def test_Compare(self):
mxKFoldRun = MxKFoldRun(5, 2)
combined5x2t = Combined5x2t()
experimentPerformance1 = mxKFoldRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
experimentPerformance2 = mxKFoldRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.iris))
self.assertAlmostEqual(
0.186,
combined5x2t.compare(experimentPerformance1,
experimentPerformance2).getPValue(), 3)
experimentPerformance1 = mxKFoldRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
experimentPerformance2 = mxKFoldRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
self.assertAlmostEqual(
0.0000059,
combined5x2t.compare(experimentPerformance1,
experimentPerformance2).getPValue(), 7)
experimentPerformance1 = mxKFoldRun.execute(
Experiment(Lda(), Parameter(1), self.dermatology))
experimentPerformance2 = mxKFoldRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.dermatology))
self.assertAlmostEqual(
0.9819,
combined5x2t.compare(experimentPerformance1,
experimentPerformance2).getPValue(), 4)
experimentPerformance1 = mxKFoldRun.execute(
Experiment(Dummy(), Parameter(1), self.nursery))
experimentPerformance2 = mxKFoldRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.nursery))
self.assertAlmostEqual(
0.0,
combined5x2t.compare(experimentPerformance1,
experimentPerformance2).getPValue(), 4)
experimentPerformance1 = mxKFoldRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.car))
experimentPerformance2 = mxKFoldRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.car))
self.assertAlmostEqual(
0.00043,
combined5x2t.compare(experimentPerformance1,
experimentPerformance2).getPValue(), 5)
experimentPerformance1 = mxKFoldRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.bupa))
experimentPerformance2 = mxKFoldRun.execute(
Experiment(Lda(), Parameter(1), self.bupa))
self.assertAlmostEqual(
0.0663,
combined5x2t.compare(experimentPerformance1,
experimentPerformance2).getPValue(), 4)
def test_LinearPerceptron(self):
linearPerceptron = LinearPerceptron()
linearPerceptronParameter = LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100)
pca = Pca(self.iris)
pca.convert()
linearPerceptron.train(self.iris.getInstanceList(), linearPerceptronParameter)
self.assertAlmostEqual(1.33, 100 * linearPerceptron.test(self.iris.getInstanceList()).getErrorRate(), 2)
linearPerceptronParameter = LinearPerceptronParameter(1, 0.01, 0.99, 0.2, 100)
pca = Pca(self.bupa)
pca.convert()
linearPerceptron.train(self.bupa.getInstanceList(), linearPerceptronParameter)
self.assertAlmostEqual(27.54, 100 * linearPerceptron.test(self.bupa.getInstanceList()).getErrorRate(), 2)
pca = Pca(self.dermatology)
pca.convert()
linearPerceptron.train(self.dermatology.getInstanceList(), linearPerceptronParameter)
self.assertAlmostEqual(3.28, 100 * linearPerceptron.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
def train(self, trainSet: InstanceList,
parameters: LinearPerceptronParameter):
"""
Training algorithm for the linear perceptron algorithm. 20 percent of the data is separated as cross-validation
data used for selecting the best weights. 80 percent of the data is used for training the linear perceptron with
gradient descent.
PARAMETERS
----------
trainSet : InstanceList
Training data given to the algorithm
parameters : LinearPerceptronParameter
Parameters of the linear perceptron.
"""
partition = Partition(trainSet, parameters.getCrossValidationRatio(),
parameters.getSeed(), True)
self.model = LinearPerceptronModel(partition.get(1), partition.get(0),
parameters)
def test_LinearPerceptron(self):
linearPerceptron = LinearPerceptron()
linearPerceptronParameter = LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100)
discreteToIndexed = LaryToBinary(self.car)
discreteToIndexed.convert()
linearPerceptron.train(self.car.getInstanceList(), linearPerceptronParameter)
self.assertAlmostEqual(29.98, 100 * linearPerceptron.test(self.car.getInstanceList()).getErrorRate(), 2)
discreteToIndexed = LaryToBinary(self.tictactoe)
discreteToIndexed.convert()
linearPerceptron.train(self.tictactoe.getInstanceList(), linearPerceptronParameter)
self.assertAlmostEqual(34.66, 100 * linearPerceptron.test(self.tictactoe.getInstanceList()).getErrorRate(), 2)
def __init__(self, trainSet: InstanceList, validationSet: InstanceList,
parameters: LinearPerceptronParameter):
"""
Constructor that takes InstanceLists as trainsSet and validationSet. Initially it allocates layer weights,
then creates an input vector by using given trainSet and finds error. Via the validationSet it finds the
classification performance and at the end it reassigns the allocated weight Matrix with the matrix that has the
best accuracy.
PARAMETERS
----------
trainSet : InstanceList
InstanceList that is used to train.
validationSet : InstanceList
InstanceList that is used to validate.
parameters : LinearPerceptronParameter
Linear perceptron parameters; learningRate, etaDecrease, crossValidationRatio, epoch.
"""
super().__init__(trainSet)
self.W = self.allocateLayerWeights(self.K, self.d + 1,
parameters.getSeed())
bestW = copy.deepcopy(self.W)
bestClassificationPerformance = ClassificationPerformance(0.0)
epoch = parameters.getEpoch()
learningRate = parameters.getLearningRate()
for i in range(epoch):
trainSet.shuffle(parameters.getSeed())
for j in range(trainSet.size()):
self.createInputVector(trainSet.get(j))
rMinusY = self.calculateRMinusY(trainSet.get(j), self.x,
self.W)
deltaW = Matrix(rMinusY, self.x)
deltaW.multiplyWithConstant(learningRate)
self.W.add(deltaW)
currentClassificationPerformance = self.testClassifier(
validationSet)
if currentClassificationPerformance.getAccuracy(
) > bestClassificationPerformance.getAccuracy():
bestClassificationPerformance = currentClassificationPerformance
bestW = copy.deepcopy(self.W)
learningRate *= parameters.getEtaDecrease()
self.W = bestW
def test_Compare(self):
kFoldRun = KFoldRun(10)
pairedt = Pairedt()
experimentPerformance1 = kFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
experimentPerformance2 = kFoldRun.execute(Experiment(LinearPerceptron(), LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100), self.iris))
self.assertAlmostEqual(0.379, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 3)
experimentPerformance1 = kFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
experimentPerformance2 = kFoldRun.execute(Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
self.assertAlmostEqual(0.00000692, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 7)
experimentPerformance1 = kFoldRun.execute(Experiment(Lda(), Parameter(1), self.dermatology))
experimentPerformance2 = kFoldRun.execute(Experiment(LinearPerceptron(), LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100), self.dermatology))
self.assertAlmostEqual(0.7842, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 4)
experimentPerformance1 = kFoldRun.execute(Experiment(Dummy(), Parameter(1), self.nursery))
experimentPerformance2 = kFoldRun.execute(Experiment(NaiveBayes(), Parameter(1), self.nursery))
self.assertAlmostEqual(0.0, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 4)
experimentPerformance1 = kFoldRun.execute(Experiment(NaiveBayes(), Parameter(1), self.car))
experimentPerformance2 = kFoldRun.execute(Experiment(Bagging(), BaggingParameter(1, 50), self.car))
self.assertAlmostEqual(0.00000336, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 7)
experimentPerformance1 = kFoldRun.execute(Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()), self.bupa))
experimentPerformance2 = kFoldRun.execute(Experiment(Lda(), Parameter(1), self.bupa))
self.assertAlmostEqual(0.1640, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 4)
def test_LinearPerceptron(self):
linearPerceptron = LinearPerceptron()
linearPerceptronParameter = LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100)
normalize = Normalize(self.iris)
normalize.convert()
linearPerceptron.train(self.iris.getInstanceList(), linearPerceptronParameter)
self.assertAlmostEqual(2.00, 100 * linearPerceptron.test(self.iris.getInstanceList()).getErrorRate(), 2)
normalize = Normalize(self.bupa)
normalize.convert()
linearPerceptron.train(self.bupa.getInstanceList(), linearPerceptronParameter)
self.assertAlmostEqual(26.67, 100 * linearPerceptron.test(self.bupa.getInstanceList()).getErrorRate(), 2)
normalize = Normalize(self.dermatology)
normalize.convert()
linearPerceptron.train(self.dermatology.getInstanceList(), linearPerceptronParameter)
self.assertAlmostEqual(1.91, 100 * linearPerceptron.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
def test_LinearPerceptron(self):
linearPerceptron = LinearPerceptron()
linearPerceptronParameter = LinearPerceptronParameter(
1, 0.1, 0.99, 0.2, 100)
discreteToContinuous = DiscreteToContinuous(self.car)
discreteToContinuous.convert()
linearPerceptron.train(self.car.getInstanceList(),
linearPerceptronParameter)
self.assertAlmostEqual(
5.73, 100 *
linearPerceptron.test(self.car.getInstanceList()).getErrorRate(),
2)
discreteToContinuous = DiscreteToContinuous(self.tictactoe)
discreteToContinuous.convert()
linearPerceptron.train(self.tictactoe.getInstanceList(),
linearPerceptronParameter)
self.assertAlmostEqual(
2.51, 100 * linearPerceptron.test(
self.tictactoe.getInstanceList()).getErrorRate(), 2)
def test_Train(self):
linearPerceptron = LinearPerceptron()
linearPerceptronParameter = LinearPerceptronParameter(
1, 0.1, 0.99, 0.2, 100)
linearPerceptron.train(self.iris.getInstanceList(),
linearPerceptronParameter)
self.assertAlmostEqual(
1.33, 100 *
linearPerceptron.test(self.iris.getInstanceList()).getErrorRate(),
2)
linearPerceptron.train(self.bupa.getInstanceList(),
linearPerceptronParameter)
self.assertAlmostEqual(
28.99, 100 *
linearPerceptron.test(self.bupa.getInstanceList()).getErrorRate(),
2)
linearPerceptron.train(self.dermatology.getInstanceList(),
linearPerceptronParameter)
self.assertAlmostEqual(
4.37, 100 * linearPerceptron.test(
self.dermatology.getInstanceList()).getErrorRate(), 2)
def test_Execute(self):
mxKFoldRun = MxKFoldRun(5, 2)
experimentPerformance = mxKFoldRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
self.assertAlmostEqual(
6.13, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = mxKFoldRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
self.assertAlmostEqual(
23.51,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = mxKFoldRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.bupa))
self.assertAlmostEqual(
37.05,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = mxKFoldRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.dermatology))
self.assertAlmostEqual(
15.41,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = mxKFoldRun.execute(
Experiment(Lda(), Parameter(1), self.bupa))
self.assertAlmostEqual(
34.72,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = mxKFoldRun.execute(
Experiment(Lda(), Parameter(1), self.dermatology))
self.assertAlmostEqual(
4.04, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = mxKFoldRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.iris))
self.assertAlmostEqual(
5.2, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = mxKFoldRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.dermatology))
self.assertAlmostEqual(
5.46, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = mxKFoldRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.car))
self.assertAlmostEqual(
16.52,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = mxKFoldRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.nursery))
self.assertAlmostEqual(
9.80, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = mxKFoldRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
self.assertAlmostEqual(
8.77, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = mxKFoldRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.car))
self.assertAlmostEqual(
9.77, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = mxKFoldRun.execute(
Experiment(Dummy(), Parameter(1), self.nursery))
self.assertAlmostEqual(
67.09,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = mxKFoldRun.execute(
Experiment(Dummy(), Parameter(1), self.iris))
self.assertAlmostEqual(
70.53,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
def test_Execute(self):
kFoldRun = KFoldRun(10)
experimentPerformance = kFoldRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
self.assertAlmostEqual(
6.00, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = kFoldRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
self.assertAlmostEqual(
18.78,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = kFoldRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.bupa))
self.assertAlmostEqual(
36.85,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = kFoldRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.dermatology))
self.assertAlmostEqual(
10.92,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = kFoldRun.execute(
Experiment(Lda(), Parameter(1), self.bupa))
self.assertAlmostEqual(
31.61,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = kFoldRun.execute(
Experiment(Lda(), Parameter(1), self.dermatology))
self.assertAlmostEqual(
3.30, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = kFoldRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.iris))
self.assertAlmostEqual(
5.33, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = kFoldRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.dermatology))
self.assertAlmostEqual(
3.81, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = kFoldRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.car))
self.assertAlmostEqual(
14.88,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = kFoldRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.nursery))
self.assertAlmostEqual(
9.71, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = kFoldRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
self.assertAlmostEqual(
3.55, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = kFoldRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.car))
self.assertAlmostEqual(
6.77, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = kFoldRun.execute(
Experiment(Dummy(), Parameter(1), self.nursery))
self.assertAlmostEqual(
67.12,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = kFoldRun.execute(
Experiment(Dummy(), Parameter(1), self.iris))
self.assertAlmostEqual(
79.33,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
def test_Execute(self):
stratifiedMxKRun = StratifiedMxKFoldRun(5, 2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
self.assertAlmostEqual(
8.00, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
self.assertAlmostEqual(
22.03,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.bupa))
self.assertAlmostEqual(
33.33,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.dermatology))
self.assertAlmostEqual(
13.66,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(Lda(), Parameter(1), self.bupa))
self.assertAlmostEqual(
33.05,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(Lda(), Parameter(1), self.dermatology))
self.assertAlmostEqual(
3.55, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.iris))
self.assertAlmostEqual(
6.00, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.dermatology))
self.assertAlmostEqual(
4.66, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.car))
self.assertAlmostEqual(
15.11,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.nursery))
self.assertAlmostEqual(
9.68, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
self.assertAlmostEqual(
9.29, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.car))
self.assertAlmostEqual(
9.20, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(Dummy(), Parameter(1), self.nursery))
self.assertAlmostEqual(
66.67,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = stratifiedMxKRun.execute(
Experiment(Dummy(), Parameter(1), self.iris))
self.assertAlmostEqual(
66.67,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
def test_Execute(self):
bootstrapRun = BootstrapRun(10)
experimentPerformance = bootstrapRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
self.assertAlmostEqual(
4.33, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = bootstrapRun.execute(
Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
self.assertAlmostEqual(
13.16,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = bootstrapRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.bupa))
self.assertAlmostEqual(
24.84,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = bootstrapRun.execute(
Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()),
self.dermatology))
self.assertAlmostEqual(
8.01, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = bootstrapRun.execute(
Experiment(Lda(), Parameter(1), self.bupa))
self.assertAlmostEqual(
32.03,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = bootstrapRun.execute(
Experiment(Lda(), Parameter(1), self.dermatology))
self.assertAlmostEqual(
2.95, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = bootstrapRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.iris))
self.assertAlmostEqual(
3.27, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = bootstrapRun.execute(
Experiment(LinearPerceptron(),
LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100),
self.dermatology))
self.assertAlmostEqual(
2.65, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = bootstrapRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.car))
self.assertAlmostEqual(
14.75,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = bootstrapRun.execute(
Experiment(NaiveBayes(), Parameter(1), self.nursery))
self.assertAlmostEqual(
9.71, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = bootstrapRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
self.assertAlmostEqual(
3.00, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = bootstrapRun.execute(
Experiment(Bagging(), BaggingParameter(1, 50), self.car))
self.assertAlmostEqual(
3.44, 100 * experimentPerformance.meanPerformance().getErrorRate(),
2)
experimentPerformance = bootstrapRun.execute(
Experiment(Dummy(), Parameter(1), self.nursery))
self.assertAlmostEqual(
66.79,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
experimentPerformance = bootstrapRun.execute(
Experiment(Dummy(), Parameter(1), self.iris))
self.assertAlmostEqual(
66.67,
100 * experimentPerformance.meanPerformance().getErrorRate(), 2)