Merge pull request #521 from quantified-uncertainty/score-dist-on-scalar-resolution

score a dist against a scalar resolution

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

@@ -146,7 +146,16 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
       }
     | ToDist(Normalize) => dist->GenericDist.normalize->Dist
     | ToScore(KLDivergence(t2)) =>
-      GenericDist.klDivergence(dist, t2, ~toPointSetFn)
+      GenericDist.Score.klDivergence(dist, t2, ~toPointSetFn)
+      ->E.R2.fmap(r => Float(r))
+      ->OutputLocal.fromResult
+    | ToScore(LogScore(answer, prior)) =>
+      GenericDist.Score.logScoreWithPointResolution(
+        ~prediction=dist,
+        ~answer,
+        ~prior,
+        ~toPointSetFn,
+      )
       ->E.R2.fmap(r => Float(r))
       ->OutputLocal.fromResult
     | ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
@@ -263,6 +272,12 @@ module Constructors = {
   let normalize = (~env, dist) => C.normalize(dist)->run(~env)->toDistR
   let isNormalized = (~env, dist) => C.isNormalized(dist)->run(~env)->toBoolR
   let klDivergence = (~env, dist1, dist2) => C.klDivergence(dist1, dist2)->run(~env)->toFloatR
+  let logScoreWithPointResolution = (
+    ~env,
+    ~prediction: DistributionTypes.genericDist,
+    ~answer: float,
+    ~prior: option<DistributionTypes.genericDist>,
+  ) => C.logScoreWithPointResolution(~prediction, ~answer, ~prior)->run(~env)->toFloatR
   let toPointSet = (~env, dist) => C.toPointSet(dist)->run(~env)->toDistR
   let toSampleSet = (~env, dist, n) => C.toSampleSet(dist, n)->run(~env)->toDistR
   let fromSamples = (~env, xs) => C.fromSamples(xs)->run(~env)->toDistR

packages/squiggle-lang/src/rescript/Distributions/DistributionOperation.resi

@@ -63,6 +63,13 @@ module Constructors: {
   @genType
   let klDivergence: (~env: env, genericDist, genericDist) => result<float, error>
   @genType
+  let logScoreWithPointResolution: (
+    ~env: env,
+    ~prediction: genericDist,
+    ~answer: float,
+    ~prior: option<genericDist>,
+  ) => result<float, error>
+  @genType
   let toPointSet: (~env: env, genericDist) => result<genericDist, error>
   @genType
   let toSampleSet: (~env: env, genericDist, int) => result<genericDist, error>

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

@@ -91,7 +91,7 @@ module DistributionOperation = {
     | ToString
     | ToSparkline(int)
 
-  type toScore = KLDivergence(genericDist)
+  type toScore = KLDivergence(genericDist) | LogScore(float, option<genericDist>)
 
   type fromDist =
     | ToFloat(toFloat)
@@ -120,6 +120,7 @@ module DistributionOperation = {
     | ToFloat(#Sample) => `sample`
     | ToFloat(#IntegralSum) => `integralSum`
     | ToScore(KLDivergence(_)) => `klDivergence`
+    | ToScore(LogScore(x, _)) => `logScore against ${E.Float.toFixed(x)}`
     | ToDist(Normalize) => `normalize`
     | ToDist(ToPointSet) => `toPointSet`
     | ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
@@ -161,6 +162,10 @@ module Constructors = {
     let truncate = (dist, left, right): t => FromDist(ToDist(Truncate(left, right)), dist)
     let inspect = (dist): t => FromDist(ToDist(Inspect), dist)
     let klDivergence = (dist1, dist2): t => FromDist(ToScore(KLDivergence(dist2)), dist1)
+    let logScoreWithPointResolution = (~prediction, ~answer, ~prior): t => FromDist(
+      ToScore(LogScore(answer, prior)),
+      prediction,
+    )
     let scalePower = (dist, n): t => FromDist(ToDist(Scale(#Power, n)), dist)
     let scaleLogarithm = (dist, n): t => FromDist(ToDist(Scale(#Logarithm, n)), dist)
     let scaleLogarithmWithThreshold = (dist, n, eps): t => FromDist(

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

@@ -59,11 +59,44 @@ let integralEndY = (t: t): float =>
 
 let isNormalized = (t: t): bool => Js.Math.abs_float(integralEndY(t) -. 1.0) < 1e-7
 
-let klDivergence = (t1, t2, ~toPointSetFn: toPointSetFn): result<float, error> => {
+module Score = {
-  let pointSets = E.R.merge(toPointSetFn(t1), toPointSetFn(t2))
+  let klDivergence = (prediction, answer, ~toPointSetFn: toPointSetFn): result<float, error> => {
-  pointSets |> E.R2.bind(((a, b)) =>
+    let pointSets = E.R.merge(toPointSetFn(prediction), toPointSetFn(answer))
-    PointSetDist.T.klDivergence(a, b)->E.R2.errMap(x => DistributionTypes.OperationError(x))
+    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))
+      )
+    }
+  }
 }
 
 let toFloatOperation = (

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

@@ -23,7 +23,15 @@ let toFloatOperation: (
   ~distToFloatOperation: DistributionTypes.DistributionOperation.toFloat,
 ) => result<float, error>
 
-let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
+module Score: {
+  let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
+  let logScoreWithPointResolution: (
+    ~prediction: t,
+    ~answer: float,
+    ~prior: option<t>,
+    ~toPointSetFn: toPointSetFn,
+  ) => result<float, error>
+}
 
 @genType
 let toPointSet: (

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

@@ -277,13 +277,12 @@ module T = Dist({
       prediction.xyShape,
       answer.xyShape,
     )
-    let xyShapeToContinuous: XYShape.xyShape => t = xyShape => {
+    newShape->E.R2.fmap(x => x->make->integralEndY)
-      xyShape: xyShape,
-      interpolation: #Linear,
-      integralSumCache: None,
-      integralCache: None,
   }
-    newShape->E.R2.fmap(x => x->xyShapeToContinuous->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)
   }
 })
 

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

@@ -229,4 +229,7 @@ module T = Dist({
       answer,
     )->E.R2.fmap(integralEndY)
   }
+  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
+    Error(Operation.NotYetImplemented)
+  }
 })

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

@@ -34,6 +34,11 @@ 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) => {
@@ -57,6 +62,7 @@ module Dist = (T: dist) => {
   let variance = T.variance
   let integralEndY = T.integralEndY
   let klDivergence = T.klDivergence
+  let logScoreWithPointResolution = T.logScoreWithPointResolution
 
   let updateIntegralCache = T.updateIntegralCache
 

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

@@ -306,6 +306,9 @@ module T = Dist({
     let klContinuousPart = Continuous.T.klDivergence(prediction.continuous, answer.continuous)
     E.R.merge(klDiscretePart, klContinuousPart)->E.R2.fmap(t => fst(t) +. snd(t))
   }
+  let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
+    Error(Operation.NotYetImplemented)
+  }
 })
 
 let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {

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

@@ -196,12 +196,21 @@ module T = Dist({
     | Continuous(m) => Continuous.T.variance(m)
     }
 
-  let klDivergence = (t1: t, t2: t) =>
+  let klDivergence = (prediction: t, answer: t) =>
-    switch (t1, t2) {
+    switch (prediction, answer) {
     | (Continuous(t1), Continuous(t2)) => Continuous.T.klDivergence(t1, t2)
     | (Discrete(t1), Discrete(t2)) => Discrete.T.klDivergence(t1, t2)
-    | (Mixed(t1), Mixed(t2)) => Mixed.T.klDivergence(t1, t2)
+    | (m1, m2) => Mixed.T.klDivergence(m1->toMixed, m2->toMixed)
-    | _ => Error(NotYetImplemented)
+    }
+
+  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)
+    }
   }
 })
 

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

@@ -14,3 +14,33 @@ module KLDivergence = {
       quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answerElement *. logFn(quot))
     }
 }
+
+module LogScoreWithPointResolution = {
+  let logFn = Js.Math.log
+  let score = (
+    ~priorPdf: option<float => float>,
+    ~predictionPdf: float => float,
+    ~answer: float,
+  ): result<float, Operation.Error.t> => {
+    let numerator = answer->predictionPdf
+    if numerator < 0.0 {
+      Operation.PdfInvalidError->Error
+    } else if numerator == 0.0 {
+      infinity->Ok
+    } else {
+      -.(
+        switch priorPdf {
+        | None => numerator->logFn
+        | Some(f) => {
+            let priorDensityOfAnswer = f(answer)
+            if priorDensityOfAnswer == 0.0 {
+              neg_infinity
+            } else {
+              (numerator /. priorDensityOfAnswer)->logFn
+            }
+          }
+        }
+      )->Ok
+    }
+  }
+}

packages/squiggle-lang/src/rescript/MagicNumbers.res

@@ -12,6 +12,7 @@ module Epsilon = {
 module Environment = {
   let defaultXYPointLength = 1000
   let defaultSampleCount = 10000
+  let sparklineLength = 20
 }
 
 module OpCost = {

packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_GenericDistribution.res

@@ -1,5 +1,5 @@
 module ExpressionValue = ReducerInterface_ExpressionValue
-type expressionValue = ReducerInterface_ExpressionValue.expressionValue
+type expressionValue = ExpressionValue.expressionValue
 
 module Helpers = {
   let arithmeticMap = r =>
@@ -162,6 +162,20 @@ module Helpers = {
       }
     }
   }
+
+  let klDivergenceWithPrior = (
+    prediction: DistributionTypes.genericDist,
+    answer: DistributionTypes.genericDist,
+    prior: DistributionTypes.genericDist,
+    env: DistributionOperation.env,
+  ) => {
+    let term1 = DistributionOperation.Constructors.klDivergence(~env, prediction, answer)
+    let term2 = DistributionOperation.Constructors.klDivergence(~env, prior, answer)
+    switch E.R.merge(term1, term2)->E.R2.fmap(((a, b)) => a -. b) {
+    | Ok(x) => x->DistributionOperation.Float->Some
+    | Error(_) => None
+    }
+  }
 }
 
 module SymbolicConstructors = {
@@ -236,7 +250,8 @@ let dispatchToGenericOutput = (
   | ("mean", [EvDistribution(dist)]) => Helpers.toFloatFn(#Mean, dist, ~env)
   | ("integralSum", [EvDistribution(dist)]) => Helpers.toFloatFn(#IntegralSum, dist, ~env)
   | ("toString", [EvDistribution(dist)]) => Helpers.toStringFn(ToString, dist, ~env)
-  | ("toSparkline", [EvDistribution(dist)]) => Helpers.toStringFn(ToSparkline(20), dist, ~env)
+  | ("toSparkline", [EvDistribution(dist)]) =>
+    Helpers.toStringFn(ToSparkline(MagicNumbers.Environment.sparklineLength), dist, ~env)
   | ("toSparkline", [EvDistribution(dist), EvNumber(n)]) =>
     Helpers.toStringFn(ToSparkline(Belt.Float.toInt(n)), dist, ~env)
   | ("exp", [EvDistribution(a)]) =>
@@ -249,8 +264,28 @@ let dispatchToGenericOutput = (
       ~env,
     )->Some
   | ("normalize", [EvDistribution(dist)]) => Helpers.toDistFn(Normalize, dist, ~env)
-  | ("klDivergence", [EvDistribution(a), EvDistribution(b)]) =>
+  | ("klDivergence", [EvDistribution(prediction), EvDistribution(answer)]) =>
-    Some(DistributionOperation.run(FromDist(ToScore(KLDivergence(b)), a), ~env))
+    Some(DistributionOperation.run(FromDist(ToScore(KLDivergence(answer)), prediction), ~env))
+  | ("klDivergence", [EvDistribution(prediction), EvDistribution(answer), EvDistribution(prior)]) =>
+    Helpers.klDivergenceWithPrior(prediction, answer, prior, env)
+  | (
+    "logScoreWithPointAnswer",
+    [EvDistribution(prediction), EvNumber(answer), EvDistribution(prior)],
+  )
+  | (
+    "logScoreWithPointAnswer",
+    [EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer))), EvDistribution(prior)],
+  ) =>
+    DistributionOperation.run(
+      FromDist(ToScore(LogScore(answer, prior->Some)), prediction),
+      ~env,
+    )->Some
+  | ("logScoreWithPointAnswer", [EvDistribution(prediction), EvNumber(answer)])
+  | (
+    "logScoreWithPointAnswer",
+    [EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer)))],
+  ) =>
+    DistributionOperation.run(FromDist(ToScore(LogScore(answer, None)), prediction), ~env)->Some
   | ("isNormalized", [EvDistribution(dist)]) => Helpers.toBoolFn(IsNormalized, dist, ~env)
   | ("toPointSet", [EvDistribution(dist)]) => Helpers.toDistFn(ToPointSet, dist, ~env)
   | ("scaleLog", [EvDistribution(dist)]) =>

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

@@ -620,6 +620,19 @@ module A = {
       | Some(o) => o
       | None => []
       }
+    // REturns `None` there are no non-`None` elements
+    let rec arrSomeToSomeArr = (optionals: array<option<'a>>): option<array<'a>> => {
+      let optionals' = optionals->Belt.List.fromArray
+      switch optionals' {
+      | list{} => []->Some
+      | list{x, ...xs} =>
+        switch x {
+        | Some(_) => xs->Belt.List.toArray->arrSomeToSomeArr
+        | None => None
+        }
+      }
+    }
+    let firstSome = x => Belt.Array.getBy(x, O.isSome)
   }
 
   module R = {

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

@@ -55,7 +55,7 @@ type operationError =
   | ComplexNumberError
   | InfinityError
   | NegativeInfinityError
-  | LogicallyInconsistentPathwayError
+  | PdfInvalidError
   | NotYetImplemented // should be removed when `klDivergence` for mixed and discrete is implemented.
 
 @genType
@@ -69,7 +69,7 @@ module Error = {
     | ComplexNumberError => "Operation returned complex result"
     | InfinityError => "Operation returned positive infinity"
     | NegativeInfinityError => "Operation returned negative infinity"
-    | LogicallyInconsistentPathwayError => "This pathway should have been logically unreachable"
+    | PdfInvalidError => "This Pdf is invalid"
     | NotYetImplemented => "This pathway is not yet implemented"
     }
 }