Mercurial > pylearn

annotate learner.py @ 96:352910e0dbf5

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added test and some restructuring for futur use
author Frederic Bastien <bastienf@iro.umontreal.ca>
date Tue, 06 May 2008 10:53:21 -0400
parents 3499918faa9d
children c4726e19b8ec
rev   line source
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2cd82666b9a7 Added statscollector and started writing dataset and learner.
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2 from dataset import *
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3
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4 class Learner(object):
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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
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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10 applied to training data returns a learned function, an object that
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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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
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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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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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,
80bf5492e571 Rewrote learner.py according to the specs in the wiki for learners.
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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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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 attribute_names(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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64 """
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65 return []
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66
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67 class TLearner(Learner):
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68 """
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69 TLearner is a virtual class of Learners that attempts to factor out of the definition
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70 of a learner the steps that are common to many implementations of learning algorithms,
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71 so as to leave only "the equations" to define in particular sub-classes, using Theano.
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72
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73 In the default implementations of use and update, it is assumed that the 'use' and 'update' methods
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74 visit examples in the input dataset sequentially. In the 'use' method only one pass through the dataset is done,
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75 whereas the sub-learner may wish to iterate over the examples multiple times. Subclasses where this
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76 basic model is not appropriate can simply redefine update or use.
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78 Sub-classes must provide the following functions and functionalities:
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79 - attributeNames(): defines all the names of attributes which can be used as fields or
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80 attributes in input/output datasets or in stats collectors.
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81 All these attributes are expected to be theano.Result objects
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82 (with a .data property and recognized by theano.Function for compilation).
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83 The sub-class constructor defines the relations between
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84 the Theano variables that may be used by 'use' and 'update'
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85 or by a stats collector.
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86 - defaultOutputFields(input_fields): return a list of default dataset output fields when
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87 None are provided by the caller of use.
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88 -
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89
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90 """
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91