatch_size |
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|
1088 assert offset>=0 and offset<len(dataset.data) |
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|
1089 assert offset+minibatch_size<=len(dataset.data) |
|
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|
1090 self.current=offset |
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|
1091 def __iter__(self): |
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|
1092 return self |
|
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|
1093 def next(self): |
|
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1094 #@todo: we suppose that MinibatchWrapAroundIterator stop the iterator |
|
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|
1095 sub_data = self.dataset.data[self.current:self.current+self.minibatch_size] |
|
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|
1096 self.minibatch._values = [sub_data[:,self.dataset.fields_columns[f]] for f in self.minibatch._names] |
|
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1097 self.current+=self.minibatch_size |
|
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|
1098 return self.minibatch |
|
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|
1099 |
|
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|
1100 return ArrayDataSetIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
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1101 |
|
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1102 |
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|
1103 class CachedDataSet(DataSet): |
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|
1104 """ |
| 167 | 1105 Wrap a L{DataSet} whose values are computationally expensive to obtain |
|
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1106 (e.g. because they involve some computation, or disk access), |
|
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|
1107 so that repeated accesses to the same example are done cheaply, |
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1108 by caching every example value that has been accessed at least once. |
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1109 |
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|
1110 Optionally, for finite-length dataset, all the values can be computed |
|
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1111 (and cached) upon construction of the CachedDataSet, rather at the |
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|
1112 first access. |
|
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1113 |
| 167 | 1114 @todo: when cache_all_upon_construction create mini-batches that are as |
|
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1115 large as possible but not so large as to fill up memory. |
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1116 |
| 167 | 1117 @todo: add disk-buffering capability, so that when the cache becomes too |
|
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1118 big for memory, we cache things on disk, trying to keep in memory only |
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1119 the record most likely to be accessed next. |
|
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1120 """ |
|
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1121 def __init__(self,source_dataset,cache_all_upon_construction=False): |
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1122 self.source_dataset=source_dataset |
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1123 self.cache_all_upon_construction=cache_all_upon_construction |
|
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1124 self.cached_examples = [] |
|
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|
1125 if cache_all_upon_construction: |
|
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1126 # this potentially brings all the source examples |
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1127 # into memory at once, which may be too much |
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1128 # the work could possibly be done by minibatches |
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1129 # that are as large as possible but no more than what memory allows. |
|
146
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|
1130 fields_values = source_dataset.minibatches(minibatch_size=len(source_dataset)).__iter__().next() |
|
152
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|
1131 assert all([len(self)==len(field_values) for field_values in fields_values]) |
|
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|
1132 for example in fields_values.examples(): |
|
171
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diff
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|
1133 self.cached_examples.append(copy.copy(example)) |
|
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|
1134 |
|
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|
1135 self.fieldNames = source_dataset.fieldNames |
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1136 self.hasFields = source_dataset.hasFields |
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|
1137 self.valuesHStack = source_dataset.valuesHStack |
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|
1138 self.valuesVStack = source_dataset.valuesVStack |
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|
1139 |
|
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|
1140 def __len__(self): |
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|
1141 return len(self.source_dataset) |
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|
1142 |
|
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|
1143 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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|
1144 class CacheIterator(object): |
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|
1145 def __init__(self,dataset): |
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|
1146 self.dataset=dataset |
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|
1147 self.current=offset |
|
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|
1148 self.all_fields = self.dataset.fieldNames()==fieldnames |
|
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|
1149 def __iter__(self): return self |
|
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|
1150 def next(self): |
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|
1151 upper = self.current+minibatch_size |
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|
1152 cache_len = len(self.dataset.cached_examples) |
|
135
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|
1153 if upper>cache_len: # whole minibatch is not already in cache |
|
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|
1154 # cache everything from current length to upper |
|
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|
1155 for example in self.dataset.source_dataset[cache_len:upper]: |
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|
1156 self.dataset.cached_examples.append(example) |
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|
1157 all_fields_minibatch = Example(self.dataset.fieldNames(), |
|
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|
1158 zip(*self.dataset.cached_examples[self.current:self.current+minibatch_size])) |
|
163
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|
1159 self.current+=minibatch_size |
|
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|
1160 if self.all_fields: |
|
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|
1161 return all_fields_minibatch |
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|
1162 return Example(fieldnames,[all_fields_minibatch[name] for name in fieldnames]) |
|
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|
1163 return CacheIterator(self) |
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|
1164 |
| 153 | 1165 def __getitem__(self,i): |
| 1166 if type(i)==int and len(self.cached_examples)>i: | |
| 1167 return self.cached_examples[i] | |
| 1168 else: | |
|
251
7e6edee187e3
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diff
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|
1169 return self.source_dataset[i] |
|
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|
1170 |
|
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|
1171 def __iter__(self): |
|
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|
1172 class CacheIteratorIter(object): |
|
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|
1173 def __init__(self,dataset): |
|
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|
1174 self.dataset=dataset |
|
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|
1175 self.l = len(dataset) |
|
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|
1176 self.current = 0 |
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|
1177 self.fieldnames = self.dataset.fieldNames() |
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1178 self.example = LookupList(self.fieldnames,[0]*len(self.fieldnames)) |
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|
1179 def __iter__(self): return self |
|
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|
1180 def next(self): |
|
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|
1181 if self.current>=self.l: |
|
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|
1182 raise StopIteration |
|
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|
1183 cache_len = len(self.dataset.cached_examples) |
|
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|
1184 if self.current>=cache_len: # whole minibatch is not already in cache |
|
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|
1185 # cache everything from current length to upper |
|
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|
1186 self.dataset.cached_examples.append( |
|
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|
1187 self.dataset.source_dataset[self.current]) |
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|
1188 self.example._values = self.dataset.cached_examples[self.current] |
|
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|
1189 self.current+=1 |
|
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|
1190 return self.example |
|
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|
1191 |
|
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|
1192 return CacheIteratorIter(self) |
|
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|
1193 |
|
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diff
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|
1194 class ApplyFunctionDataSet(DataSet): |
|
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1195 """ |
| 167 | 1196 A L{DataSet} that contains as fields the results of applying a |
| 1197 given function example-wise or minibatch-wise to all the fields of | |
| 1198 an input dataset. The output of the function should be an iterable | |
| 1199 (e.g. a list or a LookupList) over the resulting values. | |
|
197
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1200 |
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1201 The function take as input the fields of the dataset, not the examples. |
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1202 |
| 167 | 1203 In minibatch mode, the function is expected to work on minibatches |
| 1204 (takes a minibatch in input and returns a minibatch in output). More | |
| 1205 precisely, it means that each element of the input or output list | |
| 1206 should be iterable and indexable over the individual example values | |
| 1207 (typically these elements will be numpy arrays). All of the elements | |
| 1208 in the input and output lists should have the same length, which is | |
| 1209 the length of the minibatch. | |
|
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1210 |
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1211 The function is applied each time an example or a minibatch is accessed. |
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1212 To avoid re-doing computation, wrap this dataset inside a CachedDataSet. |
|
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1213 |
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1214 If the values_{h,v}stack functions are not provided, then |
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1215 the input_dataset.values{H,V}Stack functions are used by default. |
|
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1216 """ |
|
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1217 def __init__(self,input_dataset,function,output_names,minibatch_mode=True, |
|
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1218 values_hstack=None,values_vstack=None, |
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1219 description=None,fieldtypes=None): |
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1220 """ |
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1221 Constructor takes an input dataset that has as many fields as the function |
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1222 expects as inputs. The resulting dataset has as many fields as the function |
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1223 produces as outputs, and that should correspond to the number of output names |
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1224 (provided in a list). |
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1225 |
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1226 Note that the expected semantics of the function differs in minibatch mode |
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1227 (it takes minibatches of inputs and produces minibatches of outputs, as |
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1228 documented in the class comment). |
|
211
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1229 |
|
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1230 TBM: are filedtypes the old field types (from input_dataset) or the new ones |
|
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1231 (for the new dataset created)? |
|
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1232 """ |
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1233 self.input_dataset=input_dataset |
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1234 self.function=function |
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1235 self.output_names=output_names |
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1236 self.minibatch_mode=minibatch_mode |
|
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1237 DataSet.__init__(self,description,fieldtypes) |
|
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1238 self.valuesHStack = values_hstack if values_hstack else input_dataset.valuesHStack |
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1239 self.valuesVStack = values_vstack if values_vstack else input_dataset.valuesVStack |
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1240 |
|
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1241 def __len__(self): |
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1242 return len(self.input_dataset) |
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1243 |
|
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1244 def fieldNames(self): |
|
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1245 return self.output_names |
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1246 |
|
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1247 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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1248 class ApplyFunctionIterator(object): |
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1249 def __init__(self,output_dataset): |
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1250 self.input_dataset=output_dataset.input_dataset |
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1251 self.output_dataset=output_dataset |
|
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1252 self.input_iterator=self.input_dataset.minibatches(minibatch_size=minibatch_size, |
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1253 n_batches=n_batches,offset=offset).__iter__() |
|
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1254 |
|
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1255 def __iter__(self): return self |
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1256 |
|
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1257 def next(self): |
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1258 function_inputs = self.input_iterator.next() |
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1259 all_output_names = self.output_dataset.output_names |
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1260 if self.output_dataset.minibatch_mode: |
|
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1261 function_outputs = self.output_dataset.function(*function_inputs) |
|
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1262 else: |
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1263 input_examples = zip(*function_inputs) |
| 201 | 1264 output_examples = [self.output_dataset.function(*input_example) |
|
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1265 for input_example in input_examples] |
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1266 function_outputs = [self.output_dataset.valuesVStack(name,values) |
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1267 for name,values in zip(all_output_names, |
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1268 zip(*output_examples))] |
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1269 all_outputs = Example(all_output_names,function_outputs) |
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1270 if fieldnames==all_output_names: |
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1271 return all_outputs |
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1272 return Example(fieldnames,[all_outputs[name] for name in fieldnames]) |
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1273 |
|
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1274 |
|
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1275 return ApplyFunctionIterator(self) |
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1276 |
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1277 def __iter__(self): # only implemented for increased efficiency |
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1278 class ApplyFunctionSingleExampleIterator(object): |
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1279 def __init__(self,output_dataset): |
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1280 self.current=0 |
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1281 self.output_dataset=output_dataset |
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1282 self.input_iterator=output_dataset.input_dataset.__iter__() |
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1283 def __iter__(self): return self |
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1284 def next(self): |
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1285 if self.output_dataset.minibatch_mode: |
| 200 | 1286 function_inputs = [[input] for input in self.input_iterator.next()] |
| 199 | 1287 outputs = self.output_dataset.function(*function_inputs) |
| 1288 assert all([hasattr(output,'__iter__') for output in outputs]) | |
| 1289 function_outputs = [output[0] for output in outputs] | |
|
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1290 else: |
| 200 | 1291 function_inputs = self.input_iterator.next() |
| 199 | 1292 function_outputs = self.output_dataset.function(*function_inputs) |
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1293 return Example(self.output_dataset.output_names,function_outputs) |
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1294 return ApplyFunctionSingleExampleIterator(self) |
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1295 |
|
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|
1296 |
|
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1297 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
|
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1298 """ |
| 167 | 1299 Wraps an arbitrary L{DataSet} into one for supervised learning tasks |
| 1300 by forcing the user to define a set of fields as the 'input' field | |
| 1301 and a set of fields as the 'target' field. Optionally, a single | |
| 1302 weight_field can also be defined. | |
|
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1303 """ |
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1304 args = ((input_fields,'input'),(output_fields,'target')) |
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1305 if weight_field: args+=(([weight_field],'weight')) |
|
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1306 return src_dataset.merge_fields(*args) |
|
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1307 |
|
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1308 |
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1309 |
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1310 |