Mercurial > pylearn
annotate doc/v2_planning/API_learner.txt @ 1319:7185d194bb7e
Added question about doc format in file header
author | Olivier Delalleau <delallea@iro> |
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date | Fri, 08 Oct 2010 12:07:56 -0400 |
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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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17 |
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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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78 |
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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() |