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annotate doc/v2_planning/API_learner.txt @ 1195:d3ee0d2d03e6

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plugin_greenlet draft0
author James Bergstra <bergstrj@iro.umontreal.ca>
date Sun, 19 Sep 2010 13:05:48 -0400
parents 805e7c369fd1
children 317049b21b77
rev   line source
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1 .. _v2planning_learner:
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2
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3 Learner API
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4 ===========
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5
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6 # A list of "task types"
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7
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8 '''
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9 List of tasks types:
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10 Attributes
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11
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12 sequential
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13 spatial
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14 structured
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15 semi-supervised
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16 missing-values
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17
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18
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19 Supervised (x,y)
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20
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21 classification
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22 regression
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23 probabilistic classification
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24 ranking
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25 conditional density estimation
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26 collaborative filtering
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27 ordinal regression ?= ranking
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28
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29 Unsupervised (x)
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30
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31 de-noising
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32 feature learning ( transformation ) PCA, DAA
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33 density estimation
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34 inference
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35
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36 Other
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37
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38 generation (sampling)
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39 structure learning ???
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40
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41
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42 Notes on metrics & statistics:
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43 - some are applied to an example, others on a batch
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44 - most statistics are on the dataset
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45
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46 '''
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47
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48
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49 class Learner(Object):
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50 '''
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51 Takes data as inputs, and learns a prediction function (or several).
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52
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53 A learner is parametrized by hyper-parameters, which can be set from the
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54 outside (a "client" from Learner, that can be a HyperLearner, a
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55 Tester,...).
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56
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57 The data can be given all at a time as a data set, or incrementally.
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58 Some learner need to be fully trained in one step, whereas other can be
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59 trained incrementally.
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60
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61 The question of statistics collection during training remains open.
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62 '''
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63 #def use_dataset(dataset)
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64
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65 # return a dictionary of hyperparameters names(keys)
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66 # and value(values)
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67 def get_hyper_parameters()
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68 def set_hyper_parameters(dictionary)
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69
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70
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71
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72
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73 # Ver B
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74 def eval(dataset)
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75 def predict(dataset)
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76
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77 # Trainable
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78 def train(dataset) # train until complition
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79
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80 # Incremental
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81 def use_dataset(dataset)
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82 def adapt(n_steps =1)
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83 def has_converged()
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84
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85 #
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86
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87
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88 # Some example cases
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89
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90 class HyperLearner(Learner):
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91
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92 ### def get_hyper_parameter_distribution(name)
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93 def set_hyper_parameters_distribution(dictionary)
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94
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95
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96 def bagging(learner_factory):
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97 for i in range(N):
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98 learner_i = learner_factory.new()
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99 # todo: get dataset_i ??
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100 learner_i.use_dataset(dataset_i)
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101 learner_i.train()