Copyright	(c) Fabricio Olivetti de Franca 2022
License	GPL-3
Maintainer	fabricio.olivetti@gmail.com
Stability	experimental
Portability	POSIX
Safe Haskell	Safe-Inferred

MachineLearning.Utils.Config

Description

Configuration parsing and report generation.

Synopsis

allFunctions :: [Function]
data Task
- = Regression
- | RegressionNL Int
- | Classification Int
- | ClassMult Int
data Algorithm
- = GPTIR
- | SCTIR
data Penalty
- = NoPenalty
- | Len Double
- | Shape Double
data Output
- = Screen
- | PartialLog String
- | EvoLog String
data Config = Conf {
- _mutationCfg :: MutationCfg
- _ioCfg :: IOCfg
- _algorithmCfg :: AlgorithmCfg
- _constraintCfg :: ConstraintCfg
}
data MutationCfg = MutCfg {
- _kRange :: (Int, Int)
- _funs :: [Function]
- _yfuns :: [Function]
- _vars :: [Int]
- _budget :: Int
}
dfltMutCfg :: MutationCfg
data IOCfg = IOCfg {
- _trainFilename :: String
- _testFilename :: String
- _logType :: Output
}
data AlgorithmCfg = AlgCfg {
- _algorithm :: Algorithm
- _task :: Task
- _gens :: Int
- _nPop :: Int
- _pm :: Double
- _pc :: Double
- _seed :: Maybe Int
- _measures :: [Measure]
}
dfltAlgCfg :: AlgorithmCfg
data ConstraintCfg = CnsCfg {
- _penaltyType :: Penalty
- _shapes :: [Shape]
- _domains :: [(Double, Double)]
- _evaluator :: Maybe Evaluator
}
dfltCnstrCfg :: ConstraintCfg
getLogType :: Config -> Output
getSeed :: Config -> Maybe Int
getTask :: Config -> Task
getNPop :: Config -> Int
getNGens :: Config -> Int
getTrainName :: Config -> String
getTestName :: Config -> String
getDomains :: Config -> [(Double, Double)]
getImage :: Config -> Maybe (Double, Double)
getMeasures :: Config -> [Measure]
getShapes :: Config -> [Shape]
getPenalty :: Config -> Penalty
readConfig :: String -> IO Config
parseConfig :: IniParser Config

Documentation

allFunctions :: [Function] Source #

data Task Source #

Task can be Regression, Classification and One-vs-All Classification

Constructors

Regression
RegressionNL Int
Classification Int
ClassMult Int

Instances

Instances details

Read Task Source #
Instance details Defined in MachineLearning.Utils.Config Methods readsPrec :: Int -> ReadS Task readList :: ReadS [Task] readPrec :: ReadPrec Task readListPrec :: ReadPrec [Task]
Show Task Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> Task -> ShowS show :: Task -> String showList :: [Task] -> ShowS
Eq Task Source #
Instance details Defined in MachineLearning.Utils.Config Methods (==) :: Task -> Task -> Bool (/=) :: Task -> Task -> Bool

data Algorithm Source #

Current algorithm implementation are traditional Evolutionary (GPTIR) and Feasible-Infeasible two-population for shape-constraint (SCTIR).

Constructors

GPTIR
SCTIR

Instances

Instances details

Read Algorithm Source #
Instance details Defined in MachineLearning.Utils.Config Methods readsPrec :: Int -> ReadS Algorithm readList :: ReadS [Algorithm] readPrec :: ReadPrec Algorithm readListPrec :: ReadPrec [Algorithm]
Show Algorithm Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> Algorithm -> ShowS show :: Algorithm -> String showList :: [Algorithm] -> ShowS
Eq Algorithm Source #
Instance details Defined in MachineLearning.Utils.Config Methods (==) :: Algorithm -> Algorithm -> Bool (/=) :: Algorithm -> Algorithm -> Bool

data Penalty Source #

Type of penalty function

Constructors

NoPenalty
Len Double
Shape Double

Instances

Instances details

Read Penalty Source #
Instance details Defined in MachineLearning.Utils.Config Methods readsPrec :: Int -> ReadS Penalty readList :: ReadS [Penalty] readPrec :: ReadPrec Penalty readListPrec :: ReadPrec [Penalty]
Show Penalty Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> Penalty -> ShowS show :: Penalty -> String showList :: [Penalty] -> ShowS

data Output Source #

Output configuration

Constructors

Screen
PartialLog String
EvoLog String

Instances

Instances details

Read Output Source #
Instance details Defined in MachineLearning.Utils.Config Methods readsPrec :: Int -> ReadS Output readList :: ReadS [Output] readPrec :: ReadPrec Output readListPrec :: ReadPrec [Output]
Show Output Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> Output -> ShowS show :: Output -> String showList :: [Output] -> ShowS

data Config Source #

Configuration data

Constructors

Conf
Fields _mutationCfg :: MutationCfg _ioCfg :: IOCfg _algorithmCfg :: AlgorithmCfg _constraintCfg :: ConstraintCfg

Instances

Instances details

Show Config Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> Config -> ShowS show :: Config -> String showList :: [Config] -> ShowS

data MutationCfg Source #

Mutation config

Constructors

MutCfg
Fields _kRange :: (Int, Int) _funs :: [Function] _yfuns :: [Function] _vars :: [Int] _budget :: Int

Instances

Instances details

Read MutationCfg Source #
Instance details Defined in MachineLearning.Utils.Config Methods readsPrec :: Int -> ReadS MutationCfg readList :: ReadS [MutationCfg] readPrec :: ReadPrec MutationCfg readListPrec :: ReadPrec [MutationCfg]
Show MutationCfg Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> MutationCfg -> ShowS show :: MutationCfg -> String showList :: [MutationCfg] -> ShowS

dfltMutCfg :: MutationCfg Source #

data IOCfg Source #

Dataset and logging configs

Constructors

IOCfg
Fields _trainFilename :: String _testFilename :: String _logType :: Output

Instances

Instances details

Read IOCfg Source #
Instance details Defined in MachineLearning.Utils.Config Methods readsPrec :: Int -> ReadS IOCfg readList :: ReadS [IOCfg] readPrec :: ReadPrec IOCfg readListPrec :: ReadPrec [IOCfg]
Show IOCfg Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> IOCfg -> ShowS show :: IOCfg -> String showList :: [IOCfg] -> ShowS

data AlgorithmCfg Source #

Algorithm configuration

Constructors

AlgCfg
Fields _algorithm :: Algorithm _task :: Task _gens :: Int _nPop :: Int _pm :: Double _pc :: Double _seed :: Maybe Int _measures :: [Measure]

Instances

Instances details

Show AlgorithmCfg Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> AlgorithmCfg -> ShowS show :: AlgorithmCfg -> String showList :: [AlgorithmCfg] -> ShowS

dfltAlgCfg :: AlgorithmCfg Source #

data ConstraintCfg Source #

Constructors

CnsCfg
Fields _penaltyType :: Penalty _shapes :: [Shape] _domains :: [(Double, Double)] _evaluator :: Maybe Evaluator

Instances

Instances details

Read ConstraintCfg Source #
Instance details Defined in MachineLearning.Utils.Config Methods readsPrec :: Int -> ReadS ConstraintCfg readList :: ReadS [ConstraintCfg] readPrec :: ReadPrec ConstraintCfg readListPrec :: ReadPrec [ConstraintCfg]
Show ConstraintCfg Source #
Instance details Defined in MachineLearning.Utils.Config Methods showsPrec :: Int -> ConstraintCfg -> ShowS show :: ConstraintCfg -> String showList :: [ConstraintCfg] -> ShowS

dfltCnstrCfg :: ConstraintCfg Source #

getLogType :: Config -> Output Source #

getSeed :: Config -> Maybe Int Source #

getTask :: Config -> Task Source #

getNPop :: Config -> Int Source #

getNGens :: Config -> Int Source #

getTrainName :: Config -> String Source #

getTestName :: Config -> String Source #

getDomains :: Config -> [(Double, Double)] Source #

getImage :: Config -> Maybe (Double, Double) Source #

getMeasures :: Config -> [Measure] Source #

getShapes :: Config -> [Shape] Source #

getPenalty :: Config -> Penalty Source #

readConfig :: String -> IO Config Source #

parseConfig :: IniParser Config Source #

Read the config file and run the algorithm.