"weka.classifiers.bayes.NaiveBayes",
"weka.classifiers.lazy.IBk",
"weka.classifiers.trees.J48"
]
# cross-validate classifiers
for classifier in classifiers:
# classifier itself
cls = Classifier(classname=classifier)
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
print("%s: %0.0f%%" % (classifier, evl.percent_correct))
# meta with cfssubseteval
meta = SingleClassifierEnhancer(classname="weka.classifiers.meta.AttributeSelectedClassifier")
meta.options = \
["-E", "weka.attributeSelection.CfsSubsetEval",
"-S", "weka.attributeSelection.BestFirst",
"-W", classifier]
evl = Evaluation(data)
evl.crossvalidate_model(meta, data, 10, Random(1))
print("%s (cfs): %0.0f%%" % (classifier, evl.percent_correct))
# meta with wrapper
meta = SingleClassifierEnhancer(classname="weka.classifiers.meta.AttributeSelectedClassifier")
meta.options = \
["-E", "weka.attributeSelection.WrapperSubsetEval -B " + classifier,
"-S", "weka.attributeSelection.BestFirst",
"-W", classifier]
evl = Evaluation(data)
evl.crossvalidate_model(meta, data, 10, Random(1))
print("%s (wrapper): %0.0f%%" % (classifier, evl.percent_correct))
jvm.stop()
aseval = ASEvaluation(
classname="weka.attributeSelection.WrapperSubsetEval",
options=["-B", classifier])
assearch = ASSearch(classname="weka.attributeSelection.BestFirst",
options=[])
attsel = AttributeSelection()
attsel.evaluator(aseval)
attsel.search(assearch)
attsel.select_attributes(data)
reduced = attsel.reduce_dimensionality(data)
cls = Classifier(classname=classifier)
evl = Evaluation(reduced)
evl.crossvalidate_model(cls, reduced, 10, Random(1))
print("%s (reduced): %0.0f%%" % (classifier, evl.percent_correct))
# meta-classifier
for wrappercls in classifiers:
for basecls in classifiers:
meta = SingleClassifierEnhancer(
classname="weka.classifiers.meta.AttributeSelectedClassifier")
meta.options = \
["-E", "weka.attributeSelection.WrapperSubsetEval -B " + wrappercls,
"-S", "weka.attributeSelection.BestFirst",
"-W", basecls]
evl = Evaluation(data)
evl.crossvalidate_model(meta, data, 10, Random(1))
print("%s/%s: %0.0f%%" % (wrappercls, basecls, evl.percent_correct))
jvm.stop()
