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annotate sandbox/statscollector.py @ 463:8df9179b3239

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author Joseph Turian <turian@iro.umontreal.ca>
date Wed, 08 Oct 2008 17:31:26 -0400
parents d7611a3811f2
children
rev   line source
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2 # Here is how I see stats collectors:
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4 def my_stats(graph):
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5 graph.mse=examplewise_mean(square_norm(graph.residue))
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6 graph.training_loss=graph.regularizer+examplewise_sum(graph.nll)
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7 return [graph.mse,graph.training_loss]
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10 # def my_stats(residue,nll,regularizer):
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11 # mse=examplewise_mean(square_norm(residue))
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12 # training_loss=regularizer+examplewise_sum(nll)
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13 # set_names(locals())
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14 # return ((residue,nll),(regularizer),(),(mse,training_loss))
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15 # my_stats_collector = make_stats_collector(my_stats)
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16 #
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17 # where make_stats_collector calls my_stats(examplewise_fields, attributes) to
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18 # construct its update function, and figure out what are the input fields (here "residue"
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19 # and "nll") and input attributes (here "regularizer") it needs, and the output
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20 # attributes that it computes (here "mse" and "training_loss"). Remember that
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21 # fields are examplewise quantities, but attributes are not, in my jargon.
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22 # In the above example, I am highlighting that some operations done in my_stats
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23 # are examplewise and some are not. I am hoping that theano Ops can do these
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24 # kinds of internal side-effect operations (and proper initialization of these hidden
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25 # variables). I expect that a StatsCollector (returned by make_stats_collector)
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26 # knows the following methods:
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27 # stats_collector.input_fieldnames
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28 # stats_collector.input_attribute_names
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29 # stats_collector.output_attribute_names
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30 # stats_collector.update(mini_dataset)
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31 # stats_collector['mse']
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32 # where mini_dataset has the input_fieldnames() as fields and the input_attribute_names()
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33 # as attributes, and in the resulting dataset the output_attribute_names() are set to the
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34 # proper numeric values.
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37
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38 import theano
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39 from theano import tensor as t
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40 from Learner import Learner
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41 from lookup_list import LookupList
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42
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43 class StatsCollectorModel(AttributesHolder):
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44 def __init__(self,stats_collector):
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45 self.stats_collector = stats_collector
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46 self.outputs = LookupList(stats_collector.output_names,[None for name in stats_collector.output_names])
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47 # the statistics get initialized here
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48 self.update_function = theano.function(input_attributes+input_fields,output_attributes+output_fields,linker="c|py")
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49 for name,value in self.outputs.items():
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50 self.__setattribute__(name,value)
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51 def update(self,dataset):
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52 input_fields = dataset.fields()(self.stats_collector.input_field_names)
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53 input_attributes = dataset.getAttributes(self.stats_collector.input_attribute_names)
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54 self.outputs._values = self.update_function(input_attributes+input_fields)
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55 for name,value in self.outputs.items():
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56 self.__setattribute__(name,value)
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57 def __call__(self):
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58 return self.outputs
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59 def attributeNames(self):
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60 return self.outputs.keys()
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62 class StatsCollector(AttributesHolder):
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63
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64 def __init__(self,input_attributes, input_fields, outputs):
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65 self.input_attributes = input_attributes
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66 self.input_fields = input_fields
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67 self.outputs = outputs
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68 self.input_attribute_names = [v.name for v in input_attributes]
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69 self.input_field_names = [v.name for v in input_fields]
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70 self.output_names = [v.name for v in output_attributes]
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71
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72 def __call__(self,dataset=None):
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73 model = StatsCollectorModel(self)
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74 if dataset:
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75 self.update(dataset)
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76 return model
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78 if __name__ == '__main__':
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79 def my_statscollector():
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80 regularizer = t.scalar()
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81 nll = t.matrix()
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82 class_error = t.matrix()
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83 total_loss = regularizer+t.examplewise_sum(nll)
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84 avg_nll = t.examplewise_mean(nll)
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85 avg_class_error = t.examplewise_mean(class_error)
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86 for name,val in locals().items(): val.name = name
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87 return StatsCollector([regularizer],[nll,class_error],[total_loss,avg_nll,avg_class_error])
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92 # OLD DESIGN:
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93 #
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94 # class StatsCollector(object):
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95 # """A StatsCollector object is used to record performance statistics during training
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96 # or testing of a learner. It can be configured to measure different things and
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97 # accumulate the appropriate statistics. From these statistics it can be interrogated
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98 # to obtain performance measures of interest (such as maxima, minima, mean, standard
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99 # deviation, standard error, etc.). Optionally, the observations can be weighted
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100 # (yielded weighted mean, weighted variance, etc., where applicable). The statistics
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101 # that are desired can be specified among a list supported by the StatsCollector
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102 # class or subclass. When some statistics are requested, others become automatically
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103 # available (e.g., sum or mean)."""
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104 #
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105 # default_statistics = [mean,standard_deviation,min,max]
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106 #
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107 # __init__(self,n_quantities_observed, statistics=default_statistics):
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108 # self.n_quantities_observed=n_quantities_observed
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109 #
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110 # clear(self):
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111 # raise NotImplementedError
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112 #
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113 # update(self,observations):
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114 # """The observations is a numpy vector of length n_quantities_observed. Some
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115 # entries can be 'missing' (with a NaN entry) and will not be counted in the
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116 # statistics."""
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117 # raise NotImplementedError
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118 #
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119 # __getattr__(self, statistic)
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120 # """Return a particular statistic, which may be inferred from the collected statistics.
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121 # The argument is a string naming that statistic."""
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