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annotate doc/v2_planning/API_learner.txt @ 1274:9d5905d6d879

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hmc - changed updates to member fn from lambda for pickling
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 08 Sep 2010 13:17:45 -0400
parents 317049b21b77
children
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 Attributes
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10 ~~~~~~~~~~
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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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18
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19 Supervised (x,y)
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20 ~~~~~~~~~~~~~~~~
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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
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33 - de-noising
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34 - feature learning ( transformation ) PCA, DAA
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35 - density estimation
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36 - inference
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37
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38 Other
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39 ~~~~~
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40
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41 - generation (sampling)
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42 - structure learning ???
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43
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44
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45 Notes on metrics & statistics
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46 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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47 - some are applied to an example, others on a batch
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48 - most statistics are on the dataset
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49
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50
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51 The Learner class
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52 -----------------
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53
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54 .. code-block:: python
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55
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56 class Learner(Object):
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57 '''
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58 Takes data as inputs, and learns a prediction function (or several).
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59
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60 A learner is parametrized by hyper-parameters, which can be set from the
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61 outside (a "client" from Learner, that can be a HyperLearner, a
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62 Tester,...).
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63
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64 The data can be given all at a time as a data set, or incrementally.
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65 Some learner need to be fully trained in one step, whereas other can be
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66 trained incrementally.
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67
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68 The question of statistics collection during training remains open.
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69 '''
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70 #def use_dataset(dataset)
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71
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72 # return a dictionary of hyperparameters names(keys)
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73 # and value(values)
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74 def get_hyper_parameters():
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75 ...
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76 def set_hyper_parameters(dictionary):
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77 ...
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79
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80 # Ver B
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81 def eval(dataset):
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82 ...
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83 def predict(dataset):
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84 ...
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85
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86 # Trainable
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87 def train(dataset): # train until completion
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88 ...
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89
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90 # Incremental
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91 def use_dataset(dataset):
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92 ...
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93 def adapt(n_steps=1):
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94 ...
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95 def has_converged():
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96 ...
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97 #
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98
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99
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100 Some example cases
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101 ------------------
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102
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103 .. code-block:: python
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104
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105 class HyperLearner(Learner):
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106
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107 ### def get_hyper_parameter_distribution(name)
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108 def set_hyper_parameters_distribution(dictionary):
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109 ...
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110
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111
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112 def bagging(learner_factory):
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113 for i in range(N):
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114 learner_i = learner_factory.new()
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115 # todo: get dataset_i ??
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116 learner_i.use_dataset(dataset_i)
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117 learner_i.train()