split of dataset
This commit is contained in:
Johannes Merl
parent
f79355e4c1
commit
c261fcdfbb
@ -50,6 +50,7 @@ library
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, Szenario191
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, LambdaCalculus
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, IrisDataset
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, IrisData
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executable haga
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build-depends: base
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@ -83,6 +84,7 @@ executable haga
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, Szenario191
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, LambdaCalculus
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, IrisDataset
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, IrisData
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executable haga-test
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build-depends: base
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@ -117,3 +119,4 @@ executable haga-test
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, Szenario191
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, LambdaCalculus
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, IrisDataset
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, IrisData
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@ -7,6 +7,7 @@
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module IrisDataset
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( module LambdaCalculus,
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module IrisDataset,
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module IrisData,
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module GA,
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)
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where
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@ -22,17 +23,12 @@ import Data.Tuple.Extra
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import qualified Debug.Trace as DB
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import GA
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import LambdaCalculus
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import IrisData
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import qualified Language.Haskell.Interpreter as Hint
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import qualified Language.Haskell.Interpreter.Unsafe as Hint
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import Protolude
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import qualified Type.Reflection as Ref
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data IrisClass = Setosa | Virginica | Versicolor deriving (Eq, Generic, Show, Enum, Bounded)
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instance FromRecord IrisClass
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instance ToRecord IrisClass
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irisLE :: LambdaEnviroment
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irisLE =
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LambdaEnviroment
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@ -73,308 +69,8 @@ irisLEE =
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-- Path to a CSV file containing the dataset results
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trainingDatasetRes = "./res.csv",
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trainingData =
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( [ (5.1, 3.5, 1.4, 0.2),
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(4.9, 3.0, 1.4, 0.2),
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(4.7, 3.2, 1.3, 0.2),
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(4.6, 3.1, 1.5, 0.2),
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(5.0, 3.6, 1.4, 0.2),
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(5.4, 3.9, 1.7, 0.4),
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(4.6, 3.4, 1.4, 0.3),
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(5.0, 3.4, 1.5, 0.2),
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(4.4, 2.9, 1.4, 0.2),
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(4.9, 3.1, 1.5, 0.1),
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(5.4, 3.7, 1.5, 0.2),
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(4.8, 3.4, 1.6, 0.2),
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(4.8, 3.0, 1.4, 0.1),
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(4.3, 3.0, 1.1, 0.1),
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(5.8, 4.0, 1.2, 0.2),
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(5.7, 4.4, 1.5, 0.4),
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(5.4, 3.9, 1.3, 0.4),
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(5.1, 3.5, 1.4, 0.3),
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(5.7, 3.8, 1.7, 0.3),
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(5.1, 3.8, 1.5, 0.3),
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(5.4, 3.4, 1.7, 0.2),
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(5.1, 3.7, 1.5, 0.4),
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(4.6, 3.6, 1.0, 0.2),
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(5.1, 3.3, 1.7, 0.5),
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(4.8, 3.4, 1.9, 0.2),
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(5.0, 3.0, 1.6, 0.2),
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(5.0, 3.4, 1.6, 0.4),
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(5.2, 3.5, 1.5, 0.2),
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(5.2, 3.4, 1.4, 0.2),
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(4.7, 3.2, 1.6, 0.2),
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(4.8, 3.1, 1.6, 0.2),
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(5.4, 3.4, 1.5, 0.4),
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(5.2, 4.1, 1.5, 0.1),
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(5.5, 4.2, 1.4, 0.2),
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(4.9, 3.1, 1.5, 0.1),
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(5.0, 3.2, 1.2, 0.2),
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(5.5, 3.5, 1.3, 0.2),
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(4.9, 3.1, 1.5, 0.1),
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(4.4, 3.0, 1.3, 0.2),
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(5.1, 3.4, 1.5, 0.2),
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(5.0, 3.5, 1.3, 0.3),
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(4.5, 2.3, 1.3, 0.3),
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(4.4, 3.2, 1.3, 0.2),
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(5.0, 3.5, 1.6, 0.6),
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(5.1, 3.8, 1.9, 0.4),
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(4.8, 3.0, 1.4, 0.3),
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(5.1, 3.8, 1.6, 0.2),
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(4.6, 3.2, 1.4, 0.2),
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(5.3, 3.7, 1.5, 0.2),
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(5.0, 3.3, 1.4, 0.2),
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(7.0, 3.2, 4.7, 1.4),
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(6.4, 3.2, 4.5, 1.5),
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(6.9, 3.1, 4.9, 1.5),
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(5.5, 2.3, 4.0, 1.3),
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(6.5, 2.8, 4.6, 1.5),
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(5.7, 2.8, 4.5, 1.3),
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(6.3, 3.3, 4.7, 1.6),
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(4.9, 2.4, 3.3, 1.0),
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(6.6, 2.9, 4.6, 1.3),
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(5.2, 2.7, 3.9, 1.4),
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(5.0, 2.0, 3.5, 1.0),
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(5.9, 3.0, 4.2, 1.5),
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(6.0, 2.2, 4.0, 1.0),
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(6.1, 2.9, 4.7, 1.4),
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(5.6, 2.9, 3.6, 1.3),
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(6.7, 3.1, 4.4, 1.4),
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(5.6, 3.0, 4.5, 1.5),
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(5.8, 2.7, 4.1, 1.0),
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(6.2, 2.2, 4.5, 1.5),
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(5.6, 2.5, 3.9, 1.1),
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(5.9, 3.2, 4.8, 1.8),
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(6.1, 2.8, 4.0, 1.3),
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(6.3, 2.5, 4.9, 1.5),
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(6.1, 2.8, 4.7, 1.2),
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(6.4, 2.9, 4.3, 1.3),
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(6.6, 3.0, 4.4, 1.4),
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(6.8, 2.8, 4.8, 1.4),
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(6.7, 3.0, 5.0, 1.7),
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(6.0, 2.9, 4.5, 1.5),
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(5.7, 2.6, 3.5, 1.0),
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(5.5, 2.4, 3.8, 1.1),
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(5.5, 2.4, 3.7, 1.0),
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(5.8, 2.7, 3.9, 1.2),
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(6.0, 2.7, 5.1, 1.6),
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(5.4, 3.0, 4.5, 1.5),
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(6.0, 3.4, 4.5, 1.6),
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(6.7, 3.1, 4.7, 1.5),
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(6.3, 2.3, 4.4, 1.3),
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(5.6, 3.0, 4.1, 1.3),
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(5.5, 2.5, 4.0, 1.3),
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(5.5, 2.6, 4.4, 1.2),
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(6.1, 3.0, 4.6, 1.4),
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(5.8, 2.6, 4.0, 1.2),
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(5.0, 2.3, 3.3, 1.0),
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(5.6, 2.7, 4.2, 1.3),
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(5.7, 3.0, 4.2, 1.2),
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(5.7, 2.9, 4.2, 1.3),
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(6.2, 2.9, 4.3, 1.3),
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(5.1, 2.5, 3.0, 1.1),
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(5.7, 2.8, 4.1, 1.3),
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(6.3, 3.3, 6.0, 2.5),
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(5.8, 2.7, 5.1, 1.9),
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(7.1, 3.0, 5.9, 2.1),
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(6.3, 2.9, 5.6, 1.8),
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(6.5, 3.0, 5.8, 2.2),
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(7.6, 3.0, 6.6, 2.1),
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(4.9, 2.5, 4.5, 1.7),
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(7.3, 2.9, 6.3, 1.8),
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(6.7, 2.5, 5.8, 1.8),
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(7.2, 3.6, 6.1, 2.5),
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(6.5, 3.2, 5.1, 2.0),
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(6.4, 2.7, 5.3, 1.9),
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(6.8, 3.0, 5.5, 2.1),
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(5.7, 2.5, 5.0, 2.0),
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(5.8, 2.8, 5.1, 2.4),
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(6.4, 3.2, 5.3, 2.3),
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(6.5, 3.0, 5.5, 1.8),
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(7.7, 3.8, 6.7, 2.2),
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(7.7, 2.6, 6.9, 2.3),
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(6.0, 2.2, 5.0, 1.5),
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(6.9, 3.2, 5.7, 2.3),
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(5.6, 2.8, 4.9, 2.0),
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(7.7, 2.8, 6.7, 2.0),
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(6.3, 2.7, 4.9, 1.8),
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(6.7, 3.3, 5.7, 2.1),
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(7.2, 3.2, 6.0, 1.8),
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(6.2, 2.8, 4.8, 1.8),
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(6.1, 3.0, 4.9, 1.8),
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(6.4, 2.8, 5.6, 2.1),
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(7.2, 3.0, 5.8, 1.6),
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(7.4, 2.8, 6.1, 1.9),
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(7.9, 3.8, 6.4, 2.0),
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(6.4, 2.8, 5.6, 2.2),
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(6.3, 2.8, 5.1, 1.5),
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(6.1, 2.6, 5.6, 1.4),
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(7.7, 3.0, 6.1, 2.3),
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(6.3, 3.4, 5.6, 2.4),
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(6.4, 3.1, 5.5, 1.8),
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(6.0, 3.0, 4.8, 1.8),
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(6.9, 3.1, 5.4, 2.1),
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(6.7, 3.1, 5.6, 2.4),
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(6.9, 3.1, 5.1, 2.3),
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(5.8, 2.7, 5.1, 1.9),
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(6.8, 3.2, 5.9, 2.3),
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(6.7, 3.3, 5.7, 2.5),
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(6.7, 3.0, 5.2, 2.3),
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(6.3, 2.5, 5.0, 1.9),
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(6.5, 3.0, 5.2, 2.0),
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(6.2, 3.4, 5.4, 2.3),
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(5.9, 3.0, 5.1, 1.8)
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],
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[ Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Setosa,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Versicolor,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica,
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Virginica
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]
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( map fst irisTrainingData,
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map snd irisTrainingData
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),
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exTargetType = (Ref.SomeTypeRep (Ref.TypeRep @(Float -> Float -> Float -> Float -> IrisClass))),
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-- todo: kindaHacky
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@ -413,23 +109,11 @@ instance Evaluator TypeRequester LamdaExecutionEnv FittnesRes where
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calc env pop = do
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let toAdd = NE.filter (\k -> not (Map.member k (results env))) pop
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env <- loadTrainingData env
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toInsert <- Hint.runInterpreter (evalResults env toAdd)
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let insertPair (key, val) m = Map.insert key val m
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let res = foldr insertPair (results env) (fromRight undefined toInsert)
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return env {results = res}
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loadTrainingData :: LamdaExecutionEnv -> IO LamdaExecutionEnv
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loadTrainingData ex@LamdaExecutionEnv {trainingData = ([], [])} = do
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csv <- B.readFile (trainingDataset ex)
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let dat = (toList $ fromRight undefined $ decode NoHeader csv) :: [(Float, Float, Float, Float)]
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csvRes <- B.readFile (trainingDatasetRes ex)
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let decodedRes = decode NoHeader csvRes
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let recsRes = (toList $ fromRight undefined decodedRes) :: [IrisClass]
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return ex {trainingData = (dat, recsRes)}
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loadTrainingData lee@LamdaExecutionEnv {trainingData = (_ : _, _ : _)} = return lee
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loadTrainingData lee@LamdaExecutionEnv {trainingData = (_ : _, [])} = return undefined
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loadTrainingData lee@LamdaExecutionEnv {trainingData = ([], _ : _)} = return undefined
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evalResults :: LamdaExecutionEnv -> [TypeRequester] -> Hint.InterpreterT IO [(TypeRequester, FittnesRes)]
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evalResults ex trs = mapM (evalResult ex) trs
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@ -444,7 +128,7 @@ evalResult ex tr = do
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let acc = (foldr (\ts s -> if ((fst ts) == (snd ts)) then s + 1 else s) 0 resAndTarget) :: Int
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let biasWellDistributed = (foldr (*) 1 (map (\ty -> (foldr (\ts s -> if ((snd ts) == ty) then s + 1 else s) 1 resAndTarget)) ([minBound .. maxBound] :: [IrisClass]) :: [R])) ** (1 / 3) -- 1 (schlecht) bis 51 (gut)
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let biasSmall = exp (-(fromIntegral (countTrsR tr))) -- 0 (schlecht) bis 1 (gut)
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let fitness' = mean [meanOfAccuricyPerClass resAndTarget, geomeanOfDistributionAccuracy resAndTarget]
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let fitness' = meanOfAccuricyPerClass resAndTarget
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let score = fitness' + (biasSmall - 1)
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return
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( tr,
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@ -463,32 +147,3 @@ if' :: Bool -> a -> a -> a
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if' True e _ = e
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if' False _ e = e
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meanOfAccuricyPerClass :: (Enum r, Bounded r, Eq r) => [(r, r)] -> R
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meanOfAccuricyPerClass results = mean $ map (accuracyInClass results) [minBound .. maxBound]
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geomeanOfAccuricyPerClass :: (Enum r, Bounded r, Eq r) => [(r, r)] -> R
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geomeanOfAccuricyPerClass results = geomean $ map (accuracyInClass results) [minBound .. maxBound]
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geomeanOfDistributionAccuracy :: (Enum r, Bounded r, Eq r) => [(r, r)] -> R
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geomeanOfDistributionAccuracy results = geomean $ map (distributionAccuracyForClass results) [minBound .. maxBound]
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distributionAccuracyForClass :: (Eq r) => [(r, r)] -> r -> R
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distributionAccuracyForClass results clas = (1 - (min 1 (fromIntegral (abs ((length (inResClass results clas)) - (length (inClass results clas)))) / fromIntegral (length (inClass results clas))))) * 100
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mean :: (Show f, Floating f) => [f] -> f
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mean values = (sum values) * (1 / (fromIntegral (length values)))
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geomean :: (Show f, Floating f) => [f] -> f
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geomean values = (product values) ** (1 / (fromIntegral (length values)))
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accuracyInClass :: (Eq r) => [(r, r)] -> r -> R
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accuracyInClass results clas = ((accuracy'(inResClass results clas)) * 100) / fromIntegral (length (inClass results clas))
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inClass :: (Eq r) => [(r, r)] -> r -> [(r, r)]
|
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inClass results clas = (filter ((clas ==) . fst) results)
|
||||
|
||||
inResClass :: (Eq r) => [(r, r)] -> r -> [(r, r)]
|
||||
inResClass results clas = (filter ((clas ==) . snd) results)
|
||||
|
||||
accuracy' :: (Eq r) => [(r, r)] -> R
|
||||
accuracy' results = fromIntegral $ length (filter (\(target, res) -> (res == target)) results)
|
||||
|
||||
@ -466,3 +466,34 @@ eToLambdaExpressionShort (Constan (valS)) = valS
|
||||
|
||||
res :: Int -> ResClass
|
||||
res = ((\lInt0 -> ((iteClass ((eqInt ((lInt0) :: (Int)) ((1) :: (Int))) :: (Bool)) ((Class1) :: (ResClass)) ((iteClass ((eqInt ((lInt0) :: (Int)) ((2) :: (Int))) :: (Bool)) ((Class2) :: (ResClass)) ((iteClass ((eqInt ((lInt0) :: (Int)) ((3) :: (Int))) :: (Bool)) ((Class3) :: (ResClass)) ((Class3) :: (ResClass))) :: (ResClass))) :: (ResClass))) :: (ResClass))) :: (Int -> ResClass))
|
||||
|
||||
|
||||
meanOfAccuricyPerClass :: (Enum r, Bounded r, Eq r) => [(r, r)] -> R
|
||||
meanOfAccuricyPerClass results = mean $ map (accuracyInClass results) [minBound .. maxBound]
|
||||
|
||||
geomeanOfAccuricyPerClass :: (Enum r, Bounded r, Eq r) => [(r, r)] -> R
|
||||
geomeanOfAccuricyPerClass results = geomean $ map (accuracyInClass results) [minBound .. maxBound]
|
||||
|
||||
geomeanOfDistributionAccuracy :: (Enum r, Bounded r, Eq r) => [(r, r)] -> R
|
||||
geomeanOfDistributionAccuracy results = geomean $ map (distributionAccuracyForClass results) [minBound .. maxBound]
|
||||
|
||||
distributionAccuracyForClass :: (Eq r) => [(r, r)] -> r -> R
|
||||
distributionAccuracyForClass results clas = (1 - (min 1 (fromIntegral (abs ((length (inResClass results clas)) - (length (inClass results clas)))) / fromIntegral (length (inClass results clas))))) * 100
|
||||
|
||||
mean :: (Show f, Floating f) => [f] -> f
|
||||
mean values = (sum values) * (1 / (fromIntegral (length values)))
|
||||
|
||||
geomean :: (Show f, Floating f) => [f] -> f
|
||||
geomean values = (product values) ** (1 / (fromIntegral (length values)))
|
||||
|
||||
accuracyInClass :: (Eq r) => [(r, r)] -> r -> R
|
||||
accuracyInClass results clas = ((accuracy'(inResClass results clas)) * 100) / fromIntegral (length (inClass results clas))
|
||||
|
||||
inClass :: (Eq r) => [(r, r)] -> r -> [(r, r)]
|
||||
inClass results clas = (filter ((clas ==) . fst) results)
|
||||
|
||||
inResClass :: (Eq r) => [(r, r)] -> r -> [(r, r)]
|
||||
inResClass results clas = (filter ((clas ==) . snd) results)
|
||||
|
||||
accuracy' :: (Eq r) => [(r, r)] -> R
|
||||
accuracy' results = fromIntegral $ length (filter (\(target, res) -> (res == target)) results)
|
||||
|
||||
@ -32,7 +32,7 @@ options =
|
||||
( long "population-size"
|
||||
<> short 'p'
|
||||
<> metavar "N"
|
||||
<> value 1000
|
||||
<> value 50
|
||||
<> help "Population size"
|
||||
)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user