def correlation_basedfeat_sel(bunch):
"""Correlation-based Feature Subset Selection, as implemented by the CfsSubsetEval class of Weka
:param bunch: dataset
:return: new dataset
"""
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
# Instances data!
data = utilities.convertBunchToWekaInstances(bunch)
Filter = jp.JClass('weka.filters.Filter')
AttributeSelection = jp.JClass(
'weka.filters.supervised.attribute.AttributeSelection')
attsel_filter = AttributeSelection()
CfsSubsetEval = jp.JClass('weka.attributeSelection.CfsSubsetEval')
attsel_eval = CfsSubsetEval()
GreedyStepwise = jp.JClass('weka.attributeSelection.BestFirst')
attsel_search = GreedyStepwise()
# attsel_search.setSearchBackwards(True) # True, true
attsel_filter.setEvaluator(attsel_eval)
attsel_filter.setSearch(attsel_search)
attsel_filter.setInputFormat(data)
new_instances = Filter.useFilter(data, attsel_filter)
return utilities.convertWekaInstancesToBunch(new_instances)
def correlation_basedfeat_sel(bunch):
"""Correlation-based Feature Subset Selection, as implemented by the CfsSubsetEval class of Weka
:param bunch: dataset
:return: new dataset
"""
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
# Instances data!
data = utilities.convertBunchToWekaInstances(bunch)
Filter = jp.JClass('weka.filters.Filter')
AttributeSelection = jp.JClass('weka.filters.supervised.attribute.AttributeSelection')
attsel_filter = AttributeSelection()
CfsSubsetEval = jp.JClass('weka.attributeSelection.CfsSubsetEval')
attsel_eval = CfsSubsetEval()
GreedyStepwise = jp.JClass('weka.attributeSelection.BestFirst')
attsel_search = GreedyStepwise()
# attsel_search.setSearchBackwards(True) # True, true
attsel_filter.setEvaluator(attsel_eval)
attsel_filter.setSearch(attsel_search)
attsel_filter.setInputFormat(data)
new_instances = Filter.useFilter(data, attsel_filter)
return utilities.convertWekaInstancesToBunch(new_instances)
def normalize(bunch, params=None):
'''Normalizes all numeric values in the given dataset (apart from the class attribute, if set)
:param bunch: dataset
:param params: parameters in textual form to pass to the Normalize Weka class
:return: dataset
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
# Instances data!
data = utilities.convertBunchToWekaInstances(bunch)
Filter = jp.JClass('weka.filters.Filter')
Normalize = jp.JClass('weka.filters.unsupervised.attribute.Normalize')
normalize_filter = Normalize()
normalize_filter.setOptions(common.parse_options(params))
normalize_filter.setInputFormat(data)
new_instances = Filter.useFilter(data, normalize_filter)
return utilities.convertWekaInstancesToBunch(new_instances)
def normalize(bunch, params=None):
'''Normalizes all numeric values in the given dataset (apart from the class attribute, if set)
:param bunch: dataset
:param params: parameters in textual form to pass to the Normalize Weka class
:return: dataset
'''
if not jp.isThreadAttachedToJVM():
jp.attachThreadToJVM()
# Instances data!
data = utilities.convertBunchToWekaInstances(bunch)
Filter = jp.JClass('weka.filters.Filter')
Normalize = jp.JClass('weka.filters.unsupervised.attribute.Normalize')
normalize_filter = Normalize()
normalize_filter.setOptions(common.parse_options(params))
normalize_filter.setInputFormat(data)
new_instances = Filter.useFilter(data, normalize_filter)
return utilities.convertWekaInstancesToBunch(new_instances)