def runTest(self):
data = cleanData(self.filename)
knearest = getDiscreetClassifier('knearest')
resultTime = CV.doShuffleCrossValidation(knearest, data.data,
data.target)
print('knearest result: ' + str(resultTime.meanScore) +
' Time taken: ' + str(resultTime.timeTaken))
self.assertTrue(resultTime.timeTaken > 0)
def runTest(self): svm = 'svm' svmC = getDiscreetClassifier(svm) saveAble = TrainClassifier.trainWithLabels(svmC,self.data.data, self.data.target) meanScore = CV.doShuffleCrossValidation(svmC, self.data.data, self.data.target).meanScore s = saveClassifierAsString(saveAble) clf = loadClassifierFromString(s) newScore = clf.score(self.data.data,self.data.target) result = abs(meanScore - newScore) # Ensure the score after reloading the classifier is close to the cross validation score self.assertTrue(result < .05)
def runTest(self):
svm = 'svm'
svmC = getDiscreetClassifier(svm)
meanScore = CV.doShuffleCrossValidation(svmC, self.data.data,
self.data.target).meanScore
trained = TrainClassifier.trainWithLabels(getDiscreetClassifier(svm),
self.data.data,
self.data.target)
predictions = predict(trained, self.data.data)
# Ensure the score after from predicting is close to the cross validation score
correct = 0
for x in range(len(predictions)):
if (predictions[x][0] == self.data.target[x]):
correct += 1
newScore = float(correct) / len(self.data.target)
result = abs(meanScore - newScore)
self.assertTrue(result < .05)
def runTest(self):
leastSquares = 'leastSquares'
kC = getDiscreetClassifier(leastSquares)
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('LeastSquares result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
dt = 'decisionTree'
kC = getDiscreetClassifier(dt)
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('Decision Tree result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
pa = 'passiveAggressive'
kC = getDiscreetClassifier(pa)
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('Passive Aggressive result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
nCentroid = 'nearestCentroid'
kC = getDiscreetClassifier(nCentroid)
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('Nearest Centroid result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
preceptron = 'perceptron'
kC = getDiscreetClassifier(preceptron)
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('Perceptron result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
rForest = 'randomForest'
kC = getDiscreetClassifier(rForest)
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('Random Forest result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
sgd = 'sgd'
kC = getDiscreetClassifier(sgd)
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('SGD result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
gauss = 'guassNB'
kC = getDiscreetClassifier(gauss)
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('Guassian Naive Bayes result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
kC = getDiscreetClassifier('logisticRegression')
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('logisticRegression result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
kC = getDiscreetClassifier('bayesianRidge')
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('BayesianRidge result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')
def runTest(self):
kC = getDiscreetClassifier('orthogonalMatchingPursuit')
meanScoreTimeTaken = CV.doShuffleCrossValidation(kC, self.data.data, self.data.target)
print('OrthogonalMatchingPursuit result: ' + str(meanScoreTimeTaken.meanScore) + ' Time taken:' + str(meanScoreTimeTaken.timeTaken) + ' seconds')