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 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 BuildModel(self, data, labels, options): nbc = GaussianNaiveBayes(data, labels) nbc.train() return nbc