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german with cost matrix

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Johannes Merl 2024-05-12 07:46:51 +02:00
parent 98b15c8cfd
commit c07326bbfd
1 changed files with 5 additions and 2 deletions
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7
lambda/src/LambdaDatasets/GermanDataset.hs
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@ -151,6 +151,7 @@ data LamdaExecutionEnv = LamdaExecutionEnv
data FittnesRes = FittnesRes data FittnesRes = FittnesRes
{ total :: R, { total :: R,
fitnessTotal :: R, fitnessTotal :: R,
costAccordingToDataset :: N,
fitnessGeoMean :: R, fitnessGeoMean :: R,
fitnessMean :: R, fitnessMean :: R,
accuracy :: R, accuracy :: R,
@ -189,8 +190,9 @@ evalResults ex trs = do
evalResult :: LamdaExecutionEnv -> TypeRequester -> (AccountStatus -> Int -> CreditHistory -> Purpose -> Int -> Savings -> EmploymentStatus -> Int -> StatusAndSex -> OtherDebtors -> Int -> Property -> Int -> OtherPlans -> Housing -> Int -> Job -> Int -> Bool -> Bool -> GermanClass) -> (TypeRequester, FittnesRes) evalResult :: LamdaExecutionEnv -> TypeRequester -> (AccountStatus -> Int -> CreditHistory -> Purpose -> Int -> Savings -> EmploymentStatus -> Int -> StatusAndSex -> OtherDebtors -> Int -> Property -> Int -> OtherPlans -> Housing -> Int -> Job -> Int -> Bool -> Bool -> GermanClass) -> (TypeRequester, FittnesRes)
evalResult ex tr result = ( tr, evalResult ex tr result = ( tr,
FittnesRes FittnesRes
{ total = acc * 100 + (biasSmall - 1), { total = (biasSmall - 1) - (fromIntegral costAccordingToDS),
fitnessTotal = fitness', fitnessTotal = fitness',
costAccordingToDataset = costAccordingToDS,
fitnessMean = meanOfAccuricyPerClass resAndTarget, fitnessMean = meanOfAccuricyPerClass resAndTarget,
fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget, fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,
accuracy = acc, accuracy = acc,
@ -201,7 +203,8 @@ evalResult ex tr result = ( tr,
where where
res = map (\(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) -> result a b c d e f g h i j k l m n o p q r s t) (fst (dset ex)) res = map (\(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t) -> result a b c d e f g h i j k l m n o p q r s t) (fst (dset ex))
resAndTarget = (zip (snd (dset ex)) res) resAndTarget = (zip (snd (dset ex)) res)
acc = (foldr (\ts s -> if ((fst ts) == (snd ts)) then s + 1 else s) 0 resAndTarget) / fromIntegral (length resAndTarget) acc = (foldr (\(actual,predicted) s -> if (actual == predicted) then s + 1 else s) 0 resAndTarget) / fromIntegral (length resAndTarget)
costAccordingToDS = (foldr (\(actual,predicted) s -> if ((actual) == (predicted)) then s else (if actual == Deny then s+5 else s+1)) 0 resAndTarget)
biasSmall = exp ((-(fromIntegral (countTrsR tr))) / 1000) -- 0 (schlecht) bis 1 (gut) biasSmall = exp ((-(fromIntegral (countTrsR tr))) / 1000) -- 0 (schlecht) bis 1 (gut)
fitness' = meanOfAccuricyPerClass resAndTarget fitness' = meanOfAccuricyPerClass resAndTarget
score = fitness' + (biasSmall - 1) score = fitness' + (biasSmall - 1)