Mercurial > pylearn

annotate learner.py @ 113:b6bc1e769b36

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Automated merge with ssh://p-omega1@lgcm.iro.umontreal.ca/tlearn
author Frederic Bastien <bastienf@iro.umontreal.ca>
date Wed, 07 May 2008 12:12:48 -0400
parents 88257dfedf8c
children d0a1bd0378c6
rev   line source
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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
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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
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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
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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):
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62 """
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63 A Learner may have attributes that it wishes to export to other objects. To automate
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64 such export, sub-classes should define here the names (list of strings) of these attributes.
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66 @todo By default, attributeNames looks for all dictionary entries whose name does not start with _.
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67 """
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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
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101 class TLearner(Learner):
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102 """
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103 TLearner is a virtual class of Learners that attempts to factor out of the definition
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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.
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106
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107 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods
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108 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done,
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109 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this
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110 basic model is not appropriate can simply redefine update or use.
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111
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112 Sub-classes must provide the following functions and functionalities:
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113 - attributeNames(): defines all the names of attributes which can be used as fields or
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114 attributes in input/output datasets or in stats collectors.
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115 All these attributes are expected to be theano.Result objects
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116 (with a .data property and recognized by theano.Function for compilation).
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117 The sub-class constructor defines the relations between
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118 the Theano variables that may be used by 'use' and 'update'
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119 or by a stats collector.
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120 - defaultOutputFields(input_fields): return a list of default dataset output fields when
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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
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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
110
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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 """
110
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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
111
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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
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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