def runExperiment(self, classifier: Classifier, parameter: Parameter,
experimentPerformance: ExperimentPerformance,
crossValidation: CrossValidation, testSet: InstanceList):
for i in range(self.K):
trainSet = InstanceList(crossValidation.getTrainFold(i))
classifier.train(trainSet, parameter)
experimentPerformance.add(classifier.test(testSet))
def compare(self, classifier1: ExperimentPerformance,
classifier2: ExperimentPerformance) -> StatisticalTestResult:
statistic = self.__testStatistic(classifier1, classifier2)
degreeOfFreedom1 = classifier1.numberOfExperiments()
degreeOfFreedom2 = classifier2.numberOfExperiments() // 2
return StatisticalTestResult(
Distribution.fDistribution(statistic, degreeOfFreedom1,
degreeOfFreedom2), True)
def execute(self, experiment: Experiment) -> ExperimentPerformance:
"""
Execute K-fold cross-validation with separate test set with the given classifier on the given data set using the
given parameters.
PARAMETERS
----------
experiment : Experiment
Experiment to be run.
RETURNS
-------
ExperimentPerformance
An ExperimentPerformance instance.
"""
result = ExperimentPerformance()
instanceList = experiment.getDataSet().getInstanceList()
partition = Partition(instanceList, 0.25,
experiment.getParameter().getSeed(), True)
crossValidation = KFoldCrossValidation(
partition.get(1).getInstances(), self.K,
experiment.getParameter().getSeed())
self.runExperiment(experiment.getClassifier(),
experiment.getParameter(), result, crossValidation,
partition.get(0))
return result
def __testStatistic(self, classifier1: ExperimentPerformance,
classifier2: ExperimentPerformance):
if classifier1.numberOfExperiments(
) != classifier2.numberOfExperiments():
raise StatisticalTestNotApplicable(
"In order to apply a paired test, you need to have the same number of "
"experiments in both algorithms.")
if classifier1.numberOfExperiments() != 10:
raise StatisticalTestNotApplicable(
"In order to apply a 5x2 test, you need to have 10 experiments."
)
numerator = 0
difference = []
for i in range(classifier1.numberOfExperiments()):
difference.append(
classifier1.getErrorRate(i) - classifier2.getErrorRate(i))
numerator += difference[i] * difference[i]
denominator = 0
for i in range(classifier1.numberOfExperiments() // 2):
mean = (difference[2 * i] + difference[2 * i + 1]) / 2
variance = (difference[2 * i] - mean) * (difference[2 * i] - mean) + (difference[2 * i + 1] - mean) \
* (difference[2 * i + 1] - mean)
denominator += variance
denominator *= 2
if denominator == 0:
raise StatisticalTestNotApplicable("Variance is 0.")
return numerator / denominator
def __testStatistic(self, classifier1: ExperimentPerformance, classifier2: ExperimentPerformance):
if classifier1.numberOfExperiments() != classifier2.numberOfExperiments():
raise StatisticalTestNotApplicable("In order to apply a paired test, you need to have the same number of "
"experiments in both algorithms.")
difference = []
total = 0
for i in range(classifier1.numberOfExperiments()):
difference.append(classifier1.getErrorRate(i) - classifier2.getErrorRate(i))
total += difference[i]
mean = total / classifier1.numberOfExperiments()
total = 0
for i in range(classifier1.numberOfExperiments()):
total += (difference[i] - mean) * (difference[i] - mean)
standardDeviation = math.sqrt(total / (classifier1.numberOfExperiments() - 1))
if standardDeviation == 0:
raise StatisticalTestNotApplicable("Variance is 0.")
return math.sqrt(classifier1.numberOfExperiments()) * mean / standardDeviation
def execute(self, experiment: Experiment) -> ExperimentPerformance:
result = ExperimentPerformance()
for j in range(self.M):
instanceList = experiment.getDataSet().getInstanceList()
partition = Partition(instanceList, 0.25,
experiment.getParameter().getSeed(), True)
crossValidation = StratifiedKFoldCrossValidation(
Partition(partition.get(1)).getLists(), self.K,
experiment.getParameter().getSeed())
self.runExperiment(experiment.getClassifier(),
experiment.getParameter(), result,
crossValidation, partition.get(0))
return result
def execute(self, experiment: Experiment) -> ExperimentPerformance:
"""
Execute the bootstrap run with the given classifier on the given data set using the given parameters.
PARAMETERS
----------
experiment : Experiment
Experiment to be run.
RETURNS
-------
ExperimentPerformance
An ExperimentPerformance instance.
"""
result = ExperimentPerformance()
for i in range(self.__numberOfBootstraps):
bootstrap = Bootstrap(experiment.getDataSet().getInstances(),
i + experiment.getParameter().getSeed())
bootstrapSample = InstanceList(bootstrap.getSample())
experiment.getClassifier().train(bootstrapSample,
experiment.getParameter())
result.add(experiment.getClassifier().test(
experiment.getDataSet().getInstanceList()))
return result
def execute(self, experiment: Experiment) -> ExperimentPerformance:
"""
Execute Stratified K-fold cross-validation with the given classifier on the given data set using the given
parameters.
PARAMETERS
----------
experiment : Experiment
Experiment to be run.
RETURNS
-------
ExperimentPerformance
An ExperimentPerformance instance.
"""
result = ExperimentPerformance()
crossValidation = StratifiedKFoldCrossValidation(experiment.getDataSet().getClassInstances(), self.K,
experiment.getParameter().getSeed())
self.runExperiment(experiment.getClassifier(), experiment.getParameter(), result, crossValidation)
return result
def compare(self, classifier1: ExperimentPerformance, classifier2: ExperimentPerformance) -> StatisticalTestResult:
if classifier1.numberOfExperiments() != classifier2.numberOfExperiments():
raise StatisticalTestNotApplicable("In order to apply a paired test, you need to have the same number of "
"experiments in both algorithms.")
plus = 0
minus = 0
for i in range(classifier1.numberOfExperiments()):
if classifier1.getErrorRate(i) < classifier2.getErrorRate(i):
plus = plus + 1
else:
if classifier1.getErrorRate(i) > classifier2.getErrorRate(i):
minus = minus + 1
total = plus + minus
pValue = 0
if total == 0:
raise StatisticalTestNotApplicable("Variance is 0.")
for i in range(plus + 1):
pValue += self.__binomial(total, i) / math.pow(2, total)
return StatisticalTestResult(pValue, False)
def compare(self, classifier1: ExperimentPerformance, classifier2: ExperimentPerformance) -> StatisticalTestResult:
statistic = self.__testStatistic(classifier1, classifier2)
degreeOfFreedom = classifier1.numberOfExperiments() - 1
return StatisticalTestResult(Distribution.tDistribution(statistic, degreeOfFreedom), False)
