def RunQDAShogun(q):
totalTimer = Timer()
Log.Info("Loading dataset", self.verbose)
try:
# Load train and test dataset.
trainData = np.genfromtxt(self.dataset[0], delimiter=',')
trainFeat = modshogun.RealFeatures(trainData[:, :-1].T)
if len(self.dataset) == 2:
testSet = np.genfromtxt(self.dataset[1], delimiter=',')
testFeat = modshogun.RealFeatures(testData.T)
# Labels are the last row of the training set.
labels = modshogun.MulticlassLabels(
trainData[:, (trainData.shape[1] - 1)])
with totalTimer:
model = modshogun.QDA(trainFeat, labels)
model.train()
if len(self.dataset) == 2:
model.apply(testFeat).get_labels()
except Exception as e:
q.put(-1)
return -1
time = totalTimer.ElapsedTime()
q.put(time)
return time
def RunQDAShogun():
totalTimer = Timer()
Log.Info("Loading dataset", self.verbose)
try:
# Load train and test dataset.
trainData = np.genfromtxt(self.dataset[0], delimiter=',')
trainFeat = modshogun.RealFeatures(trainData[:, :-1].T)
if len(self.dataset) == 2:
testSet = np.genfromtxt(self.dataset[1], delimiter=',')
testFeat = modshogun.RealFeatures(testData.T)
if len(options) > 0:
Log.Fatal("Unknown parameters: " + str(options))
raise Exception("unknown parameters")
# Labels are the last row of the training set.
labels = modshogun.MulticlassLabels(
trainData[:, (trainData.shape[1] - 1)])
with totalTimer:
model = modshogun.QDA(trainFeat, labels)
model.train()
if len(self.dataset) == 2:
model.apply_multiclass(testFeat).get_labels()
except Exception as e:
return -1
return totalTimer.ElapsedTime()
def RunMetrics(self, options):
Log.Info("Perform QDA.", self.verbose)
results = self.QDAShogun(options)
if results < 0:
return results
metrics = {'Runtime': results}
if len(self.dataset) >= 3:
trainData, labels = SplitTrainData(self.dataset)
testData = LoadDataset(self.dataset[1])
truelabels = LoadDataset(self.dataset[2])
model = modshogun.QDA(modshogun.RealFeatures(trainData.T),
modshogun.MulticlassLabels(labels))
model.train()
predictions = model.apply(modshogun.RealFeatures(
testData.T)).get_labels()
confusionMatrix = Metrics.ConfusionMatrix(truelabels, predictions)
metrics['ACC'] = Metrics.AverageAccuracy(confusionMatrix)
metrics['MCC'] = Metrics.MCCMultiClass(confusionMatrix)
metrics['Precision'] = Metrics.AvgPrecision(confusionMatrix)
metrics['Recall'] = Metrics.AvgRecall(confusionMatrix)
metrics['MSE'] = Metrics.SimpleMeanSquaredError(
truelabels, predictions)
return metrics
def RunMetrics(self, options):
if len(self.dataset) >= 3:
trainData, labels = SplitTrainData(self.dataset)
testData = LoadDataset(self.dataset[1])
truelabels = LoadDataset(self.dataset[2])
model = modshogun.QDA(modshogun.RealFeatures(trainData.T),modshogun.MulticlassLabels(labels))
model.train()
predictions = model.apply(modshogun.RealFeatures(testData.T)).get_labels()
# Datastructure to store the results.
metrics = {}
confusionMatrix = Metrics.ConfusionMatrix(truelabels, predictions)
metrics['ACC'] = Metrics.AverageAccuracy(confusionMatrix)
metrics['MCC'] = Metrics.MCCMultiClass(confusionMatrix)
metrics['Precision'] = Metrics.AvgPrecision(confusionMatrix)
metrics['Recall'] = Metrics.AvgRecall(confusionMatrix)
metrics['MSE'] = Metrics.SimpleMeanSquaredError(truelabels, predictions)
return metrics
else:
Log.Fatal("This method requires three datasets!")