Mercurial > pylearn
annotate learner.py @ 111:88257dfedf8c
Added another work in progress, for mlp's
author | bengioy@bengiomac.local |
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date | Wed, 07 May 2008 09:16:04 -0400 |
parents | 8fa1ef2411a0 |
children | d0a1bd0378c6 |
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1 |
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2 from dataset import * |
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3 from compile import Function |
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4 |
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5 class Learner(AttributesHolder): |
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6 """Base class for learning algorithms, provides an interface |
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7 that allows various algorithms to be applicable to generic learning |
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8 algorithms. |
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9 |
10
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10 A Learner can be seen as a learning algorithm, a function that when |
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11 applied to training data returns a learned function, an object that |
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12 can be applied to other data and return some output data. |
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13 """ |
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14 |
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15 def __init__(self): |
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16 pass |
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17 |
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18 def forget(self): |
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19 """ |
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20 Reset the state of the learner to a blank slate, before seeing |
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21 training data. The operation may be non-deterministic if the |
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22 learner has a random number generator that is set to use a |
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23 different seed each time it forget() is called. |
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24 """ |
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25 raise NotImplementedError |
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26 |
14 | 27 def update(self,training_set,train_stats_collector=None): |
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28 """ |
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29 Continue training a learner, with the evidence provided by the given training set. |
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30 Hence update can be called multiple times. This is particularly useful in the |
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31 on-line setting or the sequential (Bayesian or not) settings. |
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32 The result is a function that can be applied on data, with the same |
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33 semantics of the Learner.use method. |
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34 |
14 | 35 The user may optionally provide a training StatsCollector that is used to record |
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36 some statistics of the outputs computed during training. It is update(d) during |
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37 training. |
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38 """ |
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39 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything |
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40 |
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41 |
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42 def __call__(self,training_set,train_stats_collector=None): |
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43 """ |
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44 Train a learner from scratch using the provided training set, |
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45 and return the learned function. |
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46 """ |
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47 self.forget() |
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48 return self.update(learning_task,train_stats_collector) |
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49 |
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50 def use(self,input_dataset,output_fields=None,copy_inputs=True): |
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51 """Once a Learner has been trained by one or more call to 'update', it can |
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52 be used with one or more calls to 'use'. The argument is a DataSet (possibly |
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53 containing a single example) and the result is a DataSet of the same length. |
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54 If output_fields is specified, it may be use to indicate which fields should |
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55 be constructed in the output DataSet (for example ['output','classification_error']). |
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56 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made |
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57 visible in the output DataSet returned by this method. |
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58 """ |
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59 raise NotImplementedError |
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60 |
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61 def attributeNames(self): |
78 | 62 """ |
63 A Learner may have attributes that it wishes to export to other objects. To automate | |
64 such export, sub-classes should define here the names (list of strings) of these attributes. | |
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65 |
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66 @todo By default, attributeNames looks for all dictionary entries whose name does not start with _. |
78 | 67 """ |
68 return [] | |
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69 |
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70 def updateInputAttributes(self): |
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71 """ |
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72 A subset of self.attributeNames() which are the names of attributes needed by update() in order |
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73 to do its work. |
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74 """ |
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75 raise AbstractFunction() |
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76 |
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77 def useInputAttributes(self): |
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78 """ |
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79 A subset of self.attributeNames() which are the names of attributes needed by use() in order |
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80 to do its work. |
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81 """ |
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82 raise AbstractFunction() |
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83 |
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84 def updateOutputAttributes(self): |
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85 """ |
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86 A subset of self.attributeNames() which are the names of attributes modified/created by update() in order |
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87 to do its work. |
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88 |
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89 By default these are inferred from the various update output attributes: |
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90 """ |
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91 return ["parameters"] + self.updateMinibatchOutputAttributes() + self.updateEndOutputAttributes() |
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92 |
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93 def useOutputAttributes(self): |
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94 """ |
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95 A subset of self.attributeNames() which are the names of attributes modified/created by use() in order |
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96 to do its work. |
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97 """ |
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98 raise AbstractFunction() |
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99 |
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100 |
78 | 101 class TLearner(Learner): |
102 """ | |
103 TLearner is a virtual class of Learners that attempts to factor out of the definition | |
104 of a learner the steps that are common to many implementations of learning algorithms, | |
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105 so as to leave only 'the equations' to define in particular sub-classes, using Theano. |
78 | 106 |
107 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods | |
108 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done, | |
109 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this | |
110 basic model is not appropriate can simply redefine update or use. | |
111 | |
112 Sub-classes must provide the following functions and functionalities: | |
113 - attributeNames(): defines all the names of attributes which can be used as fields or | |
114 attributes in input/output datasets or in stats collectors. | |
115 All these attributes are expected to be theano.Result objects | |
116 (with a .data property and recognized by theano.Function for compilation). | |
117 The sub-class constructor defines the relations between | |
118 the Theano variables that may be used by 'use' and 'update' | |
119 or by a stats collector. | |
120 - defaultOutputFields(input_fields): return a list of default dataset output fields when | |
121 None are provided by the caller of use. | |
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122 The following naming convention is assumed and important. |
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123 Attributes whose names are listed in attributeNames() can be of any type, |
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124 but those that can be referenced as input/output dataset fields or as |
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125 output attributes in 'use' or as input attributes in the stats collector |
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126 should be associated with a Theano Result variable. If the exported attribute |
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127 name is <name>, the corresponding Result name (an internal attribute of |
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128 the TLearner, created in the sub-class constructor) should be _<name>. |
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129 Typically <name> will be numpy ndarray and _<name> will be the corresponding |
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130 Theano Tensor (for symbolic manipulation). |
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131 |
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132 @todo pousser dans Learner toute la poutine qui peut l'etre sans etre |
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133 dependant de Theano |
78 | 134 """ |
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135 |
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136 def __init__(self): |
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137 Learner.__init__(self) |
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138 |
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139 def defaultOutputFields(self, input_fields): |
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140 """ |
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141 Return a default list of output field names (to put in the output dataset). |
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142 This will be used when None are provided (as output_fields) by the caller of the 'use' method. |
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143 This may involve looking at the input_fields (names) available in the |
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144 input_dataset. |
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145 """ |
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146 raise AbstractFunction() |
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147 |
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148 def allocate(self, minibatch): |
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149 """ |
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150 This function is called at the beginning of each updateMinibatch |
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151 and should be used to check that all required attributes have been |
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152 allocated and initialized (usually this function calls forget() |
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153 when it has to do an initialization). |
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154 """ |
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155 raise AbstractFunction() |
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156 |
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157 def minibatchwise_use_functions(self, input_fields, output_fields, stats_collector): |
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158 """ |
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159 Private helper function called by the generic TLearner.use. It returns a function |
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160 that can map the given input fields to the given output fields (along with the |
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161 attributes that the stats collector needs for its computation. The function |
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162 called also automatically makes use of the self.useInputAttributes() and |
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163 sets the self.useOutputAttributes(). |
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164 """ |
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165 if not output_fields: |
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166 output_fields = self.defaultOutputFields(input_fields) |
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167 if stats_collector: |
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168 stats_collector_inputs = stats_collector.input2UpdateAttributes() |
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169 for attribute in stats_collector_inputs: |
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170 if attribute not in input_fields: |
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171 output_fields.append(attribute) |
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172 key = (input_fields,output_fields) |
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173 if key not in self.use_functions_dictionary: |
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174 use_input_attributes = self.useInputAttributes() |
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175 use_output_attributes = self.useOutputAttributes() |
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176 complete_f = Function(self.names2OpResults(input_fields+use_input_attributes), |
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177 self.names2OpResults(output_fields+use_output_attributes)) |
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178 def f(*input_field_values): |
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179 input_attribute_values = self.names2attributes(use_input_attributes) |
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180 results = complete_f(*(input_field_values + input_attribute_values)) |
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181 output_field_values = results[0:len(output_fields)] |
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182 output_attribute_values = results[len(output_fields):len(results)] |
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183 if use_output_attributes: |
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184 self.setAttributes(use_output_attributes,output_attribute_values) |
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185 return output_field_values |
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186 self.use_functions_dictionary[key]=f |
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187 return self.use_functions_dictionary[key] |
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188 |
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189 def attributes(self,return_copy=False): |
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190 """ |
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191 Return a list with the values of the learner's attributes (or optionally, a deep copy). |
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192 """ |
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193 return self.names2attributes(self.attributeNames(),return_copy) |
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194 |
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195 def names2attributes(self,names,return_copy=False): |
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196 """ |
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197 Private helper function that maps a list of attribute names to a list |
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198 of (optionally copies) values of attributes. |
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199 """ |
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200 if return_copy: |
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201 return [copy.deepcopy(self.__getattr__(name).data) for name in names] |
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202 else: |
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203 return [self.__getattr__(name).data for name in names] |
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204 |
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205 def names2OpResults(self,names): |
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206 """ |
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207 Private helper function that maps a list of attribute names to a list |
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208 of corresponding Op Results (with the same name but with a '_' prefix). |
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209 """ |
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210 return [self.__getattr__('_'+name).data for name in names] |
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211 |
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212 def use(self,input_dataset,output_fieldnames=None,output_attributes=[], |
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213 test_stats_collector=None,copy_inputs=True, put_stats_in_output_dataset=True): |
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214 """ |
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215 The learner tries to compute in the output dataset the output fields specified |
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216 |
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217 @todo check if some of the learner attributes are actually SPECIFIED |
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218 as attributes of the input_dataset, and if so use their values instead |
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219 of the ones in the learner. |
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220 |
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221 The learner tries to compute in the output dataset the output fields specified. |
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222 If None is specified then self.defaultOutputFields(input_dataset.fieldNames()) |
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223 is called to determine the output fields. |
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224 |
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225 Attributes of the learner can also optionally be copied into the output dataset. |
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226 If output_attributes is None then all of the attributes in self.AttributeNames() |
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227 are copied in the output dataset, but if it is [] (the default), then none are copied. |
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228 If a test_stats_collector is provided, then its attributes (test_stats_collector.AttributeNames()) |
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229 are also copied into the output dataset attributes. |
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230 """ |
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231 minibatchwise_use_function = minibatchwise_use_functions(input_dataset.fieldNames(), |
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232 output_fieldnames, |
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233 test_stats_collector) |
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234 virtual_output_dataset = ApplyFunctionDataSet(input_dataset, |
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235 minibatchwise_use_function, |
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236 True,DataSet.numpy_vstack, |
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237 DataSet.numpy_hstack) |
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238 # actually force the computation |
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239 output_dataset = CachedDataSet(virtual_output_dataset,True) |
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240 if copy_inputs: |
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241 output_dataset = input_dataset | output_dataset |
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242 # copy the wanted attributes in the dataset |
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243 if output_attributes is None: |
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244 output_attributes = self.attributeNames() |
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245 if output_attributes: |
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246 assert set(attribute_names) <= set(self.attributeNames()) |
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247 output_dataset.setAttributes(output_attributes, |
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248 self.names2attributes(output_attributes,return_copy=True)) |
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249 if test_stats_collector: |
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250 test_stats_collector.update(output_dataset) |
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251 if put_stats_in_output_dataset: |
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252 output_dataset.setAttributes(test_stats_collector.attributeNames(), |
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253 test_stats_collector.attributes()) |
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254 return output_dataset |
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255 |
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256 |
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257 class MinibatchUpdatesTLearner(TLearner): |
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258 """ |
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259 This adds to TLearner a |
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260 - updateStart(), updateEnd(), updateMinibatch(minibatch), isLastEpoch(): |
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261 functions executed at the beginning, the end, in the middle |
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262 (for each minibatch) of the update method, and at the end |
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263 of each epoch. This model only |
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264 works for 'online' or one-shot learning that requires |
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265 going only once through the training data. For more complicated |
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266 models, more specialized subclasses of TLearner should be used |
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267 or a learning-algorithm specific update method should be defined. |
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268 |
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269 - a 'parameters' attribute which is a list of parameters (whose names are |
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270 specified by the user's subclass with the parameterAttributes() method) |
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271 |
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272 """ |
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273 |
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274 def __init__(self): |
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275 TLearner.__init__(self) |
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276 self.update_minibatch_function = |
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277 Function(self.names2OpResults(self.updateMinibatchOutputAttributes()+ |
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278 self.updateMinibatchInputFields()), |
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279 self.names2OpResults(self.updateMinibatchOutputAttributes())) |
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280 self.update_end_function = Function(self.names2OpResults(self.updateEndInputAttributes()), |
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281 self.names2OpResults(self.updateEndOutputAttributes())) |
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282 |
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283 def updateMinibatchInputFields(self): |
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284 raise AbstractFunction() |
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285 |
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286 def updateMinibatchInputAttributes(self): |
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287 raise AbstractFunction() |
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288 |
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289 def updateMinibatchOutputAttributes(self): |
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290 raise AbstractFunction() |
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291 |
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292 def updateEndInputAttributes(self): |
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293 raise AbstractFunction() |
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294 |
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295 def updateEndOutputAttributes(self): |
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296 raise AbstractFunction() |
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297 |
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298 def parameterAttributes(self): |
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299 raise AbstractFunction() |
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300 |
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301 def updateStart(self): pass |
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302 |
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303 def updateEnd(self): |
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304 self.setAttributes(self.updateEndOutputAttributes(), |
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305 self.update_end_function |
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306 (self.names2attributes(self.updateEndInputAttributes()))) |
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307 self.parameters = self.names2attributes(self.parameterAttributes()) |
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308 |
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309 def updateMinibatch(self,minibatch): |
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310 # make sure all required fields are allocated and initialized |
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311 self.allocate(minibatch) |
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312 self.setAttributes(self.updateMinibatchOutputAttributes(), |
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313 self.update_minibatch_function(*(self.names2attributes(self.updateMinibatchInputAttributes())) |
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314 + minibatch(self.updateMinibatchInputFields()))) |
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315 |
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316 def isLastEpoch(self): |
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317 """ |
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318 This method is called at the end of each epoch (cycling over the training set). |
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319 It returns a boolean to indicate if this is the last epoch. |
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320 By default just do one epoch. |
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321 """ |
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322 return True |
78 | 323 |
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324 def update(self,training_set,train_stats_collector=None): |
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325 """ |
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326 @todo check if some of the learner attributes are actually SPECIFIED |
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327 in as attributes of the training_set. |
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328 """ |
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329 self.updateStart(training_set) |
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330 stop=False |
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331 while not stop: |
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332 if train_stats_collector: |
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333 train_stats_collector.forget() # restart stats collectin at the beginning of each epoch |
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334 for minibatch in training_set.minibatches(self.training_set_input_fields, |
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335 minibatch_size=self.minibatch_size): |
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336 self.update_minibatch(minibatch) |
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337 if train_stats_collector: |
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338 minibatch_set = minibatch.examples() |
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339 minibatch_set.setAttributes(self.attributeNames(),self.attributes()) |
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340 train_stats_collector.update(minibatch_set) |
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341 stop = self.isLastEpoch() |
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342 self.updateEnd() |
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343 return self.use |
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344 |
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345 class OnlineGradientBasedTLearner(MinibatchUpdatesTLearner): |
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346 """ |
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347 Specialization of MinibatchUpdatesTLearner in which the minibatch updates |
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348 are obtained by performing an online (minibatch-based) gradient step. |
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349 |
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350 Sub-classes must define the following methods: |
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351 |
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352 """ |
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353 def __init__(self,truly_online=False): |
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354 """ |
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355 If truly_online then only one pass is made through the training set passed to update(). |
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356 |
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357 """ |
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358 self.truly_online=truly_online |
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359 |
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360 def isLastEpoch(self): |
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361 return self.truly_online |
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362 |
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363 |