german with cost matrix

lambda/src/LambdaDatasets/GermanDataset.hs

@@ -86,13 +86,13 @@ lE =
             ((Ref.SomeTypeRep (Ref.TypeRep @(Job))), [(fmap show (enumUniform UnemployedOrUnskilledNonResident HighlySkilled ))])
           ],
       targetType = (Ref.SomeTypeRep (Ref.TypeRep @(AccountStatus -> Int -> CreditHistory -> Purpose -> Int -> Savings -> EmploymentStatus -> Int -> StatusAndSex -> OtherDebtors -> Int -> Property -> Int -> OtherPlans -> Housing -> Int -> Job -> Int -> Bool -> Bool -> GermanClass))),
-      maxDepth = 5,
+      maxDepth = 9,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 1,
+          { lambdaSpucker = 10,
-            lambdaSchlucker = 2,
+            lambdaSchlucker = 1,
-            symbol = 30,
+            symbol = 20,
-            variable = 10,
+            variable = 100,
             constant = 5
           }
     }
@@ -151,6 +151,7 @@ data LamdaExecutionEnv = LamdaExecutionEnv
 data FittnesRes = FittnesRes
   { total :: R,
     fitnessTotal :: R,
+    costAccordingToDataset :: N,
     fitnessGeoMean :: R,
     fitnessMean :: 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 ex tr result = ( tr,
       FittnesRes
-        { total = score,
+        { total = (biasSmall - 1) - (fromIntegral costAccordingToDS),
           fitnessTotal = fitness',
+          costAccordingToDataset = costAccordingToDS,
           fitnessMean = meanOfAccuricyPerClass resAndTarget,
           fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,
           accuracy = acc,
@@ -201,7 +203,8 @@ evalResult ex tr result = ( tr,
     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))
     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)
     fitness' = meanOfAccuricyPerClass resAndTarget
     score = fitness' + (biasSmall - 1)

lambda/src/LambdaDatasets/IrisDataset.hs

@@ -53,13 +53,13 @@ lE =
             ((Ref.SomeTypeRep (Ref.TypeRep @(IrisClass))), [(fmap show (enumUniform Setosa Versicolor :: RVar IrisClass))])
           ],
       targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Float -> Float -> Float -> Float -> IrisClass))),
-      maxDepth = 5,
+      maxDepth = 9,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 1,
+          { lambdaSpucker = 10,
-            lambdaSchlucker = 2,
+            lambdaSchlucker = 1,
-            symbol = 30,
+            symbol = 20,
-            variable = 10,
+            variable = 100,
             constant = 5
           }
     }
@@ -68,7 +68,7 @@ lEE :: LamdaExecutionEnv
 lEE =
   LamdaExecutionEnv
     { -- For now these need to define all available functions and types. Generic functions can be used.
-      imports = ["LambdaDatasets.IrisDataset"],
+      imports = ["LambdaDatasets.IrisDefinition"],
       training = True,
       trainingData =
         ( map fst (takeFraktion 0.8 irisTrainingData),
@@ -89,7 +89,7 @@ shuffledLEE = do
   itD <- smpl $ shuffle irisTrainingData
   return  LamdaExecutionEnv
     { -- For now these need to define all available functions and types. Generic functions can be used.
-      imports = ["LambdaDatasets.IrisDataset"],
+      imports = ["LambdaDatasets.IrisDefinition"],
       training = True,
       trainingData =
         ( map fst (takeFraktion 0.8 itD),
@@ -155,7 +155,7 @@ evalResults ex trs = do
 evalResult :: LamdaExecutionEnv -> TypeRequester -> (Float -> Float -> Float -> Float -> IrisClass) -> (TypeRequester, FittnesRes)
 evalResult ex tr result = ( tr,
       FittnesRes
-        { total = score,
+        { total = acc * 100 + (biasSmall - 1),
           fitnessTotal = fitness',
           fitnessMean = meanOfAccuricyPerClass resAndTarget,
           fitnessGeoMean = geomeanOfDistributionAccuracy resAndTarget,

lambda/src/LambdaDatasets/NurseryDataset.hs

@@ -74,13 +74,13 @@ lE =
             ((Ref.SomeTypeRep (Ref.TypeRep @(Health))), [(fmap show (enumUniform NotRecommendHealth PriorityHealth ))])
           ],
       targetType = (Ref.SomeTypeRep (Ref.TypeRep @(Parents -> HasNurs -> Form -> Children -> Housing -> Finance -> Social -> Health -> NurseryClass))),
-      maxDepth = 5,
+      maxDepth = 9,
       weights =
         ExpressionWeights
-          { lambdaSpucker = 1,
+          { lambdaSpucker = 10,
-            lambdaSchlucker = 2,
+            lambdaSchlucker = 1,
-            symbol = 30,
+            symbol = 20,
-            variable = 10,
+            variable = 100,
             constant = 5
           }
     }

lambda/src/Main.hs

@@ -35,7 +35,7 @@ options =
       ( long "population-size"
           <> short 'p'
           <> metavar "N"
-          <> value 400
+          <> value 100
           <> help "Population size"
       )
 
@@ -59,7 +59,7 @@ main =
       selectionType = Tournament 3,
       termination = (steps (iterations opts)),
       poulationSize = (populationSize opts),
-      stepSize = 120,
+      stepSize = 90,
       elitismRatio = 5/100
     }
     pop' <- runEffect (for (run cfg) logCsv)