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dataset: Added a couple points we did not have time to discuss during meeting
author Olivier Delalleau <delallea@iro>
date Fri, 10 Sep 2010 15:36:23 -0400
parents 5c14d2ffcbb3
children 4c00af69c164
rev   line source
1002
f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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1 Discussion of Function Specification for Dataset Types
f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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2 ======================================================
f82093bf4405 adding learner.txt and dataset.txt in v2_planning/
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
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3
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a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
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4 Some talking points from the September 2 meeting:
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Dumitru Erhan <dumitru.erhan@gmail.com>
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5
a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
parents: 1002
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6 * Datasets as views/tasks (Pascal Vincent's idea): our dataset specification
a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
parents: 1002
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7 needs to be flexible enough to accommodate different (sub)tasks and views of
a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
parents: 1002
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8 the same underlying data.
a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
parents: 1002
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9 * Datasets as probability distributions from which one can sample.
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fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
Olivier Delalleau <delallea@iro>
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10 * That's not something I would consider to be a dataset-related problem to
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
Olivier Delalleau <delallea@iro>
parents: 1008
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11 tackle now: a probability distribution in Pylearn would probably be a
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
Olivier Delalleau <delallea@iro>
parents: 1008
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12 different kind of beast, and it should be easy enough to have a
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
Olivier Delalleau <delallea@iro>
parents: 1008
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13 DatasetToDistribution class for instance, that would take care of viewing a
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
Olivier Delalleau <delallea@iro>
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14 dataset as a probability distribution. -- OD
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15 * Our specification should allow transparent handling of infinite datasets (or
a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
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16 simply datasets which cannot fit in memory)
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17 * GPU/buffering issues.
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18
a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
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19 Commiteee: DE, OB, OD, AB, PV
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a154c9b68239 dataset: Dumi confirmed as leader
Olivier Delalleau <delallea@iro>
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20 Leader: DE
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21
1b61cbe0810b A very rough draft of ideas, to kick-start things
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22 Some ideas from existing ML libraries:
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23
1b61cbe0810b A very rough draft of ideas, to kick-start things
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24 - PyML: notion of dataset containers: VectorDataSet, SparseDataSet, KernelData,
1b61cbe0810b A very rough draft of ideas, to kick-start things
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25 PairDataSet, Aggregate. Ultimately, the learner decides
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26 - mlpy: very primitive notions of data (simple 2D matrices)
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20a1af112a75 dataset: Looked into datasets from some other ML libraries
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27 - PyBrain: Datasets are geared towards specific tasks: ClassificationDataSet,
20a1af112a75 dataset: Looked into datasets from some other ML libraries
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28 SequentialDataSet, ReinforcementDataSet, ... Each class is quite
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
parents: 1054
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29 constrained and may have a different interface.
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
parents: 1054
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30 - MDP: Seems to have restrictions on the type of data being passed around, as
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
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31 well as its dimensionality ("Input array data is typically assumed to be
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
parents: 1054
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32 two-dimensional and ordered such that observations of the same variable are
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
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33 stored on rows and different variables are stored on columns.")
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
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34 - Orange: Data matrices, with names and types associated to each column.
20a1af112a75 dataset: Looked into datasets from some other ML libraries
Olivier Delalleau <delallea@iro>
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35 Basically there seems to be only one base dataset class that contains the
20a1af112a75 dataset: Looked into datasets from some other ML libraries
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36 data. Data points are lists (of values corresponding to each column).
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37 - APGL: Hard to say how they deal with data from the documentation alone.
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
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38 - Monte: Data is simply numpy arrays.
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
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39 - scikits.learn: Dataset is a simple container with e.g. dataset.data being
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
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parents: 1076
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40 a 2D numpy array of input features, and dataset.target the target vector.
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
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41 - Shogun: Vade Retro C++! (may be worth looking into their feature concept
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
Olivier Delalleau <delallea@iro>
parents: 1076
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42 though).
5c14d2ffcbb3 dataset: Looked into a few more existing ML libraries
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43 - Any more worth looking at?
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44
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45 A few things that our dataset containers should support at a minimum:
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46
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47 - streams, possibly infinite
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48 - task/views of the data for different problems
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49 - indexing & slicing
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50 - pairs or triples or etc of examples
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51 - a 'distance/gram matrix' container (imagine that the data is given to you
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52 as a distance matrix)
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53 - multi-dimensional time-series (again, maybe with pairs/triples, maybe
1b61cbe0810b A very rough draft of ideas, to kick-start things
Dumitru Erhan <dumitru.erhan@gmail.com>
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54 given to you as a distance matrix over time)
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55
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56 Another question to consider is the following: how tight should it integrate
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Dumitru Erhan <dumitru.erhan@gmail.com>
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57 with Theano? Do we want to be able to store data as shared variables or just
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58 have an option for that? Theano + GPU constrains things that we can do (in terms
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59 of sizes, buffering, etc): these are things we need to think about, but it's not
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60 clear whether we should aim for building them into the interface.
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61
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62 Task views of the data for different problems: How can we achieve this? Should
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63 we simply have a set of standard dataset descriptors ('classification',
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64 'regression', 'multi-label', 'density_estimation') and have a set_view method
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65 that changes the current dataset view type?
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66
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67 There is then the question of how to approach the design of a Dataset class from
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68 an OOP perspective. So far, my (Dumi's) idea is to have an almost 'abstract class'
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69 Dataset that doesn't implement any methods except a few setters/getters. The reason
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70 to have the methods listed that way is to have a common 'specification', but classes
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71 that inherit from Dataset need not implement every single method (only the ones
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72 that are relevant) and can obviously implement other methods as appropriate. The
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73 reason to have a common specification (as abstract as it might be) is to, well,
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74 have a common specification that would make our code clearer and cleaner.
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75
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76 An example of what I (Dumi) am thinking in terms of concrete API:
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77
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78 class Dataset:
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79 def __init__(self):
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80 self.type = None
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81 self.in_memory = None
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82 self.inputs = None # list of filepaths, or objects in memory, or...
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83 self.outputs = None
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84
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85 def get_example(self,example_index):
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86 raise NotImplementedError()
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87
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88 def get_next_example(self):
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89 raise NotImplementedError()
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90
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91 def get_batch(self,batch_index):
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92 raise NotImplementedError()
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93
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94 def get_next_batch(self):
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95 raise NotImplementedError()
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96
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97 def get_slice(self,slice_object):
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98 raise NotImplementedError()
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99
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100 def set_view(self,view_type):
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101 self.view_type = view_type
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102 self.n_classes = None
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103
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104 def set_n_classes(self,n_classes):
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105 self.n_classes = n_classes
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106
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107 def set_batch_size(self,batch_size):
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108 self.batch_size = batch_size
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109
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110 You will note that there is no notion of train/valid/test in this class: I think we should
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111 just have a train dataset, a valid one and a test one instead or (if it's in one
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112 big file or infinite stream) just handle the split ourselves (via slicing, for
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113 instance). I (Dumi) am of the opinion that it keeps things cleaner, but the
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114 specification does not preclude more fine-grained 'splitting' of the data.
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115
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116 A concrete implementation would look like this (we would have one class per
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117 dataset that we use, and the class declaration contains essentially everything
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118 there is to know about the dataset):
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119
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120 class MNIST(Dataset):
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121 def __init__(self,inputs=['train_x.npy'],outputs=['train_y.npy']):
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122 self.type='standard_xy'
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123 self.in_memory = True
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124 self.inputs = inputs # load them or create
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125 self.outputs = outputs
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126 self.set_view('classification')
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127 self.set_n_classes(10)
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128 self.set_batch_size(20)
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129 self.n_batches = self._compute_n_batches()
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130
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131 def get_batch(self,batch_index):
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132 x,y = self._fetch_batch(batch_index)
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133 if self.view_type == 'classification':
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134 return x,numpy.int32(y)
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135 elif self.view_type == 'density_estimation':
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136 return x
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137 else:
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138 raise NotImplementedError()
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139
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140 def shared_data(self):
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141 shared_x = theano.shared(numpy.asarray(self.inputs, dtype=theano.config.floatX))
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142 shared_y = theano.shared(numpy.asarray(self.outputs, dtype=theano.config.floatX))
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143 return shared_x, T.cast(shared_y, 'int32')
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144
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145 def _compute_n_batches(self):
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146 pass
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147
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148 def _fetch_batch(self,batch_index):
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149 pass
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150
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151 But nothing stops you from defining get_train_batch, get_valid_batch and stuff
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152 like that!
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153
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154 So we'd use it as:
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155
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156 train_mnist = MNIST(inputs = ['train_x.npy'], outputs = ['train_y.npy'])
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157 valid_mnist = MNIST(inputs = ['valid_x.npy'], outputs = ['valid_y.npy'])
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158
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159 x,y = train_mnist.get_batch(0)
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160 train_mnist.set_view('density_estimation')
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161 x = train_mnist.get_batch(0)
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162
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163 or
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164
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165 mnist_data = MNIST(inputs = ['x.npy'], outputs = ['y.npy'])
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166 batches_train = range(int(mnist_data.n_batches*0.8))
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167 batches_valid = range(int(mnist_data.n_batches*0.8),mnist_data.n_batches)
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168
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169 xt,yt = mnist_data.get_batch(batches_train[0])
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170 xv,yv = mnist_data.get_batch(batches_valid[0])
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171
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172
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173
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174
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175 COMMENTS
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176 ~~~~~~~~
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177
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178
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179 JB asks: What may be passed as argument to the functions in Dataset, and what
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180 can be expected in return? Are there side effects (e.g. on the state of the
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181 Dataset) associated with any of the functions?
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182
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183 JB asks: What properties are part of the Dataset API? What possible types can
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184 they have, are they expected to be read-only or writeable? What do they mean?
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185
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186
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187 JB asks: What is a view? Does set_view change the Dataset or return a new
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188 Dataset with a certain view of the original (in which case call it get_view)?
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189 Does the view imply the types of the return-value of functions like
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190 get_batch? What is the difference between the view and the subclasses of
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191 Dataset in PyML?
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192
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193 JB asks: Do container formats (I'm thinking of HDF5) offer features for fast
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194 retrieval that we would like to expose via this interface?
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195
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196 JB asks: How would you recommend using this sort of dataset in a boosting
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197 algorithm where points need to be re-weighted.
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198
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199
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200 JB asks: Do we want to provide for the possibility of feedback that modifies the
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201 dataset? For example, curriculum learning might be adaptive in this sense, or
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202 if we wanted to provide a virtual world for an agent as a dataset then we need
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203 to provide 'actions' to get the next batch. Could this be done in the current
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204 API?
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205
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206
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207 Field names and attributes
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208 ~~~~~~~~~~~~~~~~~~~~~~~~~~
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209
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210 OD: One important question is how to handle fields' names and characteristics.
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211 For instance, it can be useful to know that the 3rd input field represents a
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212 number of fingers, and is a non-negative discrete field whose numeric value is
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213 meaningful (compared, to, say, an integer index that would correspond to an
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214 animal's category). We mentioned metadata during the meeting, but we did not
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215 get into its details: that may be a place where to put this kind of things.
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216
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217
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218 Freeing memory
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219 ~~~~~~~~~~~~~~
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220
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221 OD: It is sometimes useful to be able to free memory used by previous
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222 computations. A typical example is when you load in memory the original
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223 dataset, then perform various processing steps, ending with a new dataset that
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224 you also store in memory before feeding it to the learner. Unless you very
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225 carefully design your code to avoid it, your original dataset will still
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226 remain in memory (as well as maybe the results of some computations performed
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227 along the way). So there may be a use for a `clear()` method that would be
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228 called by the topmost dataset (the one doing the final memory caching), and
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229 would be forwarded iteratively to previous datasets so as to get back all this
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230 wasted memory space.
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231