work on variation

TODO.md

@@ -30,6 +30,7 @@
       - Is the best according to the papers
   - [X] Need a NoOp that opens blocks
   - [ ] Have a way to balance amount of closes with open blocks
+    - Implement "balanced" closed PushArg
 - [ ] Need to make genomes serializable (Check pysh json files)
 - [ ] Add Memory
 - [ ] Add history stack(s), like a call stack
@@ -41,3 +42,4 @@
 - [ ] Implement random simplification of genomes
   - [ ] Find a way to multi-thread this
 - [ ] Look at using `uniformShuffleList` over System.Random.Shuffle
+- [ ] Impelment selectionCases for lexicase and tournament selection.

src/HushGP/GP.hs

@@ -1,17 +1,17 @@
 -- | The main file containing information about the GP loop and various population transformation functions.
 module HushGP.GP where
 
-import System.Random
-import System.Random.Shuffle
 import Control.Monad
 import Control.Parallel.Strategies
 import Data.List (sort, uncons)
 import HushGP.GP.Downsample
+import HushGP.GP.Individual
 import HushGP.GP.PushArgs
 import HushGP.GP.PushData
 import HushGP.GP.Variation
 import HushGP.Genome
-import HushGP.GP.Individual
+import System.Random
+import System.Random.Shuffle
 
 -- import Debug.Trace (trace, traceStack)
 
@@ -45,65 +45,69 @@ gpLoop pushArgs@(PushArgs {trainingData = tData}) = do
 -- holds the evaluation count. The list of Individuals is the population. The last parameter is
 -- the training data (possibly downsampled).
 gpLoop' :: PushArgs -> Int -> Int -> [Individual] -> [PushData] -> IO ()
-gpLoop' pushArgs@(PushArgs {enableDownsampling = enableDS, solutionErrorThreshold = seThresh, downsampleParentsGens = dsParentGens, downsampleParentRate = dsParentRate, trainingData = tData})
+gpLoop'
-  generation evaluations population indexedTrainingData = do
+  pushArgs@(PushArgs {enableDownsampling = enableDS, solutionErrorThreshold = seThresh, downsampleParentsGens = dsParentGens, downsampleParentRate = dsParentRate, trainingData = tData})
-  print "Put information about each generation here."
+  generation
-  when bestIndPassesDownsample $ print $ "Semi Success Generation: " <> show generation
+  evaluations
-  parentReps <- do
+  population
-    shuffledParents <- shuffle' population (length population) <$> initStdGen
+  indexedTrainingData = do
-    if enableDS && (generation `mod` dsParentGens == 0)
+    print "Put information about each generation here."
-    then pure $ take (floor @Float (dsParentRate * (fromIntegral @Int @Float $ length population))) shuffledParents
+    when bestIndPassesDownsample $ print $ "Semi Success Generation: " <> show generation
-    else pure []
+    parentReps <- do
-  let nextAction
+      shuffledParents <- shuffle' population (length population) <$> initStdGen
-        | ( bestIndPassesDownsample
+      if enableDS && (generation `mod` dsParentGens == 0)
-              && ( (case totalFitness (updateIndividual (errorFunction epsilonPushArgs epsilonPushArgs indexedTrainingData (plushy bestInd)) bestInd) of (Just x) -> x; _ -> error "Error: Best downsample individual has no fitness!")
+        then pure $ take (floor @Float (dsParentRate * (fromIntegral @Int @Float $ length population))) shuffledParents
-                     <= solutionErrorThreshold epsilonPushArgs
+        else pure []
-                 )
+    let nextAction
-          )
+          | ( bestIndPassesDownsample
-            || (not (enableDownsampling epsilonPushArgs) && ((case totalFitness bestInd of (Just x) -> x; _ -> error "error: Best non-downsample individual has no fitness!") <= solutionErrorThreshold epsilonPushArgs)) =
+                && ( (case totalFitness (updateIndividual (errorFunction epsilonPushArgs epsilonPushArgs indexedTrainingData (plushy bestInd)) bestInd) of (Just x) -> x; _ -> error "Error: Best downsample individual has no fitness!")
-            do
+                       <= solutionErrorThreshold epsilonPushArgs
-              print $ "Successful generation: " <> show generation
+                   )
-              print $ "Successful plushy: " <> show (plushy bestInd)
+            )
-              print $ "Successful program: " <> show (plushyToPush $ plushy bestInd)
+              || (not (enableDownsampling epsilonPushArgs) && ((case totalFitness bestInd of (Just x) -> x; _ -> error "error: Best non-downsample individual has no fitness!") <= solutionErrorThreshold epsilonPushArgs)) =
-              when (useSimplification epsilonPushArgs) $
+              do
-                do
+                print $ "Successful generation: " <> show generation
-                  let simplifiedPlushy = undefined -- TODO: simplification later
+                print $ "Successful plushy: " <> show (plushy bestInd)
-                  print "Total test error simplified: " <> undefined -- Implement later
+                print $ "Successful program: " <> show (plushyToPush $ plushy bestInd)
-                  print $ "Simplified plushy: " <> undefined -- show simplifiedPlushy
+                when (useSimplification epsilonPushArgs) $
-                  print $ "Simplified program: " <> undefined -- show plushyToPush simplifiedPlushy
+                  do
-        | (not (enableDownsampling epsilonPushArgs) && (generation >= maxGenerations epsilonPushArgs))
+                    let simplifiedPlushy = undefined -- TODO: simplification later
-            || (enableDownsampling epsilonPushArgs && (evaluations >= (maxGenerations epsilonPushArgs * length population * length indexedTrainingData))) =
+                    print "Total test error simplified: " <> undefined -- Implement later
-            print $ "Best individual: " <> show (plushy bestInd)
+                    print $ "Simplified plushy: " <> undefined -- show simplifiedPlushy
-        | otherwise =
+                    print $ "Simplified program: " <> undefined -- show plushyToPush simplifiedPlushy
-            gpLoop'
+          | (not (enableDownsampling epsilonPushArgs) && (generation >= maxGenerations epsilonPushArgs))
-              pushArgs
+              || (enableDownsampling epsilonPushArgs && (evaluations >= (maxGenerations epsilonPushArgs * length population * length indexedTrainingData))) =
-              (succ generation)
+              print $ "Best individual: " <> show (plushy bestInd)
-              ( evaluations
+          | otherwise =
-                  + (populationSize pushArgs * length (trainingData pushArgs))
+              gpLoop'
-                  + (if generation `mod` downsampleParentsGens pushArgs == 0 then length parentReps * (length indexedTrainingData - length (trainingData pushArgs)) else 0)
+                pushArgs
-                  + (if bestIndPassesDownsample then length indexedTrainingData - length tData else 0)
+                (succ generation)
-              )
+                ( evaluations
-              ( if elitism pushArgs
+                    + (populationSize pushArgs * length (trainingData pushArgs))
-                  then bestInd : replicate (populationSize epsilonPushArgs - 1) (newIndividual epsilonPushArgs evaledPop)
+                    + (if generation `mod` downsampleParentsGens pushArgs == 0 then length parentReps * (length indexedTrainingData - length (trainingData pushArgs)) else 0)
-                  else replicate (populationSize epsilonPushArgs) (newIndividual epsilonPushArgs evaledPop)
+                    + (if bestIndPassesDownsample then length indexedTrainingData - length tData else 0)
-              )
+                )
-              ( if enableDS && ((generation `mod` dsParentGens) == 0)
+                ( if elitism pushArgs
-                  then updateCaseDistances repEvaluatedPop indexedTrainingData indexedTrainingData (informedDownsamplingType pushArgs) (solutionErrorThreshold pushArgs / fromIntegral @Int @Double (length indexedTrainingData))
+                    then bestInd : replicate (populationSize epsilonPushArgs - 1) (newIndividual epsilonPushArgs evaledPop)
-                  else indexedTrainingData
+                    else replicate (populationSize epsilonPushArgs) (newIndividual epsilonPushArgs evaledPop)
-              )
+                )
-  nextAction
+                ( if enableDS && ((generation `mod` dsParentGens) == 0)
-  where
+                    then updateCaseDistances repEvaluatedPop indexedTrainingData indexedTrainingData (informedDownsamplingType pushArgs) (solutionErrorThreshold pushArgs / fromIntegral @Int @Double (length indexedTrainingData))
-    -- \| This will have downsampling functionality added later.
+                    else indexedTrainingData
-    repEvaluatedPop :: [Individual]
+                )
-    repEvaluatedPop =
+    nextAction
-      if enableDS
+    where
-      then evaluatePopulation pushArgs indexedTrainingData population
+      -- \| This will have downsampling functionality added later.
-      else []
+      repEvaluatedPop :: [Individual]
-    evaledPop :: [Individual]
+      repEvaluatedPop =
-    evaledPop = evaluatePopulation pushArgs tData population
+        if enableDS
-    bestInd :: Individual
+          then evaluatePopulation pushArgs indexedTrainingData population
-    bestInd = case uncons evaledPop of Just (x, _) -> x; _ -> error "Error: Population is empty!"
+          else []
-    bestIndPassesDownsample :: Bool
+      evaledPop :: [Individual]
-    bestIndPassesDownsample = enableDS && (extractTotalFitness bestInd <= seThresh)
+      evaledPop = evaluatePopulation pushArgs tData population
-    epsilonPushArgs :: PushArgs
+      bestInd :: Individual
-    epsilonPushArgs = pushArgs {epsilons = Nothing} -- TODO: And this
+      bestInd = case uncons evaledPop of Just (x, _) -> x; _ -> error "Error: Population is empty!"
+      bestIndPassesDownsample :: Bool
+      bestIndPassesDownsample = enableDS && (extractTotalFitness bestInd <= seThresh)
+      epsilonPushArgs :: PushArgs
+      epsilonPushArgs = pushArgs {epsilons = Nothing} -- TODO: And this

src/HushGP/GP/Individual.hs

@@ -16,10 +16,13 @@ instance Ord Individual where
   ind0 <= ind1 = totalFitness ind0 <= totalFitness ind1
 
 -- | Extracts the fitnessCases from an Individual. Errors if the field is empty.
+-- Known as :errors in propeller.
 extractFitnessCases :: Individual -> [Double]
 extractFitnessCases Individual {fitnessCases = Nothing} = error "Error: fitnessCases is empty!"
 extractFitnessCases Individual {fitnessCases = Just xs} = xs
 
+-- | Extracts the total fitness from and Individual. Errors if the field is empty.
+-- Known as :total-error in propeller.
 extractTotalFitness :: Individual -> Double
 extractTotalFitness Individual {totalFitness = Nothing} = error "Error: totalFitness is empty!"
 extractTotalFitness Individual {totalFitness = Just x} = x

src/HushGP/GP/PushArgs.hs

@@ -9,10 +9,10 @@ import Data.Map qualified as Map
 -- of the evolutionary run in Hush.
 data PushArgs = PushArgs
   {
-    -- | For alternation, std deviation fo index when alternating.
+    -- | For alternation, std deviation for index when alternating.
-    alignmentDeviation :: Int,
+    alignmentDeviation :: Double,
-    -- | For alternation, probability of switching parents at each location.
+    -- | For alternation, probability of switching parents at each location. Should be a value in the range [1,100]
-    alternationRate :: Float,
+    alternationRate :: Int,
     -- | For bmx, rate genes are exchanged.
     bmxExchangeRate :: Float,
     -- | For bmx, max length of a gene.
@@ -107,8 +107,8 @@ data PushArgs = PushArgs
 -- their args from.
 defaultPushArgs :: PushArgs
 defaultPushArgs = PushArgs {
-    alignmentDeviation = 2,
+    alignmentDeviation = 2.0,
-    alternationRate = 0.1,
+    alternationRate = 10,
     bmxExchangeRate = 0.5,
     bmxGeneLengthLimit = 10,
     bmxGapChangeProbability = 0.001,

src/HushGP/GP/Variation.hs

@@ -1,7 +1,71 @@
 module HushGP.GP.Variation where
 
+import Control.Monad
+import HushGP.State
 import HushGP.GP.PushArgs
 import HushGP.GP.Individual
+import HushGP.Utility
+
+-- |Performs a uniform crossover on two parents and returns the child.
+-- Padding is placed to left of the shorter genome.
+crossover :: [Gene] -> [Gene] -> IO [Gene]
+crossover plushyA plushyB = do
+  filter (CrossoverPadding /=) <$> zipWithM (\short long -> randOneToOneHundred >>= (\num -> if num < 50 then pure short else pure long)) shorterPadded longer
+  where
+    shorter :: [Gene]
+    shorter = if length plushyA <= length plushyB then plushyA else plushyB
+    longer :: [Gene]
+    longer = if length plushyA > length plushyB then plushyA else plushyB
+    lengthDiff :: Int
+    lengthDiff = length longer - length shorter
+    shorterPadded :: [Gene]
+    shorterPadded = shorter <> replicate lengthDiff CrossoverPadding
+
+-- |Alternates between placing genes from one parent to the other in a new child based on some random numbers.
+alternation :: PushArgs -> [Gene] -> [Gene] -> IO [Gene]
+alternation pushArgs plushyA plushyB = do
+  randUsePlushyA <- randElem [True, False]
+  alternation' pushArgs 0 randUsePlushyA [] (length plushyA + length plushyB) plushyA plushyB
+
+-- |This is a chunker. The PushArgs used in the whole evolutionary run.
+-- The first Int is used in the gaussian noise calculation and as a stop condition.
+-- The Bool is used to determine which plushy is used to copy to the child.
+-- The first [Gene] is the child being created recursively.
+-- The second int is the iteration budget. Used to stop very long looping.
+-- The second [Gene] is the first plushy parent.
+-- The third [Gene] is the second plushy parent.
+-- This returns the first [Gene] when the loop is complete.
+alternation' :: PushArgs -> Int -> Bool -> [Gene] -> Int -> [Gene] -> [Gene] -> IO [Gene]
+alternation' pushArgs@PushArgs{alternationRate = altRate, alignmentDeviation = alignDeviation} n usePlushyA !resultPlushy iterationBudget plushyA plushyB = do
+  randNum <- randOneToOneHundred
+  let nextAction
+        | n >= length (if usePlushyA then plushyA else plushyB) || iterationBudget <= 0 = pure resultPlushy
+        | randNum < altRate = do
+            gNoiseFactor <- gaussianNoiseFactor
+            alternation' pushArgs (max 0 (n + round (gNoiseFactor * alignDeviation))) (not usePlushyA) resultPlushy (pred iterationBudget) plushyA plushyB
+        | otherwise = alternation' pushArgs (succ n) usePlushyA (resultPlushy <> [(if usePlushyA then plushyA else plushyB) !! n]) (pred iterationBudget) plushyA plushyB
+  nextAction
+
+-- |Performs a uniform crossover on two parents and returns the child.
+-- Padding is placed to left of the shorter genome.
+tailAlignedCrossover :: [Gene] -> [Gene] -> IO [Gene]
+tailAlignedCrossover plushyA plushyB = do
+  filter (CrossoverPadding /=) <$> zipWithM (\short long -> randOneToOneHundred >>= (\num -> if num < 50 then pure short else pure long)) shorterPadded longer
+  where
+    shorter :: [Gene]
+    shorter = if length plushyA <= length plushyB then plushyA else plushyB
+    longer :: [Gene]
+    longer = if length plushyA > length plushyB then plushyA else plushyB
+    lengthDiff :: Int
+    lengthDiff = length longer - length shorter
+    shorterPadded :: [Gene]
+    shorterPadded = replicate lengthDiff CrossoverPadding <> shorter
+
+-- |Takes the PushArgs for the evolutionary run and a singular plushy.
+-- Returns the added onto plushy. Returns the the passed plushy with
+-- new instructions possibly added before or after each existing instruction.
+uniformAddition :: PushArgs -> [Gene] -> [Gene]
+uniformAddition pushArgs plushy = undefined
 
 newIndividual :: PushArgs -> [Individual] -> Individual
 newIndividual = error "Implement this later"

src/HushGP/Genome.hs

@@ -3,24 +3,21 @@ module HushGP.Genome where
 import Data.List
 import Data.List.Split
 import Data.Map qualified as Map
+import HushGP.GP.Individual
 import HushGP.GP.PushArgs
 import HushGP.Instructions.Opens
 import HushGP.State
 import HushGP.Utility
-import HushGP.GP.Individual
-
--- import HushGP.Instructions
--- import Debug.Trace
 
 -- | Makes a random individual based on the variables in a passed PushArgs.
 makeRandomIndividual :: PushArgs -> IO Individual
 makeRandomIndividual pushArgs = do
   randomPlushy <- makeRandomPlushy pushArgs
-  return Individual {plushy = randomPlushy, totalFitness = Nothing, fitnessCases = Nothing}
+  return Individual {plushy = randomPlushy, totalFitness = Nothing, fitnessCases = Nothing, selectionCases = Nothing}
 
 -- | Makes a random plushy from variables in a passed PushArgs.
 makeRandomPlushy :: PushArgs -> IO [Gene]
-makeRandomPlushy pushArgs = randomInstructions (maxInitialPlushySize pushArgs) (instructionList pushArgs)
+makeRandomPlushy PushArgs {maxInitialPlushySize = maxInitPSize, instructionList = iList} = randomInstructions maxInitPSize iList
 
 -- | A utility function to generate an amount based on an int rather than
 --  from an argmap.

src/HushGP/Push.hs

@@ -72,7 +72,9 @@ interpretExec state@(State {_exec = e : es}) =
     (GeneVectorBoolERC (val, _)) -> interpretExec (state & exec .~ es & vectorBool .~ val : view vectorBool state)
     (GeneVectorStringERC (val, _)) -> interpretExec (state & exec .~ es & vectorString .~ val : view vectorString state)
     (GeneVectorCharERC (val, _)) -> interpretExec (state & exec .~ es & vectorChar .~ val : view vectorChar state)
-    Close -> undefined -- This should never happen. Will be converted to Blocks in the Plushy -> Exec stack process
+    Close -> error "Error: Close found in exec stack!" -- This should never happen. Will be converted to Blocks in the Plushy -> Exec stack process
-    (Open _) -> undefined -- This should also never happen. Should be converted in Plushy -> Exec stack process
+    (Open _) -> error "Error: Open found in exec stack!" -- This should also never happen. Should be converted in Plushy -> Exec stack process
-    Skip -> undefined -- This should double also never happen.
+    Skip -> error "Error: Skip found in exec stack!" -- This should double also never happen.
+    CrossoverPadding -> error "Error: CrossoverPadding found in exec stack!"
+    Gap -> error "Error: Gap found in exec stack!"
 interpretExec state = state

src/HushGP/State.hs

@@ -38,6 +38,10 @@ data Gene
   | GeneVectorBoolERC ([Bool], StdGen)
   | GeneVectorStringERC ([String], StdGen)
   | GeneVectorCharERC ([Char], StdGen)
+  | -- | This is only used in the crossover function in GP/Variation. Should not be in genome besides there.
+    CrossoverPadding
+  | -- | This is used in best match crossover (bmx in PushArgs).
+    Gap
 
 instance Eq Gene where
   GeneInt x == GeneInt y = x == y
@@ -76,6 +80,8 @@ instance Eq Gene where
   GeneVectorBoolERC (x, _) == GeneVectorBool y = x == y
   GeneVectorStringERC (x, _) == GeneVectorString y = x == y
   GeneVectorCharERC (x, _) == GeneVectorChar y = x == y
+  CrossoverPadding == CrossoverPadding = True
+  Gap == Gap = True
   _ == _ = False
 
 instance Ord Gene where
@@ -115,6 +121,8 @@ instance Ord Gene where
   GeneVectorBoolERC (x, _) <= GeneVectorBool y = x <= y
   GeneVectorStringERC (x, _) <= GeneVectorString y = x <= y
   GeneVectorCharERC (x, _) <= GeneVectorChar y = x <= y
+  CrossoverPadding <= CrossoverPadding = True
+  Gap <= Gap = True
   _ <= _ = False
 
 instance Show Gene where
@@ -144,6 +152,8 @@ instance Show Gene where
   show (GeneVectorBoolERC x) = "Bool Vec ERC: " <> show x
   show (GeneVectorStringERC x) = "String Vec ERC: " <> show x
   show (GeneVectorCharERC x) = "Char Vec ERC: " <> show x
+  show CrossoverPadding = "Crossover Padding"
+  show Gap = "Gap"
 
 -- | The structure that holds all of the values.
 data State = State

src/HushGP/Utility.hs

@@ -1,9 +1,9 @@
 module HushGP.Utility where
 
-import Data.List
 import Control.Monad
-import System.Random
+import Data.List
 import HushGP.State
+import System.Random
 
 -- | Generates a single random instruction from a list of instructions.
 randomInstruction :: [Gene] -> IO Gene
@@ -29,5 +29,18 @@ mapIndexed' count f (x : xs) = f count x : mapIndexed' (count + 1) f xs
 randElem :: [a] -> IO a
 randElem xs = (xs !!) . fst . uniformR (0, length xs - 1) <$> initStdGen
 
+-- | Used in some of the selection operations. Returns an error saying cases is empty.
 headCases :: [Int] -> Int
 headCases xs = case uncons xs of Just (y, _) -> y; _ -> error "Error: cases is empty!"
+
+-- | Almost a constant but has some randomness inside. Double for more decimal precision.
+--  Noise of mean of 0 and std dev of 1. This is a neat function to visualize on desmos!
+gaussianNoiseFactor :: IO Double
+gaussianNoiseFactor = do
+  randDecimal0 <- fst . uniformR (0.0 :: Double, 1.0 :: Double) <$> initStdGen
+  randDecimal1 <- fst . uniformR (0.0 :: Double, 1.0 :: Double) <$> initStdGen
+  pure (sqrt ((-2.0) * log randDecimal0) * cos (2.0 * pi * randDecimal1))
+
+-- | A random number between 1 and 100.
+randOneToOneHundred :: IO Int
+randOneToOneHundred = fst . uniformR (1 :: Int, 100 :: Int) <$> initStdGen