Mercurial > pylearn
annotate learner.py @ 8:d1c394486037
Replaced asarray() method by __array__ method which gets called automatically when
trying to cast into an array or numpy array or upon numpy.asarray(dataset).
author | bengioy@bengiomac.local |
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date | Mon, 24 Mar 2008 15:56:53 -0400 |
parents | 2cd82666b9a7 |
children | 80bf5492e571 |
rev | line source |
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1 |
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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 |
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8 algorithms. |
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9 |
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10 A Learner often works in stages, and the user can control when |
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11 each stage is executed by calling train repetively (with a different |
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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
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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 |
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27 attribute) or a new / different training set may be provided here (which will |
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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 |