Mercurial > pylearn

annotate learner.py @ 5:8039918516fe

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Added MinibatchIterator
author bengioy@bengiomac.local
date Sun, 23 Mar 2008 22:44:43 -0400
parents 2cd82666b9a7
children 80bf5492e571
rev   line source
1
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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1
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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2 from dataset import *
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3 from statscollector import *
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4
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5 class Learner(object):
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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
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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8 algorithms.
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9
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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10 A Learner often works in stages, and the user can control when
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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11 each stage is executed by calling train repetively (with a different
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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12 target_stage).
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13 """
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14
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15 __init__(self,training_set=None,verbosity=0):
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16 self.stage=0
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17 self.training_set=training_set
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18 if training_set:
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19 assert isinstance(training_set,DataSet), "training_set should be a DataSet"
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20 self.verbosity=verbosity
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21
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22
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23 train(self,target_stage=None,training_set=None,from_scratch=True,train_stats=None):
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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24 """The main method of a learner, to 'train' it. This modifies the Learner
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25 so that its behavior upon a next call to use may be different.
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26 The training set may have already been provided (by setting the training_set
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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27 attribute) or a new / different training set may be provided here (which will
2cd82666b9a7 Added statscollector and started writing dataset and learner.
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28 set the training_set attribute). Training may occur in stages. The current
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29 stage may be set by the user and all the stages until and including the target_stage
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30 will be performed. If from_scratch then stage is set to 0 initially.
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31 If the train_stats argument is provided, it should be a StatsCollector object.
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32 In that case, performance statistics will be computed on the training set
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33 and accumulated into the train_stats object. If from_scratch though, the
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34 train_stats object will be cleared automatically. The same train_stats object
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35 can thus be reused on consecutive calls to train (where all but the first
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36 are from scratch).
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37
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38 Subclasses may call Learner.train to set stage and training_set according
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39 to the above arguments. The actual training should then be performed
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40 within the subclass train method.
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41 """
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42 if from_scratch:
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43 stage=0
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44 if train_stats:
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45 train_stats.clear()
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46 if training_set:
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47 if training_set:
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48 self.training_set=training_set
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49 assert isinstance(training_set,DataSet), "training_set should be a DataSet"
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50
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51 return
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52
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53
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54 use(self,input_dataset,test_stats=None,output_fields=None):
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55 """Once a Learner has been trained by one or more call to 'train', it can
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56 be used with one or more calls to 'use'. The argument is a DataSet (possibly
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57 containing a single example) and the result is a DataSet of the same size.
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58 If output_fields is specified, it may be use to indicate which fields should
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59 be constructed in the output DataSet (for example ['output','classification_error']).
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60 If a test_stats object is provided, then performance statistics will be computed to
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61 measure the performance of the learner on the given dataset, and accumulated into
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62 the test_stats (hence it must be cleared before the call)."""
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63 raise NotImplementedError