Mercurial > pylearn
annotate learner.py @ 404:8cc11ac97087
Debugging simple AA a bit
| author | Joseph Turian <turian@gmail.com> |
|---|---|
| date | Thu, 10 Jul 2008 00:51:32 -0400 |
| parents | fe57b96f33d4 |
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| rev | line source |
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in __get__, problem if the i.stop was None, i being the slice, added one line replacing None by the len(self)
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3 from exceptions import * |
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4 from dataset import AttributesHolder |
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6 class OfflineLearningAlgorithm(object): |
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7 """ |
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8 Base class for offline learning algorithms, provides an interface |
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9 that allows various algorithms to be applicable to generic learning |
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10 algorithms. It is only given here to define the expected semantics. |
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12 An offline learning algorithm can be seen as a function that when |
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13 applied to training data returns a learned function (which is an object that |
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14 can be applied to other data and return some output data). |
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16 The offline learning scenario is the standard and most common one |
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17 in machine learning: an offline learning algorithm is applied |
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18 to a training dataset, |
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19 |
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20 model = learning_algorithm(training_set) |
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22 resulting in a fully trained model that can be applied to another dataset |
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23 in order to perform some desired computation: |
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24 |
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25 output_dataset = model(input_dataset) |
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26 |
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27 Note that the application of a dataset has no side-effect on the model. |
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28 In that example, the training set may for example have 'input' and 'target' |
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29 fields while the input dataset may have only 'input' (or both 'input' and |
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30 'target') and the output dataset would contain some default output fields defined |
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31 by the learning algorithm (e.g. 'output' and 'error'). The user may specifiy |
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32 what the output dataset should contain either by setting options in the |
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33 model, by the presence of particular fields in the input dataset, or with |
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34 keyword options of the __call__ method of the model (see LearnedModel.__call__). |
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35 |
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36 """ |
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37 |
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38 def __init__(self): pass |
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39 |
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40 def __call__(self, training_dataset): |
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41 """ |
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42 Return a fully trained TrainedModel. |
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43 """ |
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44 raise AbstractFunction() |
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45 |
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46 class TrainedModel(AttributesHolder): |
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47 """ |
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48 TrainedModel is a base class for models returned by instances of an |
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49 OfflineLearningAlgorithm subclass. It is only given here to define the expected semantics. |
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50 """ |
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51 def __init__(self): |
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52 pass |
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53 |
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54 def __call__(self,input_dataset,output_fieldnames=None, |
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55 test_stats_collector=None,copy_inputs=False, |
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56 put_stats_in_output_dataset=True, |
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57 output_attributes=[]): |
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58 """ |
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59 A L{TrainedModel} can be used with |
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60 with one or more calls to it. The main argument is an input L{DataSet} (possibly |
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61 containing a single example) and the result is an output L{DataSet} of the same length. |
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62 If output_fieldnames is specified, it may be use to indicate which fields should |
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63 be constructed in the output L{DataSet} (for example ['output','classification_error']). |
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64 Otherwise, some default output fields are produced (possibly depending on the input |
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65 fields available in the input_dataset). |
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66 Optionally, if copy_inputs, the input fields (of the input_dataset) can be made |
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67 visible in the output L{DataSet} returned by this method. |
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68 Optionally, attributes of the learner can be copied in the output dataset, |
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69 and statistics computed by the stats collector also put in the output dataset. |
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70 Note the distinction between fields (which are example-wise quantities, e.g. 'input') |
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71 and attributes (which are not, e.g. 'regularization_term'). |
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72 """ |
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73 raise AbstractFunction() |
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74 |
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75 |
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76 class OnlineLearningAlgorithm(object): |
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77 """ |
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78 Base class for online learning algorithms, provides an interface |
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79 that allows various algorithms to be applicable to generic online learning |
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80 algorithms. It is only given here to define the expected semantics. |
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81 |
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82 The basic setting is that the training data are only revealed in pieces |
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83 (maybe one example or a batch of example at a time): |
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84 |
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85 model = learning_algorithm() |
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86 |
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87 results in a fresh model. The model can be adapted by presenting |
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88 it with some training data, |
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89 |
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90 model.update(some_training_data) |
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91 ... |
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92 model.update(some_more_training_data) |
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93 ... |
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94 model.update(yet_more_training_data) |
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95 |
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96 and at any point one can use the model to perform some computation: |
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97 |
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98 output_dataset = model(input_dataset) |
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99 |
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100 The model should be a LearnerModel subclass instance, and LearnerModel |
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101 is a subclass of LearnedModel. |
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102 |
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103 """ |
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104 |
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105 def __init__(self): pass |
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106 |
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107 def __call__(self, training_dataset=None): |
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108 """ |
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109 Return a LearnerModel, either fresh (if training_dataset is None) or fully trained (otherwise). |
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110 """ |
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111 raise AbstractFunction() |
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112 |
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113 class LearnerModel(TrainedModel): |
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114 """ |
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115 LearnerModel is a base class for models returned by instances of a LearningAlgorithm subclass. |
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116 It is only given here to define the expected semantics. |
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117 """ |
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118 def __init__(self): |
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119 pass |
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120 |
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122 """ |
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123 Continue training a learner model, with the evidence provided by the given training set. |
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124 Hence update can be called multiple times. This is the main method used for training in the |
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125 on-line setting or the sequential (Bayesian or not) settings. |
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126 |
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127 This function has as side effect that self(data) will behave differently, |
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128 according to the adaptation achieved by update(). |
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129 |
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130 The user may optionally provide a training L{StatsCollector} that is used to record |
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131 some statistics of the outputs computed during training. It is update(d) during |
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132 training. |
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133 """ |
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134 raise AbstractFunction() |
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135 |