def get_evaluator(params, base):
pprint(params)
L = list([])
if params['weightByDistance'] == True:
L.append("-W")
L.append("-M")
L.append(str(params['sampleSize']))
L.append("-K")
L.append(str(params['numNeighbours']))
L.append("-A")
L.append(str(params['sigma']))
param_search = rk.get_params()
search = rk.get_search(param_search)
# search = ASSearch(classname="weka.attributeSelection.Ranker")
evaluator = ASEvaluation(classname="weka.attributeSelection.ReliefFAttributeEval", options=L)
clf = Classifier(classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
return clf
def get_evaluator(params, base):
pprint(params)
L = list()
if params['missing_merge'] == True:
L.append("-M")
# if params['search'] == 'GreedyStepwise':
# param_search = gs.get_params()
# search = gs.get_search(param_search)
# elif params['search'] == 'BestFirst':
# param_search = bf.get_params()
# search = bf.get_search(param_search)
# elif params['search'] == 'Ranker':
param_search = rk.get_params()
search = rk.get_search(param_search)
# search = ASSearch(classname="weka.attributeSelection."+params['search'])
evaluator = ASEvaluation(
classname="weka.attributeSelection.SymmetricalUncertAttributeEval",
options=L)
clf = Classifier(
classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
return clf
def try_params(n_instances, params, base, train, valid, test, istest):
n_instances = int(round(n_instances))
# print "n_instances:", n_instances
pprint(params)
L = list([])
if params['outputDetailedInfo'] == True:
L.append("-D")
param_search = rk.get_params()
search = rk.get_class(param_search)
# search = ASSearch(classname="weka.attributeSelection.Ranker")
evaluator = ASEvaluation(
classname="weka.attributeSelection.CorrelationAttributeEval",
options=L)
clf = Classifier(
classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
if istest:
result = test_weka_classifier(clf, train, test)
else:
result = train_and_eval_weka_classifier(clf, train, valid, n_instances)
return result
def get_evaluator(params, base):
pprint(params)
L = list([])
if params['outputDetailedInfo'] == True:
L.append("-D")
param_search = rk.get_params()
search = rk.get_search(param_search)
# search = ASSearch(classname="weka.attributeSelection.Ranker")
evaluator = ASEvaluation(
classname="weka.attributeSelection.CorrelationAttributeEval",
options=L)
clf = Classifier(
classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
return clf
def try_params(n_instances, params, base, train, valid, test, istest):
n_instances = int(round(n_instances))
pprint(params)
L = list()
if params['missing_merge'] == True:
L.append("-M")
if params['search'] == 'GreedyStepwise':
param_search = gs.get_params()
search = gs.get_search(param_search)
elif params['search'] == 'BestFirst':
param_search = bf.get_params()
search = bf.get_search(param_search)
elif params['search'] == 'Ranker':
param_search = rk.get_params()
search = rk.get_search(param_search)
# search = ASSearch(classname="weka.attributeSelection."+params['search'])
evaluator = ASEvaluation(classname="weka.attributeSelection.GainRatioAttributeEval", options=L)
clf = Classifier(classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
if istest:
result = test_weka_classifier(clf, train, test)
else:
result = train_and_eval_weka_classifier(clf, train, valid, n_instances)
return result
def get_evaluator(params, base):
pprint(params)
L = list([])
if params['missingMerge'] == False:
L.append("-M")
if params['binarizeNumericAttributes'] == True:
L.append("-B")
param_search = rk.get_params()
search = rk.get_search(param_search)
# search = ASSearch(classname="weka.attributeSelection.Ranker")
evaluator = ASEvaluation(
classname="weka.attributeSelection.InfoGainAttributeEval", options=L)
clf = Classifier(
classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
return clf
def try_params(n_instances, params, base, train, valid, test, istest):
n_instances = int(round(n_instances))
# print "n_instances:", n_instances
pprint(params)
L = list([])
if params['weightByDistance'] == True:
L.append("-W")
L.append("-M")
L.append(str(params['sampleSize']))
L.append("-K")
L.append(str(params['numNeighbours']))
L.append("-A")
L.append(str(params['sigma']))
param_search = rk.get_params()
search = rk.get_class(param_search)
# search = ASSearch(classname="weka.attributeSelection.Ranker")
evaluator = ASEvaluation(classname="weka.attributeSelection.ReliefFAttributeEval", options=L)
clf = Classifier(classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
if istest:
result = test_weka_classifier(clf, train, test)
else:
result = train_and_eval_weka_classifier(clf, train, valid, n_instances)
return result
def get_evaluator(params, base):
pprint(params)
L = list()
if params['use_training'] == True:
L.append("-D")
L.append("-S")
L.append(str(params['seed']))
L.append("-B")
L.append(str(params['minimum_bucket']))
#
# if params['search'] == 'GreedyStepwise':
# param_search = gs.get_params()
# search = gs.get_search(param_search)
# elif params['search'] == 'BestFirst':
# param_search = bf.get_params()
# search = bf.get_search(param_search)
# elif params['search'] == 'Ranker':
param_search = rk.get_params()
search = rk.get_search(param_search)
# search = ASSearch(classname="weka.attributeSelection."+params['search'])
evaluator = ASEvaluation(
classname="weka.attributeSelection.OneRAttributeEval", options=L)
clf = Classifier(
classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
return clf
def get_evaluator(params, base):
pprint(params)
L = list()
L.append("-E")
L.append(str(params['ev_measure']))
L.append("-R")
L.append(str(params['seed']))
L.append("-T")
L.append(str(params['threshold']))
if params['search'] == 'GreedyStepwise':
param_search = gs.get_params()
search = gs.get_search(param_search)
elif params['search'] == 'BestFirst':
param_search = bf.get_params()
search = bf.get_search(param_search)
elif params['search'] == 'Ranker':
param_search = rk.get_params()
search = rk.get_search(param_search)
# search = ASSearch(classname="weka.attributeSelection."+params['search'])
evaluator = ASEvaluation(
classname="weka.attributeSelection.WrapperSubsetEval", options=L)
clf = Classifier(
classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
return clf
def get_evaluator(params, base):
pprint(params)
L = list()
if params['center'] == True:
L.append("-C")
L.append("-A")
L.append(str(params['max_a']))
L.append("-R")
L.append(str(params['variance']))
# if params['search'] == 'GreedyStepwise':
# param_search = gs.get_params()
# search = gs.get_search(param_search)
# elif params['search'] == 'BestFirst':
# param_search = bf.get_params()
# search = bf.get_search(param_search)
# elif params['search'] == 'Ranker':
param_search = rk.get_params()
search = rk.get_search(param_search)
# search = ASSearch(classname="weka.attributeSelection."+params['search'])
evaluator = ASEvaluation(classname="weka.attributeSelection.PrincipalComponents", options=L)
clf = Classifier(classname="weka.classifiers.meta.AttributeSelectedClassifier")
clf.set_property("evaluator", evaluator.jobject)
clf.set_property("search", search.jobject)
clf.set_property("base", base.jobject)
return clf