klDivergence on mixed distributions works for one test case

Value: [1e-4 to 5e-2]

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

@@ -5,7 +5,6 @@ open GenericDist_Fixtures
 
 describe("klDivergence: continuous -> continuous -> float", () => {
   let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
-  exception KlFailed
 
   let testUniform = (lowAnswer, highAnswer, lowPrediction, highPrediction) => {
     test("of two uniforms is equal to the analytic expression", () => {
@@ -59,6 +58,24 @@ describe("klDivergence: continuous -> continuous -> float", () => {
       }
     }
   })
+
+  test("of a normal and a uniform is equal to the formula", () => {
+    let prediction = normalDist10
+    let answer = uniformDist
+    let kl = klDivergence(prediction, answer)
+    let analyticalKl =
+      -.Js.Math.log((10.0 -. 9.0) /. Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. 2.0 ** 2.0)) +.
+      1.0 /.
+      2.0 ** 2.0 *.
+      (10.0 ** 2.0 -. (10.0 +. 9.0) *. 10.0 +. (9.0 ** 2.0 +. 10.0 *. 9.0 +. 10.0 ** 2.0) /. 3.0)
+    switch kl {
+    | Ok(kl') => kl'->expect->toBeSoCloseTo(analyticalKl, ~digits=1)
+    | Error(err) => {
+        Js.Console.log(DistributionTypes.Error.toString(err))
+        raise(KlFailed)
+      }
+    }
+  })
 })
 
 describe("klDivergence: discrete -> discrete -> float", () => {
@@ -96,6 +113,47 @@ describe("klDivergence: discrete -> discrete -> float", () => {
   })
 })
 
+describe("klDivergence: mixed -> mixed -> float", () => {
+  let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
+  let mixture = a => DistributionTypes.DistributionOperation.Mixture(a)
+  let a' = [(floatDist, 1e0), (uniformDist, 1e0)]->mixture->run
+  let b' = [(point3, 1e0), (floatDist, 1e0), (normalDist10, 1e0)]->mixture->run
+  let (a, b) = switch (a', b') {
+  | (Dist(a''), Dist(b'')) => (a'', b'')
+  | _ => raise(MixtureFailed)
+  }
+  test("finite klDivergence returns is correct", () => {
+    let prediction = b
+    let answer = a
+    let kl = klDivergence(prediction, answer)
+    // high = 10; low = 9; mean = 10; stdev = 2
+    let analyticalKlContinuousPart =
+      Js.Math.log(Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. 2.0 ** 2.0) /. (10.0 -. 9.0)) +.
+      1.0 /.
+      2.0 ** 2.0 *.
+      (10.0 ** 2.0 -. (10.0 +. 9.0) *. 10.0 +. (9.0 ** 2.0 +. 10.0 *. 9.0 +. 10.0 ** 2.0) /. 3.0)
+    let analyticalKlDiscretePart = -2.0 /. 3.0 *. Js.Math.log(3.0 /. 2.0)
+    switch kl {
+    | Ok(kl') =>
+      kl'->expect->toBeSoCloseTo(analyticalKlContinuousPart +. analyticalKlDiscretePart, ~digits=0)
+    | Error(err) =>
+      Js.Console.log(DistributionTypes.Error.toString(err))
+      raise(KlFailed)
+    }
+  })
+  test("returns infinity when infinite", () => {
+    let prediction = a
+    let answer = b
+    let kl = klDivergence(prediction, answer)
+    switch kl {
+    | Ok(kl') => kl'->expect->toEqual(infinity)
+    | Error(err) =>
+      Js.Console.log(DistributionTypes.Error.toString(err))
+      raise(KlFailed)
+    }
+  })
+})
+
 describe("combineAlongSupportOfSecondArgument0", () => {
   // This tests the version of the function that we're NOT using. Haven't deleted the test in case we use the code later.
   test("test on two uniforms", _ => {

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

@@ -302,10 +302,9 @@ module T = Dist({
   }
 
   let klDivergence = (prediction: t, answer: t) => {
-    Error(Operation.NotYetImplemented)
-    //    combinePointwise(PointSetDist_Scoring.KLDivergence.integrand, prediction, answer) |> E.R.fmap(
-    //     integralEndY,
-    //   )
+    let klDiscretePart = Discrete.T.klDivergence(prediction.discrete, answer.discrete)
+    let klContinuousPart = Continuous.T.klDivergence(prediction.continuous, answer.continuous)
+    E.R.merge(klDiscretePart, klContinuousPart)->E.R2.fmap(t => fst(t) +. snd(t))
   }
 })
 

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

@@ -200,6 +200,7 @@ module T = Dist({
     switch (t1, t2) {
     | (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)
     | _ => Error(NotYetImplemented)
     }
 })