Mercurial > pylearn
annotate dataset.py @ 328:09140ba68e17
Added untested RenamedFieldsDataSet
| author | Yoshua Bengio <bengioy@iro.umontreal.ca> |
|---|---|
| date | Mon, 16 Jun 2008 16:06:59 -0400 |
| parents | ad8be93b3c55 |
| children | 20e08c52c98c |
| rev | line source |
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2 from lookup_list import LookupList as Example |
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3 from misc import unique_elements_list_intersection |
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4 from string import join |
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5 from sys import maxint |
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6 import numpy, copy |
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| 166 | 8 from exceptions import * |
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10 class AttributesHolder(object): |
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11 def __init__(self): pass |
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12 |
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13 def attributeNames(self): |
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14 raise AbstractFunction() |
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15 |
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16 def setAttributes(self,attribute_names,attribute_values,make_copies=False): |
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17 """ |
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18 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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19 """ |
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20 if len(attribute_names)==1 and not (isinstance(attribute_values,list) or isinstance(attribute_values,tuple) ): |
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21 attribute_values = [attribute_values] |
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22 if make_copies: |
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23 for name,value in zip(attribute_names,attribute_values): |
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24 self.__setattr__(name,copy.deepcopy(value)) |
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25 else: |
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26 for name,value in zip(attribute_names,attribute_values): |
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27 self.__setattr__(name,value) |
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28 |
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29 def getAttributes(self,attribute_names=None, return_copy=False): |
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30 """ |
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31 Return all (if attribute_names=None, in the order of attributeNames()) or a specified subset of attributes. |
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32 """ |
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33 if attribute_names is None: |
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34 attribute_names = self.attributeNames() |
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35 if return_copy: |
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36 return [copy.copy(self.__getattribute__(name)) for name in attribute_names] |
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37 else: |
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38 return [self.__getattribute__(name) for name in attribute_names] |
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39 |
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40 class DataSet(AttributesHolder): |
| 16 | 41 """A virtual base class for datasets. |
| 42 | |
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43 A DataSet can be seen as a generalization of a matrix, meant to be used in conjunction |
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44 with learning algorithms (for training and testing them): rows/records are called examples, and |
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45 columns/attributes are called fields. The field value for a particular example can be an arbitrary |
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46 python object, which depends on the particular dataset. |
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47 |
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48 We call a DataSet a 'stream' when its length is unbounded (in which case its __len__ method |
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49 should return sys.maxint). |
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| 16 | 51 A DataSet is a generator of iterators; these iterators can run through the |
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52 examples or the fields in a variety of ways. A DataSet need not necessarily have a finite |
| 16 | 53 or known length, so this class can be used to interface to a 'stream' which |
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54 feeds on-line learning (however, as noted below, some operations are not |
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55 feasible or not recommended on streams). |
| 16 | 56 |
| 57 To iterate over examples, there are several possibilities: | |
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58 - for example in dataset: |
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59 - for val1,val2,... in dataset: |
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60 - for example in dataset(field1, field2,field3, ...): |
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61 - for val1,val2,val3 in dataset(field1, field2,field3): |
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62 - for minibatch in dataset.minibatches([field1, field2, ...],minibatch_size=N): |
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63 - for mini1,mini2,mini3 in dataset.minibatches([field1, field2, field3], minibatch_size=N): |
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64 Each of these is documented below. All of these iterators are expected |
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65 to provide, in addition to the usual 'next()' method, a 'next_index()' method |
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66 which returns a non-negative integer pointing to the position of the next |
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67 example that will be returned by 'next()' (or of the first example in the |
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68 next minibatch returned). This is important because these iterators |
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69 can wrap around the dataset in order to do multiple passes through it, |
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70 in possibly unregular ways if the minibatch size is not a divisor of the |
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71 dataset length. |
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72 |
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73 To iterate over fields, one can do |
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74 - for field in dataset.fields(): |
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75 for field_value in field: # iterate over the values associated to that field for all the dataset examples |
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76 - for field in dataset(field1,field2,...).fields() to select a subset of fields |
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77 - for field in dataset.fields(field1,field2,...) to select a subset of fields |
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78 and each of these fields is iterable over the examples: |
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79 - for field_examples in dataset.fields(): |
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80 for example_value in field_examples: |
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81 ... |
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82 but when the dataset is a stream (unbounded length), it is not recommended to do |
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83 such things because the underlying dataset may refuse to access the different fields in |
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84 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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85 The result of fields() is a L{DataSetFields} object, which iterates over fields, |
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86 and whose elements are iterable over examples. A DataSetFields object can |
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87 be turned back into a DataSet with its examples() method:: |
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88 dataset2 = dataset1.fields().examples() |
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89 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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90 |
| 16 | 91 Note: Fields are not mutually exclusive, i.e. two fields can overlap in their actual content. |
| 92 | |
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93 Note: The content of a field can be of any type. Field values can also be 'missing' |
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94 (e.g. to handle semi-supervised learning), and in the case of numeric (numpy array) |
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95 fields (i.e. an ArrayFieldsDataSet), NaN plays the role of a missing value. |
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96 What about non-numeric values? None. |
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97 |
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98 Dataset elements can be indexed and sub-datasets (with a subset |
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99 of examples) can be extracted. These operations are not supported |
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100 by default in the case of streams. |
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101 |
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102 - dataset[:n] returns an Example with the n first examples. |
| 16 | 103 |
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104 - dataset[i1:i2:s] returns an Example with the examples i1,i1+s,...i2-s. |
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105 |
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106 - dataset[i] returns an Example. |
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107 |
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108 - dataset[[i1,i2,...in]] returns a. Example with examples i1,i2,...in. |
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109 |
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110 A similar command gives you a DataSet instead of Examples : |
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111 |
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112 - dataset.subset[:n] returns a DataSet with the n first examples. |
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113 |
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114 - dataset.subset[i1:i2:s] returns a DataSet with the examples i1,i1+s,...i2-s. |
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115 |
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116 - dataset.subset[i] returns a DataSet. |
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117 |
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118 - dataset.subset[[i1,i2,...in]] returns a DataSet with examples i1,i2,...in. |
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119 |
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120 |
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121 - dataset.<property> returns the value of a property associated with |
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122 the name <property>. The following properties should be supported: |
| 41 | 123 - 'description': a textual description or name for the dataset |
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124 - 'fieldtypes': a list of types (one per field) |
| 78 | 125 A DataSet may have other attributes that it makes visible to other objects. These are |
| 126 used to store information that is not example-wise but global to the dataset. | |
| 127 The list of names of these attributes is given by the attribute_names() method. | |
| 41 | 128 |
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129 Datasets can be concatenated either vertically (increasing the length) or |
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130 horizontally (augmenting the set of fields), if they are compatible, using |
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131 the following operations (with the same basic semantics as numpy.hstack |
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132 and numpy.vstack): |
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133 |
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134 - dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3]) |
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135 |
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136 creates a new dataset whose list of fields is the concatenation of the list of |
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137 fields of the argument datasets. This only works if they all have the same length. |
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138 |
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139 - dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3]) |
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140 |
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141 creates a new dataset that concatenates the examples from the argument datasets |
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142 (and whose length is the sum of the length of the argument datasets). This only |
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143 works if they all have the same fields. |
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144 |
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145 According to the same logic, and viewing a DataSetFields object associated to |
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146 a DataSet as a kind of transpose of it, fields1 & fields2 concatenates fields of |
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147 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their |
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148 examples. |
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149 |
| 41 | 150 A dataset can hold arbitrary key-value pairs that may be used to access meta-data |
| 151 or other properties of the dataset or associated with the dataset or the result | |
| 152 of a computation stored in a dataset. These can be accessed through the [key] syntax | |
| 153 when key is a string (or more specifically, neither an integer, a slice, nor a list). | |
| 78 | 154 |
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155 A DataSet sub-class should always redefine the following methods: |
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156 - __len__ if it is not a stream |
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157 - fieldNames |
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158 - minibatches_nowrap (called by DataSet.minibatches()) |
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159 For efficiency of implementation, a sub-class might also want to redefine |
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160 - valuesHStack |
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161 - valuesVStack |
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162 - hasFields |
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163 - __getitem__ may not be feasible with some streams |
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164 - __iter__ |
| 78 | 165 A sub-class should also append attributes to self._attribute_names |
| 166 (the default value returned by attributeNames()). | |
| 167 By convention, attributes not in attributeNames() should have a name | |
| 168 starting with an underscore. | |
| 169 @todo enforce/test that convention! | |
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170 """ |
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171 |
| 83 | 172 numpy_vstack = lambda fieldname,values: numpy.vstack(values) |
| 173 numpy_hstack = lambda fieldnames,values: numpy.hstack(values) | |
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174 |
| 292 | 175 def __init__(self, description=None, fieldnames=None, fieldtypes=None): |
| 176 """ | |
| 177 @type fieldnames: list of strings | |
| 178 @type fieldtypes: list of python types, same length as fieldnames | |
| 179 @type description: string | |
| 180 @param description: description/name for this dataset | |
| 181 """ | |
| 182 def default_desc(): | |
| 183 return type(self).__name__ \ | |
| 184 + " ( " + join([x.__name__ for x in type(self).__bases__]) + " )" | |
| 185 | |
| 186 #self.fieldnames = fieldnames | |
| 187 | |
| 188 self.fieldtypes = fieldtypes if fieldtypes is not None \ | |
| 189 else [None]*1 #len(fieldnames) | |
| 190 | |
| 191 self.description = default_desc() if description is None \ | |
| 192 else description | |
| 78 | 193 self._attribute_names = ["description"] |
| 292 | 194 |
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195 |
| 292 | 196 attributeNames = property(lambda self: copy.copy(self._attribute_names)) |
| 197 | |
| 198 def __contains__(self, fieldname): | |
| 199 return (fieldname in self.fieldNames()) \ | |
| 200 or (fieldname in self.attributeNames()) | |
| 201 | |
| 202 def __iter__(self): | |
| 203 """Supports the syntax "for i in dataset: ..." | |
| 78 | 204 |
| 292 | 205 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 206 all the fields of DataSet self. Every field of "i" will give access to | |
| 207 a field of a single example. Fields should be accessible via | |
| 208 i["fielname"] or i[3] (in the order defined by the elements of the | |
| 209 Example returned by this iterator), but the derived class is free | |
| 210 to accept any type of identifier, and add extra functionality to the iterator. | |
| 211 | |
| 212 The default implementation calls the minibatches iterator and extracts the first example of each field. | |
| 213 """ | |
| 214 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) | |
| 215 | |
| 216 def __len__(self): | |
| 217 """ | |
| 218 len(dataset) returns the number of examples in the dataset. | |
| 219 By default, a DataSet is a 'stream', i.e. it has an unbounded length (sys.maxint). | |
| 220 Sub-classes which implement finite-length datasets should redefine this method. | |
| 221 Some methods only make sense for finite-length datasets. | |
| 222 """ | |
| 223 return None | |
| 224 | |
| 78 | 225 |
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226 class MinibatchToSingleExampleIterator(object): |
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227 """ |
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228 Converts the result of minibatch iterator with minibatch_size==1 into |
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229 single-example values in the result. Therefore the result of |
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230 iterating on the dataset itself gives a sequence of single examples |
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231 (whereas the result of iterating over minibatches gives in each |
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232 Example field an iterable object over the individual examples in |
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233 the minibatch). |
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234 """ |
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235 def __init__(self, minibatch_iterator): |
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236 self.minibatch_iterator = minibatch_iterator |
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237 self.minibatch = None |
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238 def __iter__(self): #makes for loop work |
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239 return self |
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240 def next(self): |
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241 size1_minibatch = self.minibatch_iterator.next() |
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242 if not self.minibatch: |
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243 self.minibatch = Example(size1_minibatch.keys(),[value[0] for value in size1_minibatch.values()]) |
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244 else: |
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245 self.minibatch._values = [value[0] for value in size1_minibatch.values()] |
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246 return self.minibatch |
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247 |
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248 def next_index(self): |
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249 return self.minibatch_iterator.next_index() |
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250 |
|
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251 class MinibatchWrapAroundIterator(object): |
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252 """ |
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253 An iterator for minibatches that handles the case where we need to wrap around the |
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254 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
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255 a dataset that provides a minibatch iterator that does not need to handle that problem. |
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256 This class is a utility for dataset subclass writers, so that they do not have to handle |
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257 this issue multiple times, nor check that fieldnames are valid, nor handle the |
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258 empty fieldnames (meaning 'use all the fields'). |
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259 """ |
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260 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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261 self.dataset=dataset |
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262 self.fieldnames=fieldnames |
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263 self.minibatch_size=minibatch_size |
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264 self.n_batches=n_batches |
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265 self.n_batches_done=0 |
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266 self.next_row=offset |
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267 self.L=len(dataset) |
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268 self.offset=offset % self.L |
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269 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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270 if n_batches is not None: |
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271 ds_nbatches = min(n_batches,ds_nbatches) |
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272 if fieldnames: |
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273 assert dataset.hasFields(*fieldnames) |
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274 else: |
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275 self.fieldnames=dataset.fieldNames() |
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276 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, ds_nbatches,self.next_row) |
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277 |
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278 def __iter__(self): |
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279 return self |
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280 |
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281 def next_index(self): |
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282 return self.next_row |
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283 |
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284 def next(self): |
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285 if self.n_batches and self.n_batches_done==self.n_batches: |
|
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286 raise StopIteration |
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287 elif not self.n_batches and self.next_row ==self.L: |
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288 raise StopIteration |
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289 upper = self.next_row+self.minibatch_size |
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290 if upper <=self.L: |
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291 minibatch = self.iterator.next() |
|
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292 else: |
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293 if not self.n_batches: |
|
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294 upper=min(upper, self.L) |
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295 # if their is not a fixed number of batch, we continue to the end of the dataset. |
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296 # this can create a minibatch that is smaller then the minibatch_size |
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297 assert (self.L-self.next_row)<=self.minibatch_size |
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298 minibatch = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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299 else: |
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300 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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301 # first get the beginning of our minibatch (top of dataset) |
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302 first_part = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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303 second_part = self.dataset.minibatches_nowrap(self.fieldnames,upper-self.L,1,0).next() |
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304 minibatch = Example(self.fieldnames, |
|
268
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305 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
|
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306 for name in self.fieldnames]) |
|
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307 self.next_row=upper |
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308 self.n_batches_done+=1 |
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309 if upper >= self.L and self.n_batches: |
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310 self.next_row -= self.L |
|
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311 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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312 if self.n_batches is not None: |
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313 ds_nbatches = min(self.n_batches,ds_nbatches) |
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314 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, |
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315 ds_nbatches,self.next_row) |
|
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316 return DataSetFields(MinibatchDataSet(minibatch,self.dataset.valuesVStack, |
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317 self.dataset.valuesHStack), |
|
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318 minibatch.keys()) |
|
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319 |
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320 |
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321 minibatches_fieldnames = None |
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322 minibatches_minibatch_size = 1 |
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323 minibatches_n_batches = None |
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324 def minibatches(self, |
|
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325 fieldnames = minibatches_fieldnames, |
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326 minibatch_size = minibatches_minibatch_size, |
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327 n_batches = minibatches_n_batches, |
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328 offset = 0): |
|
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329 """ |
|
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330 Return an iterator that supports three forms of syntax: |
|
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331 |
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332 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 333 |
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334 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 335 |
|
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336 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 337 |
|
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338 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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339 tuple, or Example instance. In both cases, i[k] is a list-like container |
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340 of a batch of current examples. In the second case, i[0] is |
|
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341 list-like container of the f1 field of a batch current examples, i[1] is |
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342 a list-like container of the f2 field, etc. |
|
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343 |
|
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344 Using the first syntax, all the fields will be returned in "i". |
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345 Using the third syntax, i1, i2, i3 will be list-like containers of the |
|
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346 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
|
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347 |
|
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348 The minibatches iterator is expected to return upon each call to next() |
|
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349 a DataSetFields object, which is a Example (indexed by the field names) whose |
| 80 | 350 elements are iterable and indexable over the minibatch examples, and which keeps a pointer to |
|
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351 a sub-dataset that can be used to iterate over the individual examples |
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352 in the minibatch. Hence a minibatch can be converted back to a regular |
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353 dataset or its fields can be looked at individually (and possibly iterated over). |
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354 |
|
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355 PARAMETERS |
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356 - fieldnames (list of any type, default None): |
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357 The loop variables i1, i2, i3 (in the example above) should contain the |
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358 f1, f2, and f3 fields of the current batch of examples. If None, the |
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359 derived class can choose a default, e.g. all fields. |
| 16 | 360 |
|
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361 - minibatch_size (integer, default 1) |
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362 On every iteration, the variables i1, i2, i3 will have |
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363 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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364 |
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365 - n_batches (integer, default None) |
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366 The iterator will loop exactly this many times, and then stop. If None, |
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367 the derived class can choose a default. If (-1), then the returned |
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368 iterator should support looping indefinitely. |
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369 |
|
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370 - offset (integer, default 0) |
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371 The iterator will start at example 'offset' in the dataset, rather than the default. |
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372 |
|
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373 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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374 any other object that supports integer indexing and slicing. |
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375 |
|
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376 """ |
|
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377 return DataSet.MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
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378 |
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379 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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380 """ |
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381 This is the minibatches iterator generator that sub-classes must define. |
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382 It does not need to worry about wrapping around multiple times across the dataset, |
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383 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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384 The next() method of the returned iterator does not even need to worry about |
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385 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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386 before an improper call to minibatches_nowrap's next() is made). |
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387 That next() method can assert that its next row will always be within [0,len(dataset)). |
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388 The iterator returned by minibatches_nowrap does not need to implement |
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389 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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390 """ |
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391 raise AbstractFunction() |
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392 |
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393 def is_unbounded(self): |
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394 """ |
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395 Tests whether a dataset is unbounded (e.g. a stream). |
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396 """ |
| 123 | 397 return len(self)==maxint |
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398 |
|
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399 def hasFields(self,*fieldnames): |
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400 """ |
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401 Return true if the given field name (or field names, if multiple arguments are |
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402 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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403 of the iterators). |
|
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404 |
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405 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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406 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
|
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407 """ |
|
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408 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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409 |
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410 def fieldNames(self): |
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411 """ |
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412 Return the list of field names that are supported by the iterators, |
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413 and for which hasFields(fieldname) would return True. |
|
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414 """ |
|
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415 raise AbstractFunction() |
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416 |
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417 def __call__(self,*fieldnames): |
|
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418 """ |
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419 Return a dataset that sees only the fields whose name are specified. |
|
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420 """ |
|
42
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421 assert self.hasFields(*fieldnames) |
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422 return self.fields(*fieldnames).examples() |
|
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423 |
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424 def fields(self,*fieldnames): |
|
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425 """ |
|
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426 Return a DataSetFields object associated with this dataset. |
|
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427 """ |
|
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428 return DataSetFields(self,fieldnames) |
|
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429 |
|
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430 def getitem_key(self, fieldname): |
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431 """A not-so-well thought-out place to put code that used to be in |
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432 getitem. |
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433 """ |
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434 #removing as per discussion June 4. --JSB |
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435 |
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436 i = fieldname |
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437 # else check for a fieldname |
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438 if self.hasFields(i): |
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439 return self.minibatches(fieldnames=[i],minibatch_size=len(self),n_batches=1,offset=0).next()[0] |
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440 # else we are trying to access a property of the dataset |
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441 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
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442 return self.__dict__[i] |
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443 |
|
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444 def __getitem__(self,i): |
|
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445 """ |
|
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446 @rtype: Example |
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447 @returns: single or multiple examples |
|
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448 |
|
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449 @type i: integer or slice or <iterable> of integers |
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450 @param i: |
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451 dataset[i] returns the (i+1)-th example of the dataset. |
|
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452 dataset[i:j] returns a LookupList with examples i,i+1,...,j-1. |
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453 dataset[i:j:s] returns a LookupList with examples i,i+2,i+4...,j-2. |
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454 dataset[[i1,i2,..,in]] returns a LookupList with examples i1,i2,...,in. |
|
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455 |
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456 @note: |
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457 Some stream datasets may be unable to implement random access, i.e. |
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458 arbitrary slicing/indexing because they can only iterate through |
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459 examples one or a minibatch at a time and do not actually store or keep |
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460 past (or future) examples. |
|
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461 |
|
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462 The default implementation of getitem uses the minibatches iterator |
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463 to obtain one example, one slice, or a list of examples. It may not |
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464 always be the most efficient way to obtain the result, especially if |
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465 the data are actually stored in a memory array. |
|
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466 """ |
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467 |
|
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468 if type(i) is int: |
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469 assert i >= 0 # TBM: see if someone complains and want negative i |
|
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470 if i >= len(self) : |
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471 raise IndexError |
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472 i_batch = self.minibatches_nowrap(self.fieldNames(), |
| 293 | 473 minibatch_size=1, n_batches=1, offset=i) |
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474 return DataSet.MinibatchToSingleExampleIterator(i_batch).next() |
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475 |
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476 #if i is a contiguous slice |
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477 if type(i) is slice and (i.step in (None, 1)): |
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478 offset = 0 if i.start is None else i.start |
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479 upper_bound = len(self) if i.stop is None else i.stop |
|
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480 upper_bound = min(len(self) , upper_bound) |
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481 #return MinibatchDataSet(self.minibatches_nowrap(self.fieldNames(), |
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482 # minibatch_size=upper_bound - offset, |
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483 # n_batches=1, |
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484 # offset=offset).next()) |
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485 # now returns a LookupList |
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486 return self.minibatches_nowrap(self.fieldNames(), |
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487 minibatch_size=upper_bound - offset, |
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488 n_batches=1, |
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489 offset=offset).next() |
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490 |
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491 # if slice has a step param, convert it to list and handle it with the |
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492 # list code |
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493 if type(i) is slice: |
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494 offset = 0 if i.start is None else i.start |
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495 upper_bound = len(self) if i.stop is None else i.stop |
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496 upper_bound = min(len(self) , upper_bound) |
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497 i = list(range(offset, upper_bound, i.step)) |
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498 |
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499 # handle tuples, arrays, lists |
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500 if hasattr(i, '__getitem__'): |
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501 for idx in i: |
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502 #dis-allow nested slices |
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503 if not isinstance(idx, int): |
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504 raise TypeError(idx) |
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505 if idx >= len(self) : |
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506 raise IndexError |
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507 # call back into self.__getitem__ |
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508 examples = [self.minibatches_nowrap(self.fieldNames(), |
| 293 | 509 minibatch_size=1, n_batches=1, offset=ii).next() |
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510 for ii in i] |
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511 # re-index the fields in each example by field instead of by example |
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512 field_values = [[] for blah in self.fieldNames()] |
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513 for e in examples: |
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514 for f,v in zip(field_values, e): |
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515 f.append(v) |
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516 #build them into a LookupList (a.ka. Example) |
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517 zz = zip(self.fieldNames(),field_values) |
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518 vst = [self.valuesVStack(fieldname,field_values) for fieldname,field_values in zz] |
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519 example = Example(self.fieldNames(), vst) |
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520 #return MinibatchDataSet(example, self.valuesVStack, self.valuesHStack) |
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521 # now returns a LookupList |
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522 return example |
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523 |
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524 # what in the world is i? |
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525 raise TypeError(i, type(i)) |
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526 |
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527 |
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528 """ |
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529 Enables the call dataset.subset[a:b:c] that will return a DataSet |
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530 around the examples returned by __getitem__(slice(a,b,c)) |
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531 |
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532 @SEE DataSet.__getsubset(self) |
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533 """ |
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534 subset = property(lambda s : s.__getsubset(),doc="returns a subset as a DataSet") |
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535 |
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536 |
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537 def __getsubset(self) : |
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538 """ |
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539 Enables the call data.subset[a:b:c], returns a DataSet. |
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540 Default implementation is a simple wrap around __getitem__() using MinibatchDataSet. |
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541 |
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542 @RETURN DataSet |
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543 @SEE DataSet.subset = property(lambda s : s.__getsubset()) |
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544 """ |
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545 _self = self |
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546 class GetSliceReturnsDataSet(object) : |
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547 def __getitem__(self,slice) : |
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548 return MinibatchDataSet(_self.__getitem__(slice)) |
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549 return GetSliceReturnsDataSet() |
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550 |
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551 |
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552 |
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553 def valuesHStack(self,fieldnames,fieldvalues): |
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554 """ |
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555 Return a value that corresponds to concatenating (horizontally) several field values. |
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556 This can be useful to merge some fields. The implementation of this operation is likely |
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557 to involve a copy of the original values. When the values are numpy arrays, the |
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558 result should be numpy.hstack(values). If it makes sense, this operation should |
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559 work as well when each value corresponds to multiple examples in a minibatch |
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560 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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561 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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562 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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563 pointing to the original values for other data types. |
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564 """ |
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565 all_numpy=True |
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566 for value in fieldvalues: |
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567 if not type(value) is numpy.ndarray: |
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568 all_numpy=False |
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569 if all_numpy: |
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570 return numpy.hstack(fieldvalues) |
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571 # the default implementation of horizontal stacking is to put values in a list |
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572 return fieldvalues |
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573 |
|
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574 def valuesVStack(self,fieldname,values): |
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575 """ |
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576 @param fieldname: the name of the field from which the values were taken |
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577 @type fieldname: any type |
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578 |
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579 @param values: bits near the beginning or end of the dataset |
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580 @type values: list of minibatches (returned by minibatch_nowrap) |
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581 |
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582 @return: the concatenation (stacking) of the values |
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583 @rtype: something suitable as a minibatch field |
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584 """ |
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585 rval = [] |
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586 for v in values: |
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587 rval.extend(v) |
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588 return rval |
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589 |
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590 def __or__(self,other): |
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591 """ |
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592 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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593 fields of the argument datasets. This only works if they all have the same length. |
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594 """ |
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595 return HStackedDataSet([self,other]) |
|
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596 |
|
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597 def __and__(self,other): |
|
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598 """ |
|
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599 dataset1 & dataset2 is a dataset that concatenates the examples from the argument datasets |
|
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600 (and whose length is the sum of the length of the argument datasets). This only |
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601 works if they all have the same fields. |
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602 """ |
|
135
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603 return VStackedDataSet([self,other]) |
|
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604 |
|
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605 def hstack(datasets): |
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606 """ |
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607 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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608 which is a dataset whose fields list is the concatenation of the fields |
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609 of the individual datasets. |
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610 """ |
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611 assert len(datasets)>0 |
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612 if len(datasets)==1: |
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613 return datasets[0] |
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614 return HStackedDataSet(datasets) |
|
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615 |
|
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616 def vstack(datasets): |
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617 """ |
|
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618 vstack(dataset1,dataset2,...) returns dataset1 & datataset2 & ... |
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619 which is a dataset which iterates first over the examples of dataset1, then |
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620 over those of dataset2, etc. |
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621 """ |
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622 assert len(datasets)>0 |
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623 if len(datasets)==1: |
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624 return datasets[0] |
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625 return VStackedDataSet(datasets) |
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626 |
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627 class FieldsSubsetDataSet(DataSet): |
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628 """ |
| 167 | 629 A sub-class of L{DataSet} that selects a subset of the fields. |
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630 """ |
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631 def __init__(self,src,fieldnames): |
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632 self.src=src |
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633 self.fieldnames=fieldnames |
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634 assert src.hasFields(*fieldnames) |
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635 self.valuesHStack = src.valuesHStack |
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636 self.valuesVStack = src.valuesVStack |
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637 |
|
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638 def __len__(self): return len(self.src) |
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639 |
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640 def fieldNames(self): |
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641 return self.fieldnames |
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642 |
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643 def __iter__(self): |
|
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644 class FieldsSubsetIterator(object): |
|
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645 def __init__(self,ds): |
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646 self.ds=ds |
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647 self.src_iter=ds.src.__iter__() |
|
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648 self.example=None |
|
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649 def __iter__(self): return self |
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650 def next(self): |
|
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651 complete_example = self.src_iter.next() |
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652 if self.example: |
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653 self.example._values=[complete_example[field] |
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654 for field in self.ds.fieldnames] |
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655 else: |
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656 self.example=Example(self.ds.fieldnames, |
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657 [complete_example[field] for field in self.ds.fieldnames]) |
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658 return self.example |
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659 return FieldsSubsetIterator(self) |
|
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660 |
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661 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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662 assert self.hasFields(*fieldnames) |
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663 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
|
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664 def dontuse__getitem__(self,i): |
|
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665 return FieldsSubsetDataSet(self.src[i],self.fieldnames) |
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666 |
|
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667 class RenamedFieldsDataSet(DataSet): |
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668 """ |
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|
669 A sub-class of L{DataSet} that selects and renames a subset of the fields. |
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670 """ |
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671 def __init__(self,src,src_fieldnames,new_fieldnames): |
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672 self.src=src |
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673 self.src_fieldnames=src_fieldnames |
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674 self.new_fieldnames=new_fieldnames |
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675 assert src.hasFields(*src_fieldnames) |
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676 assert len(src_fieldnames)==len(new_fieldnames) |
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677 self.valuesHStack = src.valuesHStack |
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678 self.valuesVStack = src.valuesVStack |
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|
679 |
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680 def __len__(self): return len(self.src) |
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681 |
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682 def fieldNames(self): |
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683 return self.new_fieldnames |
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684 |
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|
685 def __iter__(self): |
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686 class FieldsSubsetIterator(object): |
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687 def __init__(self,ds): |
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688 self.ds=ds |
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689 self.src_iter=ds.src.__iter__() |
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690 self.example=None |
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691 def __iter__(self): return self |
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692 def next(self): |
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693 complete_example = self.src_iter.next() |
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694 if self.example: |
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695 self.example._values=[complete_example[field] |
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696 for field in self.ds.src_fieldnames] |
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697 else: |
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698 self.example=Example(self.ds.new_fieldnames, |
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|
699 [complete_example[field] |
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700 for field in self.ds.src_fieldnames]) |
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701 return self.example |
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702 return FieldsSubsetIterator(self) |
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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 assert self.hasFields(*fieldnames) |
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706 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
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|
707 def __getitem__(self,i): |
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708 return FieldsSubsetDataSet(self.src[i],self.new_fieldnames) |
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|
709 |
|
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|
710 |
|
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|
711 class DataSetFields(Example): |
|
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712 """ |
| 167 | 713 Although a L{DataSet} iterates over examples (like rows of a matrix), an associated |
|
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|
714 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
|
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diff
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|
715 as a transpose of the associated dataset. |
|
17
759d17112b23
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diff
changeset
|
716 |
|
36
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diff
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|
717 To iterate over fields, one can do |
|
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diff
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|
718 * for fields in dataset.fields() |
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diff
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|
719 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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|
720 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
|
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|
721 and each of these fields is iterable over the examples: |
|
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diff
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|
722 * for field_examples in dataset.fields(): |
|
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diff
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|
723 for example_value in field_examples: |
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|
724 ... |
|
241
ddb88a8e9fd2
If I understand properly, the length of an unbounded stream is sys.maxint
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diff
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|
725 but when the dataset is a stream (unbounded length), it is not recommended to do |
|
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|
726 such things because the underlying dataset may refuse to access the different fields in |
|
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|
727 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
|
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|
728 The result of fields() is a DataSetFields object, which iterates over fields, |
|
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|
729 and whose elements are iterable over examples. A DataSetFields object can |
|
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|
730 be turned back into a DataSet with its examples() method: |
|
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|
731 dataset2 = dataset1.fields().examples() |
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|
732 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
|
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88fd1cce08b9
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diff
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|
733 |
|
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diff
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|
734 DataSetFields can be concatenated vertically or horizontally. To be consistent with |
|
88fd1cce08b9
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diff
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|
735 the syntax used for DataSets, the | concatenates the fields and the & concatenates |
|
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|
736 the examples. |
|
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|
737 """ |
|
74
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diff
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|
738 def __init__(self,dataset,fieldnames): |
|
65
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|
739 original_dataset=dataset |
|
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|
740 if not fieldnames: |
|
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|
741 fieldnames=dataset.fieldNames() |
|
274
ed70580f2324
bugfix in FieldSubsetDataSet
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diff
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|
742 elif not list(fieldnames)==list(dataset.fieldNames()): |
|
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bugfix in FieldSubsetDataSet
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diff
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|
743 #we must cast to list, othersize('x','y')!=['x','y'] |
|
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diff
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|
744 dataset = FieldsSubsetDataSet(dataset,fieldnames) |
|
37
73c4212ba5b3
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diff
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|
745 assert dataset.hasFields(*fieldnames) |
|
42
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Testing basic functionality and removing obvious bugs
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diff
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|
746 self.dataset=dataset |
|
66
dde1fb1b63ba
fixed test and removed print
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diff
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|
747 |
|
64
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diff
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|
748 if isinstance(dataset,MinibatchDataSet): |
|
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|
749 Example.__init__(self,fieldnames,list(dataset._fields)) |
|
65
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diff
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|
750 elif isinstance(original_dataset,MinibatchDataSet): |
|
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|
751 Example.__init__(self,fieldnames, |
|
65
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diff
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|
752 [original_dataset._fields[field] |
|
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diff
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|
753 for field in fieldnames]) |
|
64
863da25a60f1
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diff
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|
754 else: |
|
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diff
changeset
|
755 minibatch_iterator = dataset.minibatches(fieldnames, |
|
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diff
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|
756 minibatch_size=len(dataset), |
|
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parents:
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diff
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|
757 n_batches=1) |
|
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|
758 minibatch=minibatch_iterator.next() |
|
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|
759 Example.__init__(self,fieldnames,minibatch) |
|
42
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diff
changeset
|
760 |
|
36
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|
761 def examples(self): |
|
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|
762 return self.dataset |
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|
763 |
|
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|
764 def __or__(self,other): |
|
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|
765 """ |
|
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|
766 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
|
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|
767 of the list of examples of DataSetFields fields1 and fields2. |
|
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changeset
|
768 """ |
|
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|
769 return (self.examples() + other.examples()).fields() |
|
17
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bergstrj@iro.umontreal.ca
diff
changeset
|
770 |
|
40
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diff
changeset
|
771 def __and__(self,other): |
|
17
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diff
changeset
|
772 """ |
|
36
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changeset
|
773 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
|
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|
774 of the fields of DataSetFields fields1 and fields2. |
|
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|
775 """ |
|
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|
776 return (self.examples() | other.examples()).fields() |
|
17
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bergstrj@iro.umontreal.ca
diff
changeset
|
777 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
778 |
|
36
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diff
changeset
|
779 class MinibatchDataSet(DataSet): |
|
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|
780 """ |
|
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diff
changeset
|
781 Turn a L{Example} of same-length (iterable) fields into an example-iterable dataset. |
|
36
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changeset
|
782 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
|
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|
783 """ |
|
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diff
changeset
|
784 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
|
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diff
changeset
|
785 values_hstack=DataSet().valuesHStack): |
|
17
759d17112b23
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bergstrj@iro.umontreal.ca
diff
changeset
|
786 """ |
|
36
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|
787 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
|
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diff
changeset
|
788 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
|
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diff
changeset
|
789 semantics as the DataSet methods of the same name (but without the self argument). |
|
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diff
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|
790 """ |
|
211
bd728c83faff
in __get__, problem if the i.stop was None, i being the slice, added one line replacing None by the len(self)
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
203
diff
changeset
|
791 |
|
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:
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diff
changeset
|
792 self._fields=fields_lookuplist |
|
36
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diff
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|
793 assert len(fields_lookuplist)>0 |
|
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|
794 self.length=len(fields_lookuplist[0]) |
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|
795 for field in fields_lookuplist[1:]: |
|
223
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
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diff
changeset
|
796 if self.length != len(field) : |
|
517364d48ae0
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diff
changeset
|
797 print 'self.length = ',self.length |
|
517364d48ae0
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diff
changeset
|
798 print 'len(field) = ', len(field) |
|
517364d48ae0
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diff
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|
799 print 'self._fields.keys() = ', self._fields.keys() |
|
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diff
changeset
|
800 print 'field=',field |
|
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|
801 print 'fields_lookuplist=', fields_lookuplist |
|
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|
802 assert self.length==len(field) |
|
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|
803 self.valuesVStack=values_vstack |
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|
804 self.valuesHStack=values_hstack |
|
3
378b68d5c4ad
Added first (untested) version of ArrayDataSet
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parents:
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diff
changeset
|
805 |
|
378b68d5c4ad
Added first (untested) version of ArrayDataSet
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parents:
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diff
changeset
|
806 def __len__(self): |
|
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diff
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|
807 return self.length |
|
28
541a273bc89f
Removed __array__ method from dataset, whose
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diff
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|
808 |
|
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|
809 def dontuse__getitem__(self,i): |
| 80 | 810 if type(i) in (slice,list): |
|
48
b6730f9a336d
Fixing MinibatchDataSet getitem
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diff
changeset
|
811 return DataSetFields(MinibatchDataSet( |
| 80 | 812 Example(self._fields.keys(),[field[i] for field in self._fields])),self.fieldNames()) |
| 813 if type(i) is int: | |
| 85 | 814 return Example(self._fields.keys(),[field[i] for field in self._fields]) |
|
48
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Fixing MinibatchDataSet getitem
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diff
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|
815 if self.hasFields(i): |
|
61
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Frederic Bastien <bastienf@iro.umontreal.ca>
parents:
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diff
changeset
|
816 return self._fields[i] |
|
55
66619ce44497
Efficient implementation of getitem for ArrayDataSet
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
48
diff
changeset
|
817 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
|
48
b6730f9a336d
Fixing MinibatchDataSet getitem
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parents:
46
diff
changeset
|
818 return self.__dict__[i] |
|
11
be128b9127c8
Debugged (to the extent of my tests) the new version of dataset
bengioy@esprit.iro.umontreal.ca
parents:
9
diff
changeset
|
819 |
|
29
46c5c90019c2
Changed apply_function so that it propagates methods of the source.
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diff
changeset
|
820 def fieldNames(self): |
|
61
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diff
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|
821 return self._fields.keys() |
|
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diff
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|
822 |
|
37
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diff
changeset
|
823 def hasFields(self,*fieldnames): |
|
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|
824 for fieldname in fieldnames: |
|
61
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|
825 if fieldname not in self._fields.keys(): |
|
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|
826 return False |
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diff
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|
827 return True |
|
20
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Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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parents:
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diff
changeset
|
828 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
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36
diff
changeset
|
829 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
223
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diff
changeset
|
830 #@TODO bug somewhere here, fieldnames doesnt seem to be well handled |
|
36
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|
831 class Iterator(object): |
|
223
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diff
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|
832 def __init__(self,ds,fieldnames): |
|
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diff
changeset
|
833 # tbm: added two next lines to handle fieldnames |
|
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|
834 if fieldnames is None: fieldnames = ds._fields.keys() |
|
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diff
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|
835 self.fieldnames = fieldnames |
|
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|
836 |
|
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|
837 self.ds=ds |
|
37
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Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
838 self.next_example=offset |
|
290
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diff
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|
839 assert minibatch_size >= 0 |
| 41 | 840 if offset+minibatch_size > ds.length: |
|
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diff
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|
841 raise NotImplementedError() |
|
20
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parents:
19
diff
changeset
|
842 def __iter__(self): |
|
266c68cb6136
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diff
changeset
|
843 return self |
|
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Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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parents:
19
diff
changeset
|
844 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
|
845 upper = self.next_example+minibatch_size |
|
37
73c4212ba5b3
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diff
changeset
|
846 assert upper<=self.ds.length |
|
223
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diff
changeset
|
847 #minibatch = Example(self.ds._fields.keys(), |
|
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diff
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|
848 # [field[self.next_example:upper] |
|
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changeset
|
849 # for field in self.ds._fields]) |
|
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diff
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|
850 # tbm: modif to use fieldnames |
|
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diff
changeset
|
851 values = [] |
|
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diff
changeset
|
852 for f in self.fieldnames : |
|
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diff
changeset
|
853 #print 'we have field',f,'in fieldnames' |
|
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diff
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|
854 values.append( self.ds._fields[f][self.next_example:upper] ) |
|
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diff
changeset
|
855 minibatch = Example(self.fieldnames,values) |
|
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parents:
218
diff
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|
856 #print minibatch |
|
36
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diff
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|
857 self.next_example+=minibatch_size |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
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72
diff
changeset
|
858 return minibatch |
|
20
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diff
changeset
|
859 |
|
223
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diff
changeset
|
860 # tbm: added fieldnames to handle subset of fieldnames |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
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218
diff
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|
861 return Iterator(self,fieldnames) |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
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diff
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|
862 |
|
36
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diff
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|
863 class HStackedDataSet(DataSet): |
|
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diff
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|
864 """ |
| 167 | 865 A L{DataSet} that wraps several datasets and shows a view that includes all their fields, |
|
36
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diff
changeset
|
866 i.e. whose list of fields is the concatenation of their lists of fields. |
|
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diff
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|
867 |
|
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diff
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|
868 If a field name is found in more than one of the datasets, then either an error is |
|
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diff
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|
869 raised or the fields are renamed (either by prefixing the __name__ attribute |
|
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|
870 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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|
871 |
| 167 | 872 @todo: automatically detect a chain of stacked datasets due to A | B | C | D ... |
|
36
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diff
changeset
|
873 """ |
| 41 | 874 def __init__(self,datasets,accept_nonunique_names=False,description=None,field_types=None): |
| 875 DataSet.__init__(self,description,field_types) | |
|
36
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|
876 self.datasets=datasets |
|
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|
877 self.accept_nonunique_names=accept_nonunique_names |
|
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|
878 self.fieldname2dataset={} |
|
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diff
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|
879 |
|
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diff
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|
880 def rename_field(fieldname,dataset,i): |
|
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Rewriting dataset.py completely
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parents:
29
diff
changeset
|
881 if hasattr(dataset,"__name__"): |
|
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diff
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|
882 return dataset.__name__ + "." + fieldname |
|
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parents:
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diff
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|
883 return fieldname+"."+str(i) |
|
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diff
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|
884 |
|
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|
885 # make sure all datasets have the same length and unique field names |
|
438440ba0627
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diff
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|
886 self.length=None |
|
438440ba0627
Rewriting dataset.py completely
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diff
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|
887 names_to_change=[] |
|
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diff
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|
888 for i in xrange(len(datasets)): |
|
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parents:
29
diff
changeset
|
889 dataset = datasets[i] |
|
438440ba0627
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parents:
29
diff
changeset
|
890 length=len(dataset) |
|
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parents:
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diff
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|
891 if self.length: |
|
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parents:
29
diff
changeset
|
892 assert self.length==length |
|
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parents:
29
diff
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|
893 else: |
|
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parents:
29
diff
changeset
|
894 self.length=length |
|
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parents:
29
diff
changeset
|
895 for fieldname in dataset.fieldNames(): |
|
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Rewriting dataset.py completely
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parents:
29
diff
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|
896 if fieldname in self.fieldname2dataset: # name conflict! |
|
438440ba0627
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parents:
29
diff
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|
897 if accept_nonunique_names: |
|
438440ba0627
Rewriting dataset.py completely
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diff
changeset
|
898 fieldname=rename_field(fieldname,dataset,i) |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
899 names2change.append((fieldname,i)) |
|
438440ba0627
Rewriting dataset.py completely
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parents:
29
diff
changeset
|
900 else: |
|
438440ba0627
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parents:
29
diff
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|
901 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
|
438440ba0627
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parents:
29
diff
changeset
|
902 self.fieldname2dataset[fieldname]=i |
|
438440ba0627
Rewriting dataset.py completely
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parents:
29
diff
changeset
|
903 for fieldname,i in names_to_change: |
|
438440ba0627
Rewriting dataset.py completely
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parents:
29
diff
changeset
|
904 del self.fieldname2dataset[fieldname] |
|
438440ba0627
Rewriting dataset.py completely
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parents:
29
diff
changeset
|
905 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
|
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parents:
29
diff
changeset
|
906 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
907 def hasFields(self,*fieldnames): |
|
36
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diff
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|
908 for fieldname in fieldnames: |
|
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29
diff
changeset
|
909 if not fieldname in self.fieldname2dataset: |
|
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29
diff
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|
910 return False |
|
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diff
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|
911 return True |
|
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29
diff
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|
912 |
|
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diff
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|
913 def fieldNames(self): |
|
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diff
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|
914 return self.fieldname2dataset.keys() |
|
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diff
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|
915 |
| 41 | 916 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
36
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diff
changeset
|
917 |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
918 class HStackedIterator(object): |
|
36
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diff
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|
919 def __init__(self,hsds,iterators): |
|
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|
920 self.hsds=hsds |
|
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diff
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|
921 self.iterators=iterators |
|
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diff
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|
922 def __iter__(self): |
|
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diff
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|
923 return self |
|
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diff
changeset
|
924 def next(self): |
|
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diff
changeset
|
925 # concatenate all the fields of the minibatches |
|
290
9b533cc7874a
trying to get default implemenations to work
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
274
diff
changeset
|
926 l=Example() |
| 140 | 927 for iter in self.iterators: |
| 928 l.append_lookuplist(iter.next()) | |
| 929 return l | |
|
36
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diff
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|
930 |
| 125 | 931 assert self.hasFields(*fieldnames) |
|
36
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diff
changeset
|
932 # find out which underlying datasets are necessary to service the required fields |
|
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diff
changeset
|
933 # and construct corresponding minibatch iterators |
| 140 | 934 if fieldnames and fieldnames!=self.fieldNames(): |
|
36
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diff
changeset
|
935 datasets=set([]) |
|
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diff
changeset
|
936 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
|
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diff
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|
937 for fieldname in fieldnames: |
| 136 | 938 dataset=self.datasets[self.fieldname2dataset[fieldname]] |
|
36
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diff
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|
939 datasets.add(dataset) |
|
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diff
changeset
|
940 fields_in_dataset[dataset].append(fieldname) |
|
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diff
changeset
|
941 datasets=list(datasets) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
942 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
|
36
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diff
changeset
|
943 for dataset in datasets] |
|
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diff
changeset
|
944 else: |
|
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diff
changeset
|
945 datasets=self.datasets |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
946 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
|
947 return HStackedIterator(self,iterators) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
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diff
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|
948 |
|
438440ba0627
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diff
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|
949 |
|
290
9b533cc7874a
trying to get default implemenations to work
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
274
diff
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|
950 def untested_valuesVStack(self,fieldname,fieldvalues): |
|
36
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Rewriting dataset.py completely
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parents:
29
diff
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|
951 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
|
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29
diff
changeset
|
952 |
|
290
9b533cc7874a
trying to get default implemenations to work
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
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diff
changeset
|
953 def untested_valuesHStack(self,fieldnames,fieldvalues): |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
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29
diff
changeset
|
954 """ |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
955 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
956 to do the work, hoping that it can cope with the other values (i.e. won't care |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
957 about the incompatible fieldnames). Hence this heuristic will always work if |
|
438440ba0627
Rewriting dataset.py completely
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29
diff
changeset
|
958 all the fieldnames are of the same sub-dataset. |
|
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Rewriting dataset.py completely
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diff
changeset
|
959 """ |
|
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diff
changeset
|
960 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
|
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parents:
29
diff
changeset
|
961 |
|
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Rewriting dataset.py completely
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diff
changeset
|
962 class VStackedDataSet(DataSet): |
|
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Rewriting dataset.py completely
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diff
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|
963 """ |
| 167 | 964 A L{DataSet} that wraps several datasets and shows a view that includes all their examples, |
|
36
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parents:
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diff
changeset
|
965 in the order provided. This clearly assumes that they all have the same field names |
|
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Rewriting dataset.py completely
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parents:
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diff
changeset
|
966 and all (except possibly the last one) are of finite length. |
|
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Rewriting dataset.py completely
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diff
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|
967 |
| 167 | 968 @todo: automatically detect a chain of stacked datasets due to A + B + C + D ... |
|
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|
969 """ |
|
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diff
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|
970 def __init__(self,datasets): |
|
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diff
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|
971 self.datasets=datasets |
|
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changeset
|
972 self.length=0 |
|
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changeset
|
973 self.index2dataset={} |
|
37
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Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
974 assert len(datasets)>0 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
975 fieldnames = datasets[-1].fieldNames() |
|
38
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|
976 self.datasets_start_row=[] |
|
37
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diff
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|
977 # We use this map from row index to dataset index for constant-time random access of examples, |
|
73c4212ba5b3
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diff
changeset
|
978 # to avoid having to search for the appropriate dataset each time and slice is asked for. |
|
36
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diff
changeset
|
979 for dataset,k in enumerate(datasets[0:-1]): |
|
48
b6730f9a336d
Fixing MinibatchDataSet getitem
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parents:
46
diff
changeset
|
980 assert dataset.is_unbounded() # All VStacked datasets (except possibly the last) must be bounded (have a length). |
|
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
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diff
changeset
|
981 L=len(dataset) |
|
36
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diff
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|
982 for i in xrange(L): |
|
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diff
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|
983 self.index2dataset[self.length+i]=k |
|
38
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diff
changeset
|
984 self.datasets_start_row.append(self.length) |
|
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|
985 self.length+=L |
|
37
73c4212ba5b3
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diff
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|
986 assert dataset.fieldNames()==fieldnames |
|
38
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diff
changeset
|
987 self.datasets_start_row.append(self.length) |
|
36
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diff
changeset
|
988 self.length+=len(datasets[-1]) |
|
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|
989 # If length is very large, we should use a more memory-efficient mechanism |
|
73c4212ba5b3
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parents:
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diff
changeset
|
990 # that does not store all indices |
|
73c4212ba5b3
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parents:
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diff
changeset
|
991 if self.length>1000000: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
992 # 1 million entries would require about 60 meg for the index2dataset map |
|
73c4212ba5b3
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parents:
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diff
changeset
|
993 # TODO |
|
73c4212ba5b3
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bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
994 print "A more efficient mechanism for index2dataset should be implemented" |
|
73c4212ba5b3
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parents:
36
diff
changeset
|
995 |
|
73c4212ba5b3
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diff
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|
996 def __len__(self): |
|
73c4212ba5b3
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changeset
|
997 return self.length |
|
73c4212ba5b3
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diff
changeset
|
998 |
|
73c4212ba5b3
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parents:
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diff
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|
999 def fieldNames(self): |
|
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|
1000 return self.datasets[0].fieldNames() |
|
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Factored the minibatch-writing code into an iterator class inside DataSet
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diff
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|
1001 |
|
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diff
changeset
|
1002 def hasFields(self,*fieldnames): |
|
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|
1003 return self.datasets[0].hasFields(*fieldnames) |
|
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diff
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|
1004 |
|
38
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changeset
|
1005 def locate_row(self,row): |
|
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parents:
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|
1006 """Return (dataset_index, row_within_dataset) for global row number""" |
|
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|
1007 dataset_index = self.index2dataset[row] |
|
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changeset
|
1008 row_within_dataset = self.datasets_start_row[dataset_index] |
|
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|
1009 return dataset_index, row_within_dataset |
|
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|
1010 |
| 41 | 1011 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
44
5a85fda9b19b
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parents:
43
diff
changeset
|
1012 |
|
5a85fda9b19b
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43
diff
changeset
|
1013 class VStackedIterator(object): |
|
37
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|
1014 def __init__(self,vsds): |
|
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|
1015 self.vsds=vsds |
|
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|
1016 self.next_row=offset |
|
38
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|
1017 self.next_dataset_index,self.next_dataset_row=self.vsds.locate_row(offset) |
|
37
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|
1018 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
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|
1019 self.next_iterator(vsds.datasets[0],offset,n_batches) |
|
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diff
changeset
|
1020 |
|
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diff
changeset
|
1021 def next_iterator(self,dataset,starting_offset,batches_left): |
|
73c4212ba5b3
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diff
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|
1022 L=len(dataset) |
|
73c4212ba5b3
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diff
changeset
|
1023 ds_nbatches = (L-starting_offset)/minibatch_size |
|
73c4212ba5b3
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|
1024 if batches_left is not None: |
|
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diff
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|
1025 ds_nbatches = max(batches_left,ds_nbatches) |
|
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diff
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|
1026 if minibatch_size>L: |
|
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changeset
|
1027 ds_minibatch_size=L |
|
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changeset
|
1028 n_left_in_mb=minibatch_size-L |
|
38
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changeset
|
1029 ds_nbatches=1 |
|
d637ad8f7352
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changeset
|
1030 else: |
|
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|
1031 n_left_in_mb=0 |
|
37
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diff
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|
1032 return dataset.minibatches(fieldnames,minibatch_size,ds_nbatches,starting_offset), \ |
|
73c4212ba5b3
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diff
changeset
|
1033 L-(starting_offset+ds_nbatches*minibatch_size), n_left_in_mb |
|
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diff
changeset
|
1034 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
1035 def move_to_next_dataset(self): |
|
38
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diff
changeset
|
1036 if self.n_left_at_the_end_of_ds>0: |
|
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diff
changeset
|
1037 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
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diff
changeset
|
1038 self.next_iterator(vsds.datasets[self.next_dataset_index], |
|
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diff
changeset
|
1039 self.n_left_at_the_end_of_ds,1) |
|
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diff
changeset
|
1040 else: |
|
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changeset
|
1041 self.next_dataset_index +=1 |
|
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diff
changeset
|
1042 if self.next_dataset_index==len(self.vsds.datasets): |
|
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diff
changeset
|
1043 self.next_dataset_index = 0 |
|
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diff
changeset
|
1044 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
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diff
changeset
|
1045 self.next_iterator(vsds.datasets[self.next_dataset_index],starting_offset,n_batches) |
|
37
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diff
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|
1046 |
|
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|
1047 def __iter__(self): |
|
73c4212ba5b3
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diff
changeset
|
1048 return self |
|
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diff
changeset
|
1049 |
|
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changeset
|
1050 def next(self): |
|
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diff
changeset
|
1051 dataset=self.vsds.datasets[self.next_dataset_index] |
|
73c4212ba5b3
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diff
changeset
|
1052 mb = self.next_iterator.next() |
|
73c4212ba5b3
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diff
changeset
|
1053 if self.n_left_in_mb: |
|
73c4212ba5b3
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changeset
|
1054 extra_mb = [] |
|
73c4212ba5b3
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diff
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|
1055 while self.n_left_in_mb>0: |
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1056 self.move_to_next_dataset() |
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1057 extra_mb.append(self.next_iterator.next()) |
|
73
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1058 mb = Example(fieldnames, |
|
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1059 [dataset.valuesVStack(name, |
|
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|
1060 [mb[name]]+[b[name] for b in extra_mb]) |
|
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1061 for name in fieldnames]) |
|
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1062 |
|
37
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1063 self.next_row+=minibatch_size |
|
38
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1064 self.next_dataset_row+=minibatch_size |
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1065 if self.next_row+minibatch_size>len(dataset): |
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1066 self.move_to_next_dataset() |
|
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1067 return examples |
|
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1068 return VStackedIterator(self) |
|
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1069 |
| 41 | 1070 class ArrayFieldsDataSet(DataSet): |
| 1071 """ | |
| 1072 Virtual super-class of datasets whose field values are numpy array, | |
| 1073 thus defining valuesHStack and valuesVStack for sub-classes. | |
| 1074 """ | |
|
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1075 def __init__(self,description=None,field_types=None): |
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1076 DataSet.__init__(self,description,field_types) |
|
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1077 def untested_valuesHStack(self,fieldnames,fieldvalues): |
| 41 | 1078 """Concatenate field values horizontally, e.g. two vectors |
| 1079 become a longer vector, two matrices become a wider matrix, etc.""" | |
| 1080 return numpy.hstack(fieldvalues) | |
|
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1081 def untested_valuesVStack(self,fieldname,values): |
| 41 | 1082 """Concatenate field values vertically, e.g. two vectors |
| 1083 become a two-row matrix, two matrices become a longer matrix, etc.""" | |
| 1084 return numpy.vstack(values) | |
| 1085 | |
|
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1086 |
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1087 |
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1088 class NArraysDataSet(ArrayFieldsDataSet) : |
|
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1089 """ |
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1090 An NArraysDataSet stores fields that are numpy tensor, whose first axis |
|
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1091 iterates over examples. It's a generalization of ArrayDataSet. |
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1092 """ |
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1093 #@TODO not completely implemented yet |
|
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1094 def __init__(self, data_arrays, fieldnames, **kwargs) : |
|
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1095 """ |
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1096 Construct an NArraysDataSet from a list of numpy tensor (data_arrays) and a list |
|
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1097 of fieldnames. The number of arrays must be the same as the number of |
|
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1098 fieldnames. Each set of numpy tensor must have the same first dimension (first |
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|
1099 axis) corresponding to the number of examples. |
|
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1100 |
|
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1101 Every tensor is treated as a numpy array (using numpy.asarray) |
|
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1102 """ |
|
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1103 ArrayFieldsDataSet.__init__(self,**kwargs) |
|
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1104 assert len(data_arrays) == len(fieldnames) |
|
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1105 assert len(fieldnames) > 0 |
|
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|
1106 ndarrays = [numpy.asarray(a) for a in data_arrays] |
|
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1107 lens = [a.shape[0] for a in ndarrays] |
|
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1108 num_examples = lens[0] #they must all be equal anyway |
|
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1109 self._fieldnames = fieldnames |
|
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|
1110 for k in ndarrays : |
|
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1111 assert k.shape[0] == num_examples |
|
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1112 self._datas = ndarrays |
|
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1113 # create dict |
|
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1114 self.map_field_idx = dict() |
|
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1115 for k in range(len(fieldnames)): |
|
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1116 self.map_field_idx[fieldnames[k]] = k |
|
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1117 |
|
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1118 |
|
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1119 def __len__(self) : |
|
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1120 """ |
|
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1121 Length of the dataset is based on the first array = data_arrays[0], using its shape |
|
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1122 """ |
|
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1123 return self._datas[0].shape[0] |
|
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1124 |
|
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1125 def fieldNames(self) : |
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1126 """ |
|
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1127 Returns the fieldnames as set in self.__init__ |
|
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1128 """ |
|
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1129 return self._fieldnames |
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1130 |
|
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1131 def field_pos(self,fieldname) : |
|
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1132 """ |
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1133 Returns the index of a given fieldname. Fieldname must exists! see fieldNames(). |
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1134 """ |
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1135 return self.map_field_idx[fieldname] |
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1136 |
|
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1137 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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1138 cursor = Example(fieldnames,[0]*len(fieldnames)) |
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1139 fieldnames = self.fieldNames() if fieldnames is None else fieldnames |
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1140 for n in xrange(n_batches): |
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1141 if offset == len(self): |
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1142 break |
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1143 for f in range(len(cursor._names)) : |
|
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1144 idx = self.field_pos(cursor._names[f]) |
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1145 sub_data = self._datas[idx][offset : offset+minibatch_size] |
|
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1146 cursor._values[f] = sub_data |
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1147 offset += len(sub_data) #can be less than minibatch_size at end |
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1148 yield cursor |
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1149 |
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|
1150 #return ArrayDataSetIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
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1151 |
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1152 |
|
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1153 |
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1154 |
| 41 | 1155 class ArrayDataSet(ArrayFieldsDataSet): |
| 1156 """ | |
| 1157 An ArrayDataSet stores the fields as groups of columns in a numpy tensor, | |
| 1158 whose first axis iterates over examples, second axis determines fields. | |
| 1159 If the underlying array is N-dimensional (has N axes), then the field | |
| 1160 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | |
| 1161 """ | |
| 1162 | |
|
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1163 def __init__(self, data_array, fields_columns, **kwargs): |
|
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1164 """ |
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1165 Construct an ArrayDataSet from the underlying numpy array (data) and |
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1166 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified |
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1167 using the standard arguments for indexing/slicing: integer for a column index, |
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1168 slice for an interval of columns (with possible stride), or iterable of column indices. |
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1169 """ |
|
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1170 ArrayFieldsDataSet.__init__(self, **kwargs) |
| 41 | 1171 self.data=data_array |
|
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1172 self.fields_columns=fields_columns |
| 41 | 1173 |
| 1174 # check consistency and complete slices definitions | |
|
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|
1175 for fieldname, fieldcolumns in self.fields_columns.items(): |
| 41 | 1176 if type(fieldcolumns) is int: |
| 1177 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1] | |
|
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1178 if 1: |
|
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1179 #I changed this because it didn't make sense to me, |
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1180 # and it made it more difficult to write my learner. |
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1181 # If it breaks stuff, let's talk about it. |
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1182 # - James 22/05/2008 |
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1183 self.fields_columns[fieldname]=[fieldcolumns] |
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1184 else: |
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1185 self.fields_columns[fieldname]=fieldcolumns |
| 41 | 1186 elif type(fieldcolumns) is slice: |
| 1187 start,step=None,None | |
| 1188 if not fieldcolumns.start: | |
| 1189 start=0 | |
| 1190 if not fieldcolumns.step: | |
| 1191 step=1 | |
| 1192 if start or step: | |
|
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|
1193 self.fields_columns[fieldname]=slice(start,fieldcolumns.stop,step) |
| 41 | 1194 elif hasattr(fieldcolumns,"__iter__"): # something like a list |
| 1195 for i in fieldcolumns: | |
| 1196 assert i>=0 and i<data_array.shape[1] | |
| 1197 | |
|
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|
1198 def fieldNames(self): |
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|
1199 return self.fields_columns.keys() |
| 41 | 1200 |
|
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|
1201 def __len__(self): |
|
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|
1202 return len(self.data) |
| 41 | 1203 |
|
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|
1204 def __getitem__(self,key): |
|
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|
1205 """More efficient implementation than the default __getitem__""" |
|
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|
1206 fieldnames=self.fields_columns.keys() |
|
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|
1207 values=self.fields_columns.values() |
| 80 | 1208 if type(key) is int: |
|
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|
1209 return Example(fieldnames, |
|
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|
1210 [self.data[key,col] for col in values]) |
| 80 | 1211 if type(key) is slice: |
|
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|
1212 return Example(fieldnames,[self.data[key,col] for col in values]) |
| 80 | 1213 if type(key) is list: |
| 1214 for i in range(len(key)): | |
| 1215 if self.hasFields(key[i]): | |
| 1216 key[i]=self.fields_columns[key[i]] | |
|
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|
1217 return Example(fieldnames, |
|
5fe6d0c93109
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|
1218 #we must separate differently for list as numpy |
|
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|
1219 # doesn't support self.data[[i1,...],[i2,...]] |
|
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|
1220 # when their is more then two i1 and i2 |
|
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|
1221 [self.data[key,:][:,col] |
|
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|
1222 if isinstance(col,list) else |
|
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|
1223 self.data[key,col] for col in values]) |
| 80 | 1224 |
|
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|
1225 # else check for a fieldname |
| 80 | 1226 if self.hasFields(key): |
|
105
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Frederic Bastien <bastienf@iro.umontreal.ca>
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diff
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|
1227 return self.data[:,self.fields_columns[key]] |
|
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|
1228 # else we are trying to access a property of the dataset |
| 80 | 1229 assert key in self.__dict__ # else it means we are trying to access a non-existing property |
| 1230 return self.__dict__[key] | |
|
55
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|
1231 |
|
290
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diff
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|
1232 def dontuse__iter__(self): |
|
270
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diff
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|
1233 class ArrayDataSetIteratorIter(object): |
|
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|
1234 def __init__(self,dataset,fieldnames): |
|
228
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|
1235 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
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|
1236 # store the resulting minibatch in a lookup-list of values |
|
290
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|
1237 self.minibatch = Example(fieldnames,[0]*len(fieldnames)) |
|
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|
1238 self.dataset=dataset |
|
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|
1239 self.current=0 |
|
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|
1240 self.columns = [self.dataset.fields_columns[f] |
|
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|
1241 for f in self.minibatch._names] |
|
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|
1242 self.l = self.dataset.data.shape[0] |
|
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|
1243 def __iter__(self): |
|
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|
1244 return self |
|
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|
1245 def next(self): |
|
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|
1246 #@todo: we suppose that we need to stop only when minibatch_size == 1. |
|
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|
1247 # Otherwise, MinibatchWrapAroundIterator do it. |
|
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|
1248 if self.current>=self.l: |
|
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|
1249 raise StopIteration |
|
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|
1250 sub_data = self.dataset.data[self.current] |
|
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|
1251 self.minibatch._values = [sub_data[c] for c in self.columns] |
|
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|
1252 |
|
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|
1253 self.current+=1 |
|
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|
1254 return self.minibatch |
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|
1255 |
|
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|
1256 return ArrayDataSetIteratorIter(self,self.fieldNames()) |
|
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|
1257 |
|
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|
1258 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
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|
1259 cursor = Example(fieldnames,[0]*len(fieldnames)) |
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1260 fieldnames = self.fieldNames() if fieldnames is None else fieldnames |
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|
1261 for n in xrange(n_batches): |
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|
1262 if offset == len(self): |
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|
1263 break |
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|
1264 sub_data = self.data[offset : offset+minibatch_size] |
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|
1265 offset += len(sub_data) #can be less than minibatch_size at end |
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|
1266 cursor._values = [sub_data[:,self.fields_columns[f]] for f in cursor._names] |
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|
1267 yield cursor |
|
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|
1268 |
|
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|
1269 #return ArrayDataSetIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
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diff
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|
1270 |
|
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diff
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|
1271 |
|
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diff
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|
1272 class CachedDataSet(DataSet): |
|
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|
1273 """ |
| 167 | 1274 Wrap a L{DataSet} whose values are computationally expensive to obtain |
|
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|
1275 (e.g. because they involve some computation, or disk access), |
|
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diff
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|
1276 so that repeated accesses to the same example are done cheaply, |
|
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|
1277 by caching every example value that has been accessed at least once. |
|
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|
1278 |
|
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|
1279 Optionally, for finite-length dataset, all the values can be computed |
|
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|
1280 (and cached) upon construction of the CachedDataSet, rather at the |
|
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|
1281 first access. |
|
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1282 |
| 167 | 1283 @todo: when cache_all_upon_construction create mini-batches that are as |
|
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|
1284 large as possible but not so large as to fill up memory. |
|
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1285 |
| 167 | 1286 @todo: add disk-buffering capability, so that when the cache becomes too |
|
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1287 big for memory, we cache things on disk, trying to keep in memory only |
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1288 the record most likely to be accessed next. |
|
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|
1289 """ |
|
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1290 def __init__(self,source_dataset,cache_all_upon_construction=False): |
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1291 self.source_dataset=source_dataset |
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1292 self.cache_all_upon_construction=cache_all_upon_construction |
|
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1293 self.cached_examples = [] |
|
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1294 if cache_all_upon_construction: |
|
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1295 # this potentially brings all the source examples |
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1296 # into memory at once, which may be too much |
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1297 # the work could possibly be done by minibatches |
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1298 # that are as large as possible but no more than what memory allows. |
|
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1299 fields_values = source_dataset.minibatches(minibatch_size=len(source_dataset)).__iter__().next() |
|
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1300 assert all([len(self)==len(field_values) for field_values in fields_values]) |
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1301 for example in fields_values.examples(): |
|
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1302 self.cached_examples.append(copy.copy(example)) |
|
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|
1303 |
|
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1304 self.fieldNames = source_dataset.fieldNames |
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1305 self.hasFields = source_dataset.hasFields |
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1306 self.valuesHStack = source_dataset.valuesHStack |
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1307 self.valuesVStack = source_dataset.valuesVStack |
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|
1308 |
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1309 def __len__(self): |
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1310 return len(self.source_dataset) |
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1311 |
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1312 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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1313 class CacheIterator(object): |
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1314 def __init__(self,dataset): |
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1315 self.dataset=dataset |
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1316 self.current=offset |
|
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1317 self.all_fields = self.dataset.fieldNames()==fieldnames |
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1318 def __iter__(self): return self |
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1319 def next(self): |
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|
1320 upper = self.current+minibatch_size |
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|
1321 cache_len = len(self.dataset.cached_examples) |
|
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|
1322 if upper>cache_len: # whole minibatch is not already in cache |
|
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1323 # cache everything from current length to upper |
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1324 for example in self.dataset.source_dataset[cache_len:upper]: |
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|
1325 self.dataset.cached_examples.append(example) |
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|
1326 all_fields_minibatch = Example(self.dataset.fieldNames(), |
|
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|
1327 zip(*self.dataset.cached_examples[self.current:self.current+minibatch_size])) |
|
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|
1328 self.current+=minibatch_size |
|
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1329 if self.all_fields: |
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|
1330 return all_fields_minibatch |
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|
1331 return Example(fieldnames,[all_fields_minibatch[name] for name in fieldnames]) |
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|
1332 return CacheIterator(self) |
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|
1333 |
|
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|
1334 def dontuse__getitem__(self,i): |
| 153 | 1335 if type(i)==int and len(self.cached_examples)>i: |
| 1336 return self.cached_examples[i] | |
| 1337 else: | |
|
251
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diff
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|
1338 return self.source_dataset[i] |
|
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|
1339 |
|
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|
1340 def __iter__(self): |
|
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|
1341 class CacheIteratorIter(object): |
|
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|
1342 def __init__(self,dataset): |
|
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|
1343 self.dataset=dataset |
|
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|
1344 self.l = len(dataset) |
|
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|
1345 self.current = 0 |
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|
1346 self.fieldnames = self.dataset.fieldNames() |
|
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|
1347 self.example = Example(self.fieldnames,[0]*len(self.fieldnames)) |
|
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|
1348 def __iter__(self): return self |
|
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|
1349 def next(self): |
|
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|
1350 if self.current>=self.l: |
|
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|
1351 raise StopIteration |
|
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|
1352 cache_len = len(self.dataset.cached_examples) |
|
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|
1353 if self.current>=cache_len: # whole minibatch is not already in cache |
|
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|
1354 # cache everything from current length to upper |
|
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|
1355 self.dataset.cached_examples.append( |
|
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|
1356 self.dataset.source_dataset[self.current]) |
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|
1357 self.example._values = self.dataset.cached_examples[self.current] |
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|
1358 self.current+=1 |
|
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|
1359 return self.example |
|
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|
1360 |
|
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|
1361 return CacheIteratorIter(self) |
|
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|
1362 |
|
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|
1363 class ApplyFunctionDataSet(DataSet): |
|
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|
1364 """ |
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|
1365 A L{DataSet} that contains as fields the results of applying a |
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|
1366 given function example-wise or minibatch-wise to all the fields of |
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|
1367 an input dataset. The output of the function should be an iterable |
|
296
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|
1368 (e.g. a list or a LookupList) over the resulting values. |
|
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|
1369 |
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|
1370 The function take as input the fields of the dataset, not the examples. |
|
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|
1371 |
|
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|
1372 In minibatch mode, the function is expected to work on minibatches |
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1373 (takes a minibatch in input and returns a minibatch in output). More |
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|
1374 precisely, it means that each element of the input or output list |
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|
1375 should be iterable and indexable over the individual example values |
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|
1376 (typically these elements will be numpy arrays). All of the elements |
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|
1377 in the input and output lists should have the same length, which is |
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|
1378 the length of the minibatch. |
|
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|
1379 |
|
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|
1380 The function is applied each time an example or a minibatch is accessed. |
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|
1381 To avoid re-doing computation, wrap this dataset inside a CachedDataSet. |
|
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|
1382 |
|
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|
1383 If the values_{h,v}stack functions are not provided, then |
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|
1384 the input_dataset.values{H,V}Stack functions are used by default. |
|
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|
1385 |
|
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|
1386 """ |
|
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|
1387 |
|
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|
1388 def __init__(self,input_dataset,function,output_names,minibatch_mode=True, |
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|
1389 values_hstack=None,values_vstack=None, |
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|
1390 description=None,fieldtypes=None): |
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|
1391 """ |
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1392 Constructor takes an input dataset that has as many fields as the function |
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1393 expects as inputs. The resulting dataset has as many fields as the function |
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1394 produces as outputs, and that should correspond to the number of output names |
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1395 (provided in a list). |
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1396 |
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1397 Note that the expected semantics of the function differs in minibatch mode |
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1398 (it takes minibatches of inputs and produces minibatches of outputs, as |
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1399 documented in the class comment). |
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1400 |
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1401 TBM: are filedtypes the old field types (from input_dataset) or the new ones |
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1402 (for the new dataset created)? |
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1403 """ |
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1404 self.input_dataset=input_dataset |
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1405 self.function=function |
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1406 self.output_names=output_names |
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1407 self.minibatch_mode=minibatch_mode |
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1408 DataSet.__init__(self,description,fieldtypes) |
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1409 self.valuesHStack = values_hstack if values_hstack else input_dataset.valuesHStack |
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1410 self.valuesVStack = values_vstack if values_vstack else input_dataset.valuesVStack |
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1411 |
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1412 def __len__(self): |
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1413 return len(self.input_dataset) |
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1414 |
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1415 def fieldNames(self): |
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1416 return self.output_names |
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1417 |
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1418 def minibatches_nowrap(self, fieldnames, *args, **kwargs): |
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1419 all_input_fieldNames = self.input_dataset.fieldNames() |
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1420 mbnw = self.input_dataset.minibatches_nowrap |
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1421 |
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1422 for input_fields in mbnw(all_input_fieldNames, *args, **kwargs): |
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1423 if self.minibatch_mode: |
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1424 all_output_fields = self.function(*input_fields) |
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1425 else: |
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1426 input_examples = zip(*input_fields) #makes so that [i] means example i |
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1427 output_examples = [self.function(*input_example) |
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1428 for input_example in input_examples] |
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1429 all_output_fields = zip(*output_examples) |
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1430 |
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1431 all_outputs = Example(self.output_names, all_output_fields) |
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1432 #print 'input_fields', input_fields |
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1433 #print 'all_outputs', all_outputs |
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1434 if fieldnames==self.output_names: |
| 293 | 1435 rval = all_outputs |
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1436 else: |
| 293 | 1437 rval = Example(fieldnames,[all_outputs[name] for name in fieldnames]) |
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1438 #print 'rval', rval |
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1439 #print '--------' |
| 293 | 1440 yield rval |
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1441 |
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1442 def untested__iter__(self): # only implemented for increased efficiency |
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1443 class ApplyFunctionSingleExampleIterator(object): |
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1444 def __init__(self,output_dataset): |
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1445 self.current=0 |
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1446 self.output_dataset=output_dataset |
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1447 self.input_iterator=output_dataset.input_dataset.__iter__() |
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1448 def __iter__(self): return self |
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1449 def next(self): |
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1450 if self.output_dataset.minibatch_mode: |
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1451 function_inputs = [[input] for input in self.input_iterator.next()] |
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1452 outputs = self.output_dataset.function(*function_inputs) |
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1453 assert all([hasattr(output,'__iter__') for output in outputs]) |
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1454 function_outputs = [output[0] for output in outputs] |
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1455 else: |
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1456 function_inputs = self.input_iterator.next() |
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1457 function_outputs = self.output_dataset.function(*function_inputs) |
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1458 return Example(self.output_dataset.output_names,function_outputs) |
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1459 return ApplyFunctionSingleExampleIterator(self) |
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1460 |
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1461 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
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1462 """ |
| 167 | 1463 Wraps an arbitrary L{DataSet} into one for supervised learning tasks |
| 1464 by forcing the user to define a set of fields as the 'input' field | |
| 1465 and a set of fields as the 'target' field. Optionally, a single | |
| 1466 weight_field can also be defined. | |
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1467 """ |
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1468 args = ((input_fields,'input'),(output_fields,'target')) |
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1469 if weight_field: args+=(([weight_field],'weight')) |
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1470 return src_dataset.merge_fields(*args) |
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1471 |
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1472 |
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1473 |
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1474 |