Module discretise

source code

Classes

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Discretise

Discretiser

Functions

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get_chunks_with_frequency(values, freq)

source code

ranges_from_chunks(chunks)

source code

Variables

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a_help = 'Selects the discretisation algorithm ...

f_help = 'Base name of attribute, klass, training, test and go...

t_help = 'Base name of training file for discretisation. '

T_help = 'Base name of test file to be discterised. '

g_help = 'Base name of gold file to be discretised. '

A_help = 'Comma separated list of attribute indices. '

o_help = 'Algorithm specific options ...

UNSUPERVISED_EQUAL_WIDTH = 'UEW'

UNSUPERVISED_EQUAL_FREQUENCY = 'UEF'

NAIVE_SUPERVISED = 'NS'

NAIVE_SUPERVISED_V1 = 'NS1'

NAIVE_SUPERVISED_V2 = 'NS2'

ENTROPY_BASED_SUPERVISED = 'ES'

ALGORITHM_MAPPINGS = {'ES': 'entropy_based_supervised', 'NS': ...

Variables Details

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a_help

Value:

'Selects the discretisation algorithm                 Options: UEW for
 Unsupervised Equal Width                     UEF for Unsupervised Equ
al Frequency                 NS for Naive Supervised                  
            NS1 for Naive Supervised version 1                   NS2 f
or Naive Supervised version 2                   ES for Entropy Based S
upervised             Default: UEW.'

f_help

Value:

'Base name of attribute, klass, training, test and gold files.        
                                      '

o_help

Value:

'Algorithm specific options                           UEW: Comma separ
ated list of number of parts in which     each attribute should be spl
it.                 '

ALGORITHM_MAPPINGS

Value:

{'ES': 'entropy_based_supervised',
 'NS': 'naive_supervised',
 'NS1': 'naive_supervised_v1',
 'NS2': 'naive_supervised_v2',
 'UEF': 'unsupervised_equal_frequency',
 'UEW': 'unsupervised_equal_width'}