Mercurial > pylearn
comparison learner.py @ 1:2cd82666b9a7
Added statscollector and started writing dataset and learner.
| author | bengioy@esprit.iro.umontreal.ca |
|---|---|
| date | Fri, 14 Mar 2008 11:28:08 -0400 |
| parents | 586dcaa4b2df |
| children | 80bf5492e571 |
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| 0:586dcaa4b2df | 1:2cd82666b9a7 |
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| 1 | |
| 2 from dataset import * | |
| 3 from statscollector import * | |
| 4 | |
| 5 class Learner(object): | |
| 6 """Base class for learning algorithms, provides an interface | |
| 7 that allows various algorithms to be applicable to generic learning | |
| 8 algorithms. | |
| 9 | |
| 10 A Learner often works in stages, and the user can control when | |
| 11 each stage is executed by calling train repetively (with a different | |
| 12 target_stage). | |
| 13 """ | |
| 14 | |
| 15 __init__(self,training_set=None,verbosity=0): | |
| 16 self.stage=0 | |
| 17 self.training_set=training_set | |
| 18 if training_set: | |
| 19 assert isinstance(training_set,DataSet), "training_set should be a DataSet" | |
| 20 self.verbosity=verbosity | |
| 21 | |
| 22 | |
| 23 train(self,target_stage=None,training_set=None,from_scratch=True,train_stats=None): | |
| 24 """The main method of a learner, to 'train' it. This modifies the Learner | |
| 25 so that its behavior upon a next call to use may be different. | |
| 26 The training set may have already been provided (by setting the training_set | |
| 27 attribute) or a new / different training set may be provided here (which will | |
| 28 set the training_set attribute). Training may occur in stages. The current | |
| 29 stage may be set by the user and all the stages until and including the target_stage | |
| 30 will be performed. If from_scratch then stage is set to 0 initially. | |
| 31 If the train_stats argument is provided, it should be a StatsCollector object. | |
| 32 In that case, performance statistics will be computed on the training set | |
| 33 and accumulated into the train_stats object. If from_scratch though, the | |
| 34 train_stats object will be cleared automatically. The same train_stats object | |
| 35 can thus be reused on consecutive calls to train (where all but the first | |
| 36 are from scratch). | |
| 37 | |
| 38 Subclasses may call Learner.train to set stage and training_set according | |
| 39 to the above arguments. The actual training should then be performed | |
| 40 within the subclass train method. | |
| 41 """ | |
| 42 if from_scratch: | |
| 43 stage=0 | |
| 44 if train_stats: | |
| 45 train_stats.clear() | |
| 46 if training_set: | |
| 47 if training_set: | |
| 48 self.training_set=training_set | |
| 49 assert isinstance(training_set,DataSet), "training_set should be a DataSet" | |
| 50 | |
| 51 return | |
| 52 | |
| 53 | |
| 54 use(self,input_dataset,test_stats=None,output_fields=None): | |
| 55 """Once a Learner has been trained by one or more call to 'train', it can | |
| 56 be used with one or more calls to 'use'. The argument is a DataSet (possibly | |
| 57 containing a single example) and the result is a DataSet of the same size. | |
| 58 If output_fields is specified, it may be use to indicate which fields should | |
| 59 be constructed in the output DataSet (for example ['output','classification_error']). | |
| 60 If a test_stats object is provided, then performance statistics will be computed to | |
| 61 measure the performance of the learner on the given dataset, and accumulated into | |
| 62 the test_stats (hence it must be cleared before the call).""" | |
| 63 raise NotImplementedError |