Example #1
0
 def __init__(self, path, bTrain):
    self.bTrain = bTrain
    if bTrain:
       self.file = open(path+'.tab', "w")
    else:
       data = orange.ExampleTable(path)
       self.file = orange.C45Learner(data, prune=False)
    self.names = None
    self.types = None
    self.classes = None
    self.examples = []
Example #2
0
def train_classifier(data, type, filter):
    if type == "tree" or type == "c4.5" or type == "decision_tree":
        learner = orange.C45Learner()
    elif type == "bayes" or type == "naive" or type == "naive_bayes":
        learner = orange.BayesLearner()
    elif type == "svm" or type == "linear_svm":
        learner = Orange.classification.svm.LinearSVMLearner()
    #elif type == "logreg" or type == "regression":
    #	learner = Orange.classification.logreg.LogRegLearner()
    else:
        print "Invalid Learner Type\n"
        exit()

    if filter == 0:
        classifier = learner(data)
    else:
        filtered_learner = Orange.feature.selection.FilteredLearner(
            learner,
            filter=Orange.feature.selection.FilterBestN(n=filter),
            name='filtered')
        classifier = filtered_learner(data)

    return classifier
Example #3
0
# Description: Shows how to use C4.5 learner
# Category:    learning
# Classes:     C45Learner, C45Classifier
# Uses:        iris
# Referenced:  C45Learner.htm

import orange

#data = orange.ExampleTable("lenses.tab")
data = orange.ExampleTable("iris")
tree = orange.C45Learner(data)

print "\n\nC4.5 with default arguments"
for i in data[:5]:
    print tree(i), i.getclass()

print "\n\nC4.5 with m=100"
tree = orange.C45Learner(data, m=100)
for i in data[:5]:
    print tree(i), i.getclass()

print "\n\nC4.5 with minObjs=100"
tree = orange.C45Learner(data, minObjs=100)
for i in data[:5]:
    print tree(i), i.getclass()

print "\n\nC4.5 with -m 1 and -s"
lrn = orange.C45Learner()
lrn.commandline("-m 1 -s")
tree = lrn(data)
for i in data:
Example #4
0
def c45_tree(input_dict):
    import orange
    output_dict = {}
    output_dict['c45out'] = orange.C45Learner(name="C4.5 Tree (Orange)")
    return output_dict
Example #5
0
 def test_pickling_on(self, dataset):
     try:
         orange.C45Learner()
     except orange.KernelException:
         raise unittest.SkipTest("C45 dll not found")
     testing.LearnerTestCase.test_pickling_on(self, dataset)
Example #6
0
##f.close()
##print tree.name

##treeC45 = orange.C45Learner(trainD, minObjs=5)
##treeC45.name    = "tree - C45       "
##f = file(trainData+'o.txt.C45tree', 'w')
##dumpC45Tree(treeC45,f)
##f.close()
##print treeC45.name

t = orngTree.TreeLearner(measure='gainRatio', binarization=0, minSubset=2, minExamples=2, sameMajorityPruning=1, mForPruning=2);
bsTree = BLearner(learner=t, examples=trainD)
bsTree.name     = "bsTree           "
print bsTree.name

c45 = orange.C45Learner(minObjs=2)
bsC45 = BLearner(learner=c45, examples=trainD)
bsC45.name      = "bsC45            "
print bsC45.name

##svm = orange.SVMLearner(trainD)
##svm.name      = "SVM              "

##classifiers = [majority, tree, bsTree, treeC45, bsC45]
classifiers = [majority, bsTree, bsC45]

# compute accuracies
print '='*80
acc = accuracy(trainD, classifiers)
print "Classification accuracies - train:"
print '-'*80