Mercurial > pylearn
annotate dataset.py @ 298:5987415496df
better testing of the MultiLengthDataSet, now called exotic1
| author | Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca> |
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| date | Fri, 06 Jun 2008 17:55:14 -0400 |
| parents | f5d33f9c0b9c |
| children | 923de30457f0 |
| 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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9 |
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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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50 |
| 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 a dataset with the n first examples. |
| 16 | 103 |
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104 - dataset[i1:i2:s] returns a dataset 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 dataset with examples i1,i2,...in. |
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109 |
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110 - dataset.<property> returns the value of a property associated with |
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111 the name <property>. The following properties should be supported: |
| 41 | 112 - 'description': a textual description or name for the dataset |
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113 - 'fieldtypes': a list of types (one per field) |
| 78 | 114 A DataSet may have other attributes that it makes visible to other objects. These are |
| 115 used to store information that is not example-wise but global to the dataset. | |
| 116 The list of names of these attributes is given by the attribute_names() method. | |
| 41 | 117 |
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118 Datasets can be concatenated either vertically (increasing the length) or |
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119 horizontally (augmenting the set of fields), if they are compatible, using |
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120 the following operations (with the same basic semantics as numpy.hstack |
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121 and numpy.vstack): |
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122 |
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123 - dataset1 | dataset2 | dataset3 == dataset.hstack([dataset1,dataset2,dataset3]) |
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124 |
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125 creates a new dataset whose list of fields is the concatenation of the list of |
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126 fields of the argument datasets. This only works if they all have the same length. |
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127 |
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128 - dataset1 & dataset2 & dataset3 == dataset.vstack([dataset1,dataset2,dataset3]) |
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129 |
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130 creates a new dataset that concatenates the examples from the argument datasets |
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131 (and whose length is the sum of the length of the argument datasets). This only |
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132 works if they all have the same fields. |
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133 |
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134 According to the same logic, and viewing a DataSetFields object associated to |
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135 a DataSet as a kind of transpose of it, fields1 & fields2 concatenates fields of |
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136 a DataSetFields fields1 and fields2, and fields1 | fields2 concatenates their |
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137 examples. |
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138 |
| 41 | 139 A dataset can hold arbitrary key-value pairs that may be used to access meta-data |
| 140 or other properties of the dataset or associated with the dataset or the result | |
| 141 of a computation stored in a dataset. These can be accessed through the [key] syntax | |
| 142 when key is a string (or more specifically, neither an integer, a slice, nor a list). | |
| 78 | 143 |
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144 A DataSet sub-class should always redefine the following methods: |
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145 - __len__ if it is not a stream |
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146 - fieldNames |
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147 - minibatches_nowrap (called by DataSet.minibatches()) |
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148 For efficiency of implementation, a sub-class might also want to redefine |
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149 - valuesHStack |
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150 - valuesVStack |
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151 - hasFields |
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152 - __getitem__ may not be feasible with some streams |
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153 - __iter__ |
| 78 | 154 A sub-class should also append attributes to self._attribute_names |
| 155 (the default value returned by attributeNames()). | |
| 156 By convention, attributes not in attributeNames() should have a name | |
| 157 starting with an underscore. | |
| 158 @todo enforce/test that convention! | |
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159 """ |
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160 |
| 83 | 161 numpy_vstack = lambda fieldname,values: numpy.vstack(values) |
| 162 numpy_hstack = lambda fieldnames,values: numpy.hstack(values) | |
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163 |
| 292 | 164 def __init__(self, description=None, fieldnames=None, fieldtypes=None): |
| 165 """ | |
| 166 @type fieldnames: list of strings | |
| 167 @type fieldtypes: list of python types, same length as fieldnames | |
| 168 @type description: string | |
| 169 @param description: description/name for this dataset | |
| 170 """ | |
| 171 def default_desc(): | |
| 172 return type(self).__name__ \ | |
| 173 + " ( " + join([x.__name__ for x in type(self).__bases__]) + " )" | |
| 174 | |
| 175 #self.fieldnames = fieldnames | |
| 176 | |
| 177 self.fieldtypes = fieldtypes if fieldtypes is not None \ | |
| 178 else [None]*1 #len(fieldnames) | |
| 179 | |
| 180 self.description = default_desc() if description is None \ | |
| 181 else description | |
| 78 | 182 self._attribute_names = ["description"] |
| 292 | 183 |
| 184 attributeNames = property(lambda self: copy.copy(self._attribute_names)) | |
| 185 | |
| 186 def __contains__(self, fieldname): | |
| 187 return (fieldname in self.fieldNames()) \ | |
| 188 or (fieldname in self.attributeNames()) | |
| 189 | |
| 190 def __iter__(self): | |
| 191 """Supports the syntax "for i in dataset: ..." | |
| 78 | 192 |
| 292 | 193 Using this syntax, "i" will be an Example instance (or equivalent) with |
| 194 all the fields of DataSet self. Every field of "i" will give access to | |
| 195 a field of a single example. Fields should be accessible via | |
| 196 i["fielname"] or i[3] (in the order defined by the elements of the | |
| 197 Example returned by this iterator), but the derived class is free | |
| 198 to accept any type of identifier, and add extra functionality to the iterator. | |
| 199 | |
| 200 The default implementation calls the minibatches iterator and extracts the first example of each field. | |
| 201 """ | |
| 202 return DataSet.MinibatchToSingleExampleIterator(self.minibatches(None, minibatch_size = 1)) | |
| 203 | |
| 204 def __len__(self): | |
| 205 """ | |
| 206 len(dataset) returns the number of examples in the dataset. | |
| 207 By default, a DataSet is a 'stream', i.e. it has an unbounded length (sys.maxint). | |
| 208 Sub-classes which implement finite-length datasets should redefine this method. | |
| 209 Some methods only make sense for finite-length datasets. | |
| 210 """ | |
| 211 return None | |
| 212 | |
| 78 | 213 |
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214 class MinibatchToSingleExampleIterator(object): |
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215 """ |
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216 Converts the result of minibatch iterator with minibatch_size==1 into |
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217 single-example values in the result. Therefore the result of |
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218 iterating on the dataset itself gives a sequence of single examples |
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219 (whereas the result of iterating over minibatches gives in each |
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220 Example field an iterable object over the individual examples in |
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221 the minibatch). |
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222 """ |
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223 def __init__(self, minibatch_iterator): |
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224 self.minibatch_iterator = minibatch_iterator |
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225 self.minibatch = None |
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226 def __iter__(self): #makes for loop work |
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227 return self |
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228 def next(self): |
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229 size1_minibatch = self.minibatch_iterator.next() |
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230 if not self.minibatch: |
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231 self.minibatch = Example(size1_minibatch.keys(),[value[0] for value in size1_minibatch.values()]) |
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232 else: |
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233 self.minibatch._values = [value[0] for value in size1_minibatch.values()] |
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234 return self.minibatch |
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235 |
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236 def next_index(self): |
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237 return self.minibatch_iterator.next_index() |
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238 |
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239 class MinibatchWrapAroundIterator(object): |
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240 """ |
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241 An iterator for minibatches that handles the case where we need to wrap around the |
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242 dataset because n_batches*minibatch_size > len(dataset). It is constructed from |
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243 a dataset that provides a minibatch iterator that does not need to handle that problem. |
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244 This class is a utility for dataset subclass writers, so that they do not have to handle |
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245 this issue multiple times, nor check that fieldnames are valid, nor handle the |
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246 empty fieldnames (meaning 'use all the fields'). |
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247 """ |
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248 def __init__(self,dataset,fieldnames,minibatch_size,n_batches,offset): |
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249 self.dataset=dataset |
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250 self.fieldnames=fieldnames |
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251 self.minibatch_size=minibatch_size |
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252 self.n_batches=n_batches |
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253 self.n_batches_done=0 |
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254 self.next_row=offset |
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255 self.L=len(dataset) |
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256 self.offset=offset % self.L |
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257 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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258 if n_batches is not None: |
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259 ds_nbatches = min(n_batches,ds_nbatches) |
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260 if fieldnames: |
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261 assert dataset.hasFields(*fieldnames) |
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262 else: |
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263 self.fieldnames=dataset.fieldNames() |
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264 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, ds_nbatches,self.next_row) |
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265 |
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266 def __iter__(self): |
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267 return self |
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268 |
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269 def next_index(self): |
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270 return self.next_row |
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271 |
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272 def next(self): |
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273 if self.n_batches and self.n_batches_done==self.n_batches: |
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274 raise StopIteration |
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275 elif not self.n_batches and self.next_row ==self.L: |
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276 raise StopIteration |
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277 upper = self.next_row+self.minibatch_size |
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278 if upper <=self.L: |
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279 minibatch = self.iterator.next() |
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280 else: |
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281 if not self.n_batches: |
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282 upper=min(upper, self.L) |
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283 # if their is not a fixed number of batch, we continue to the end of the dataset. |
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284 # this can create a minibatch that is smaller then the minibatch_size |
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285 assert (self.L-self.next_row)<=self.minibatch_size |
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286 minibatch = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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287 else: |
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288 # we must concatenate (vstack) the bottom and top parts of our minibatch |
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289 # first get the beginning of our minibatch (top of dataset) |
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290 first_part = self.dataset.minibatches_nowrap(self.fieldnames,self.L-self.next_row,1,self.next_row).next() |
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291 second_part = self.dataset.minibatches_nowrap(self.fieldnames,upper-self.L,1,0).next() |
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292 minibatch = Example(self.fieldnames, |
|
268
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293 [self.dataset.valuesVStack(name,[first_part[name],second_part[name]]) |
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294 for name in self.fieldnames]) |
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295 self.next_row=upper |
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296 self.n_batches_done+=1 |
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297 if upper >= self.L and self.n_batches: |
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298 self.next_row -= self.L |
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299 ds_nbatches = (self.L-self.next_row)/self.minibatch_size |
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300 if self.n_batches is not None: |
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301 ds_nbatches = min(self.n_batches,ds_nbatches) |
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302 self.iterator = self.dataset.minibatches_nowrap(self.fieldnames,self.minibatch_size, |
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303 ds_nbatches,self.next_row) |
|
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304 return DataSetFields(MinibatchDataSet(minibatch,self.dataset.valuesVStack, |
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305 self.dataset.valuesHStack), |
|
74
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306 minibatch.keys()) |
|
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307 |
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308 |
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309 minibatches_fieldnames = None |
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310 minibatches_minibatch_size = 1 |
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311 minibatches_n_batches = None |
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312 def minibatches(self, |
|
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313 fieldnames = minibatches_fieldnames, |
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314 minibatch_size = minibatches_minibatch_size, |
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315 n_batches = minibatches_n_batches, |
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316 offset = 0): |
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317 """ |
|
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318 Return an iterator that supports three forms of syntax: |
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319 |
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320 for i in dataset.minibatches(None,**kwargs): ... |
| 16 | 321 |
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322 for i in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 323 |
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324 for i1, i2, i3 in dataset.minibatches([f1, f2, f3],**kwargs): ... |
| 16 | 325 |
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326 Using the first two syntaxes, "i" will be an indexable object, such as a list, |
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327 tuple, or Example instance. In both cases, i[k] is a list-like container |
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328 of a batch of current examples. In the second case, i[0] is |
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329 list-like container of the f1 field of a batch current examples, i[1] is |
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330 a list-like container of the f2 field, etc. |
|
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331 |
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332 Using the first syntax, all the fields will be returned in "i". |
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333 Using the third syntax, i1, i2, i3 will be list-like containers of the |
|
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334 f1, f2, and f3 fields of a batch of examples on each loop iteration. |
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335 |
|
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336 The minibatches iterator is expected to return upon each call to next() |
|
290
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337 a DataSetFields object, which is a Example (indexed by the field names) whose |
| 80 | 338 elements are iterable and indexable over the minibatch examples, and which keeps a pointer to |
|
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339 a sub-dataset that can be used to iterate over the individual examples |
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340 in the minibatch. Hence a minibatch can be converted back to a regular |
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341 dataset or its fields can be looked at individually (and possibly iterated over). |
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342 |
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343 PARAMETERS |
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344 - fieldnames (list of any type, default None): |
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345 The loop variables i1, i2, i3 (in the example above) should contain the |
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346 f1, f2, and f3 fields of the current batch of examples. If None, the |
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347 derived class can choose a default, e.g. all fields. |
| 16 | 348 |
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349 - minibatch_size (integer, default 1) |
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350 On every iteration, the variables i1, i2, i3 will have |
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351 exactly minibatch_size elements. e.g. len(i1) == minibatch_size |
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352 |
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353 - n_batches (integer, default None) |
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354 The iterator will loop exactly this many times, and then stop. If None, |
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355 the derived class can choose a default. If (-1), then the returned |
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356 iterator should support looping indefinitely. |
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357 |
|
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358 - offset (integer, default 0) |
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359 The iterator will start at example 'offset' in the dataset, rather than the default. |
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360 |
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361 Note: A list-like container is something like a tuple, list, numpy.ndarray or |
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362 any other object that supports integer indexing and slicing. |
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363 |
|
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364 """ |
|
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365 return DataSet.MinibatchWrapAroundIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
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366 |
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367 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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368 """ |
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369 This is the minibatches iterator generator that sub-classes must define. |
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370 It does not need to worry about wrapping around multiple times across the dataset, |
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371 as this is handled by MinibatchWrapAroundIterator when DataSet.minibatches() is called. |
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372 The next() method of the returned iterator does not even need to worry about |
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373 the termination condition (as StopIteration will be raised by DataSet.minibatches |
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374 before an improper call to minibatches_nowrap's next() is made). |
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375 That next() method can assert that its next row will always be within [0,len(dataset)). |
|
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376 The iterator returned by minibatches_nowrap does not need to implement |
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377 a next_index() method either, as this will be provided by MinibatchWrapAroundIterator. |
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378 """ |
|
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379 raise AbstractFunction() |
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380 |
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381 def is_unbounded(self): |
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382 """ |
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383 Tests whether a dataset is unbounded (e.g. a stream). |
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384 """ |
| 123 | 385 return len(self)==maxint |
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386 |
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387 def hasFields(self,*fieldnames): |
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388 """ |
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389 Return true if the given field name (or field names, if multiple arguments are |
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390 given) is recognized by the DataSet (i.e. can be used as a field name in one |
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391 of the iterators). |
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392 |
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393 The default implementation may be inefficient (O(# fields in dataset)), as it calls the fieldNames() |
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394 method. Many datasets may store their field names in a dictionary, which would allow more efficiency. |
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395 """ |
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396 return len(unique_elements_list_intersection(fieldnames,self.fieldNames()))>0 |
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397 |
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398 def fieldNames(self): |
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399 """ |
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400 Return the list of field names that are supported by the iterators, |
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401 and for which hasFields(fieldname) would return True. |
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402 """ |
|
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403 raise AbstractFunction() |
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404 |
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405 def __call__(self,*fieldnames): |
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406 """ |
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407 Return a dataset that sees only the fields whose name are specified. |
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408 """ |
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409 assert self.hasFields(*fieldnames) |
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410 return self.fields(*fieldnames).examples() |
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411 |
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412 def fields(self,*fieldnames): |
|
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413 """ |
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414 Return a DataSetFields object associated with this dataset. |
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415 """ |
|
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416 return DataSetFields(self,fieldnames) |
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417 |
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418 def getitem_key(self, fieldname): |
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419 """A not-so-well thought-out place to put code that used to be in |
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420 getitem. |
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421 """ |
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422 #removing as per discussion June 4. --JSB |
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423 |
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424 i = fieldname |
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425 # else check for a fieldname |
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426 if self.hasFields(i): |
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427 return self.minibatches(fieldnames=[i],minibatch_size=len(self),n_batches=1,offset=0).next()[0] |
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428 # else we are trying to access a property of the dataset |
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429 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
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430 return self.__dict__[i] |
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431 |
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432 def __getitem__(self,i): |
|
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433 """ |
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434 @rtype: Example |
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435 @returns: single or multiple examples |
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436 |
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437 @type i: integer or slice or <iterable> of integers |
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438 @param i: |
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439 dataset[i] returns the (i+1)-th example of the dataset. |
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440 dataset[i:j] returns the subdataset with examples i,i+1,...,j-1. |
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441 dataset[i:j:s] returns the subdataset with examples i,i+2,i+4...,j-2. |
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442 dataset[[i1,i2,..,in]] returns the subdataset with examples i1,i2,...,in. |
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443 |
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444 @note: |
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445 Some stream datasets may be unable to implement random access, i.e. |
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446 arbitrary slicing/indexing because they can only iterate through |
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447 examples one or a minibatch at a time and do not actually store or keep |
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448 past (or future) examples. |
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449 |
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450 The default implementation of getitem uses the minibatches iterator |
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451 to obtain one example, one slice, or a list of examples. It may not |
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452 always be the most efficient way to obtain the result, especially if |
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453 the data are actually stored in a memory array. |
|
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454 """ |
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455 |
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456 if type(i) is int: |
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457 #TODO: consider asserting that i >= 0 |
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458 i_batch = self.minibatches_nowrap(self.fieldNames(), |
| 293 | 459 minibatch_size=1, n_batches=1, offset=i) |
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460 return DataSet.MinibatchToSingleExampleIterator(i_batch).next() |
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461 |
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462 #if i is a contiguous slice |
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463 if type(i) is slice and (i.step in (None, 1)): |
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464 offset = 0 if i.start is None else i.start |
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465 upper_bound = len(self) if i.stop is None else i.stop |
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466 return MinibatchDataSet(self.minibatches_nowrap(self.fieldNames(), |
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467 minibatch_size=upper_bound - offset, |
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468 n_batches=1, |
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469 offset=offset).next()) |
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470 |
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471 # if slice has a step param, convert it to list and handle it with the |
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472 # list code |
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473 if type(i) is slice: |
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474 offset = 0 if i.start is None else i.start |
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475 upper_bound = len(self) if i.stop is None else i.stop |
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476 i = list(range(offset, upper_bound, i.step)) |
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477 |
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478 # handle tuples, arrays, lists |
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479 if hasattr(i, '__getitem__'): |
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480 for idx in i: |
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481 #dis-allow nested slices |
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482 if not isinstance(idx, int): |
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483 raise TypeError(idx) |
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484 # call back into self.__getitem__ |
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485 examples = [self.minibatches_nowrap(self.fieldNames(), |
| 293 | 486 minibatch_size=1, n_batches=1, offset=ii).next() |
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487 for ii in i] |
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488 # re-index the fields in each example by field instead of by example |
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489 field_values = [[] for blah in self.fieldNames()] |
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490 for e in examples: |
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491 for f,v in zip(field_values, e): |
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492 f.append(v) |
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493 #build them into a LookupList (a.ka. Example) |
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494 zz = zip(self.fieldNames(),field_values) |
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495 vst = [self.valuesVStack(fieldname,field_values) for fieldname,field_values in zz] |
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496 example = Example(self.fieldNames(), vst) |
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497 return MinibatchDataSet(example, self.valuesVStack, self.valuesHStack) |
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498 raise TypeError(i, type(i)) |
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499 |
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500 def valuesHStack(self,fieldnames,fieldvalues): |
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501 """ |
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502 Return a value that corresponds to concatenating (horizontally) several field values. |
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503 This can be useful to merge some fields. The implementation of this operation is likely |
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504 to involve a copy of the original values. When the values are numpy arrays, the |
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505 result should be numpy.hstack(values). If it makes sense, this operation should |
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506 work as well when each value corresponds to multiple examples in a minibatch |
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507 e.g. if each value is a Ni-vector and a minibatch of length L is a LxNi matrix, |
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508 then the result should be a Lx(N1+N2+..) matrix equal to numpy.hstack(values). |
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509 The default is to use numpy.hstack for numpy.ndarray values, and a list |
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510 pointing to the original values for other data types. |
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511 """ |
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512 all_numpy=True |
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513 for value in fieldvalues: |
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514 if not type(value) is numpy.ndarray: |
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515 all_numpy=False |
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516 if all_numpy: |
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517 return numpy.hstack(fieldvalues) |
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518 # the default implementation of horizontal stacking is to put values in a list |
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519 return fieldvalues |
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520 |
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521 def valuesVStack(self,fieldname,values): |
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522 """ |
|
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523 @param fieldname: the name of the field from which the values were taken |
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524 @type fieldname: any type |
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525 |
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526 @param values: bits near the beginning or end of the dataset |
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527 @type values: list of minibatches (returned by minibatch_nowrap) |
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528 |
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529 @return: the concatenation (stacking) of the values |
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530 @rtype: something suitable as a minibatch field |
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531 """ |
|
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532 rval = [] |
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533 for v in values: |
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534 rval.extend(v) |
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535 return rval |
|
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536 |
|
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537 def __or__(self,other): |
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538 """ |
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539 dataset1 | dataset2 returns a dataset whose list of fields is the concatenation of the list of |
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540 fields of the argument datasets. This only works if they all have the same length. |
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541 """ |
|
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542 return HStackedDataSet([self,other]) |
|
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543 |
|
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544 def __and__(self,other): |
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545 """ |
|
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546 dataset1 & dataset2 is a dataset that concatenates the examples from the argument datasets |
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547 (and whose length is the sum of the length of the argument datasets). This only |
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548 works if they all have the same fields. |
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549 """ |
|
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550 return VStackedDataSet([self,other]) |
|
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551 |
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552 def hstack(datasets): |
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553 """ |
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554 hstack(dataset1,dataset2,...) returns dataset1 | datataset2 | ... |
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555 which is a dataset whose fields list is the concatenation of the fields |
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556 of the individual datasets. |
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557 """ |
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558 assert len(datasets)>0 |
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559 if len(datasets)==1: |
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560 return datasets[0] |
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561 return HStackedDataSet(datasets) |
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562 |
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563 def vstack(datasets): |
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564 """ |
|
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565 vstack(dataset1,dataset2,...) returns dataset1 & datataset2 & ... |
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566 which is a dataset which iterates first over the examples of dataset1, then |
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567 over those of dataset2, etc. |
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568 """ |
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569 assert len(datasets)>0 |
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570 if len(datasets)==1: |
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571 return datasets[0] |
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572 return VStackedDataSet(datasets) |
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|
573 |
|
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574 class FieldsSubsetDataSet(DataSet): |
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575 """ |
| 167 | 576 A sub-class of L{DataSet} that selects a subset of the fields. |
|
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577 """ |
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578 def __init__(self,src,fieldnames): |
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579 self.src=src |
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580 self.fieldnames=fieldnames |
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581 assert src.hasFields(*fieldnames) |
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582 self.valuesHStack = src.valuesHStack |
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583 self.valuesVStack = src.valuesVStack |
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584 |
|
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585 def __len__(self): return len(self.src) |
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586 |
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587 def fieldNames(self): |
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588 return self.fieldnames |
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589 |
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590 def __iter__(self): |
|
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591 class FieldsSubsetIterator(object): |
|
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592 def __init__(self,ds): |
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593 self.ds=ds |
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594 self.src_iter=ds.src.__iter__() |
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595 self.example=None |
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596 def __iter__(self): return self |
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597 def next(self): |
|
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598 complete_example = self.src_iter.next() |
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599 if self.example: |
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600 self.example._values=[complete_example[field] |
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601 for field in self.ds.fieldnames] |
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602 else: |
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603 self.example=Example(self.ds.fieldnames, |
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604 [complete_example[field] for field in self.ds.fieldnames]) |
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605 return self.example |
|
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606 return FieldsSubsetIterator(self) |
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607 |
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608 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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609 assert self.hasFields(*fieldnames) |
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610 return self.src.minibatches_nowrap(fieldnames,minibatch_size,n_batches,offset) |
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611 def dontuse__getitem__(self,i): |
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612 return FieldsSubsetDataSet(self.src[i],self.fieldnames) |
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613 |
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614 |
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615 class DataSetFields(Example): |
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616 """ |
| 167 | 617 Although a L{DataSet} iterates over examples (like rows of a matrix), an associated |
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618 DataSetFields iterates over fields (like columns of a matrix), and can be understood |
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619 as a transpose of the associated dataset. |
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620 |
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621 To iterate over fields, one can do |
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622 * for fields in dataset.fields() |
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623 * for fields in dataset(field1,field2,...).fields() to select a subset of fields |
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624 * for fields in dataset.fields(field1,field2,...) to select a subset of fields |
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625 and each of these fields is iterable over the examples: |
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626 * for field_examples in dataset.fields(): |
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627 for example_value in field_examples: |
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628 ... |
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629 but when the dataset is a stream (unbounded length), it is not recommended to do |
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630 such things because the underlying dataset may refuse to access the different fields in |
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631 an unsynchronized ways. Hence the fields() method is illegal for streams, by default. |
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632 The result of fields() is a DataSetFields object, which iterates over fields, |
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633 and whose elements are iterable over examples. A DataSetFields object can |
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634 be turned back into a DataSet with its examples() method: |
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635 dataset2 = dataset1.fields().examples() |
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636 and dataset2 should behave exactly like dataset1 (in fact by default dataset2==dataset1). |
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637 |
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638 DataSetFields can be concatenated vertically or horizontally. To be consistent with |
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639 the syntax used for DataSets, the | concatenates the fields and the & concatenates |
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640 the examples. |
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641 """ |
|
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642 def __init__(self,dataset,fieldnames): |
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643 original_dataset=dataset |
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644 if not fieldnames: |
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645 fieldnames=dataset.fieldNames() |
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646 elif not list(fieldnames)==list(dataset.fieldNames()): |
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647 #we must cast to list, othersize('x','y')!=['x','y'] |
|
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648 dataset = FieldsSubsetDataSet(dataset,fieldnames) |
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649 assert dataset.hasFields(*fieldnames) |
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650 self.dataset=dataset |
|
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651 |
|
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652 if isinstance(dataset,MinibatchDataSet): |
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653 Example.__init__(self,fieldnames,list(dataset._fields)) |
|
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654 elif isinstance(original_dataset,MinibatchDataSet): |
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655 Example.__init__(self,fieldnames, |
|
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656 [original_dataset._fields[field] |
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657 for field in fieldnames]) |
|
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658 else: |
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659 minibatch_iterator = dataset.minibatches(fieldnames, |
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660 minibatch_size=len(dataset), |
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661 n_batches=1) |
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662 minibatch=minibatch_iterator.next() |
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663 Example.__init__(self,fieldnames,minibatch) |
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664 |
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665 def examples(self): |
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666 return self.dataset |
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667 |
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668 def __or__(self,other): |
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669 """ |
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670 fields1 | fields2 is a DataSetFields that whose list of examples is the concatenation |
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671 of the list of examples of DataSetFields fields1 and fields2. |
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672 """ |
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673 return (self.examples() + other.examples()).fields() |
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674 |
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675 def __and__(self,other): |
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676 """ |
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677 fields1 + fields2 is a DataSetFields that whose list of fields is the concatenation |
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678 of the fields of DataSetFields fields1 and fields2. |
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679 """ |
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680 return (self.examples() | other.examples()).fields() |
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681 |
|
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682 |
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683 class MinibatchDataSet(DataSet): |
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684 """ |
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685 Turn a L{Example} of same-length (iterable) fields into an example-iterable dataset. |
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686 Each element of the lookup-list should be an iterable and sliceable, all of the same length. |
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687 """ |
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688 def __init__(self,fields_lookuplist,values_vstack=DataSet().valuesVStack, |
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689 values_hstack=DataSet().valuesHStack): |
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690 """ |
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691 The user can (and generally should) also provide values_vstack(fieldname,fieldvalues) |
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692 and a values_hstack(fieldnames,fieldvalues) functions behaving with the same |
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693 semantics as the DataSet methods of the same name (but without the self argument). |
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694 """ |
|
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695 |
|
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696 self._fields=fields_lookuplist |
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697 assert len(fields_lookuplist)>0 |
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698 self.length=len(fields_lookuplist[0]) |
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699 for field in fields_lookuplist[1:]: |
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700 if self.length != len(field) : |
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701 print 'self.length = ',self.length |
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702 print 'len(field) = ', len(field) |
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703 print 'self._fields.keys() = ', self._fields.keys() |
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704 print 'field=',field |
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705 print 'fields_lookuplist=', fields_lookuplist |
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706 assert self.length==len(field) |
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707 self.valuesVStack=values_vstack |
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708 self.valuesHStack=values_hstack |
|
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709 |
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710 def __len__(self): |
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711 return self.length |
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712 |
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713 def dontuse__getitem__(self,i): |
| 80 | 714 if type(i) in (slice,list): |
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715 return DataSetFields(MinibatchDataSet( |
| 80 | 716 Example(self._fields.keys(),[field[i] for field in self._fields])),self.fieldNames()) |
| 717 if type(i) is int: | |
| 85 | 718 return Example(self._fields.keys(),[field[i] for field in self._fields]) |
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719 if self.hasFields(i): |
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720 return self._fields[i] |
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721 assert i in self.__dict__ # else it means we are trying to access a non-existing property |
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48
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
722 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
|
723 |
|
29
46c5c90019c2
Changed apply_function so that it propagates methods of the source.
bengioy@grenat.iro.umontreal.ca
parents:
28
diff
changeset
|
724 def fieldNames(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
|
725 return self._fields.keys() |
|
36
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Rewriting dataset.py completely
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parents:
29
diff
changeset
|
726 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
727 def hasFields(self,*fieldnames): |
|
36
438440ba0627
Rewriting dataset.py completely
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parents:
29
diff
changeset
|
728 for fieldname in fieldnames: |
|
61
a8b70a9117ad
bugfix: in MinibatchDataSet renamed the class variable fields to _fields as parent class have a function called field.
Frederic Bastien <bastienf@iro.umontreal.ca>
parents:
60
diff
changeset
|
729 if fieldname not in self._fields.keys(): |
|
36
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parents:
29
diff
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|
730 return False |
|
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parents:
29
diff
changeset
|
731 return True |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
732 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
733 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
223
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
734 #@TODO bug somewhere here, fieldnames doesnt seem to be well handled |
|
36
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Rewriting dataset.py completely
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parents:
29
diff
changeset
|
735 class Iterator(object): |
|
223
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
736 def __init__(self,ds,fieldnames): |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
737 # tbm: added two next lines to handle fieldnames |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
738 if fieldnames is None: fieldnames = ds._fields.keys() |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
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parents:
218
diff
changeset
|
739 self.fieldnames = fieldnames |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
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|
740 |
|
36
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diff
changeset
|
741 self.ds=ds |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
742 self.next_example=offset |
|
290
9b533cc7874a
trying to get default implemenations to work
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
274
diff
changeset
|
743 assert minibatch_size >= 0 |
| 41 | 744 if offset+minibatch_size > ds.length: |
|
36
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Rewriting dataset.py completely
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parents:
29
diff
changeset
|
745 raise NotImplementedError() |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
746 def __iter__(self): |
|
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
747 return self |
|
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
748 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
|
749 upper = self.next_example+minibatch_size |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
750 assert upper<=self.ds.length |
|
223
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
751 #minibatch = Example(self.ds._fields.keys(), |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
752 # [field[self.next_example:upper] |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
753 # for field in self.ds._fields]) |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
754 # tbm: modif to use fieldnames |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
755 values = [] |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
756 for f in self.fieldnames : |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
757 #print 'we have field',f,'in fieldnames' |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
758 values.append( self.ds._fields[f][self.next_example:upper] ) |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
759 minibatch = Example(self.fieldnames,values) |
|
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
760 #print minibatch |
|
36
438440ba0627
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parents:
29
diff
changeset
|
761 self.next_example+=minibatch_size |
|
73
69f97aad3faf
Coded untested ApplyFunctionDataSet and CacheDataSet
bengioy@bengiomac.local
parents:
72
diff
changeset
|
762 return minibatch |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
763 |
|
223
517364d48ae0
should have solved the problem with minibatches not handling subsets of fieldnames, although maybe not super efficient
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
764 # 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
Thierry Bertin-Mahieux <bertinmt@iro.umontreal.ca>
parents:
218
diff
changeset
|
765 return Iterator(self,fieldnames) |
|
20
266c68cb6136
Minor editions, plus adding untested ApplyFunctionDataset for GradientLearner in the works.
bengioy@bengiomac.local
parents:
19
diff
changeset
|
766 |
|
36
438440ba0627
Rewriting dataset.py completely
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parents:
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diff
changeset
|
767 class HStackedDataSet(DataSet): |
|
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diff
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|
768 """ |
| 167 | 769 A L{DataSet} that wraps several datasets and shows a view that includes all their fields, |
|
36
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parents:
29
diff
changeset
|
770 i.e. whose list of fields is the concatenation of their lists of fields. |
|
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diff
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|
771 |
|
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diff
changeset
|
772 If a field name is found in more than one of the datasets, then either an error is |
|
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parents:
29
diff
changeset
|
773 raised or the fields are renamed (either by prefixing the __name__ attribute |
|
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diff
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|
774 of the dataset + ".", if it exists, or by suffixing the dataset index in the argument list). |
|
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parents:
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diff
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|
775 |
| 167 | 776 @todo: automatically detect a chain of stacked datasets due to A | B | C | D ... |
|
36
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parents:
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diff
changeset
|
777 """ |
| 41 | 778 def __init__(self,datasets,accept_nonunique_names=False,description=None,field_types=None): |
| 779 DataSet.__init__(self,description,field_types) | |
|
36
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parents:
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diff
changeset
|
780 self.datasets=datasets |
|
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|
781 self.accept_nonunique_names=accept_nonunique_names |
|
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diff
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|
782 self.fieldname2dataset={} |
|
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diff
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|
783 |
|
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diff
changeset
|
784 def rename_field(fieldname,dataset,i): |
|
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diff
changeset
|
785 if hasattr(dataset,"__name__"): |
|
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diff
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|
786 return dataset.__name__ + "." + fieldname |
|
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diff
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|
787 return fieldname+"."+str(i) |
|
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diff
changeset
|
788 |
|
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|
789 # make sure all datasets have the same length and unique field names |
|
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|
790 self.length=None |
|
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parents:
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|
791 names_to_change=[] |
|
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diff
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|
792 for i in xrange(len(datasets)): |
|
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|
793 dataset = datasets[i] |
|
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changeset
|
794 length=len(dataset) |
|
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|
795 if self.length: |
|
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diff
changeset
|
796 assert self.length==length |
|
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parents:
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diff
changeset
|
797 else: |
|
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|
798 self.length=length |
|
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diff
changeset
|
799 for fieldname in dataset.fieldNames(): |
|
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diff
changeset
|
800 if fieldname in self.fieldname2dataset: # name conflict! |
|
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|
801 if accept_nonunique_names: |
|
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diff
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|
802 fieldname=rename_field(fieldname,dataset,i) |
|
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diff
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|
803 names2change.append((fieldname,i)) |
|
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|
804 else: |
|
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diff
changeset
|
805 raise ValueError("Incompatible datasets: non-unique field name = "+fieldname) |
|
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diff
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|
806 self.fieldname2dataset[fieldname]=i |
|
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diff
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|
807 for fieldname,i in names_to_change: |
|
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diff
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|
808 del self.fieldname2dataset[fieldname] |
|
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diff
changeset
|
809 self.fieldname2dataset[rename_field(fieldname,self.datasets[i],i)]=i |
|
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diff
changeset
|
810 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
811 def hasFields(self,*fieldnames): |
|
36
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|
812 for fieldname in fieldnames: |
|
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|
813 if not fieldname in self.fieldname2dataset: |
|
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|
814 return False |
|
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|
815 return True |
|
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diff
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|
816 |
|
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|
817 def fieldNames(self): |
|
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|
818 return self.fieldname2dataset.keys() |
|
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diff
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|
819 |
| 41 | 820 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
36
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|
821 |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
822 class HStackedIterator(object): |
|
36
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diff
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|
823 def __init__(self,hsds,iterators): |
|
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|
824 self.hsds=hsds |
|
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|
825 self.iterators=iterators |
|
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diff
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|
826 def __iter__(self): |
|
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|
827 return self |
|
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changeset
|
828 def next(self): |
|
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changeset
|
829 # concatenate all the fields of the minibatches |
|
290
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James Bergstra <bergstrj@iro.umontreal.ca>
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diff
changeset
|
830 l=Example() |
| 140 | 831 for iter in self.iterators: |
| 832 l.append_lookuplist(iter.next()) | |
| 833 return l | |
|
36
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diff
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|
834 |
| 125 | 835 assert self.hasFields(*fieldnames) |
|
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diff
changeset
|
836 # find out which underlying datasets are necessary to service the required fields |
|
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diff
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|
837 # and construct corresponding minibatch iterators |
| 140 | 838 if fieldnames and fieldnames!=self.fieldNames(): |
|
36
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diff
changeset
|
839 datasets=set([]) |
|
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diff
changeset
|
840 fields_in_dataset=dict([(dataset,[]) for dataset in datasets]) |
|
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diff
changeset
|
841 for fieldname in fieldnames: |
| 136 | 842 dataset=self.datasets[self.fieldname2dataset[fieldname]] |
|
36
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diff
changeset
|
843 datasets.add(dataset) |
|
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diff
changeset
|
844 fields_in_dataset[dataset].append(fieldname) |
|
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parents:
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diff
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|
845 datasets=list(datasets) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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parents:
36
diff
changeset
|
846 iterators=[dataset.minibatches(fields_in_dataset[dataset],minibatch_size,n_batches,offset) |
|
36
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diff
changeset
|
847 for dataset in datasets] |
|
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diff
changeset
|
848 else: |
|
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Rewriting dataset.py completely
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parents:
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diff
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|
849 datasets=self.datasets |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
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diff
changeset
|
850 iterators=[dataset.minibatches(None,minibatch_size,n_batches,offset) for dataset in datasets] |
|
44
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Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
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diff
changeset
|
851 return HStackedIterator(self,iterators) |
|
36
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changeset
|
852 |
|
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parents:
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diff
changeset
|
853 |
|
290
9b533cc7874a
trying to get default implemenations to work
James Bergstra <bergstrj@iro.umontreal.ca>
parents:
274
diff
changeset
|
854 def untested_valuesVStack(self,fieldname,fieldvalues): |
|
36
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parents:
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diff
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|
855 return self.datasets[self.fieldname2dataset[fieldname]].valuesVStack(fieldname,fieldvalues) |
|
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diff
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|
856 |
|
290
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trying to get default implemenations to work
James Bergstra <bergstrj@iro.umontreal.ca>
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|
857 def untested_valuesHStack(self,fieldnames,fieldvalues): |
|
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Rewriting dataset.py completely
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diff
changeset
|
858 """ |
|
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bengioy@zircon.iro.umontreal.ca
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diff
changeset
|
859 We will use the sub-dataset associated with the first fieldname in the fieldnames list |
|
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Rewriting dataset.py completely
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parents:
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diff
changeset
|
860 to do the work, hoping that it can cope with the other values (i.e. won't care |
|
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Rewriting dataset.py completely
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parents:
29
diff
changeset
|
861 about the incompatible fieldnames). Hence this heuristic will always work if |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
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29
diff
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|
862 all the fieldnames are of the same sub-dataset. |
|
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Rewriting dataset.py completely
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|
863 """ |
|
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Rewriting dataset.py completely
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diff
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|
864 return self.datasets[self.fieldname2dataset[fieldnames[0]]].valuesHStack(fieldnames,fieldvalues) |
|
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diff
changeset
|
865 |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
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diff
changeset
|
866 class VStackedDataSet(DataSet): |
|
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Rewriting dataset.py completely
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29
diff
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|
867 """ |
| 167 | 868 A L{DataSet} that wraps several datasets and shows a view that includes all their examples, |
|
36
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Rewriting dataset.py completely
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diff
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|
869 in the order provided. This clearly assumes that they all have the same field names |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
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diff
changeset
|
870 and all (except possibly the last one) are of finite length. |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
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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 ... |
|
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diff
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|
873 """ |
|
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Rewriting dataset.py completely
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parents:
29
diff
changeset
|
874 def __init__(self,datasets): |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
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diff
changeset
|
875 self.datasets=datasets |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
876 self.length=0 |
|
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Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
877 self.index2dataset={} |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
878 assert len(datasets)>0 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
879 fieldnames = datasets[-1].fieldNames() |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
880 self.datasets_start_row=[] |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
881 # We use this map from row index to dataset index for constant-time random access of examples, |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
882 # to avoid having to search for the appropriate dataset each time and slice is asked for. |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
883 for dataset,k in enumerate(datasets[0:-1]): |
|
48
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
884 assert dataset.is_unbounded() # All VStacked datasets (except possibly the last) must be bounded (have a length). |
|
b6730f9a336d
Fixing MinibatchDataSet getitem
bengioy@grenat.iro.umontreal.ca
parents:
46
diff
changeset
|
885 L=len(dataset) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
886 for i in xrange(L): |
|
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
887 self.index2dataset[self.length+i]=k |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
888 self.datasets_start_row.append(self.length) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
889 self.length+=L |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
890 assert dataset.fieldNames()==fieldnames |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
891 self.datasets_start_row.append(self.length) |
|
36
438440ba0627
Rewriting dataset.py completely
bengioy@zircon.iro.umontreal.ca
parents:
29
diff
changeset
|
892 self.length+=len(datasets[-1]) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
893 # If length is very large, we should use a more memory-efficient mechanism |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
894 # that does not store all indices |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
895 if self.length>1000000: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
896 # 1 million entries would require about 60 meg for the index2dataset map |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
897 # TODO |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
898 print "A more efficient mechanism for index2dataset should be implemented" |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
899 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
900 def __len__(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
901 return self.length |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
902 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
903 def fieldNames(self): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
904 return self.datasets[0].fieldNames() |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
905 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
906 def hasFields(self,*fieldnames): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
907 return self.datasets[0].hasFields(*fieldnames) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
908 |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
909 def locate_row(self,row): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
910 """Return (dataset_index, row_within_dataset) for global row number""" |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
911 dataset_index = self.index2dataset[row] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
912 row_within_dataset = self.datasets_start_row[dataset_index] |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
913 return dataset_index, row_within_dataset |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
914 |
| 41 | 915 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
|
44
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
916 |
|
5a85fda9b19b
Fixed some more iterator bugs
bengioy@grenat.iro.umontreal.ca
parents:
43
diff
changeset
|
917 class VStackedIterator(object): |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
918 def __init__(self,vsds): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
919 self.vsds=vsds |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
920 self.next_row=offset |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
921 self.next_dataset_index,self.next_dataset_row=self.vsds.locate_row(offset) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
922 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
923 self.next_iterator(vsds.datasets[0],offset,n_batches) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
924 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
925 def next_iterator(self,dataset,starting_offset,batches_left): |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
926 L=len(dataset) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
927 ds_nbatches = (L-starting_offset)/minibatch_size |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
928 if batches_left is not None: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
929 ds_nbatches = max(batches_left,ds_nbatches) |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
930 if minibatch_size>L: |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
931 ds_minibatch_size=L |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
932 n_left_in_mb=minibatch_size-L |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
933 ds_nbatches=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
934 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
935 n_left_in_mb=0 |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
936 return dataset.minibatches(fieldnames,minibatch_size,ds_nbatches,starting_offset), \ |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
937 L-(starting_offset+ds_nbatches*minibatch_size), n_left_in_mb |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
938 |
|
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
939 def move_to_next_dataset(self): |
|
38
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
940 if self.n_left_at_the_end_of_ds>0: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
941 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
942 self.next_iterator(vsds.datasets[self.next_dataset_index], |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
943 self.n_left_at_the_end_of_ds,1) |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
944 else: |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
945 self.next_dataset_index +=1 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
946 if self.next_dataset_index==len(self.vsds.datasets): |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
947 self.next_dataset_index = 0 |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
948 self.current_iterator,self.n_left_at_the_end_of_ds,self.n_left_in_mb= \ |
|
d637ad8f7352
Finished first untested version of VStackedDataset
bengioy@esprit.iro.umontreal.ca
parents:
37
diff
changeset
|
949 self.next_iterator(vsds.datasets[self.next_dataset_index],starting_offset,n_batches) |
|
37
73c4212ba5b3
Factored the minibatch-writing code into an iterator class inside DataSet
bengioy@esprit.iro.umontreal.ca
parents:
36
diff
changeset
|
950 |
|
73c4212ba5b3
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|
951 def __iter__(self): |
|
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|
952 return self |
|
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|
953 |
|
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|
954 def next(self): |
|
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|
955 dataset=self.vsds.datasets[self.next_dataset_index] |
|
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diff
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|
956 mb = self.next_iterator.next() |
|
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|
957 if self.n_left_in_mb: |
|
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|
958 extra_mb = [] |
|
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diff
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|
959 while self.n_left_in_mb>0: |
|
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diff
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|
960 self.move_to_next_dataset() |
|
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diff
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|
961 extra_mb.append(self.next_iterator.next()) |
|
73
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|
962 mb = Example(fieldnames, |
|
40
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|
963 [dataset.valuesVStack(name, |
|
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diff
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|
964 [mb[name]]+[b[name] for b in extra_mb]) |
|
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|
965 for name in fieldnames]) |
|
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diff
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|
966 |
|
37
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|
967 self.next_row+=minibatch_size |
|
38
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|
968 self.next_dataset_row+=minibatch_size |
|
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diff
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|
969 if self.next_row+minibatch_size>len(dataset): |
|
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diff
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|
970 self.move_to_next_dataset() |
|
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diff
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|
971 return examples |
|
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diff
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|
972 return VStackedIterator(self) |
|
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|
973 |
| 41 | 974 class ArrayFieldsDataSet(DataSet): |
| 975 """ | |
| 976 Virtual super-class of datasets whose field values are numpy array, | |
| 977 thus defining valuesHStack and valuesVStack for sub-classes. | |
| 978 """ | |
|
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979 def __init__(self,description=None,field_types=None): |
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|
980 DataSet.__init__(self,description,field_types) |
|
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|
981 def untested_valuesHStack(self,fieldnames,fieldvalues): |
| 41 | 982 """Concatenate field values horizontally, e.g. two vectors |
| 983 become a longer vector, two matrices become a wider matrix, etc.""" | |
| 984 return numpy.hstack(fieldvalues) | |
|
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|
985 def untested_valuesVStack(self,fieldname,values): |
| 41 | 986 """Concatenate field values vertically, e.g. two vectors |
| 987 become a two-row matrix, two matrices become a longer matrix, etc.""" | |
| 988 return numpy.vstack(values) | |
| 989 | |
| 990 class ArrayDataSet(ArrayFieldsDataSet): | |
| 991 """ | |
| 992 An ArrayDataSet stores the fields as groups of columns in a numpy tensor, | |
| 993 whose first axis iterates over examples, second axis determines fields. | |
| 994 If the underlying array is N-dimensional (has N axes), then the field | |
| 995 values are (N-2)-dimensional objects (i.e. ordinary numbers if N=2). | |
| 996 """ | |
| 997 | |
|
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|
998 def __init__(self, data_array, fields_columns, **kwargs): |
|
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|
999 """ |
|
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|
1000 Construct an ArrayDataSet from the underlying numpy array (data) and |
|
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1001 a map (fields_columns) from fieldnames to field columns. The columns of a field are specified |
|
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|
1002 using the standard arguments for indexing/slicing: integer for a column index, |
|
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1003 slice for an interval of columns (with possible stride), or iterable of column indices. |
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|
1004 """ |
|
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|
1005 ArrayFieldsDataSet.__init__(self, **kwargs) |
| 41 | 1006 self.data=data_array |
|
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diff
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|
1007 self.fields_columns=fields_columns |
| 41 | 1008 |
| 1009 # check consistency and complete slices definitions | |
|
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diff
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|
1010 for fieldname, fieldcolumns in self.fields_columns.items(): |
| 41 | 1011 if type(fieldcolumns) is int: |
| 1012 assert fieldcolumns>=0 and fieldcolumns<data_array.shape[1] | |
|
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1013 if 1: |
|
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|
1014 #I changed this because it didn't make sense to me, |
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|
1015 # and it made it more difficult to write my learner. |
|
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|
1016 # If it breaks stuff, let's talk about it. |
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|
1017 # - James 22/05/2008 |
|
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|
1018 self.fields_columns[fieldname]=[fieldcolumns] |
|
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|
1019 else: |
|
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|
1020 self.fields_columns[fieldname]=fieldcolumns |
| 41 | 1021 elif type(fieldcolumns) is slice: |
| 1022 start,step=None,None | |
| 1023 if not fieldcolumns.start: | |
| 1024 start=0 | |
| 1025 if not fieldcolumns.step: | |
| 1026 step=1 | |
| 1027 if start or step: | |
|
42
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diff
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|
1028 self.fields_columns[fieldname]=slice(start,fieldcolumns.stop,step) |
| 41 | 1029 elif hasattr(fieldcolumns,"__iter__"): # something like a list |
| 1030 for i in fieldcolumns: | |
| 1031 assert i>=0 and i<data_array.shape[1] | |
| 1032 | |
|
42
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diff
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|
1033 def fieldNames(self): |
|
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diff
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|
1034 return self.fields_columns.keys() |
| 41 | 1035 |
|
42
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diff
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|
1036 def __len__(self): |
|
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diff
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|
1037 return len(self.data) |
| 41 | 1038 |
|
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|
1039 def dontuse__getitem__(self,key): |
|
55
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|
1040 """More efficient implementation than the default __getitem__""" |
|
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|
1041 fieldnames=self.fields_columns.keys() |
|
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|
1042 values=self.fields_columns.values() |
| 80 | 1043 if type(key) is int: |
|
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|
1044 return Example(fieldnames, |
|
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|
1045 [self.data[key,col] for col in values]) |
| 80 | 1046 if type(key) is slice: |
|
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|
1047 return MinibatchDataSet(Example(fieldnames, |
|
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|
1048 [self.data[key,col] for col in values])) |
| 80 | 1049 if type(key) is list: |
| 1050 for i in range(len(key)): | |
| 1051 if self.hasFields(key[i]): | |
| 1052 key[i]=self.fields_columns[key[i]] | |
| 1053 return MinibatchDataSet(Example(fieldnames, | |
|
88
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85
diff
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|
1054 #we must separate differently for list as numpy |
| 128 | 1055 # doesn't support self.data[[i1,...],[i2,...]] |
|
88
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diff
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|
1056 # when their is more then two i1 and i2 |
|
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|
1057 [self.data[key,:][:,col] |
|
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|
1058 if isinstance(col,list) else |
|
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|
1059 self.data[key,col] for col in values]), |
|
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|
1060 |
| 128 | 1061 |
|
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|
1062 self.valuesVStack,self.valuesHStack) |
| 80 | 1063 |
|
55
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|
1064 # else check for a fieldname |
| 80 | 1065 if self.hasFields(key): |
|
105
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|
1066 return self.data[:,self.fields_columns[key]] |
|
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|
1067 # else we are trying to access a property of the dataset |
| 80 | 1068 assert key in self.__dict__ # else it means we are trying to access a non-existing property |
| 1069 return self.__dict__[key] | |
|
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|
1070 |
|
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|
1071 def dontuse__iter__(self): |
|
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|
1072 class ArrayDataSetIteratorIter(object): |
|
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|
1073 def __init__(self,dataset,fieldnames): |
|
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|
1074 if fieldnames is None: fieldnames = dataset.fieldNames() |
|
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|
1075 # store the resulting minibatch in a lookup-list of values |
|
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|
1076 self.minibatch = Example(fieldnames,[0]*len(fieldnames)) |
|
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|
1077 self.dataset=dataset |
|
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|
1078 self.current=0 |
|
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|
1079 self.columns = [self.dataset.fields_columns[f] |
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|
1080 for f in self.minibatch._names] |
|
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|
1081 self.l = self.dataset.data.shape[0] |
|
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1082 def __iter__(self): |
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|
1083 return self |
|
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|
1084 def next(self): |
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|
1085 #@todo: we suppose that we need to stop only when minibatch_size == 1. |
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|
1086 # Otherwise, MinibatchWrapAroundIterator do it. |
|
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|
1087 if self.current>=self.l: |
|
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|
1088 raise StopIteration |
|
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|
1089 sub_data = self.dataset.data[self.current] |
|
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|
1090 self.minibatch._values = [sub_data[c] for c in self.columns] |
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|
1091 |
|
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|
1092 self.current+=1 |
|
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|
1093 return self.minibatch |
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|
1094 |
|
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|
1095 return ArrayDataSetIteratorIter(self,self.fieldNames()) |
|
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|
1096 |
|
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1097 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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1098 cursor = Example(fieldnames,[0]*len(fieldnames)) |
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1099 fieldnames = self.fieldNames() if fieldnames is None else fieldnames |
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1100 for n in xrange(n_batches): |
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1101 if offset == len(self): |
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1102 break |
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1103 sub_data = self.data[offset : offset+minibatch_size] |
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1104 offset += len(sub_data) #can be less than minibatch_size at end |
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1105 cursor._values = [sub_data[:,self.fields_columns[f]] for f in cursor._names] |
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1106 yield cursor |
|
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1107 |
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1108 #return ArrayDataSetIterator(self,fieldnames,minibatch_size,n_batches,offset) |
|
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1109 |
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1110 |
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1111 class CachedDataSet(DataSet): |
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1112 """ |
| 167 | 1113 Wrap a L{DataSet} whose values are computationally expensive to obtain |
|
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1114 (e.g. because they involve some computation, or disk access), |
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1115 so that repeated accesses to the same example are done cheaply, |
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1116 by caching every example value that has been accessed at least once. |
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1117 |
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1118 Optionally, for finite-length dataset, all the values can be computed |
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1119 (and cached) upon construction of the CachedDataSet, rather at the |
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1120 first access. |
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1121 |
| 167 | 1122 @todo: when cache_all_upon_construction create mini-batches that are as |
|
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1123 large as possible but not so large as to fill up memory. |
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1124 |
| 167 | 1125 @todo: add disk-buffering capability, so that when the cache becomes too |
|
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1126 big for memory, we cache things on disk, trying to keep in memory only |
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1127 the record most likely to be accessed next. |
|
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1128 """ |
|
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1129 def __init__(self,source_dataset,cache_all_upon_construction=False): |
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1130 self.source_dataset=source_dataset |
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1131 self.cache_all_upon_construction=cache_all_upon_construction |
|
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1132 self.cached_examples = [] |
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1133 if cache_all_upon_construction: |
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1134 # this potentially brings all the source examples |
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1135 # into memory at once, which may be too much |
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1136 # the work could possibly be done by minibatches |
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1137 # that are as large as possible but no more than what memory allows. |
|
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1138 fields_values = source_dataset.minibatches(minibatch_size=len(source_dataset)).__iter__().next() |
|
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1139 assert all([len(self)==len(field_values) for field_values in fields_values]) |
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1140 for example in fields_values.examples(): |
|
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1141 self.cached_examples.append(copy.copy(example)) |
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1142 |
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1143 self.fieldNames = source_dataset.fieldNames |
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1144 self.hasFields = source_dataset.hasFields |
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1145 self.valuesHStack = source_dataset.valuesHStack |
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1146 self.valuesVStack = source_dataset.valuesVStack |
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1147 |
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1148 def __len__(self): |
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1149 return len(self.source_dataset) |
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1150 |
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1151 def minibatches_nowrap(self,fieldnames,minibatch_size,n_batches,offset): |
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1152 class CacheIterator(object): |
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1153 def __init__(self,dataset): |
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1154 self.dataset=dataset |
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1155 self.current=offset |
|
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1156 self.all_fields = self.dataset.fieldNames()==fieldnames |
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1157 def __iter__(self): return self |
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1158 def next(self): |
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1159 upper = self.current+minibatch_size |
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1160 cache_len = len(self.dataset.cached_examples) |
|
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1161 if upper>cache_len: # whole minibatch is not already in cache |
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1162 # cache everything from current length to upper |
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1163 for example in self.dataset.source_dataset[cache_len:upper]: |
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1164 self.dataset.cached_examples.append(example) |
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1165 all_fields_minibatch = Example(self.dataset.fieldNames(), |
|
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1166 zip(*self.dataset.cached_examples[self.current:self.current+minibatch_size])) |
|
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1167 self.current+=minibatch_size |
|
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1168 if self.all_fields: |
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1169 return all_fields_minibatch |
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1170 return Example(fieldnames,[all_fields_minibatch[name] for name in fieldnames]) |
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1171 return CacheIterator(self) |
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1172 |
|
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1173 def dontuse__getitem__(self,i): |
| 153 | 1174 if type(i)==int and len(self.cached_examples)>i: |
| 1175 return self.cached_examples[i] | |
| 1176 else: | |
|
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1177 return self.source_dataset[i] |
|
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1178 |
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1179 def __iter__(self): |
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1180 class CacheIteratorIter(object): |
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1181 def __init__(self,dataset): |
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1182 self.dataset=dataset |
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1183 self.l = len(dataset) |
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1184 self.current = 0 |
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1185 self.fieldnames = self.dataset.fieldNames() |
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1186 self.example = Example(self.fieldnames,[0]*len(self.fieldnames)) |
|
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1187 def __iter__(self): return self |
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1188 def next(self): |
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1189 if self.current>=self.l: |
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1190 raise StopIteration |
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1191 cache_len = len(self.dataset.cached_examples) |
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1192 if self.current>=cache_len: # whole minibatch is not already in cache |
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1193 # cache everything from current length to upper |
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1194 self.dataset.cached_examples.append( |
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1195 self.dataset.source_dataset[self.current]) |
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1196 self.example._values = self.dataset.cached_examples[self.current] |
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1197 self.current+=1 |
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1198 return self.example |
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1199 |
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1200 return CacheIteratorIter(self) |
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1201 |
|
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1202 class ApplyFunctionDataSet(DataSet): |
|
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1203 """ |
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1204 A L{DataSet} that contains as fields the results of applying a |
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1205 given function example-wise or minibatch-wise to all the fields of |
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1206 an input dataset. The output of the function should be an iterable |
|
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1207 (e.g. a list or a LookupList) over the resulting values. |
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1208 |
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1209 The function take as input the fields of the dataset, not the examples. |
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1210 |
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1211 In minibatch mode, the function is expected to work on minibatches |
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1212 (takes a minibatch in input and returns a minibatch in output). More |
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1213 precisely, it means that each element of the input or output list |
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1214 should be iterable and indexable over the individual example values |
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1215 (typically these elements will be numpy arrays). All of the elements |
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1216 in the input and output lists should have the same length, which is |
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1217 the length of the minibatch. |
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1218 |
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1219 The function is applied each time an example or a minibatch is accessed. |
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1220 To avoid re-doing computation, wrap this dataset inside a CachedDataSet. |
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1221 |
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1222 If the values_{h,v}stack functions are not provided, then |
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1223 the input_dataset.values{H,V}Stack functions are used by default. |
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1224 |
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1225 """ |
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1226 |
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1227 def __init__(self,input_dataset,function,output_names,minibatch_mode=True, |
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1228 values_hstack=None,values_vstack=None, |
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1229 description=None,fieldtypes=None): |
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1230 """ |
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1231 Constructor takes an input dataset that has as many fields as the function |
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1232 expects as inputs. The resulting dataset has as many fields as the function |
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1233 produces as outputs, and that should correspond to the number of output names |
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1234 (provided in a list). |
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1235 |
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1236 Note that the expected semantics of the function differs in minibatch mode |
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1237 (it takes minibatches of inputs and produces minibatches of outputs, as |
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1238 documented in the class comment). |
|
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1239 |
|
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1240 TBM: are filedtypes the old field types (from input_dataset) or the new ones |
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1241 (for the new dataset created)? |
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1242 """ |
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1243 self.input_dataset=input_dataset |
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1244 self.function=function |
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1245 self.output_names=output_names |
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1246 self.minibatch_mode=minibatch_mode |
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1247 DataSet.__init__(self,description,fieldtypes) |
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1248 self.valuesHStack = values_hstack if values_hstack else input_dataset.valuesHStack |
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1249 self.valuesVStack = values_vstack if values_vstack else input_dataset.valuesVStack |
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1250 |
|
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1251 def __len__(self): |
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1252 return len(self.input_dataset) |
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1253 |
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1254 def fieldNames(self): |
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1255 return self.output_names |
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1256 |
|
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1257 def minibatches_nowrap(self, fieldnames, *args, **kwargs): |
|
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1258 all_input_fieldNames = self.input_dataset.fieldNames() |
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1259 mbnw = self.input_dataset.minibatches_nowrap |
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1260 |
|
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1261 for input_fields in mbnw(all_input_fieldNames, *args, **kwargs): |
|
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1262 if self.minibatch_mode: |
|
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1263 all_output_fields = self.function(*input_fields) |
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1264 else: |
|
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1265 input_examples = zip(*input_fields) #makes so that [i] means example i |
|
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1266 output_examples = [self.function(*input_example) |
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1267 for input_example in input_examples] |
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1268 all_output_fields = zip(*output_examples) |
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1269 |
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1270 all_outputs = Example(self.output_names, all_output_fields) |
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1271 #print 'input_fields', input_fields |
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1272 #print 'all_outputs', all_outputs |
|
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1273 if fieldnames==self.output_names: |
| 293 | 1274 rval = all_outputs |
|
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1275 else: |
| 293 | 1276 rval = Example(fieldnames,[all_outputs[name] for name in fieldnames]) |
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1277 #print 'rval', rval |
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1278 #print '--------' |
| 293 | 1279 yield rval |
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1280 |
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1281 def untested__iter__(self): # only implemented for increased efficiency |
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1282 class ApplyFunctionSingleExampleIterator(object): |
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1283 def __init__(self,output_dataset): |
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1284 self.current=0 |
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1285 self.output_dataset=output_dataset |
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1286 self.input_iterator=output_dataset.input_dataset.__iter__() |
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1287 def __iter__(self): return self |
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1288 def next(self): |
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1289 if self.output_dataset.minibatch_mode: |
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1290 function_inputs = [[input] for input in self.input_iterator.next()] |
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1291 outputs = self.output_dataset.function(*function_inputs) |
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1292 assert all([hasattr(output,'__iter__') for output in outputs]) |
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1293 function_outputs = [output[0] for output in outputs] |
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1294 else: |
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1295 function_inputs = self.input_iterator.next() |
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1296 function_outputs = self.output_dataset.function(*function_inputs) |
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1297 return Example(self.output_dataset.output_names,function_outputs) |
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1298 return ApplyFunctionSingleExampleIterator(self) |
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1299 |
|
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1300 def supervised_learning_dataset(src_dataset,input_fields,target_fields,weight_field=None): |
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1301 """ |
| 167 | 1302 Wraps an arbitrary L{DataSet} into one for supervised learning tasks |
| 1303 by forcing the user to define a set of fields as the 'input' field | |
| 1304 and a set of fields as the 'target' field. Optionally, a single | |
| 1305 weight_field can also be defined. | |
|
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1306 """ |
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1307 args = ((input_fields,'input'),(output_fields,'target')) |
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1308 if weight_field: args+=(([weight_field],'weight')) |
|
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1309 return src_dataset.merge_fields(*args) |
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1310 |
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1311 |
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1312 |
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1313 |