def testGradingTraining(self):
# Coordinates plane for classification task
# 1111111
# X 01234567890123456 #Y
grid = [' 1 1 1 1 1 1 0', #0
' 1 1 1 1 0', #1
' 1 1 1 0', #2
' 1 1 0 0', #3
' 1 1 0 0 0 ', #4
' 1 1 0 0 0', #5
' 1 0 0 00', #6
' 1 0 0 00 0', #7
]
dataset = ClassificationDataSet(2, nb_classes=2, class_labels= ['0', '1'])
createFromGrid(grid, dataset)
optimizer = GradientOptimizer(minChange=1e-6)
optimizer.maxLearningSteps = 1000
optimizer.verbose = False
lrf = LogisticRegressionFactory(optimizer)
gradingFactory = GradingFactory([lrf, lrf, lrf], lrf, 0.7, MajorVoting)
gradingClassifier = gradingFactory.buildClassifier(dataset)
self.assertEqual(gradingClassifier.getPrediction([0, 0]), 1)
self.assertEqual(gradingClassifier.getPrediction([16, 7]), 0)
def testUsingPredictions(self):
# Coordinates plane for classification task
# 1111111
# X 01234567890123456 #Y
grid = [' 0 0 11 11', #0
' 0 0 1 1 1', #1
' 0 1 ', #2
' 0 0 1 1', #3
' 0 1 1', #4
' 0 0 0 111', #5
' 0 0 11', #6
' 0 0 0 0 11', #7
]
dataset = ClassificationDataSet(2, nb_classes=2, class_labels= ['0', '1'])
createFromGrid(grid, dataset)
optimizer = GradientOptimizer(minChange=1e-6)
optimizer.maxLearningSteps = 1000
optimizer.verbose = False
lrf = LogisticRegressionFactory(optimizer)
stackingFactory = StackingFactory([lrf, lrf], lrf, useDistributions=True)
stackingClassifier = stackingFactory.buildClassifier(dataset)
self.assertEqual(stackingClassifier.getPrediction([2, 6]), 0)
self.assertEqual(stackingClassifier.getPrediction([1, 8]), 0)
self.assertEqual(stackingClassifier.getPrediction([3, 7]), 0)
self.assertEqual(stackingClassifier.getPrediction([14, 2]), 1)
self.assertEqual(stackingClassifier.getPrediction([13, 0]), 1)
self.assertEqual(stackingClassifier.getPrediction([16, 4]), 1)
def testLogisticWithMulticlass(self):
# Coordinates plane for classification task
# 1111111
# X 01234567890123456 #Y
grid = [' 0 0 ', #0
' 0 0 2 2', #1
' ', #2
' 2 2', #3
' ', #4
' 1 1 ', #5
' ', #6
' 1 1 ', #7
]
dataset = ClassificationDataSet(2, nb_classes=3, class_labels= ['0', '1', '2'])
createFromGrid(grid, dataset)
optimizer = GradientOptimizer(minChange=1e-6)
optimizer.maxLearningSteps = 1000
optimizer.verbose = False
lrf = LogisticRegressionFactory(optimizer)
allVsAll = AllVsAllFactory(lrf)
classifier = allVsAll.buildClassifier(dataset)
self.assertEqual(classifier.getPrediction([3, 0]), 0)
self.assertEqual(classifier.getPrediction([4, 1]), 0)
self.assertEqual(classifier.getPrediction([6, 5]), 1)
self.assertEqual(classifier.getPrediction([6, 6]), 1)
self.assertEqual(classifier.getPrediction([15, 2]), 2)
self.assertEqual(classifier.getPrediction([14, 2]), 2)
def testTwoDimensionalClassification1(self):
# Coordinates plane for classification task
# 1111111
# X 01234567890123456 #Y
grid = [
" 1 1 ", # 0
" 1 1 0 0", # 1
" ", # 2
" 0 0", # 3
" ", # 4
" 0 0 ", # 5
" ", # 6
" 0 0 ", # 7
]
dataset = ClassificationDataSet(2, nb_classes=2, class_labels=["0", "1"])
createFromGrid(grid, dataset)
optimizer = GradientOptimizer(minChange=1e-6)
optimizer.maxLearningSteps = 1000
optimizer.verbose = False
lrf = LogisticRegressionFactory(optimizer)
classifier = lrf.buildClassifier(dataset)
self.assertEqual(classifier.getPrediction([3, 0]), 1)
self.assertEqual(classifier.getPrediction([4, 1]), 1)
def testMulticlassTaskWithProbabilityEstimation(self):
# Coordinates plane for classification task
# 1111111
# X 01234567890123456 #Y
grid = [' 0 0 ', #0
' 0 0 2 2', #1
' ', #2
' 2 2', #3
' ', #4
' 1 1 ', #5
' ', #6
' 1 1 ', #7
]
dataset = ClassificationDataSet(2, nb_classes=3, class_labels= ['0', '1', '2'])
createFromGrid(grid, dataset)
svmf = SVMFactory(C=100, estimateProbability=True)
classifier = svmf.buildClassifier(dataset)
self.assertEqual(classifier.getPrediction([3, 0]), 0)
self.assertEqual(classifier.getPrediction([4, 1]), 0)
self.assertEqual(classifier.getPrediction([6, 5]), 1)
self.assertEqual(classifier.getPrediction([6, 6]), 1)
self.assertEqual(classifier.getPrediction([15, 2]), 2)
self.assertEqual(classifier.getPrediction([14, 2]), 2)
def testLinearSeparableNuSVCTypeEstimateProbability(self):
# Coordinates plane for classification task
# 1111111
# X 01234567890123456 #Y
grid = [' 1 1 ', #0
' 1 1 0 0', #1
' ', #2
' 0 0', #3
' ', #4
' 0 0 ', #5
' ', #6
' 0 0 ', #7
]
dataset = ClassificationDataSet(2, nb_classes=2, class_labels= ['0', '1'])
createFromGrid(grid, dataset)
svmf = SVMFactory(nu = 0.3, estimateProbability=True, svmType=SVMType.NU_SVC)
classifier = svmf.buildClassifier(dataset)
self.assertEqual(classifier.getPrediction([3, 0]), 1)
self.assertEqual(classifier.getPrediction([4, 1]), 1)
self.assertEqual(classifier.getPrediction([6, 5]), 0)
self.assertEqual(classifier.getPrediction([6, 6]), 0)
self.assertEqual(classifier.getPrediction([15, 2]), 0)
self.assertEqual(classifier.getPrediction([14, 2]), 0)
