logScore now in interface.

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

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

@@ -145,7 +145,11 @@ 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(prediction, answer)) =>
+      GenericDist.Score.logScore(dist, prediction, answer, ~toPointSetFn)
       ->E.R2.fmap(r => Float(r))
       ->OutputLocal.fromResult
     | ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
@@ -262,6 +266,8 @@ 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 logScore = (~env, prior, prediction, answer) =>
+    C.logScore(prior, prediction, answer)->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

@@ -62,6 +62,8 @@ module Constructors: {
   @genType
   let klDivergence: (~env: env, genericDist, genericDist) => result<float, error>
   @genType
+  let logScore: (~env: env, genericDist, genericDist, float) => 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(genericDist, float)
 
   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 logScore = (prior, prediction, answer): t => FromDist(
+      ToScore(LogScore(prediction, answer)),
+      prior,
+    )
     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

@@ -59,6 +59,7 @@ let integralEndY = (t: t): float =>
 
 let isNormalized = (t: t): bool => Js.Math.abs_float(integralEndY(t) -. 1.0) < 1e-7
 
+module Score = {
   let klDivergence = (t1, t2, ~toPointSetFn: toPointSetFn): result<float, error> => {
     let pointSets = E.R.merge(toPointSetFn(t1), toPointSetFn(t2))
     pointSets |> E.R2.bind(((a, b)) =>
@@ -66,6 +67,14 @@ let klDivergence = (t1, t2, ~toPointSetFn: toPointSetFn): result<float, error> =
     )
   }
 
+  let logScore = (prior, prediction, answer, ~toPointSetFn: toPointSetFn): result<float, error> => {
+    let pointSets = E.R.merge(toPointSetFn(prior), toPointSetFn(prediction))
+    pointSets |> E.R2.bind(((a, b)) =>
+      PointSetDist.T.logScore(a, b, answer)->E.R2.errMap(x => DistributionTypes.OperationError(x))
+    )
+  }
+}
+
 let toFloatOperation = (
   t,
   ~toPointSetFn: toPointSetFn,

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

@@ -23,7 +23,10 @@ let toFloatOperation: (
   ~distToFloatOperation: DistributionTypes.DistributionOperation.toFloat,
 ) => result<float, error>
 
+module Score: {
   let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
+  let logScore: (t, t, float, ~toPointSetFn: toPointSetFn) => result<float, error>
+}
 
 @genType
 let toPointSet: (

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

@@ -287,10 +287,6 @@ module T = Dist({
     )
     newShape->E.R2.fmap(x => x->make->integralEndY)
   }
-  let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
-    let prior = make({xs: prediction.xyShape.xs, ys: E.A.fmap(_ => 1.0, prediction.xyShape.xs)})
-    logScore(prior, prediction, answer)
-  }
 })
 
 let isNormalized = (t: t): bool => {

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

@@ -232,7 +232,4 @@ module T = Dist({
   let logScore = (prior: t, prediction: t, answer: float) => {
     Error(Operation.NotYetImplemented)
   }
-  let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
-    Error(Operation.NotYetImplemented)
-  }
 })

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

@@ -35,7 +35,6 @@ module type dist = {
   let variance: t => float
   let klDivergence: (t, t) => result<float, Operation.Error.t>
   let logScore: (t, t, float) => result<float, Operation.Error.t>
-  let logScoreAgainstImproperPrior: (t, float) => result<float, Operation.Error.t>
 }
 
 module Dist = (T: dist) => {
@@ -60,7 +59,6 @@ module Dist = (T: dist) => {
   let integralEndY = T.integralEndY
   let klDivergence = T.klDivergence
   let logScore = T.logScore
-  let logScoreAgainstImproperPrior = T.logScoreAgainstImproperPrior
 
   let updateIntegralCache = T.updateIntegralCache
 

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

@@ -309,9 +309,6 @@ module T = Dist({
   let logScore = (prior: t, prediction: t, answer: float) => {
     Error(Operation.NotYetImplemented)
   }
-  let logScoreAgainstImproperPrior = (prediction: t, answer: float) => {
-    Error(Operation.NotYetImplemented)
-  }
 })
 
 let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {

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

@@ -157,6 +157,20 @@ module Helpers = {
       }
     }
   }
+  let constructNonNormalizedPointSet = (
+    ~supportOf: DistributionTypes.genericDist,
+    fn: float => float,
+  ): option<DistributionTypes.genericDist> => {
+    switch supportOf {
+    | PointSet(Continuous(dist)) =>
+      {xs: dist.xyShape.xs, ys: E.A.fmap(fn, dist.xyShape.xs)}
+      ->Continuous.make
+      ->Continuous
+      ->PointSet
+      ->Some
+    | _ => None
+    }
+  }
 }
 
 module SymbolicConstructors = {
@@ -219,7 +233,8 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment)
   | ("mean", [EvDistribution(dist)]) => Helpers.toFloatFn(#Mean, dist)
   | ("integralSum", [EvDistribution(dist)]) => Helpers.toFloatFn(#IntegralSum, dist)
   | ("toString", [EvDistribution(dist)]) => Helpers.toStringFn(ToString, dist)
-  | ("toSparkline", [EvDistribution(dist)]) => Helpers.toStringFn(ToSparkline(20), dist)
+  | ("toSparkline", [EvDistribution(dist)]) =>
+    Helpers.toStringFn(ToSparkline(MagicNumbers.Environment.sparklineLength), dist)
   | ("toSparkline", [EvDistribution(dist), EvNumber(n)]) =>
     Helpers.toStringFn(ToSparkline(Belt.Float.toInt(n)), dist)
   | ("exp", [EvDistribution(a)]) =>
@@ -233,6 +248,21 @@ let dispatchToGenericOutput = (call: ExpressionValue.functionCall, _environment)
   | ("normalize", [EvDistribution(dist)]) => Helpers.toDistFn(Normalize, dist)
   | ("klDivergence", [EvDistribution(a), EvDistribution(b)]) =>
     Some(runGenericOperation(FromDist(ToScore(KLDivergence(b)), a)))
+  | ("logScore", [EvDistribution(prior), EvDistribution(prediction), EvNumber(answer)])
+  | (
+    "logScore",
+    [EvDistribution(prior), EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer)))],
+  ) =>
+    Some(runGenericOperation(FromDist(ToScore(LogScore(prediction, answer)), prior)))
+  | ("logScoreAgainstImproperPrior", [EvDistribution(prediction), EvNumber(answer)])
+  | (
+    "logScoreAgainstImproperPrior",
+    [EvDistribution(prediction), EvDistribution(Symbolic(#Float(answer)))],
+  ) =>
+    E.O.fmap(
+      d => runGenericOperation(FromDist(ToScore(LogScore(prediction, answer)), d)),
+      Helpers.constructNonNormalizedPointSet(~supportOf=prediction, _ => 1.0),
+    )
   | ("isNormalized", [EvDistribution(dist)]) => Helpers.toBoolFn(IsNormalized, dist)
   | ("toPointSet", [EvDistribution(dist)]) => Helpers.toDistFn(ToPointSet, dist)
   | ("scaleLog", [EvDistribution(dist)]) =>