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annotate learner.py @ 110:8fa1ef2411a0

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Worked on OneShotTLearner and implementation of LinearRegression
author bengioy@bengiomac.local
date Tue, 06 May 2008 22:24:55 -0400
parents d97f6fe6bdf9
children 88257dfedf8c
rev   line source
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2cd82666b9a7 Added statscollector and started writing dataset and learner.
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1
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2 from dataset import *
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4 class Learner(AttributesHolder):
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5 """Base class for learning algorithms, provides an interface
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6 that allows various algorithms to be applicable to generic learning
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7 algorithms.
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8
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9 A Learner can be seen as a learning algorithm, a function that when
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10 applied to training data returns a learned function, an object that
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11 can be applied to other data and return some output data.
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12 """
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13
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14 def __init__(self):
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15 pass
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16
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17 def forget(self):
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18 """
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19 Reset the state of the learner to a blank slate, before seeing
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20 training data. The operation may be non-deterministic if the
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21 learner has a random number generator that is set to use a
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22 different seed each time it forget() is called.
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23 """
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24 raise NotImplementedError
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25
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26 def update(self,training_set,train_stats_collector=None):
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27 """
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28 Continue training a learner, with the evidence provided by the given training set.
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29 Hence update can be called multiple times. This is particularly useful in the
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30 on-line setting or the sequential (Bayesian or not) settings.
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31 The result is a function that can be applied on data, with the same
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32 semantics of the Learner.use method.
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33
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34 The user may optionally provide a training StatsCollector that is used to record
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35 some statistics of the outputs computed during training. It is update(d) during
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36 training.
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37 """
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38 return self.use # default behavior is 'non-adaptive', i.e. update does not do anything
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39
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40
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41 def __call__(self,training_set,train_stats_collector=None):
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42 """
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43 Train a learner from scratch using the provided training set,
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44 and return the learned function.
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45 """
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46 self.forget()
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47 return self.update(learning_task,train_stats_collector)
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48
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49 def use(self,input_dataset,output_fields=None,copy_inputs=True):
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50 """Once a Learner has been trained by one or more call to 'update', it can
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51 be used with one or more calls to 'use'. The argument is a DataSet (possibly
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52 containing a single example) and the result is a DataSet of the same length.
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53 If output_fields is specified, it may be use to indicate which fields should
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54 be constructed in the output DataSet (for example ['output','classification_error']).
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55 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made
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56 visible in the output DataSet returned by this method.
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57 """
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58 raise NotImplementedError
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60 def attributeNames(self):
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61 """
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62 A Learner may have attributes that it wishes to export to other objects. To automate
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63 such export, sub-classes should define here the names (list of strings) of these attributes.
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65 @todo By default, attributeNames looks for all dictionary entries whose name does not start with _.
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66 """
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67 return []
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69 def updateInputAttributes(self):
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70 """
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71 A subset of self.attributeNames() which are the names of attributes needed by update() in order
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72 to do its work.
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73 """
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74 raise AbstractFunction()
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76 def useInputAttributes(self):
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77 """
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78 A subset of self.attributeNames() which are the names of attributes needed by use() in order
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79 to do its work.
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80 """
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81 raise AbstractFunction()
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82
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83 def updateOutputAttributes(self):
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84 """
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85 A subset of self.attributeNames() which are the names of attributes modified/created by update() in order
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86 to do its work.
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87 """
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88 raise AbstractFunction()
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89
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90 def useOutputAttributes(self):
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91 """
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92 A subset of self.attributeNames() which are the names of attributes modified/created by use() in order
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93 to do its work.
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94 """
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95 raise AbstractFunction()
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97
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98 class TLearner(Learner):
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99 """
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100 TLearner is a virtual class of Learners that attempts to factor out of the definition
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101 of a learner the steps that are common to many implementations of learning algorithms,
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102 so as to leave only 'the equations' to define in particular sub-classes, using Theano.
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103
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104 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods
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105 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done,
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106 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this
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107 basic model is not appropriate can simply redefine update or use.
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108
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109 Sub-classes must provide the following functions and functionalities:
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110 - attributeNames(): defines all the names of attributes which can be used as fields or
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111 attributes in input/output datasets or in stats collectors.
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112 All these attributes are expected to be theano.Result objects
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113 (with a .data property and recognized by theano.Function for compilation).
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114 The sub-class constructor defines the relations between
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115 the Theano variables that may be used by 'use' and 'update'
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116 or by a stats collector.
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117 - defaultOutputFields(input_fields): return a list of default dataset output fields when
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118 None are provided by the caller of use.
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119 The following naming convention is assumed and important.
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120 Attributes whose names are listed in attributeNames() can be of any type,
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121 but those that can be referenced as input/output dataset fields or as
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122 output attributes in 'use' or as input attributes in the stats collector
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123 should be associated with a Theano Result variable. If the exported attribute
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124 name is <name>, the corresponding Result name (an internal attribute of
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125 the TLearner, created in the sub-class constructor) should be _<name>.
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126 Typically <name> will be numpy ndarray and _<name> will be the corresponding
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127 Theano Tensor (for symbolic manipulation).
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128
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129 @todo pousser dans Learner toute la poutine qui peut l'etre sans etre
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130 dependant de Theano
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131 """
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132
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133 def __init__(self):
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134 Learner.__init__(self)
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135
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136 def defaultOutputFields(self, input_fields):
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137 """
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138 Return a default list of output field names (to put in the output dataset).
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139 This will be used when None are provided (as output_fields) by the caller of the 'use' method.
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140 This may involve looking at the input_fields (names) available in the
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141 input_dataset.
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142 """
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143 raise AbstractFunction()
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144
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145 def allocate(self, minibatch):
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146 """
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147 This function is called at the beginning of each updateMinibatch
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148 and should be used to check that all required attributes have been
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149 allocated and initialized (usually this function calls forget()
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150 when it has to do an initialization).
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151 """
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152 raise AbstractFunction()
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153
110
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154 def minibatchwise_use_functions(self, input_fields, output_fields, stats_collector):
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155 """
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156 Private helper function called by the generic TLearner.use. It returns a function
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157 that can map the given input fields to the given output fields (along with the
110
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158 attributes that the stats collector needs for its computation. The function
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159 called also automatically makes use of the self.useInputAttributes() and
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160 sets the self.useOutputAttributes().
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161 """
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162 if not output_fields:
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163 output_fields = self.defaultOutputFields(input_fields)
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164 if stats_collector:
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165 stats_collector_inputs = stats_collector.input2UpdateAttributes()
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166 for attribute in stats_collector_inputs:
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167 if attribute not in input_fields:
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168 output_fields.append(attribute)
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169 key = (input_fields,output_fields)
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170 if key not in self.use_functions_dictionary:
110
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171 use_input_attributes = self.useInputAttributes()
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172 use_output_attributes = self.useOutputAttributes()
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173 complete_f = Function(self.names2OpResults(input_fields+use_input_attributes),
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174 self.names2OpResults(output_fields+use_output_attributes))
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175 def f(*input_field_values):
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176 input_attribute_values = self.names2attributes(use_input_attributes)
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177 results = complete_f(*(input_field_values + input_attribute_values))
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178 output_field_values = results[0:len(output_fields)]
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179 output_attribute_values = results[len(output_fields):len(results)]
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180 if use_output_attributes:
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181 self.setAttributes(use_output_attributes,output_attribute_values)
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182 return output_field_values
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183 self.use_functions_dictionary[key]=f
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184 return self.use_functions_dictionary[key]
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185
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186 def attributes(self,return_copy=False):
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187 """
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188 Return a list with the values of the learner's attributes (or optionally, a deep copy).
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189 """
110
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190 return self.names2attributes(self.attributeNames(),return_copy)
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191
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192 def names2attributes(self,names,return_copy=False):
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193 """
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194 Private helper function that maps a list of attribute names to a list
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195 of (optionally copies) values of attributes.
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196 """
110
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197 if return_copy:
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198 return [copy.deepcopy(self.__getattr__(name).data) for name in names]
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199 else:
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200 return [self.__getattr__(name).data for name in names]
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201
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202 def names2OpResults(self,names):
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203 """
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204 Private helper function that maps a list of attribute names to a list
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205 of corresponding Op Results (with the same name but with a '_' prefix).
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206 """
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207 return [self.__getattr__('_'+name).data for name in names]
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208
107
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209 def use(self,input_dataset,output_fieldnames=None,output_attributes=[],
110
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210 test_stats_collector=None,copy_inputs=True, put_stats_in_output_dataset=True):
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211 """
107
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212 The learner tries to compute in the output dataset the output fields specified
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213
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214 @todo check if some of the learner attributes are actually SPECIFIED
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215 as attributes of the input_dataset, and if so use their values instead
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216 of the ones in the learner.
109
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217
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218 The learner tries to compute in the output dataset the output fields specified.
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219 If None is specified then self.defaultOutputFields(input_dataset.fieldNames())
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220 is called to determine the output fields.
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221
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222 Attributes of the learner can also optionally be copied into the output dataset.
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223 If output_attributes is None then all of the attributes in self.AttributeNames()
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224 are copied in the output dataset, but if it is [] (the default), then none are copied.
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225 If a test_stats_collector is provided, then its attributes (test_stats_collector.AttributeNames())
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226 are also copied into the output dataset attributes.
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227 """
110
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228 minibatchwise_use_function = minibatchwise_use_functions(input_dataset.fieldNames(),
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229 output_fieldnames,
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230 test_stats_collector)
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231 virtual_output_dataset = ApplyFunctionDataSet(input_dataset,
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232 minibatchwise_use_function,
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233 True,DataSet.numpy_vstack,
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234 DataSet.numpy_hstack)
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235 # actually force the computation
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236 output_dataset = CachedDataSet(virtual_output_dataset,True)
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237 if copy_inputs:
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238 output_dataset = input_dataset | output_dataset
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239 # copy the wanted attributes in the dataset
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240 if output_attributes is None:
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241 output_attributes = self.attributeNames()
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242 if output_attributes:
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243 assert set(attribute_names) <= set(self.attributeNames())
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244 output_dataset.setAttributes(output_attributes,
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245 self.names2attributes(output_attributes,return_copy=True))
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246 if test_stats_collector:
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247 test_stats_collector.update(output_dataset)
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248 if put_stats_in_output_dataset:
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249 output_dataset.setAttributes(test_stats_collector.attributeNames(),
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250 test_stats_collector.attributes())
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251 return output_dataset
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252
107
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253
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254 class OneShotTLearner(TLearner):
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255 """
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256 This adds to TLearner a
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257 - updateStart(), updateEnd(), updateMinibatch(minibatch), isLastEpoch():
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258 functions executed at the beginning, the end, in the middle
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259 (for each minibatch) of the update method, and at the end
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260 of each epoch. This model only
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261 works for 'online' or one-shot learning that requires
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262 going only once through the training data. For more complicated
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263 models, more specialized subclasses of TLearner should be used
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264 or a learning-algorithm specific update method should be defined.
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265 """
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266
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267 def __init__(self):
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268 TLearner.__init__(self)
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269 self.update_minibatch_function =
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270 Function(self.names2OpResults(self.updateMinibatchOutputAttributes()+
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271 self.updateMinibatchInputFields()),
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272 self.names2OpResults(self.updateMinibatchOutputAttributes()))
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273 self.update_end_function = Function(self.names2OpResults(self.updateEndInputAttributes()),
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274 self.names2OpResults(self.updateEndOutputAttributes()))
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275
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276 def updateMinibatchInputFields(self):
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277 raise AbstractFunction()
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278
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279 def updateMinibatchInputAttributes(self):
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280 raise AbstractFunction()
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281
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282 def updateMinibatchOutputAttributes(self):
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283 raise AbstractFunction()
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284
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285 def updateEndInputAttributes(self):
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286 raise AbstractFunction()
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287
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288 def updateEndOutputAttributes(self):
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289 raise AbstractFunction()
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290
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291 def updateStart(self): pass
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292
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293 def updateEnd(self):
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294 self.setAttributes(self.updateEndOutputAttributes(),
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295 self.update_end_function
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296 (self.names2attributes(self.updateEndInputAttributes())))
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297
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298 def updateMinibatch(self,minibatch):
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299 # make sure all required fields are allocated and initialized
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300 self.allocate(minibatch)
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301 self.setAttributes(self.updateMinibatchOutputAttributes(),
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302 self.update_minibatch_function(*(self.names2attributes(self.updateMinibatchInputAttributes()))
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303 + minibatch(self.updateMinibatchInputFields())))
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304
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305 def isLastEpoch(self):
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306 """
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307 This method is called at the end of each epoch (cycling over the training set).
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308 It returns a boolean to indicate if this is the last epoch.
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309 By default just do one epoch.
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310 """
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311 return True
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312
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313 def update(self,training_set,train_stats_collector=None):
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314 """
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315 @todo check if some of the learner attributes are actually SPECIFIED
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316 in as attributes of the training_set.
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317 """
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318 self.updateStart(training_set)
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319 stop=False
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320 while not stop:
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321 if train_stats_collector:
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322 train_stats_collector.forget() # restart stats collectin at the beginning of each epoch
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323 for minibatch in training_set.minibatches(self.training_set_input_fields,
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324 minibatch_size=self.minibatch_size):
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325 self.update_minibatch(minibatch)
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326 if train_stats_collector:
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327 minibatch_set = minibatch.examples()
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328 minibatch_set.setAttributes(self.attributeNames(),self.attributes())
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329 train_stats_collector.update(minibatch_set)
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330 stop = self.isLastEpoch()
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331 self.updateEnd()
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332 return self.use
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333