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dataset: Rewrote my rambling about the links between dataset and learner
author Olivier Delalleau <delallea@iro>
date Fri, 10 Sep 2010 20:24:51 -0400
parents 7e6e77d50eeb
children 65ac0f493830
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>
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3
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4 Some talking points from the September 2 meeting:
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5
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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
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7 needs to be flexible enough to accommodate different (sub)tasks and views of
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8 the same underlying data.
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9 * Datasets as probability distributions from which one can sample.
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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>
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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>
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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
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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
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19 Commiteee: DE, OB, OD, AB, PV
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20 Leader: DE
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21
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22 Some ideas from existing ML libraries:
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23
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24 - PyML: notion of dataset containers: VectorDataSet, SparseDataSet, KernelData,
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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
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29 constrained and may have a different interface.
20a1af112a75 dataset: Looked into datasets from some other ML libraries
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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
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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
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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
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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
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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
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35 Basically there seems to be only one base dataset class that contains the
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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.
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38 - Monte: Data is simply numpy arrays.
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39 - scikits.learn: Dataset is a simple container with e.g. dataset.data being
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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
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42 though).
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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
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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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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
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232 What is a mini-batch?
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233 ~~~~~~~~~~~~~~~~~~~~~
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234
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235 This is a follow-up to the meeting's discussion about whether a mini-batch
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236 returned by a dataset should be itself a dataset.
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237
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238 OD: During the meeting I was voting in favor of a 'yes', mostly because it
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239 made sense to me (a mini-batch is a subset of a dataset and thus should be a
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240 dataset), but now I tend towards 'no'. The main reason is it is not clear yet
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241 what the dataset interface will be, so that it is hard to judge whether this
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242 is good idea (my main concern is how much additional work would be required by
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243 the writer of a new dataset subclass). Anyway, maybe a first thing we could
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244 think about is what we want a mini-batch to be. I think we can agree that we
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245 would like to be able to do something like:
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246 for mb in dataset.mini_batches(size=10):
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247 learner.update(mb.input, mb.target)
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248 so that it should be ok for a mini-batch to be an object whose fields
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249 (that should have the same name as those of the dataset) are numpy arrays.
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250 More generally, we would like to be able to iterate on samples in a
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251 mini-batch, or do random access on them, so a mini-batch should implement
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252 __iter__ and __getitem__.
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253 Besides this, is there any other typical use-case of a mini-batch? In
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254 particular, is there any reason to want an infinite mini-batch? (in which case
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255 we may need to revise our idea of what 'mini' means) Hopefully the answer to
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256 that last question is no, as I think it would definitely keep things simpler,
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257 since we could simply use numpy arrays (for numeric data) or lists (for
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258 anything else) to store mini-batches' data. So I vote for 'no'.
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259
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260 A dataset is a learner
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261 ~~~~~~~~~~~~~~~~~~~~~~
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262
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263 OD: (this is hopefully a clearer re-write of the original version from
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264 r7e6e77d50eeb, which I was not happy with).
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265 There are typically three kinds of objects that spit out data:
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266 1. Datasets that are loaded from disk or are able to generate data all by
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267 themselves (i.e. without any other dataset as input)
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268 2. Datasets that transform their input dataset in some way (e.g. filtering
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269 samples or features, normalizing data, etc.)
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270 3. Datasets that are the output of a transformation whose parameters are
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271 learned on a potentially different dataset (e.g. PCA when you want to learn the
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272 projection space on the training set in order to transform both the training
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273 and test sets).
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274 My impression currently is that we would use dataset subclasses to handle 1
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275 and 2. However, 3 requires a learner framework, so you would need to have
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276 something like a LearnerOutputDataset(trained_learner, dataset).
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277
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278 Note however that 2 is a special case of 3 (where training does nothing), and
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279 1 is a special case of 2 (where we do not care about being given an input
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280 dataset). Thus you could decide to also implement 1 and 2 as learners wrapped
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281 by LearnerOutputDataset.
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282
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283 The main advantages I find in this approach (that I have been using at
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284 Ubisoft) are:
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285 - You only need to learn how to subclass the learner class. The only dataset
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286 class is LearnerOutputDataset, which you could just name Dataset.
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287 - You do not have different ways to achieve the same result (having to figure
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288 out which one is most appropriate).
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289 - Upgrading code from 2 to 3 is more straighforward. Such a situation can
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290 happen e.g. if you write some code that normalizes your input dataset
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291 (situation 2), then realize later you would like to be able to normalize new
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292 datasets using the same parameters (e.g. same shift & rescaling), which
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293 requires situation 3.
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294 - It can make your life easier when thinking about how to plug things together
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295 (something that has not been discussed yet), because the interfaces of the
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296 various components are less varied.
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297
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298 I am not saying that we should necessarily do it this way, but I think it is
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299 worth at least keeping in mind this close relationship between simple
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300 processing and learning, and thinking about what are the benefits / drawbacks
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301 in keeping them separate in the class hierarchy.