def runTrainError(dataFile, classFile, vocab, samples):
print "Samples:" , samples
print "Vocab size:", vocab
data = ExampleData(int(samples), int(vocab))
data.readDataVectorFile(dataFile)
data.readClassificationFile(classFile)
learner = Learner(data)
error = dict()
for cl in learner.clf.keys():
learner.learn(cl)
error[cl] = learner.test(cl, data.Xdata, data.Ydata)
print "NB train acc:%f\tLinearSVC train acc:%f\tSVM train acc:%f\tSGD train acc:%f" %(error["NB"], error["LinearSVC"], error["SVM"], error["SGD"])
def runML(dataFile, classFile, vocab, samples):
print "Samples:" , samples
print "Vocab size:", vocab
data = ExampleData(int(samples), int(vocab))
data.readDataVectorFile(dataFile)
data.readClassificationFile(classFile)
avgNB = 0
avgLin = 0
avgSVC = 0
avgSGD = 0
numTrials = 5
learner = Learner(None)
for n in range(numTrials):
score = learner.test_kf("NB", data.Xdata, data.Ydata)
meanNB = score.mean()
avgNB += meanNB
print "NB:", meanNB
score = learner.test_kf("LinearSVC", data.Xdata, data.Ydata)
meanLin = score.mean()
avgLin += meanLin
print "LinearSVC:", meanLin
score = learner.test_kf("SGD", data.Xdata, data.Ydata)
meanSGD = score.mean()
avgSGD += meanSGD
print "SGD:", meanSGD
score = learner.test_kf("SVM", data.Xdata, data.Ydata)
meanSVC = score.mean()
avgSVC += meanSVC
print "SVM:", meanSVC
avgNB /= numTrials
avgLin /=numTrials
avgSGD /=numTrials
avgSVC /=numTrials
print "NB Avg:%f\tLinearSVC Avg:%f\tSVM Avg:%f\tSGD Avg:%f" %(avgNB, avgLin, avgSVC, avgSGD)