def testExtraCredit(setFunc=setEntropy, infoFunc=infoGain): examples, attrValues, labelName, labelValues = getExtraCreditDataset() print 'Testing Nursery dataset. Number of examples %d.' % len(examples) tree = makeTree(examples, attrValues, labelName, setFunc, infoFunc) f = open('nursery.out', 'w') f.write(str(tree)) f.close() print 'Tree size: %d.\n' % tree.count() print 'Entire tree written out to nursery.out in local directory\n' dataset = getExtraCreditDataset() evaluation = getAverageClassificaionRate( (examples, attrValues, labelName, labelValues), runs=10, testSize=2000) print 'Results for training set:\n%s\n' % str(evaluation) printDemarcation() return (tree, evaluation)
def testAdultSet(setFunc=setEntropy, infoFunc=infoGain): """Correct classification averate rate is about 0.95""" examples, attrValues, labelName, labelValues = getExtraCreditDataset() print 'Testing Adult dataset. Number of examples %d.' % len(examples) start = time.time() tree = makeTree(examples, attrValues, labelName, setFunc, infoFunc) end = time.time() print "Training time: ", (end - start) f = open('adult.out', 'w') f.write(str(tree)) f.close() print 'Tree size: %d.\n' % tree.count() print 'Entire tree written out to adult.out in local directory\n' dataset = getExtraCreditDataset() evaluation = getAverageClassificaionRate( (examples, attrValues, labelName, labelValues)) print 'Results for training set:\n%s\n' % str(evaluation) printDemarcation() return (tree, evaluation)
def testextra(setFunc=setEntropy, infoFunc=infoGain): """Correct classification averate rate is about 0.75""" examples, attrValues, labelName, labelValues = getExtraCreditDataset() print 'Testing Connect4 dataset. Number of examples %d.' % len(examples) tree = makeTree(examples, attrValues, labelName, setFunc, infoFunc) f = open('extra.out', 'w') print 'Tree size: %d.\n' % tree.count() print 'Entire tree written out to extra.out in local directory\n' f.write(str(tree)) f.close() evaluation = getAverageClassificaionRate( (examples, attrValues, labelName, labelValues)) print 'Results for training set:\n%s\n' % str(evaluation) printDemarcation() return (tree, evaluation)