def localInputAndChecks(self,xmlNode):
"""
Function to read the portion of the xml input that belongs to this specialized class
and initialize some stuff based on the inputs got
@ In, xmlNode, xml.etree.ElementTree.Element, Xml element node
@ Out, None
"""
HybridModelBase.localInputAndChecks(self, xmlNode)
paramInput = HybridModel.getInputSpecification()()
paramInput.parseNode(xmlNode)
for child in paramInput.subparts:
if child.getName() == 'TargetEvaluation':
self.targetEvaluationInstance = child.value.strip()
if child.getName() == 'ROM':
romName = child.value.strip()
self.romsDictionary[romName] = {'Instance': None, 'Converged': False, 'Valid': False}
if child.getName() == 'settings':
for childChild in child.subparts:
if childChild.getName() == 'maxTrainSize':
self.romTrainMaxSize = utils.intConversion(childChild.value)
if childChild.getName() == 'minInitialTrainSize':
self.romTrainStartSize = utils.intConversion(childChild.value)
if childChild.getName() == 'tolerance':
self.romConvergence = utils.floatConversion(childChild.value)
if child.getName() == 'validationMethod':
name = child.parameterValues['name']
self.validationMethod[name] = {}
for childChild in child.subparts:
if childChild.getName() == 'threshold':
self.validationMethod[name]['threshold'] = utils.floatConversion(childChild.value)
if name != 'CrowdingDistance':
self.raiseAnError(IOError, "Validation method ", name, " is not implemented yet!")
def checkCV(self, trainingSize):
"""
The function will check whether we can use Cross Validation or not
@ In, trainingSize, int, the size of current training size
@ Out, None
"""
useCV = True
initDict = self.cvInstance.interface.initializationOptionDict
if 'SciKitLearn' in initDict.keys() and 'n_splits' in initDict['SciKitLearn'].keys():
if trainingSize < utils.intConversion(initDict['SciKitLearn']['n_splits']):
useCV = False
return useCV