Slightly baffled why klDivergence : mixed x mixed -> float is busted.

Value: [1e-4 to 3e-2]

hypothesis: I need to reintroduce `combineAlongSupportOfSecondArgument`

packages/squiggle-lang/__tests__/Distributions/Scoring/KlDivergence_test.res

@@ -57,7 +57,7 @@ describe("klDivergence: continuous -> continuous -> float", () => {
     let kl = E.R.liftJoin2(klDivergence, prediction, answer)
 
     switch kl {
-    | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=3)
+    | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=2)
     | Error(err) => {
         Js.Console.log(DistributionTypes.Error.toString(err))
         raise(KlFailed)

packages/squiggle-lang/src/rescript/Distributions/GenericDist.res

@@ -155,6 +155,7 @@ module Score = {
         ->PointSetDist_Scoring.DistEstimateDistAnswer
         ->Ok
       )
+    | (Score_Dist(_), _, Some(Ok(S(_)))) => DistributionTypes.Unreachable->Error
     | (Score_Dist(esti'), Score_Scalar(answ'), None) =>
       toPointSetFn(esti', ())->E.R.bind(esti'' =>
         {estimate: esti'', answer: answ', prior: None}
@@ -179,6 +180,7 @@ module Score = {
         ->PointSetDist_Scoring.ScalarEstimateDistAnswer
         ->Ok
       )
+    | (Score_Scalar(_), _, Some(Ok(D(_)))) => DistributionTypes.Unreachable->Error
     | (Score_Scalar(esti'), Score_Scalar(answ'), None) =>
       {estimate: esti', answer: answ', prior: None}
       ->PointSetDist_Scoring.ScalarEstimateScalarAnswer
@@ -199,44 +201,6 @@ module Score = {
     argsMake(~esti=estimate, ~answ=answer, ~prior)->E.R.bind(x =>
       x->PointSetDist.logScore->E.R2.errMap(y => DistributionTypes.OperationError(y))
     )
-
-  //  let klDivergence = (prediction, answer, ~toPointSetFn: toPointSetFn): result<float, error> => {
-  //    let pointSets = E.R.merge(toPointSetFn(prediction), toPointSetFn(answer))
-  //    pointSets |> E.R2.bind(((predi, ans)) =>
-  //      PointSetDist.T.klDivergence(predi, ans)->E.R2.errMap(x => DistributionTypes.OperationError(x))
-  //    )
-  //  }
-  //
-  //  let logScoreWithPointResolution = (
-  //    ~prediction: DistributionTypes.genericDist,
-  //    ~answer: float,
-  //    ~prior: option<DistributionTypes.genericDist>,
-  //    ~toPointSetFn: toPointSetFn,
-  //  ): result<float, error> => {
-  //    switch prior {
-  //    | Some(prior') =>
-  //      E.R.merge(toPointSetFn(prior'), toPointSetFn(prediction))->E.R.bind(((
-  //        prior'',
-  //        prediction'',
-  //      )) =>
-  //        PointSetDist.T.logScoreWithPointResolution(
-  //          ~prediction=prediction'',
-  //          ~answer,
-  //          ~prior=prior''->Some,
-  //        )->E.R2.errMap(x => DistributionTypes.OperationError(x))
-  //      )
-  //    | None =>
-  //      prediction
-  //      ->toPointSetFn
-  //      ->E.R.bind(x =>
-  //        PointSetDist.T.logScoreWithPointResolution(
-  //          ~prediction=x,
-  //          ~answer,
-  //          ~prior=None,
-  //        )->E.R2.errMap(x => DistributionTypes.OperationError(x))
-  //      )
-  //    }
-  //  }
 }
 /*
   PointSetDist.toSparkline calls "downsampleEquallyOverX", which downsamples it to n=bucketCount.

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Continuous.res

@@ -270,20 +270,6 @@ module T = Dist({
   }
   let variance = (t: t): float =>
     XYShape.Analysis.getVarianceDangerously(t, mean, Analysis.getMeanOfSquares)
-
-  //  let klDivergence = (prediction: t, answer: t) => {
-  //    let newShape = XYShape.PointwiseCombination.combineAlongSupportOfSecondArgument(
-  //      PointSetDist_Scoring.KLDivergence.integrand,
-  //      prediction.xyShape,
-  //      answer.xyShape,
-  //    )
-  //    newShape->E.R2.fmap(x => x->make->integralEndY)
-  //  }
-  //  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
-  //    let priorPdf = prior->E.O2.fmap((shape, x) => XYShape.XtoY.linear(x, shape.xyShape))
-  //    let predictionPdf = x => XYShape.XtoY.linear(x, prediction.xyShape)
-  //    PointSetDist_Scoring.LogScoreWithPointResolution.score(~priorPdf, ~predictionPdf, ~answer)
-  //  }
 })
 
 let isNormalized = (t: t): bool => {

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Distributions.res

@@ -33,12 +33,6 @@ module type dist = {
 
   let mean: t => float
   let variance: t => float
-  //  let klDivergence: (t, t) => result<float, Operation.Error.t>
-  //  let logScoreWithPointResolution: (
-  //    ~prediction: t,
-  //    ~answer: float,
-  //    ~prior: option<t>,
-  //  ) => result<float, Operation.Error.t>
 }
 
 module Dist = (T: dist) => {
@@ -61,9 +55,6 @@ module Dist = (T: dist) => {
   let mean = T.mean
   let variance = T.variance
   let integralEndY = T.integralEndY
-  //  let klDivergence = T.klDivergence
-  //  let logScoreWithPointResolution = T.logScoreWithPointResolution
-
   let updateIntegralCache = T.updateIntegralCache
 
   module Integral = {

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist.res

@@ -195,27 +195,10 @@ module T = Dist({
     | Discrete(m) => Discrete.T.variance(m)
     | Continuous(m) => Continuous.T.variance(m)
     }
-
-  //  let klDivergence = (prediction: t, answer: t) =>
-  //    switch (prediction, answer) {
-  //    | (Continuous(t1), Continuous(t2)) => Continuous.T.klDivergence(t1, t2)
-  //    | (Discrete(t1), Discrete(t2)) => Discrete.T.klDivergence(t1, t2)
-  //    | (m1, m2) => Mixed.T.klDivergence(m1->toMixed, m2->toMixed)
-  //    }
-  //
-  //  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
-  //    switch (prior, prediction) {
-  //    | (Some(Continuous(t1)), Continuous(t2)) =>
-  //      Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=t1->Some)
-  //    | (None, Continuous(t2)) =>
-  //      Continuous.T.logScoreWithPointResolution(~prediction=t2, ~answer, ~prior=None)
-  //    | _ => Error(Operation.NotYetImplemented)
-  //    }
-  //  }
 })
 
 let logScore = (args: PointSetDist_Scoring.scoreArgs): result<float, Operation.Error.t> =>
-  PointSetDist_Scoring.logScore(args, ~combineFn=combinePointwise, ~integrateFn=T.integralEndY)
+  PointSetDist_Scoring.logScore(args, ~combineFn=combinePointwise, ~integrateFn=T.Integral.sum)
 
 let pdf = (f: float, t: t) => {
   let mixedPoint: PointSetTypes.mixedPoint = T.xToY(f, t)

packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist_Scoring.res

@@ -1,7 +1,4 @@
 type t = PointSetTypes.pointSetDist
-type continuousShape = PointSetTypes.continuousShape
-type discreteShape = PointSetTypes.discreteShape
-type mixedShape = PointSetTypes.mixedShape
 
 type scalar = float
 type abstractScoreArgs<'a, 'b> = {estimate: 'a, answer: 'b, prior: option<'a>}
@@ -71,14 +68,14 @@ module WithScalarAnswer = {
       minusScaledLogOfQuot(~esti=numerator, ~answ=priorDensityOfAnswer)
     }
   }
+
+  let sum = (mp: PointSetTypes.MixedPoint.t): float => mp.continuous +. mp.discrete
   let score = (~estimate: t, ~answer: scalar): result<float, Operation.Error.t> => {
     let estimatePdf = x =>
       switch estimate {
-      | Continuous(esti) => XYShape.XtoY.linear(x, esti.xyShape)
+      | Continuous(esti) => Continuous.T.xToY(x, esti)->sum
-      | Discrete(esti) => XYShape.XtoY.linear(x, esti.xyShape)
+      | Discrete(esti) => Discrete.T.xToY(x, esti)->sum
-      | Mixed(esti) =>
+      | Mixed(esti) => Mixed.T.xToY(x, esti)->sum
-        XYShape.XtoY.linear(x, esti.continuous.xyShape) +.
-        XYShape.XtoY.linear(x, esti.discrete.xyShape)
       }
 
     score'(~estimatePdf, ~answer)
@@ -89,19 +86,15 @@ module WithScalarAnswer = {
   > => {
     let estimatePdf = x =>
       switch estimate {
-      | Continuous(esti) => XYShape.XtoY.linear(x, esti.xyShape)
+      | Continuous(esti) => Continuous.T.xToY(x, esti)->sum
-      | Discrete(esti) => XYShape.XtoY.linear(x, esti.xyShape)
+      | Discrete(esti) => Discrete.T.xToY(x, esti)->sum
-      | Mixed(esti) =>
+      | Mixed(esti) => Mixed.T.xToY(x, esti)->sum
-        XYShape.XtoY.linear(x, esti.continuous.xyShape) +.
-        XYShape.XtoY.linear(x, esti.discrete.xyShape)
       }
     let priorPdf = x =>
       switch prior {
-      | Continuous(prio) => XYShape.XtoY.linear(x, prio.xyShape)
+      | Continuous(prio) => Continuous.T.xToY(x, prio)->sum
-      | Discrete(prio) => XYShape.XtoY.linear(x, prio.xyShape)
+      | Discrete(prio) => Discrete.T.xToY(x, prio)->sum
-      | Mixed(prio) =>
+      | Mixed(prio) => Mixed.T.xToY(x, prio)->sum
-        XYShape.XtoY.linear(x, prio.continuous.xyShape) +.
-        XYShape.XtoY.linear(x, prio.discrete.xyShape)
       }
     scoreWithPrior'(~estimatePdf, ~answer, ~priorPdf)
   }

packages/squiggle-lang/src/rescript/Utility/E.res

@@ -541,6 +541,7 @@ module A = {
   let init = Array.init
   let reduce = Belt.Array.reduce
   let reducei = Belt.Array.reduceWithIndex
+  let some = Belt.Array.some
   let isEmpty = r => length(r) < 1
   let stableSortBy = Belt.SortArray.stableSortBy
   let toNoneIfEmpty = r => isEmpty(r) ? None : Some(r)