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v2_planning dataset - added questions
author James Bergstra <bergstrj@iro.umontreal.ca>
date Wed, 08 Sep 2010 16:20:20 -0400
parents 1b61cbe0810b
children 20a1af112a75
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
1008
a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
parents: 1002
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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
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Dumitru Erhan <dumitru.erhan@gmail.com>
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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.
1023
fb6cae14fd07 dataset: Comment about viewing a dataset as a distribution
Olivier Delalleau <delallea@iro>
parents: 1008
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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
1008
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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>
parents: 1002
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16 simply datasets which cannot fit in memory)
a5886b394bda Updating with talking points from Sept. 2 discussion
Dumitru Erhan <dumitru.erhan@gmail.com>
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17 * GPU/buffering issues.
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18
a5886b394bda Updating with talking points from Sept. 2 discussion
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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
1047
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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
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27 - (still going through the other ones)
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28
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29 A few things that our dataset containers should support at a minimum:
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30
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31 - streams, possibly infinite
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32 - task/views of the data for different problems
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33 - indexing & slicing
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34 - pairs or triples or etc of examples
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35 - a 'distance/gram matrix' container (imagine that the data is given to you
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36 as a distance matrix)
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37 - multi-dimensional time-series (again, maybe with pairs/triples, maybe
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38 given to you as a distance matrix over time)
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39
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40 Another question to consider is the following: how tight should it integrate
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41 with Theano? Do we want to be able to store data as shared variables or just
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42 have an option for that? Theano + GPU constrains things that we can do (in terms
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43 of sizes, buffering, etc): these are things we need to think about, but it's not
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44 clear whether we should aim for building them into the interface.
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45
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46 Task views of the data for different problems: How can we achieve this? Should
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47 we simply have a set of standard dataset descriptors ('classification',
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48 'regression', 'multi-label', 'density_estimation') and have a set_view method
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49 that changes the current dataset view type?
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50
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51 There is then the question of how to approach the design of a Dataset class from
1b61cbe0810b A very rough draft of ideas, to kick-start things
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52 an OOP perspective. So far, my (Dumi's) idea is to have an almost 'abstract class'
1b61cbe0810b A very rough draft of ideas, to kick-start things
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53 Dataset that doesn't implement any methods except a few setters/getters. The reason
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54 to have the methods listed that way is to have a common 'specification', but classes
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55 that inherit from Dataset need not implement every single method (only the ones
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56 that are relevant) and can obviously implement other methods as appropriate. The
1b61cbe0810b A very rough draft of ideas, to kick-start things
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57 reason to have a common specification (as abstract as it might be) is to, well,
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58 have a common specification that would make our code clearer and cleaner.
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59
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60 An example of what I (Dumi) am thinking in terms of concrete API:
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61
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62 class Dataset:
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63 def __init__(self):
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64 self.type = None
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65 self.in_memory = None
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66 self.inputs = None # list of filepaths, or objects in memory, or...
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67 self.outputs = None
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68
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69 def get_example(self,example_index):
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70 raise NotImplementedError()
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71
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72 def get_next_example(self):
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73 raise NotImplementedError()
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74
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75 def get_batch(self,batch_index):
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76 raise NotImplementedError()
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77
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78 def get_next_batch(self):
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79 raise NotImplementedError()
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80
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81 def get_slice(self,slice_object):
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82 raise NotImplementedError()
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83
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84 def set_view(self,view_type):
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85 self.view_type = view_type
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86 self.n_classes = None
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87
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88 def set_n_classes(self,n_classes):
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89 self.n_classes = n_classes
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90
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91 def set_batch_size(self,batch_size):
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92 self.batch_size = batch_size
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93
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94 You will note that there is no notion of train/valid/test in this class: I think we should
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95 just have a train dataset, a valid one and a test one instead or (if it's in one
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96 big file or infinite stream) just handle the split ourselves (via slicing, for
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97 instance). I (Dumi) am of the opinion that it keeps things cleaner, but the
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98 specification does not preclude more fine-grained 'splitting' of the data.
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99
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100 A concrete implementation would look like this (we would have one class per
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101 dataset that we use, and the class declaration contains essentially everything
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102 there is to know about the dataset):
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103
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104 class MNIST(Dataset):
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105 def __init__(self,inputs=['train_x.npy'],outputs=['train_y.npy']):
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106 self.type='standard_xy'
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107 self.in_memory = True
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108 self.inputs = inputs # load them or create
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109 self.outputs = outputs
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110 self.set_view('classification')
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111 self.set_n_classes(10)
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112 self.set_batch_size(20)
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113 self.n_batches = self._compute_n_batches()
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114
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115 def get_batch(self,batch_index):
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116 x,y = self._fetch_batch(batch_index)
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117 if self.view_type == 'classification':
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118 return x,numpy.int32(y)
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119 elif self.view_type == 'density_estimation':
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120 return x
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121 else:
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122 raise NotImplementedError()
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123
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124 def shared_data(self):
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125 shared_x = theano.shared(numpy.asarray(self.inputs, dtype=theano.config.floatX))
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126 shared_y = theano.shared(numpy.asarray(self.outputs, dtype=theano.config.floatX))
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127 return shared_x, T.cast(shared_y, 'int32')
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128
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129 def _compute_n_batches(self):
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130 pass
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131
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132 def _fetch_batch(self,batch_index):
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133 pass
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134
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135 But nothing stops you from defining get_train_batch, get_valid_batch and stuff
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136 like that!
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137
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138 So we'd use it as:
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139
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140 train_mnist = MNIST(inputs = ['train_x.npy'], outputs = ['train_y.npy'])
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141 valid_mnist = MNIST(inputs = ['valid_x.npy'], outputs = ['valid_y.npy'])
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142
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143 x,y = train_mnist.get_batch(0)
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144 train_mnist.set_view('density_estimation')
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145 x = train_mnist.get_batch(0)
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146
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147 or
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148
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149 mnist_data = MNIST(inputs = ['x.npy'], outputs = ['y.npy'])
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150 batches_train = range(int(mnist_data.n_batches*0.8))
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151 batches_valid = range(int(mnist_data.n_batches*0.8),mnist_data.n_batches)
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152
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153 xt,yt = mnist_data.get_batch(batches_train[0])
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154 xv,yv = mnist_data.get_batch(batches_valid[0])
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155
1054
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156
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157
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158
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159 COMMENTS
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160 ~~~~~~~~
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161
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162
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163 JB asks: What may be passed as argument to the functions in Dataset, and what
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164 can be expected in return? Are there side effects (e.g. on the state of the
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165 Dataset) associated with any of the functions?
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166
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167 JB asks: What properties are part of the Dataset API? What possible types can
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168 they have, are they expected to be read-only or writeable? What do they mean?
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169
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170
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171 JB asks: What is a view? Does set_view change the Dataset or return a new
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172 Dataset with a certain view of the original (in which case call it get_view)?
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173 Does the view imply the types of the return-value of functions like
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174 get_batch? What is the difference between the view and the subclasses of
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175 Dataset in PyML?
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176
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177 JB asks: Do container formats (I'm thinking of HDF5) offer features for fast
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178 retrieval that we would like to expose via this interface?
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179
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180 JB asks: How would you recommend using this sort of dataset in a boosting
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181 algorithm where points need to be re-weighted.
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182
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183
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184 JB asks: Do we want to provide for the possibility of feedback that modifies the
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185 dataset? For example, curriculum learning might be adaptive in this sense, or
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186 if we wanted to provide a virtual world for an agent as a dataset then we need
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187 to provide 'actions' to get the next batch. Could this be done in the current
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188 API?
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189
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190
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191