def RunNBCShogun(q):
totalTimer = Timer()
Log.Info("Loading dataset", self.verbose)
try:
# Load train and test dataset.
trainData = np.genfromtxt(self.dataset[0], delimiter=',')
testData = np.genfromtxt(self.dataset[1], delimiter=',')
# Labels are the last row of the training set.
labels = MulticlassLabels(trainData[:, (trainData.shape[1] - 1)])
with totalTimer:
# Transform into features.
trainFeat = RealFeatures(trainData[:,:-1].T)
testFeat = RealFeatures(testData.T)
# Create and train the classifier.
nbc = GaussianNaiveBayes(trainFeat, labels)
nbc.train()
# Run Naive Bayes Classifier on the test dataset.
nbc.apply(testFeat).get_labels()
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def classifier_gaussiannaivebayes_modular (train_fname=traindat,test_fname=testdat,label_train_fname=label_traindat): from modshogun import RealFeatures, MulticlassLabels, GaussianNaiveBayes, CSVFile feats_train=RealFeatures(CSVFile(train_fname)) feats_test=RealFeatures(CSVFile(test_fname)) labels=MulticlassLabels(CSVFile(label_train_fname)) gnb=GaussianNaiveBayes(feats_train, labels) gnb_train = gnb.train() output=gnb.apply(feats_test).get_labels() return gnb, gnb_train, output
def RunNBCShogun(q):
totalTimer = Timer()
Log.Info("Loading dataset", self.verbose)
try:
# Load train and test dataset.
trainData = np.genfromtxt(self.dataset[0], delimiter=',')
testData = np.genfromtxt(self.dataset[1], delimiter=',')
# Labels are the last row of the training set.
labels = MulticlassLabels(trainData[:,
(trainData.shape[1] - 1)])
with totalTimer:
# Transform into features.
trainFeat = RealFeatures(trainData[:, :-1].T)
testFeat = RealFeatures(testData.T)
# Create and train the classifier.
nbc = GaussianNaiveBayes(trainFeat, labels)
nbc.train()
# Run Naive Bayes Classifier on the test dataset.
nbc.apply(testFeat).get_labels()
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def BuildModel(self, data, labels, options): nbc = GaussianNaiveBayes(data, labels) nbc.train() return nbc
def BuildModel(self, data, labels, options):
nbc = GaussianNaiveBayes(data, labels)
nbc.train()
return nbc
