Mercurial > pylearn
annotate dataset.py @ 231:38beb81f4e8b
Automated merge with ssh://projects@lgcm.iro.umontreal.ca/hg/pylearn
| author | Frederic Bastien <bastienf@iro.umontreal.ca> |
|---|---|
| date | Tue, 27 May 2008 13:46:03 -0400 |
| parents | 17c5d080964b 6f55e301c687 |
| children | a70f2c973ea5 ddb88a8e9fd2 |
| 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 (otherwise its __len__ method |
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51 should return sys.maxint). |
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52 |
| 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 recommanded 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 recommanded 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[fieldname] an iterable over the values of the field fieldname across |
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113 the dataset (the iterable is obtained by default by calling valuesVStack |
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114 over the values for individual examples). |
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115 |
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116 - dataset.<property> returns the value of a property associated with |
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117 the name <property>. The following properties should be supported: |
| 41 | 118 - 'description': a textual description or name for the dataset |
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119 - 'fieldtypes': a list of types (one per field) |
| 78 | 120 A DataSet may have other attributes that it makes visible to other objects. These are |
| 121 used to store information that is not example-wise but global to the dataset. | |
| 122 The list of names of these attributes is given by the attribute_names() method. | |
| 41 | 123 |
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124 Datasets can be concatenated either vertically (increasing the length) or |
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125 horizontally (augmenting the set of fields), if they are compatible, using |
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126 the following operations (with the same basic semantics as numpy.hstack |
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127 and numpy.vstack): |
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128 |
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129 - dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3]) |
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130 |
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131 creates a new dataset whose list of fields is the concatenation of the list of |
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132 fields of the argument datasets. This only works if they all have the same length. |
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133 |
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134 - dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3]) |
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135 |
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136 creates a new dataset that concatenates the examples from the argument datasets |
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137 (and whose length is the sum of the length of the argument datasets). This only |
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138 works if they all have the same fields. |
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139 |
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140 According to the same logic, and viewing a DataSetFields object associated to |
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141 a DataSet as a kind of transpose of it, fields1 & fields2 concatenates fields of |
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142 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their |
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143 examples. |
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144 |
| 41 | 145 A dataset can hold arbitrary key-value pairs that may be used to access meta-data |
| 146 or other properties of the dataset or associated with the dataset or the result | |
| 147 of a computation stored in a dataset. These can be accessed through the [key] syntax | |
| 148 when key is a string (or more specifically, neither an integer, a slice, nor a list). | |
| 78 | 149 |
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150 A DataSet sub-class should always redefine the following methods: |
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151 - __len__ if it is not a stream |
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152 - fieldNames |
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153 - minibatches_nowrap (called by DataSet.minibatches()) |
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154 - valuesHStack |
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155 - valuesVStack |
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156 For efficiency of implementation, a sub-class might also want to redefine |
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157 - hasFields |
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158 - __getitem__ may not be feasible with some streams |
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159 - __iter__ |
| 78 | 160 A sub-class should also append attributes to self._attribute_names |
| 161 (the default value returned by attributeNames()). | |
| 162 By convention, attributes not in attributeNames() should have a name | |
| 163 starting with an underscore. | |
| 164 @todo enforce/test that convention! | |
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165 """ |
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166 |
| 83 | 167 numpy_vstack = lambda fieldname,values: numpy.vstack(values) |
| 168 numpy_hstack = lambda fieldnames,values: numpy.hstack(values) | |
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169 |
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170 def __init__(self,description=None,fieldtypes=None): |
| 41 | 171 if description is None: |
| 172 # by default return "<DataSetType>(<SuperClass1>,<SuperClass2>,...)" | |
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173 description = type(self).__name__ + " ( " + join([x.__name__ for x in type(self).__bases__]) + " )" |
| 41 | 174 self.description=description |
| 60 | 175 self.fieldtypes=fieldtypes |
| 78 | 176 self._attribute_names = ["description"] |
| 177 if fieldtypes: | |
| 178 self._attribute_names.append("fieldtypes") | |
| 179 | |
| 180 def attributeNames(self): return self._attribute_names | |
| 181 | |
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182 class MinibatchToSingleExampleIterator(object): |
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183 """ |
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184 Converts the result of minibatch iterator with minibatch_size==1 into |
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185 single-example values in the result. Therefore the result of |
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186 iterating on the dataset itself gives a sequence of single examples |
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187 (whereas the result of iterating over minibatches gives in each |
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188 Example field an iterable object over the individual examples in |
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189 the minibatch). |
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190 """ |
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191 def __init__(self, minibatch_iterator): |
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192 self.minibatch_iterator = minibatch_iterator |
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193 self.minibatch = None |
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194 def __iter__(self): #makes for loop work |
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195 return self |
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196 def next(self): |
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197 size1_minibatch = self.minibatch_iterator.next() |
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198 if not self.minibatch: |
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199 self.minibatch = Example(size1_minibatch.keys(),[value[0] for value in size1_minibatch.values()]) |
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200 else: |
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201 self.minibatch._values = [value[0] for value in size1_minibatch.values()] |
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202 return self.minibatch |
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203 |
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204 def next_index(self): |
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205 return self.minibatch_iterator.next_index() |
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206 |
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207 def __iter__(self): |
| 16 | 208 """Supports the syntax "for i in dataset: ..." |
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209 |
| 16 | 210 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 211 all the fields of DataSet self. Every field of "i" will give access to | |
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212 a field of a single example. Fields should be accessible via |
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213 i["fielname"] or i[3] (in the order defined by the elements of the |
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214 Example returned by this iterator), but the derived class is free |
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215 to accept any type of identifier, and add extra functionality to the iterator. |
| 16 | 216 |
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217 The default implementation calls the minibatches iterator and extracts the first example of each field. |
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218 """ |
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219 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) |
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220 |
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221 def __contains__(self, fieldname): |
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222 return (fieldname in self.fieldNames()) \ |
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223 or (fieldname in self.attributeNames()) |
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224 |
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225 class MinibatchWrapAroundIterator(object): |
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226 """ |
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227 An iterator for minibatches that handles the case where we need to wrap around the |
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228 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
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229 a dataset that provides a minibatch iterator that does not need to handle that problem. |
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230 This class is a utility for dataset subclass writers, so that they do not have to handle |
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231 this issue multiple times, nor check that fieldnames are valid, nor handle the |
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232 empty fieldnames (meaning 'use all the fields'). |
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233 """ |
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234 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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235 self.dataset=dataset |
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236 self.fieldnames=fieldnames |
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237 self.minibatch_size=minibatch_size |
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238 self.n_batches=n_batches |
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239 self.n_batches_done=0 |
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240 self.next_row=offset |
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241 self.offset=offset |
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242 self.L=len(dataset) |
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243 assert offset+minibatch_size<=self.L |
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244 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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245 if n_batches is not None: |
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246 ds_nbatches = min(n_batches,ds_nbatches) |
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247 if fieldnames: |
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248 assert dataset.hasFields(*fieldnames) |
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249 else: |
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250 self.fieldnames=dataset.fieldNames() |
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251 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, |
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252 ds_nbatches,self.next_row) |
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253 |
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254 def __iter__(self): |
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255 return self |
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256 |
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257 def next_index(self): |
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258 return self.next_row |
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259 |
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260 def next(self): |
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261 if self.n_batches and self.n_batches_done==self.n_batches: |
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262 raise StopIteration |
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263 elif not self.n_batches and self.next_row ==self.L: |
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264 raise StopIteration |
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265 upper = self.next_row+self.minibatch_size |
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266 if upper <=self.L: |
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267 minibatch = self.iterator.next() |
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268 else: |
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269 if not self.n_batches: |
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270 upper=min(upper, self.L) |
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271 # if their is not a fixed number of batch, we continue to the end of the dataset. |
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272 # this can create a minibatch that is smaller then the minibatch_size |
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273 assert (self.L-self.next_row)<=self.minibatch_size |
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274 minibatch = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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275 else: |
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276 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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277 # first get the beginning of our minibatch (top of dataset) |
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278 first_part = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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279 second_part = self.dataset.minibatches_nowrap(self.fieldnames,upper-self.L,1,0).next() |
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280 minibatch = Example(self.fieldnames, |
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281 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
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282 for name in self.fieldnames]) |
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283 self.next_row=upper |
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284 self.n_batches_done+=1 |
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285 if upper >= self.L and self.n_batches: |
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286 self.next_row -= self.L |
|
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287 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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288 if self.n_batches is not None: |
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289 ds_nbatches = min(self.n_batches,ds_nbatches) |
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290 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, |
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291 ds_nbatches,self.next_row) |
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292 return DataSetFields(MinibatchDataSet(minibatch,self.dataset.valuesVStack, |
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293 self.dataset.valuesHStack), |
|
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294 minibatch.keys()) |
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295 |
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296 |
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297 minibatches_fieldnames = None |
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298 minibatches_minibatch_size = 1 |
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299 minibatches_n_batches = None |
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300 def minibatches(self, |
|
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301 fieldnames = minibatches_fieldnames, |
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302 minibatch_size = minibatches_minibatch_size, |
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303 n_batches = minibatches_n_batches, |
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304 offset = 0): |
|
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305 """ |
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306 Return an iterator that supports three forms of syntax: |
|
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307 |
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308 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 309 |
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310 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 311 |
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312 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 313 |
|
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314 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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315 tuple, or Example instance. In both cases, i[k] is a list-like container |
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316 of a batch of current examples. In the second case, i[0] is |
|
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317 list-like container of the f1 field of a batch current examples, i[1] is |
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318 a list-like container of the f2 field, etc. |
|
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319 |
|
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320 Using the first syntax, all the fields will be returned in "i". |
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321 Using the third syntax, i1, i2, i3 will be list-like containers of the |
|
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322 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
|
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323 |
|
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324 The minibatches iterator is expected to return upon each call to next() |
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325 a DataSetFields object, which is a LookupList (indexed by the field names) whose |
| 80 | 326 elements are iterable and indexable over the minibatch examples, and which keeps a pointer to |
|
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327 a sub-dataset that can be used to iterate over the individual examples |
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328 in the minibatch. Hence a minibatch can be converted back to a regular |
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329 dataset or its fields can be looked at individually (and possibly iterated over). |
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330 |
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331 PARAMETERS |
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332 - fieldnames (list of any type, default None): |
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333 The loop variables i1, i2, i3 (in the example above) should contain the |
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334 f1, f2, and f3 fields of the current batch of examples. If None, the |
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335 derived class can choose a default, e.g. all fields. |
| 16 | 336 |
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337 - minibatch_size (integer, default 1) |
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338 On every iteration, the variables i1, i2, i3 will have |
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339 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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340 |
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341 - n_batches (integer, default None) |
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342 The iterator will loop exactly this many times, and then stop. If None, |
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343 the derived class can choose a default. If (-1), then the returned |
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344 iterator should support looping indefinitely. |
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345 |
|
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346 - offset (integer, default 0) |
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347 The iterator will start at example 'offset' in the dataset, rather than the default. |
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348 |
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349 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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350 any other object that supports integer indexing and slicing. |
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351 |
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352 """ |
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353 return DataSet.MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
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354 |
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355 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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356 """ |
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357 This is the minibatches iterator generator that sub-classes must define. |
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358 It does not need to worry about wrapping around multiple times across the dataset, |
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359 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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360 The next() method of the returned iterator does not even need to worry about |
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361 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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362 before an improper call to minibatches_nowrap's next() is made). |
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363 That next() method can assert that its next row will always be within [0,len(dataset)). |
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364 The iterator returned by minibatches_nowrap does not need to implement |
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365 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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366 """ |
|
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367 raise AbstractFunction() |
|
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368 |
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369 def __len__(self): |
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370 """ |
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371 len(dataset) returns the number of examples in the dataset. |
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372 By default, a DataSet is a 'stream', i.e. it has an unbounded length (sys.maxint). |
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373 Sub-classes which implement finite-length datasets should redefine this method. |
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374 Some methods only make sense for finite-length datasets. |
|
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375 """ |
| 123 | 376 return maxint |
|
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377 |
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378 def is_unbounded(self): |
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379 """ |
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380 Tests whether a dataset is unbounded (e.g. a stream). |
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381 """ |
| 123 | 382 return len(self)==maxint |
|
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383 |
|
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384 def hasFields(self,*fieldnames): |
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385 """ |
|
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386 Return true if the given field name (or field names, if multiple arguments are |
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387 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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388 of the iterators). |
|
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389 |
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390 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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391 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
|
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392 """ |
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393 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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394 |
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395 def fieldNames(self): |
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396 """ |
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397 Return the list of field names that are supported by the iterators, |
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398 and for which hasFields(fieldname) would return True. |
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399 """ |
|
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400 raise AbstractFunction() |
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401 |
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402 def __call__(self,*fieldnames): |
|
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403 """ |
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404 Return a dataset that sees only the fields whose name are specified. |
|
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405 """ |
|
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406 assert self.hasFields(*fieldnames) |
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407 return self.fields(*fieldnames).examples() |
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408 |
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409 def fields(self,*fieldnames): |
|
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410 """ |
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411 Return a DataSetFields object associated with this dataset. |
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412 """ |
|
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413 return DataSetFields(self,fieldnames) |
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414 |
|
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415 def __getitem__(self,i): |
|
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416 """ |
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417 dataset[i] returns the (i+1)-th example of the dataset. |
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418 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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419 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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420 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
| 41 | 421 dataset['key'] returns a property associated with the given 'key' string. |
| 422 If 'key' is a fieldname, then the VStacked field values (iterable over | |
| 423 field values) for that field is returned. Other keys may be supported | |
| 424 by different dataset subclasses. The following key names are encouraged: | |
| 425 - 'description': a textual description or name for the dataset | |
| 426 - '<fieldname>.type': a type name or value for a given <fieldname> | |
|
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427 |
| 39 | 428 Note that some stream datasets may be unable to implement random access, i.e. |
| 429 arbitrary slicing/indexing | |
|
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430 because they can only iterate through examples one or a minibatch at a time |
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431 and do not actually store or keep past (or future) examples. |
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432 |
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433 The default implementation of getitem uses the minibatches iterator |
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434 to obtain one example, one slice, or a list of examples. It may not |
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435 always be the most efficient way to obtain the result, especially if |
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436 the data are actually stored in a memory array. |
|
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437 """ |
| 41 | 438 # check for an index |
|
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439 if type(i) is int: |
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440 return DataSet.MinibatchToSingleExampleIterator( |
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441 self.minibatches(minibatch_size=1,n_batches=1,offset=i)).next() |
| 41 | 442 rows=None |
| 443 # or a slice | |
|
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444 if type(i) is slice: |
|
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445 #print 'i=',i |
|
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446 if not i.start: i=slice(0,i.stop,i.step) |
|
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447 if not i.stop: i=slice(i.start,len(self),i.step) |
|
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448 if not i.step: i=slice(i.start,i.stop,1) |
|
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449 if i.step is 1: |
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450 return self.minibatches(minibatch_size=i.stop-i.start,n_batches=1,offset=i.start).next().examples() |
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451 rows = range(i.start,i.stop,i.step) |
| 41 | 452 # or a list of indices |
| 453 elif type(i) is list: | |
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454 rows = i |
| 41 | 455 if rows is not None: |
|
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456 examples = [self[row] for row in rows] |
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457 fields_values = zip(*examples) |
|
45
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458 return MinibatchDataSet( |
| 41 | 459 Example(self.fieldNames(),[ self.valuesVStack(fieldname,field_values) |
| 460 for fieldname,field_values | |
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461 in zip(self.fieldNames(),fields_values)]), |
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462 self.valuesVStack,self.valuesHStack) |
| 41 | 463 # else check for a fieldname |
| 464 if self.hasFields(i): | |
| 465 return self.minibatches(fieldnames=[i],minibatch_size=len(self),n_batches=1,offset=0).next()[0] | |
| 466 # else we are trying to access a property of the dataset | |
| 467 assert i in self.__dict__ # else it means we are trying to access a non-existing property | |
| 468 return self.__dict__[i] | |
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469 |
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470 def valuesHStack(self,fieldnames,fieldvalues): |
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471 """ |
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472 Return a value that corresponds to concatenating (horizontally) several field values. |
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473 This can be useful to merge some fields. The implementation of this operation is likely |
|
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474 to involve a copy of the original values. When the values are numpy arrays, the |
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475 result should be numpy.hstack(values). If it makes sense, this operation should |
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476 work as well when each value corresponds to multiple examples in a minibatch |
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477 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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478 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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479 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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480 pointing to the original values for other data types. |
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481 """ |
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482 all_numpy=True |
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483 for value in fieldvalues: |
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484 if not type(value) is numpy.ndarray: |
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485 all_numpy=False |
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486 if all_numpy: |
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487 return numpy.hstack(fieldvalues) |
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488 # the default implementation of horizontal stacking is to put values in a list |
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489 return fieldvalues |
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490 |
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491 |
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492 def valuesVStack(self,fieldname,values): |
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493 """ |
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494 Return a value that corresponds to concatenating (vertically) several values of the |
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495 same field. This can be important to build a minibatch out of individual examples. This |
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496 is likely to involve a copy of the original values. When the values are numpy arrays, the |
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497 result should be numpy.vstack(values). |
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498 The default is to use numpy.vstack for numpy.ndarray values, and a list |
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499 pointing to the original values for other data types. |
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500 """ |
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501 all_numpy=True |
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502 for value in values: |
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503 if not type(value) is numpy.ndarray: |
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504 all_numpy=False |
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505 if all_numpy: |
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506 return numpy.vstack(values) |
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507 # the default implementation of vertical stacking is to put values in a list |
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508 return values |
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509 |
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510 def __or__(self,other): |
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511 """ |
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512 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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513 fields of the argument datasets. This only works if they all have the same length. |
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514 """ |
|
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515 return HStackedDataSet([self,other]) |
|
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516 |
|
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517 def __and__(self,other): |
|
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518 """ |
|
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519 dataset1 & dataset2 is a dataset that concatenates the examples from the argument datasets |
|
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520 (and whose length is the sum of the length of the argument datasets). This only |
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521 works if they all have the same fields. |
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522 """ |
|
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523 return VStackedDataSet([self,other]) |
|
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524 |
|
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525 def hstack(datasets): |
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526 """ |
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527 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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528 which is a dataset whose fields list is the concatenation of the fields |
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529 of the individual datasets. |
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530 """ |
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531 assert len(datasets)>0 |
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532 if len(datasets)==1: |
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533 return datasets[0] |
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534 return HStackedDataSet(datasets) |
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535 |
|
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536 def vstack(datasets): |
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537 """ |
|
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538 vstack(dataset1,dataset2,...) returns dataset1 & datataset2 & ... |
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539 which is a dataset which iterates first over the examples of dataset1, then |
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540 over those of dataset2, etc. |
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541 """ |
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542 assert len(datasets)>0 |
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543 if len(datasets)==1: |
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544 return datasets[0] |
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545 return VStackedDataSet(datasets) |
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546 |
|
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|
547 class FieldsSubsetDataSet(DataSet): |
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548 """ |
| 167 | 549 A sub-class of L{DataSet} that selects a subset of the fields. |
|
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550 """ |
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551 def __init__(self,src,fieldnames): |
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552 self.src=src |
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|
553 self.fieldnames=fieldnames |
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554 assert src.hasFields(*fieldnames) |
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|
555 self.valuesHStack = src.valuesHStack |
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556 self.valuesVStack = src.valuesVStack |
|
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557 |
|
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558 def __len__(self): return len(self.src) |
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|
559 |
|
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|
560 def fieldNames(self): |
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561 return self.fieldnames |
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562 |
|
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|
563 def __iter__(self): |
|
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|
564 class FieldsSubsetIterator(object): |
|
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565 def __init__(self,ds): |
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|
566 self.ds=ds |
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567 self.src_iter=ds.src.__iter__() |
|
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|
568 self.example=None |
|
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|
569 def __iter__(self): return self |
|
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|
570 def next(self): |
|
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|
571 complete_example = self.src_iter.next() |
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572 if self.example: |
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573 self.example._values=[complete_example[field] |
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574 for field in self.ds.fieldnames] |
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575 else: |
|
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576 self.example=Example(self.ds.fieldnames, |
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|
577 [complete_example[field] for field in self.ds.fieldnames]) |
|
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578 return self.example |
|
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|
579 return FieldsSubsetIterator(self) |
|
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580 |
|
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|
581 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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|
582 assert self.hasFields(*fieldnames) |
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583 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
|
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|
584 def __getitem__(self,i): |
|
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585 return FieldsSubsetDataSet(self.src[i],self.fieldnames) |
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|
586 |
|
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|
587 |
|
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588 class DataSetFields(LookupList): |
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589 """ |
| 167 | 590 Although a L{DataSet} iterates over examples (like rows of a matrix), an associated |
|
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|
591 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
|
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|
592 as a transpose of the associated dataset. |
|
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593 |
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594 To iterate over fields, one can do |
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595 * for fields in dataset.fields() |
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596 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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597 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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598 and each of these fields is iterable over the examples: |
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599 * for field_examples in dataset.fields(): |
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600 for example_value in field_examples: |
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601 ... |
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602 but when the dataset is a stream (unbounded length), it is not recommanded to do |
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603 such things because the underlying dataset may refuse to access the different fields in |
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604 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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605 The result of fields() is a DataSetFields object, which iterates over fields, |
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606 and whose elements are iterable over examples. A DataSetFields object can |
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607 be turned back into a DataSet with its examples() method: |
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608 dataset2 = dataset1.fields().examples() |
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609 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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610 |
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611 DataSetFields can be concatenated vertically or horizontally. To be consistent with |
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612 the syntax used for DataSets, the | concatenates the fields and the & concatenates |
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613 the examples. |
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614 """ |
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615 def __init__(self,dataset,fieldnames): |
|
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616 original_dataset=dataset |
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617 if not fieldnames: |
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618 fieldnames=dataset.fieldNames() |
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619 elif not fieldnames==dataset.fieldNames(): |
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620 dataset = FieldsSubsetDataSet(dataset,fieldnames) |
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621 assert dataset.hasFields(*fieldnames) |
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622 self.dataset=dataset |
|
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623 |
|
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624 if isinstance(dataset,MinibatchDataSet): |
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625 LookupList.__init__(self,fieldnames,list(dataset._fields)) |
|
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626 elif isinstance(original_dataset,MinibatchDataSet): |
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627 LookupList.__init__(self,fieldnames, |
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628 [original_dataset._fields[field] |
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629 for field in fieldnames]) |
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630 else: |
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631 minibatch_iterator = dataset.minibatches(fieldnames, |
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632 minibatch_size=len(dataset), |
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633 n_batches=1) |
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634 minibatch=minibatch_iterator.next() |
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635 LookupList.__init__(self,fieldnames,minibatch) |
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636 |
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637 def examples(self): |
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638 return self.dataset |
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639 |
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640 def __or__(self,other): |
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641 """ |
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642 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
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643 of the list of examples of DataSetFields fields1 and fields2. |
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644 """ |
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645 return (self.examples() + other.examples()).fields() |
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646 |
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647 def __and__(self,other): |
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648 """ |
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649 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
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650 of the fields of DataSetFields fields1 and fields2. |
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651 """ |
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652 return (self.examples() | other.examples()).fields() |
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653 |
|
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654 |
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655 class MinibatchDataSet(DataSet): |
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656 """ |
| 167 | 657 Turn a L{LookupList} of same-length (iterable) fields into an example-iterable dataset. |
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658 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
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659 """ |
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660 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
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661 values_hstack=DataSet().valuesHStack): |
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662 """ |
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663 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
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664 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
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665 semantics as the DataSet methods of the same name (but without the self argument). |
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666 """ |
|
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667 |
|
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668 self._fields=fields_lookuplist |
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669 assert len(fields_lookuplist)>0 |
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670 self.length=len(fields_lookuplist[0]) |
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671 for field in fields_lookuplist[1:]: |
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672 if self.length != len(field) : |
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673 print 'self.length = ',self.length |
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674 print 'len(field) = ', len(field) |
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675 print 'self._fields.keys() = ', self._fields.keys() |
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676 print 'field=',field |
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677 assert self.length==len(field) |
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678 self.values_vstack=values_vstack |
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679 self.values_hstack=values_hstack |
|
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680 |
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681 def __len__(self): |
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682 return self.length |
|
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683 |
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684 def __getitem__(self,i): |
| 80 | 685 if type(i) in (slice,list): |
|
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686 return DataSetFields(MinibatchDataSet( |
| 80 | 687 Example(self._fields.keys(),[field[i] for field in self._fields])),self.fieldNames()) |
| 688 if type(i) is int: | |
| 85 | 689 return Example(self._fields.keys(),[field[i] for field in self._fields]) |
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690 if self.hasFields(i): |
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691 return self._fields[i] |
|
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692 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
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693 return self.__dict__[i] |
|
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694 |
|
29
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695 def fieldNames(self): |
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696 return self._fields.keys() |
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697 |
|
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698 def hasFields(self,*fieldnames): |
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699 for fieldname in fieldnames: |
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700 if fieldname not in self._fields.keys(): |
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701 return False |
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702 return True |
|
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703 |
|
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704 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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705 #@TODO bug somewhere here, fieldnames doesnt seem to be well handled |
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706 class Iterator(object): |
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707 def __init__(self,ds,fieldnames): |
|
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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218
diff
changeset
|
708 # tbm: added two next lines to handle fieldnames |
|
517364d48ae0
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diff
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|
709 if fieldnames is None: fieldnames = ds._fields.keys() |
|
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diff
changeset
|
710 self.fieldnames = fieldnames |
|
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diff
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|
711 |
|
36
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diff
changeset
|
712 self.ds=ds |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
713 self.next_example=offset |
|
36
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diff
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|
714 assert minibatch_size > 0 |
| 41 | 715 if offset+minibatch_size > ds.length: |
|
36
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diff
changeset
|
716 raise NotImplementedError() |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
717 def __iter__(self): |
|
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
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diff
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|
718 return self |
|
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
719 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
|
720 upper = self.next_example+minibatch_size |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
721 assert upper<=self.ds.length |
|
223
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diff
changeset
|
722 #minibatch = Example(self.ds._fields.keys(), |
|
517364d48ae0
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diff
changeset
|
723 # [field[self.next_example:upper] |
|
517364d48ae0
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diff
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|
724 # for field in self.ds._fields]) |
|
517364d48ae0
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parents:
218
diff
changeset
|
725 # tbm: modif to use fieldnames |
|
517364d48ae0
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diff
changeset
|
726 values = [] |
|
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diff
changeset
|
727 for f in self.fieldnames : |
|
517364d48ae0
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diff
changeset
|
728 #print 'we have field',f,'in fieldnames' |
|
517364d48ae0
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diff
changeset
|
729 values.append( self.ds._fields[f][self.next_example:upper] ) |
|
517364d48ae0
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diff
changeset
|
730 minibatch = Example(self.fieldnames,values) |
|
517364d48ae0
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parents:
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diff
changeset
|
731 #print minibatch |
|
36
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diff
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|
732 self.next_example+=minibatch_size |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
bengioy@bengiomac.local
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72
diff
changeset
|
733 return minibatch |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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diff
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|
734 |
|
223
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diff
changeset
|
735 # tbm: added fieldnames to handle subset of fieldnames |
|
517364d48ae0
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218
diff
changeset
|
736 return Iterator(self,fieldnames) |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
737 |
|
36
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diff
changeset
|
738 def valuesVStack(self,fieldname,fieldvalues): |
|
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Rewriting dataset.py completely
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diff
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|
739 return self.values_vstack(fieldname,fieldvalues) |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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parents:
19
diff
changeset
|
740 |
|
36
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diff
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|
741 def valuesHStack(self,fieldnames,fieldvalues): |
|
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diff
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|
742 return self.values_hstack(fieldnames,fieldvalues) |
|
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diff
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|
743 |
|
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diff
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|
744 class HStackedDataSet(DataSet): |
|
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diff
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|
745 """ |
| 167 | 746 A L{DataSet} that wraps several datasets and shows a view that includes all their fields, |
|
36
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diff
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|
747 i.e. whose list of fields is the concatenation of their lists of fields. |
|
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diff
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|
748 |
|
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Rewriting dataset.py completely
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diff
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|
749 If a field name is found in more than one of the datasets, then either an error is |
|
438440ba0627
Rewriting dataset.py completely
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29
diff
changeset
|
750 raised or the fields are renamed (either by prefixing the __name__ attribute |
|
438440ba0627
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diff
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|
751 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list). |
|
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diff
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|
752 |
| 167 | 753 @todo: automatically detect a chain of stacked datasets due to A | B | C | D ... |
|
36
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Rewriting dataset.py completely
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diff
changeset
|
754 """ |
| 41 | 755 def __init__(self,datasets,accept_nonunique_names=False,description=None,field_types=None): |
| 756 DataSet.__init__(self,description,field_types) | |
|
36
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diff
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|
757 self.datasets=datasets |
|
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|
758 self.accept_nonunique_names=accept_nonunique_names |
|
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diff
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|
759 self.fieldname2dataset={} |
|
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diff
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|
760 |
|
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diff
changeset
|
761 def rename_field(fieldname,dataset,i): |
|
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29
diff
changeset
|
762 if hasattr(dataset,"__name__"): |
|
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diff
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|
763 return dataset.__name__ + "." + fieldname |
|
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diff
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|
764 return fieldname+"."+str(i) |
|
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diff
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|
765 |
|
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diff
changeset
|
766 # make sure all datasets have the same length and unique field names |
|
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diff
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|
767 self.length=None |
|
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|
768 names_to_change=[] |
|
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|
769 for i in xrange(len(datasets)): |
|
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|
770 dataset = datasets[i] |
|
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|
771 length=len(dataset) |
|
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|
772 if self.length: |
|
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|
773 assert self.length==length |
|
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diff
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|
774 else: |
|
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|
775 self.length=length |
|
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diff
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|
776 for fieldname in dataset.fieldNames(): |
|
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diff
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|
777 if fieldname in self.fieldname2dataset: # name conflict! |
|
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diff
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|
778 if accept_nonunique_names: |
|
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diff
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|
779 fieldname=rename_field(fieldname,dataset,i) |
|
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diff
changeset
|
780 names2change.append((fieldname,i)) |
|
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|
781 else: |
|
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|
782 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
|
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diff
changeset
|
783 self.fieldname2dataset[fieldname]=i |
|
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changeset
|
784 for fieldname,i in names_to_change: |
|
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diff
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|
785 del self.fieldname2dataset[fieldname] |
|
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diff
changeset
|
786 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
|
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diff
changeset
|
787 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
788 def hasFields(self,*fieldnames): |
|
36
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diff
changeset
|
789 for fieldname in fieldnames: |
|
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diff
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|
790 if not fieldname in self.fieldname2dataset: |
|
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diff
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|
791 return False |
|
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|
792 return True |
|
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|
793 |
|
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|
794 def fieldNames(self): |
|
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|
795 return self.fieldname2dataset.keys() |
|
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|
796 |
| 41 | 797 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
36
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diff
changeset
|
798 |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
799 class HStackedIterator(object): |
|
36
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diff
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|
800 def __init__(self,hsds,iterators): |
|
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|
801 self.hsds=hsds |
|
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|
802 self.iterators=iterators |
|
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|
803 def __iter__(self): |
|
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|
804 return self |
|
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|
805 def next(self): |
|
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diff
changeset
|
806 # concatenate all the fields of the minibatches |
| 140 | 807 l=LookupList() |
| 808 for iter in self.iterators: | |
| 809 l.append_lookuplist(iter.next()) | |
| 810 return l | |
|
36
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|
811 |
| 125 | 812 assert self.hasFields(*fieldnames) |
|
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changeset
|
813 # find out which underlying datasets are necessary to service the required fields |
|
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changeset
|
814 # and construct corresponding minibatch iterators |
| 140 | 815 if fieldnames and fieldnames!=self.fieldNames(): |
|
36
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|
816 datasets=set([]) |
|
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changeset
|
817 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
|
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|
818 for fieldname in fieldnames: |
| 136 | 819 dataset=self.datasets[self.fieldname2dataset[fieldname]] |
|
36
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|
820 datasets.add(dataset) |
|
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changeset
|
821 fields_in_dataset[dataset].append(fieldname) |
|
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diff
changeset
|
822 datasets=list(datasets) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
823 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
|
36
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diff
changeset
|
824 for dataset in datasets] |
|
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diff
changeset
|
825 else: |
|
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diff
changeset
|
826 datasets=self.datasets |
|
37
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Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
827 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
|
828 return HStackedIterator(self,iterators) |
|
36
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diff
changeset
|
829 |
|
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|
830 |
|
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831 def valuesVStack(self,fieldname,fieldvalues): |
|
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832 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
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|
833 |
|
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|
834 def valuesHStack(self,fieldnames,fieldvalues): |
|
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|
835 """ |
|
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|
836 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
|
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|
837 to do the work, hoping that it can cope with the other values (i.e. won't care |
|
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|
838 about the incompatible fieldnames). Hence this heuristic will always work if |
|
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|
839 all the fieldnames are of the same sub-dataset. |
|
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|
840 """ |
|
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841 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
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|
842 |
|
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843 class VStackedDataSet(DataSet): |
|
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|
844 """ |
| 167 | 845 A L{DataSet} that wraps several datasets and shows a view that includes all their examples, |
|
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846 in the order provided. This clearly assumes that they all have the same field names |
|
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|
847 and all (except possibly the last one) are of finite length. |
|
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|
848 |
| 167 | 849 @todo: automatically detect a chain of stacked datasets due to A + B + C + D ... |
|
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850 """ |
|
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851 def __init__(self,datasets): |
|
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852 self.datasets=datasets |
|
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|
853 self.length=0 |
|
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|
854 self.index2dataset={} |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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|
855 assert len(datasets)>0 |
|
73c4212ba5b3
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parents:
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|
856 fieldnames = datasets[-1].fieldNames() |
|
38
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857 self.datasets_start_row=[] |
|
37
73c4212ba5b3
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|
858 # We use this map from row index to dataset index for constant-time random access of examples, |
|
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859 # to avoid having to search for the appropriate dataset each time and slice is asked for. |
|
36
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|
860 for dataset,k in enumerate(datasets[0:-1]): |
|
48
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|
861 assert dataset.is_unbounded() # All VStacked datasets (except possibly the last) must be bounded (have a length). |
|
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Fixing MinibatchDataSet getitem
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|
862 L=len(dataset) |
|
36
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|
863 for i in xrange(L): |
|
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864 self.index2dataset[self.length+i]=k |
|
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|
865 self.datasets_start_row.append(self.length) |
|
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|
866 self.length+=L |
|
37
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|
867 assert dataset.fieldNames()==fieldnames |
|
38
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|
868 self.datasets_start_row.append(self.length) |
|
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|
869 self.length+=len(datasets[-1]) |
|
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|
870 # If length is very large, we should use a more memory-efficient mechanism |
|
73c4212ba5b3
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|
871 # that does not store all indices |
|
73c4212ba5b3
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diff
changeset
|
872 if self.length>1000000: |
|
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diff
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|
873 # 1 million entries would require about 60 meg for the index2dataset map |
|
73c4212ba5b3
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diff
changeset
|
874 # TODO |
|
73c4212ba5b3
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diff
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|
875 print "A more efficient mechanism for index2dataset should be implemented" |
|
73c4212ba5b3
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diff
changeset
|
876 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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|
877 def __len__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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|
878 return self.length |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
879 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
880 def fieldNames(self): |
|
73c4212ba5b3
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diff
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|
881 return self.datasets[0].fieldNames() |
|
73c4212ba5b3
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diff
changeset
|
882 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
883 def hasFields(self,*fieldnames): |
|
73c4212ba5b3
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diff
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|
884 return self.datasets[0].hasFields(*fieldnames) |
|
73c4212ba5b3
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diff
changeset
|
885 |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
886 def locate_row(self,row): |
|
d637ad8f7352
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parents:
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diff
changeset
|
887 """Return (dataset_index, row_within_dataset) for global row number""" |
|
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Finished first untested version of VStackedDataset
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diff
changeset
|
888 dataset_index = self.index2dataset[row] |
|
d637ad8f7352
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diff
changeset
|
889 row_within_dataset = self.datasets_start_row[dataset_index] |
|
d637ad8f7352
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changeset
|
890 return dataset_index, row_within_dataset |
|
d637ad8f7352
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37
diff
changeset
|
891 |
| 41 | 892 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
44
5a85fda9b19b
Fixed some more iterator bugs
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43
diff
changeset
|
893 |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
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43
diff
changeset
|
894 class VStackedIterator(object): |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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|
895 def __init__(self,vsds): |
|
73c4212ba5b3
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|
896 self.vsds=vsds |
|
73c4212ba5b3
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diff
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|
897 self.next_row=offset |
|
38
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Finished first untested version of VStackedDataset
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changeset
|
898 self.next_dataset_index,self.next_dataset_row=self.vsds.locate_row(offset) |
|
37
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
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diff
changeset
|
899 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
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diff
changeset
|
900 self.next_iterator(vsds.datasets[0],offset,n_batches) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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36
diff
changeset
|
901 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
902 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:
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diff
changeset
|
903 L=len(dataset) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
904 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
|
905 if batches_left is not None: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
906 ds_nbatches = max(batches_left,ds_nbatches) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
907 if minibatch_size>L: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
908 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
|
909 n_left_in_mb=minibatch_size-L |
|
38
d637ad8f7352
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bengioy@esprit.iro.umontreal.ca
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37
diff
changeset
|
910 ds_nbatches=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
911 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
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diff
changeset
|
912 n_left_in_mb=0 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
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diff
changeset
|
913 return dataset.minibatches(fieldnames,minibatch_size,ds_nbatches,starting_offset), \ |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
914 L-(starting_offset+ds_nbatches*minibatch_size), n_left_in_mb |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
915 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
916 def move_to_next_dataset(self): |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
917 if self.n_left_at_the_end_of_ds>0: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
918 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
|
919 self.next_iterator(vsds.datasets[self.next_dataset_index], |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
920 self.n_left_at_the_end_of_ds,1) |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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37
diff
changeset
|
921 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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diff
changeset
|
922 self.next_dataset_index +=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
923 if self.next_dataset_index==len(self.vsds.datasets): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
924 self.next_dataset_index = 0 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
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parents:
37
diff
changeset
|
925 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
|
926 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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diff
changeset
|
927 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
928 def __iter__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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diff
changeset
|
929 return self |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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36
diff
changeset
|
930 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
931 def next(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
932 dataset=self.vsds.datasets[self.next_dataset_index] |
|
73c4212ba5b3
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36
diff
changeset
|
933 mb = self.next_iterator.next() |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
934 if self.n_left_in_mb: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
935 extra_mb = [] |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
936 while self.n_left_in_mb>0: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
937 self.move_to_next_dataset() |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
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|
938 extra_mb.append(self.next_iterator.next()) |
|
73
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|
939 mb = Example(fieldnames, |
|
40
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|
940 [dataset.valuesVStack(name, |
|
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diff
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|
941 [mb[name]]+[b[name] for b in extra_mb]) |
|
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|
942 for name in fieldnames]) |
|
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|
943 |
|
37
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diff
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|
944 self.next_row+=minibatch_size |
|
38
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|
945 self.next_dataset_row+=minibatch_size |
|
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diff
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|
946 if self.next_row+minibatch_size>len(dataset): |
|
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diff
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|
947 self.move_to_next_dataset() |
|
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|
948 return examples |
|
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|
949 return VStackedIterator(self) |
|
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|
950 |
| 41 | 951 class ArrayFieldsDataSet(DataSet): |
| 952 """ | |
| 953 Virtual super-class of datasets whose field values are numpy array, | |
| 954 thus defining valuesHStack and valuesVStack for sub-classes. | |
| 955 """ | |
| 956 def __init__(self,description=None,field_types=None): | |
| 957 DataSet.__init__(self,description,field_types) | |
| 958 def valuesHStack(self,fieldnames,fieldvalues): | |
| 959 """Concatenate field values horizontally, e.g. two vectors | |
| 960 become a longer vector, two matrices become a wider matrix, etc.""" | |
| 961 return numpy.hstack(fieldvalues) | |
| 962 def valuesVStack(self,fieldname,values): | |
| 963 """Concatenate field values vertically, e.g. two vectors | |
| 964 become a two-row matrix, two matrices become a longer matrix, etc.""" | |
| 965 return numpy.vstack(values) | |
| 966 | |
| 967 class ArrayDataSet(ArrayFieldsDataSet): | |
| 968 """ | |
| 969 An ArrayDataSet stores the fields as groups of columns in a numpy tensor, | |
| 970 whose first axis iterates over examples, second axis determines fields. | |
| 971 If the underlying array is N-dimensional (has N axes), then the field | |
| 972 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | |
| 973 """ | |
| 974 | |
|
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|
975 def __init__(self, data_array, fields_columns, **kwargs): |
|
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|
976 """ |
|
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|
977 Construct an ArrayDataSet from the underlying numpy array (data) and |
|
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|
978 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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|
979 using the standard arguments for indexing/slicing: integer for a column index, |
|
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|
980 slice for an interval of columns (with possible stride), or iterable of column indices. |
|
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diff
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|
981 """ |
|
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|
982 ArrayFieldsDataSet.__init__(self, **kwargs) |
| 41 | 983 self.data=data_array |
|
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diff
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|
984 self.fields_columns=fields_columns |
| 41 | 985 |
| 986 # check consistency and complete slices definitions | |
|
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diff
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|
987 for fieldname, fieldcolumns in self.fields_columns.items(): |
| 41 | 988 if type(fieldcolumns) is int: |
| 989 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1] | |
|
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|
990 if 0: |
|
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|
991 #I changed this because it didn't make sense to me, |
|
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|
992 # and it made it more difficult to write my learner. |
|
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|
993 # If it breaks stuff, let's talk about it. |
|
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|
994 # - James 22/05/2008 |
|
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|
995 self.fields_columns[fieldname]=[fieldcolumns] |
|
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|
996 else: |
|
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|
997 self.fields_columns[fieldname]=fieldcolumns |
| 41 | 998 elif type(fieldcolumns) is slice: |
| 999 start,step=None,None | |
| 1000 if not fieldcolumns.start: | |
| 1001 start=0 | |
| 1002 if not fieldcolumns.step: | |
| 1003 step=1 | |
| 1004 if start or step: | |
|
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diff
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|
1005 self.fields_columns[fieldname]=slice(start,fieldcolumns.stop,step) |
| 41 | 1006 elif hasattr(fieldcolumns,"__iter__"): # something like a list |
| 1007 for i in fieldcolumns: | |
| 1008 assert i>=0 and i<data_array.shape[1] | |
| 1009 | |
|
42
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41
diff
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|
1010 def fieldNames(self): |
|
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diff
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|
1011 return self.fields_columns.keys() |
| 41 | 1012 |
|
42
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diff
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|
1013 def __len__(self): |
|
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diff
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|
1014 return len(self.data) |
| 41 | 1015 |
| 80 | 1016 def __getitem__(self,key): |
|
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|
1017 """More efficient implementation than the default __getitem__""" |
|
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|
1018 fieldnames=self.fields_columns.keys() |
| 80 | 1019 if type(key) is int: |
|
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|
1020 return Example(fieldnames, |
| 80 | 1021 [self.data[key,self.fields_columns[f]] for f in fieldnames]) |
| 1022 if type(key) is slice: | |
|
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diff
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|
1023 return MinibatchDataSet(Example(fieldnames, |
| 80 | 1024 [self.data[key,self.fields_columns[f]] for f in fieldnames])) |
| 1025 if type(key) is list: | |
| 1026 for i in range(len(key)): | |
| 1027 if self.hasFields(key[i]): | |
| 1028 key[i]=self.fields_columns[key[i]] | |
| 1029 return MinibatchDataSet(Example(fieldnames, | |
|
88
6749d18e11c8
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diff
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|
1030 #we must separate differently for list as numpy |
| 128 | 1031 # doesn't support self.data[[i1,...],[i2,...]] |
|
88
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|
1032 # when their is more then two i1 and i2 |
|
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diff
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|
1033 [self.data[key,:][:,self.fields_columns[f]] |
|
6749d18e11c8
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diff
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|
1034 if isinstance(self.fields_columns[f],list) else |
| 128 | 1035 self.data[key,self.fields_columns[f]] for f in fieldnames]), |
| 1036 | |
|
73
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|
1037 self.valuesVStack,self.valuesHStack) |
| 80 | 1038 |
|
55
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|
1039 # else check for a fieldname |
| 80 | 1040 if self.hasFields(key): |
|
105
8c0a1b11b007
bugfix, we keep all the line, but only a some columns
Frederic Bastien <bastienf@iro.umontreal.ca>
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diff
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|
1041 return self.data[:,self.fields_columns[key]] |
|
55
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diff
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|
1042 # else we are trying to access a property of the dataset |
| 80 | 1043 assert key in self.__dict__ # else it means we are trying to access a non-existing property |
| 1044 return self.__dict__[key] | |
|
55
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|
1045 |
|
228
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|
1046 def __iter__(self): |
|
6f55e301c687
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|
1047 class ArrayDataSetIterator2(object): |
|
6f55e301c687
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|
1048 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
|
6f55e301c687
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|
1049 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
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|
1050 # store the resulting minibatch in a lookup-list of values |
|
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|
1051 self.minibatch = LookupList(fieldnames,[0]*len(fieldnames)) |
|
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|
1052 self.dataset=dataset |
|
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|
1053 self.minibatch_size=minibatch_size |
|
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|
1054 assert offset>=0 and offset<len(dataset.data) |
|
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|
1055 assert offset+minibatch_size<=len(dataset.data) |
|
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|
1056 self.current=offset |
|
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|
1057 def __iter__(self): |
|
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|
1058 return self |
|
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|
1059 def next(self): |
|
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|
1060 #@todo: we suppose that we need to stop only when minibatch_size == 1. |
|
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|
1061 # Otherwise, MinibatchWrapAroundIterator do it. |
|
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|
1062 if self.current>=self.dataset.data.shape[0]: |
|
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|
1063 raise StopIteration |
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|
1064 sub_data = self.dataset.data[self.current] |
|
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|
1065 self.minibatch._values = [sub_data[self.dataset.fields_columns[f]] for f in self.minibatch._names] |
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1066 self.current+=self.minibatch_size |
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|
1067 return self.minibatch |
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|
1068 |
|
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|
1069 return ArrayDataSetIterator2(self,self.fieldNames(),1,0,0) |
|
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|
1070 |
|
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|
1071 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
44
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|
1072 class ArrayDataSetIterator(object): |
|
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|
1073 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
|
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|
1074 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
44
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|
1075 # store the resulting minibatch in a lookup-list of values |
|
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|
1076 self.minibatch = LookupList(fieldnames,[0]*len(fieldnames)) |
|
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|
1077 self.dataset=dataset |
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diff
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|
1078 self.minibatch_size=minibatch_size |
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diff
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|
1079 assert offset>=0 and offset<len(dataset.data) |
|
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diff
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|
1080 assert offset+minibatch_size<=len(dataset.data) |
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|
1081 self.current=offset |
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|
1082 def __iter__(self): |
|
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|
1083 return self |
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|
1084 def next(self): |
|
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|
1085 #@todo: we suppose that MinibatchWrapAroundIterator stop the iterator |
|
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|
1086 sub_data = self.dataset.data[self.current:self.current+self.minibatch_size] |
|
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|
1087 self.minibatch._values = [sub_data[:,self.dataset.fields_columns[f]] for f in self.minibatch._names] |
|
43
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|
1088 self.current+=self.minibatch_size |
|
44
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|
1089 return self.minibatch |
|
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|
1090 |
|
44
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1091 return ArrayDataSetIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
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1092 |
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1093 |
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1094 class CachedDataSet(DataSet): |
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1095 """ |
| 167 | 1096 Wrap a L{DataSet} whose values are computationally expensive to obtain |
|
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1097 (e.g. because they involve some computation, or disk access), |
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1098 so that repeated accesses to the same example are done cheaply, |
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1099 by caching every example value that has been accessed at least once. |
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|
1100 |
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1101 Optionally, for finite-length dataset, all the values can be computed |
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1102 (and cached) upon construction of the CachedDataSet, rather at the |
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|
1103 first access. |
|
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1104 |
| 167 | 1105 @todo: when cache_all_upon_construction create mini-batches that are as |
|
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1106 large as possible but not so large as to fill up memory. |
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1107 |
| 167 | 1108 @todo: add disk-buffering capability, so that when the cache becomes too |
|
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1109 big for memory, we cache things on disk, trying to keep in memory only |
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1110 the record most likely to be accessed next. |
|
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1111 """ |
|
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1112 def __init__(self,source_dataset,cache_all_upon_construction=False): |
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1113 self.source_dataset=source_dataset |
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1114 self.cache_all_upon_construction=cache_all_upon_construction |
|
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1115 self.cached_examples = [] |
|
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1116 if cache_all_upon_construction: |
|
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1117 # this potentially brings all the source examples |
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1118 # into memory at once, which may be too much |
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1119 # the work could possibly be done by minibatches |
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1120 # that are as large as possible but no more than what memory allows. |
|
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|
1121 fields_values = source_dataset.minibatches(minibatch_size=len(source_dataset)).__iter__().next() |
|
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1122 assert all([len(self)==len(field_values) for field_values in fields_values]) |
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|
1123 for example in fields_values.examples(): |
|
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1124 self.cached_examples.append(copy.copy(example)) |
|
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1125 |
|
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1126 self.fieldNames = source_dataset.fieldNames |
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1127 self.hasFields = source_dataset.hasFields |
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1128 self.valuesHStack = source_dataset.valuesHStack |
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1129 self.valuesVStack = source_dataset.valuesVStack |
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|
1130 |
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1131 def __len__(self): |
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1132 return len(self.source_dataset) |
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1133 |
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1134 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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|
1135 class CacheIterator(object): |
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1136 def __init__(self,dataset): |
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|
1137 self.dataset=dataset |
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|
1138 self.current=offset |
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|
1139 def __iter__(self): return self |
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|
1140 def next(self): |
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1141 upper = self.current+minibatch_size |
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|
1142 cache_len = len(self.dataset.cached_examples) |
|
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1143 if upper>cache_len: # whole minibatch is not already in cache |
|
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1144 # cache everything from current length to upper |
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1145 for example in self.dataset.source_dataset[cache_len:upper]: |
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1146 self.dataset.cached_examples.append(example) |
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1147 all_fields_minibatch = Example(self.dataset.fieldNames(), |
|
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|
1148 zip(*self.dataset.cached_examples[self.current:self.current+minibatch_size])) |
|
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|
1149 self.current+=minibatch_size |
|
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1150 if self.dataset.fieldNames()==fieldnames: |
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1151 return all_fields_minibatch |
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1152 return Example(fieldnames,[all_fields_minibatch[name] for name in fieldnames]) |
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1153 return CacheIterator(self) |
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|
1154 |
| 153 | 1155 def __getitem__(self,i): |
| 1156 if type(i)==int and len(self.cached_examples)>i: | |
| 1157 return self.cached_examples[i] | |
| 1158 else: | |
| 1159 return DataSet.__getitem__(self,i) | |
|
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|
1160 |
|
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|
1161 class ApplyFunctionDataSet(DataSet): |
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|
1162 """ |
| 167 | 1163 A L{DataSet} that contains as fields the results of applying a |
| 1164 given function example-wise or minibatch-wise to all the fields of | |
| 1165 an input dataset. The output of the function should be an iterable | |
| 1166 (e.g. a list or a LookupList) over the resulting values. | |
|
197
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|
1167 |
|
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1168 The function take as input the fields of the dataset, not the examples. |
|
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1169 |
| 167 | 1170 In minibatch mode, the function is expected to work on minibatches |
| 1171 (takes a minibatch in input and returns a minibatch in output). More | |
| 1172 precisely, it means that each element of the input or output list | |
| 1173 should be iterable and indexable over the individual example values | |
| 1174 (typically these elements will be numpy arrays). All of the elements | |
| 1175 in the input and output lists should have the same length, which is | |
| 1176 the length of the minibatch. | |
|
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|
1177 |
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|
1178 The function is applied each time an example or a minibatch is accessed. |
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|
1179 To avoid re-doing computation, wrap this dataset inside a CachedDataSet. |
|
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|
1180 |
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|
1181 If the values_{h,v}stack functions are not provided, then |
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1182 the input_dataset.values{H,V}Stack functions are used by default. |
|
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1183 """ |
|
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1184 def __init__(self,input_dataset,function,output_names,minibatch_mode=True, |
|
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1185 values_hstack=None,values_vstack=None, |
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1186 description=None,fieldtypes=None): |
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1187 """ |
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|
1188 Constructor takes an input dataset that has as many fields as the function |
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1189 expects as inputs. The resulting dataset has as many fields as the function |
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1190 produces as outputs, and that should correspond to the number of output names |
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1191 (provided in a list). |
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|
1192 |
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|
1193 Note that the expected semantics of the function differs in minibatch mode |
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1194 (it takes minibatches of inputs and produces minibatches of outputs, as |
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|
1195 documented in the class comment). |
|
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|
1196 |
|
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|
1197 TBM: are filedtypes the old field types (from input_dataset) or the new ones |
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1198 (for the new dataset created)? |
|
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1199 """ |
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1200 self.input_dataset=input_dataset |
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1201 self.function=function |
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1202 self.output_names=output_names |
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1203 self.minibatch_mode=minibatch_mode |
|
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1204 DataSet.__init__(self,description,fieldtypes) |
|
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1205 self.valuesHStack = values_hstack if values_hstack else input_dataset.valuesHStack |
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1206 self.valuesVStack = values_vstack if values_vstack else input_dataset.valuesVStack |
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|
1207 |
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|
1208 def __len__(self): |
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1209 return len(self.input_dataset) |
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|
1210 |
|
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|
1211 def fieldNames(self): |
|
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1212 return self.output_names |
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1213 |
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1214 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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1215 class ApplyFunctionIterator(object): |
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1216 def __init__(self,output_dataset): |
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1217 self.input_dataset=output_dataset.input_dataset |
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1218 self.output_dataset=output_dataset |
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1219 self.input_iterator=self.input_dataset.minibatches(minibatch_size=minibatch_size, |
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1220 n_batches=n_batches,offset=offset).__iter__() |
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1221 |
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1222 def __iter__(self): return self |
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1223 |
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1224 def next(self): |
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1225 function_inputs = self.input_iterator.next() |
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1226 all_output_names = self.output_dataset.output_names |
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1227 if self.output_dataset.minibatch_mode: |
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1228 function_outputs = self.output_dataset.function(*function_inputs) |
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1229 else: |
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1230 input_examples = zip(*function_inputs) |
| 201 | 1231 output_examples = [self.output_dataset.function(*input_example) |
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1232 for input_example in input_examples] |
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1233 function_outputs = [self.output_dataset.valuesVStack(name,values) |
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1234 for name,values in zip(all_output_names, |
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1235 zip(*output_examples))] |
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1236 all_outputs = Example(all_output_names,function_outputs) |
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1237 if fieldnames==all_output_names: |
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1238 return all_outputs |
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1239 return Example(fieldnames,[all_outputs[name] for name in fieldnames]) |
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1240 |
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1241 |
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1242 return ApplyFunctionIterator(self) |
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1243 |
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1244 def __iter__(self): # only implemented for increased efficiency |
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1245 class ApplyFunctionSingleExampleIterator(object): |
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1246 def __init__(self,output_dataset): |
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1247 self.current=0 |
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1248 self.output_dataset=output_dataset |
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1249 self.input_iterator=output_dataset.input_dataset.__iter__() |
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1250 def __iter__(self): return self |
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1251 def next(self): |
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1252 if self.output_dataset.minibatch_mode: |
| 200 | 1253 function_inputs = [[input] for input in self.input_iterator.next()] |
| 199 | 1254 outputs = self.output_dataset.function(*function_inputs) |
| 1255 assert all([hasattr(output,'__iter__') for output in outputs]) | |
| 1256 function_outputs = [output[0] for output in outputs] | |
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1257 else: |
| 200 | 1258 function_inputs = self.input_iterator.next() |
| 199 | 1259 function_outputs = self.output_dataset.function(*function_inputs) |
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1260 return Example(self.output_dataset.output_names,function_outputs) |
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1261 return ApplyFunctionSingleExampleIterator(self) |
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1262 |
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1263 |
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1264 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
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1265 """ |
| 167 | 1266 Wraps an arbitrary L{DataSet} into one for supervised learning tasks |
| 1267 by forcing the user to define a set of fields as the 'input' field | |
| 1268 and a set of fields as the 'target' field. Optionally, a single | |
| 1269 weight_field can also be defined. | |
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1270 """ |
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1271 args = ((input_fields,'input'),(output_fields,'target')) |
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1272 if weight_field: args+=(([weight_field],'weight')) |
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1273 return src_dataset.merge_fields(*args) |
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1274 |
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1275 |
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1276 |
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1277 |