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annotate doc/v2_planning/learn_meeting.py @ 1135:a1957faecc9b

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revised plugin interface and implementation
author Olivier Breuleux <breuleuo@iro.umontreal.ca>
date Thu, 16 Sep 2010 02:58:24 -0400
parents 8c448829db30
children
rev   line source
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1
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2
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3 def bagging(learner_factory):
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4 for i in range(N):
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5 learner_i = learner_factory.new()
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6 # todo: get dataset_i ??
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7 learner_i.use_dataset(dataset_i)
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8 learner_i.train()
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9 '''
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10 List of tasks types:
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11 Attributes
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12
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13 sequential
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14 spatial
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15 structured
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16 semi-supervised
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17 missing-values
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18
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20 Supervised (x,y)
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21
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22 classification
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23 regression
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24 probabilistic classification
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25 ranking
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26 conditional density estimation
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27 collaborative filtering
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28 ordinal regression ?= ranking
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29
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30 Unsupervised (x)
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31
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32 de-noising
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33 feature learning ( transformation ) PCA, DAA
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34 density estimation
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35 inference
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36
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37 Other
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38
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39 generation (sampling)
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40 structure learning ???
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41
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42
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43 Notes on metrics & statistics:
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44 - some are applied to an example, others on a batch
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45 - most statistics are on the dataset
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46 '''
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47 class Learner(Object):
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48
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49 #def use_dataset(dataset)
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50
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51 # return a dictionary of hyperparameters names(keys)
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52 # and value(values)
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53 def get_hyper_parameters()
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54 def set_hyper_parameters(dictionary)
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57
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58
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59 # Ver B
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60 def eval(dataset)
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61 def predict(dataset)
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62
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63 # Trainable
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64 def train(dataset) # train until complition
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65
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66 # Incremental
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67 def use_dataset(dataset)
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68 def adapt(n_steps =1)
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69 def has_converged()
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70
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71 #
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72
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73 class HyperLearner(Learner):
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74
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75 ### def get_hyper_parameter_distribution(name)
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76 def set_hyper_parameters_distribution(dictionary)