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Adding an option for target normalization in SVR #1853

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Adding an option for target normalization in SVR #1853

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33a4e53
adding target normalization for ROM that require normalization
Jimmy-INL Apr 23, 2022
477f0b9
removing unnecessary commented lines
Jimmy-INL Apr 23, 2022
a128fb4
Merge remote-tracking branch 'upstream/devel' into Jimmy-Fixing-RomTa…
Jimmy-INL May 16, 2022
7829ccc
trying to fix tests
Jimmy-INL Jun 1, 2022
31dc4ca
to fix the classifier issue merged before
Jimmy-INL Jun 1, 2022
da65882
fixing defaults
Jimmy-INL Jun 14, 2022
bac131b
removing commented lines and adding a test
Jimmy-INL Jun 15, 2022
c722786
adding gold files
Jimmy-INL Jun 15, 2022
31ba1b0
resolving a conflict in Merge conflict in ravenframework/SupervisedLe…
Jimmy-INL Jun 15, 2022
0b44ced
regolding
Jimmy-INL Jun 16, 2022
07d300b
regolding two files
Jimmy-INL Jun 17, 2022
075becb
adding SVRProjectile test
Jimmy-INL Jun 17, 2022
29f02b7
updating tests
Jimmy-INL Jun 17, 2022
8ed094d
updating tests and image diff
Jimmy-INL Jun 17, 2022
58a49f9
moving the target normalization option to parent classes
Jimmy-INL Nov 24, 2022
8177c19
removing unnecessary commented lines
Jimmy-INL Nov 24, 2022
b7d1ce5
wake it work for multitarget static ROMs
Jimmy-INL Nov 26, 2022
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adding target normalization for ROM that require normalization
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@Jimmy-INL
Jimmy-INL committed Apr 23, 2022
commit 33a4e530d77e730cb99603a5fb7f49dc045c213e
2 changes: 1 addition & 1 deletion framework/SupervisedLearning/ScikitLearn/SVM/LinearSVR.py
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Expand Up @@ -15,7 +15,7 @@
Created on Jan 21, 2020

@author: alfoa, wangc
Linear Support Vector Classifier
Linear Support Vector Regressor

"""
#Internal Modules (Lazy Importer)--------------------------------------------------------------------
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11 changes: 9 additions & 2 deletions framework/SupervisedLearning/SupervisedLearning.py
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Expand Up @@ -264,9 +264,13 @@ def train(self, tdict, indexMap=None):
# valueToUse can be either a matrix (for who can handle time-dep data) or a vector (for who can not)
if self.dynamicFeatures:
featureValues[:, :, cnt] = (valueToUse[:, :]- self.muAndSigmaFeatures[feat][0])/self.muAndSigmaFeatures[feat][1]
# targetValues[:,cnt] = (targetValues[:]- self.muAndSigmaFeatures[self.target[0]][0])/self.muAndSigmaFeatures[self.target[0]][1]
else:
featureValues[:,cnt] = ( (valueToUse[:,0] if len(valueToUse.shape) > 1 else valueToUse[:]) - self.muAndSigmaFeatures[feat][0])/self.muAndSigmaFeatures[feat][1]

# targetValues[cnt] = ( (targetValues[0] if len(valueToUse.shape) > 1 else targetValues[:]) - self.muAndSigmaFeatures[self.target[0]][0])/self.muAndSigmaFeatures[self.target[0]][1]
# self.targetMean = np.mean(targetValues)
# self.targetStd = np.std(targetValues)
targetValues = (targetValues - self.muAndSigmaFeatures[self.target[0]][0])/self.muAndSigmaFeatures[self.target[0]][1]
self.__trainLocal__(featureValues,targetValues)
self.amITrained = True

Expand All @@ -280,6 +284,7 @@ def _localNormalizeData(self,values,names,feat):
@ Out, None
"""
self.muAndSigmaFeatures[feat] = mathUtils.normalizationFactors(values[names.index(feat)])
self.muAndSigmaFeatures[self.target[0]] = mathUtils.normalizationFactors(values[names.index(self.target[0])])

def confidence(self, edict):
"""
Expand Down Expand Up @@ -356,7 +361,9 @@ def evaluate(self,edict):
featureValues[:, :, cnt] = ((values[names.index(feat)] - self.muAndSigmaFeatures[feat][0]))/self.muAndSigmaFeatures[feat][1]
else:
featureValues[:,cnt] = ((values[names.index(feat)] - self.muAndSigmaFeatures[feat][0]))/self.muAndSigmaFeatures[feat][1]
return self.__evaluateLocal__(featureValues)
target = self.__evaluateLocal__(featureValues)
target.update((x, y * self.muAndSigmaFeatures[self.target[0]][1] + self.muAndSigmaFeatures[self.target[0]][0]) for x, y in target.items())
return target

def reset(self):
"""
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