reimplement splitContinuousAndDiscreteForMinWeight

packages/squiggle-lang/__tests__/E/splitContinuousAndDiscrete_test.res

@@ -9,19 +9,19 @@ let prepareInputs = (ar, minWeight) =>
 describe("Continuous and discrete splits", () => {
   makeTest(
     "is empty, with no common elements",
-    prepareInputs([1.432, 1.33455, 2.0], 2),
+    prepareInputs([1.33455, 1.432, 2.0], 2),
     ([1.33455, 1.432, 2.0], []),
   )
 
   makeTest(
     "only stores 3.5 as discrete when minWeight is 3",
-    prepareInputs([1.432, 1.33455, 2.0, 2.0, 3.5, 3.5, 3.5], 3),
+    prepareInputs([1.33455, 1.432, 2.0, 2.0, 3.5, 3.5, 3.5], 3),
     ([1.33455, 1.432, 2.0, 2.0], [(3.5, 3.0)]),
   )
 
   makeTest(
     "doesn't store 3.5 as discrete when minWeight is 5",
-    prepareInputs([1.432, 1.33455, 2.0, 2.0, 3.5, 3.5, 3.5], 5),
+    prepareInputs([1.33455, 1.432, 2.0, 2.0, 3.5, 3.5, 3.5], 5),
     ([1.33455, 1.432, 2.0, 2.0, 3.5, 3.5, 3.5], []),
   )
 

packages/squiggle-lang/scripts/bench-sampleset-to-pointset.mjs

@@ -0,0 +1,22 @@
+#!/usr/bin/env node
+import { SqProject } from "@quri/squiggle-lang";
+import { measure } from "./lib.mjs";
+
+const maxP = 7;
+
+for (let p = 0; p <= maxP; p++) {
+  const size = Math.pow(10, p);
+  const project = SqProject.create();
+  project.setSource(
+    "main",
+    `
+    List.upTo(1, ${size}) -> map({|x|
+        normal(x,2) -> SampleSet.fromDist -> PointSet.fromDist
+    })->List.last
+    `
+  );
+  const time = measure(() => {
+    project.run("main");
+  });
+  console.log(`1e${p}`, "\t", time);
+}

packages/squiggle-lang/src/rescript/Distributions/SampleSetDist/SampleSetDist_ToPointSet.res

@@ -33,19 +33,19 @@ module Internals = {
 
   module KDE = {
     let normalSampling = (samples, outputXYPoints, kernelWidth) =>
-      samples |> JS.samplesToContinuousPdf(_, outputXYPoints, kernelWidth) |> JS.jsToDist
+      samples -> JS.samplesToContinuousPdf(outputXYPoints, kernelWidth) -> JS.jsToDist
   }
 
   module T = {
     type t = array<float>
 
     let xWidthToUnitWidth = (samples, outputXYPoints, xWidth) => {
-      let xyPointRange = E.A.Sorted.range(samples) |> E.O.default(0.0)
+      let xyPointRange = E.A.Sorted.range(samples) -> E.O2.default(0.0)
       let xyPointWidth = xyPointRange /. float_of_int(outputXYPoints)
       xWidth /. xyPointWidth
     }
 
-    let formatUnitWidth = w => Jstat.max([w, 1.0]) |> int_of_float
+    let formatUnitWidth = w => Jstat.max([w, 1.0]) -> int_of_float
 
     let suggestedUnitWidth = (samples, outputXYPoints) => {
       let suggestedXWidth = SampleSetDist_Bandwidth.nrd0(samples)
@@ -62,23 +62,24 @@ let toPointSetDist = (
   ~samplingInputs: SamplingInputs.samplingInputs,
   (),
 ): Internals.Types.outputs => {
-  let samples = Js.Array2.copy(samples)
-  Array.fast_sort(compare, samples)
+  let samples = samples->Js.Array2.copy->Js.Array2.sortInPlaceWith(compare)
+
   let minDiscreteToKeep = MagicNumbers.ToPointSet.minDiscreteToKeep(samples)
   let (continuousPart, discretePart) = E.A.Floats.Sorted.splitContinuousAndDiscreteForMinWeight(
     samples,
     ~minDiscreteWeight=minDiscreteToKeep,
   )
-  let length = samples |> E.A.length |> float_of_int
+
+  let length = samples->E.A.length->float_of_int
   let discrete: PointSetTypes.discreteShape =
     discretePart
-    |> E.FloatFloatMap.fmap(r => r /. length)
-    |> E.FloatFloatMap.toArray
-    |> XYShape.T.fromZippedArray
-    |> Discrete.make
+    ->E.FloatFloatMap.fmap(r => r /. length, _)
+    ->E.FloatFloatMap.toArray
+    ->XYShape.T.fromZippedArray
+    ->Discrete.make
 
   let pdf =
-    continuousPart |> E.A.length > 5
+    continuousPart->E.A.length > 5
       ? {
           let _suggestedXWidth = SampleSetDist_Bandwidth.nrd0(continuousPart)
           // todo: This does some recalculating from the last step.
@@ -86,7 +87,7 @@ let toPointSetDist = (
             continuousPart,
             samplingInputs.outputXYPoints,
           )
-          let usedWidth = samplingInputs.kernelWidth |> E.O.default(_suggestedXWidth)
+          let usedWidth = samplingInputs.kernelWidth -> E.O2.default(_suggestedXWidth)
           let usedUnitWidth = Internals.T.xWidthToUnitWidth(
             samples,
             samplingInputs.outputXYPoints,
@@ -101,18 +102,18 @@ let toPointSetDist = (
             bandwidthUnitImplemented: usedUnitWidth,
           }
           continuousPart
-          |> Internals.T.kde(
+          ->Internals.T.kde(
             ~samples=_,
             ~outputXYPoints=samplingInputs.outputXYPoints,
             Internals.T.formatUnitWidth(usedUnitWidth),
           )
-          |> Continuous.make
-          |> (r => Some((r, samplingStats)))
+          ->Continuous.make
+          ->(r => Some((r, samplingStats)))
         }
       : None
 
   let pointSetDist = MixedShapeBuilder.buildSimple(
-    ~continuous=pdf |> E.O.fmap(fst),
+    ~continuous=pdf->E.O2.fmap(fst),
     ~discrete=Some(discrete),
   )
 
@@ -125,7 +126,7 @@ let toPointSetDist = (
   let normalizedPointSet = pointSetDist->E.O2.fmap(PointSetDist.T.normalize)
 
   let samplesParse: Internals.Types.outputs = {
-    continuousParseParams: pdf |> E.O.fmap(snd),
+    continuousParseParams: pdf -> E.O2.fmap(snd),
     pointSetDist: normalizedPointSet,
   }
 

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

@@ -305,55 +305,49 @@ module Floats = {
     /*
       This function goes through a sorted array and divides it into two different clusters:
       continuous samples and discrete samples. The discrete samples are stored in a mutable map.
-      Samples are thought to be discrete if they have any duplicates.
- */
-    let _splitContinuousAndDiscreteForDuplicates = (sortedArray: array<float>) => {
-      let continuous: array<float> = []
-      let discrete = FloatFloatMap.empty()
-      Belt.Array.forEachWithIndex(sortedArray, (index, element) => {
-        let maxIndex = (sortedArray |> Array.length) - 1
-        let possiblySimilarElements = switch index {
-        | 0 => [index + 1]
-        | n if n == maxIndex => [index - 1]
-        | _ => [index - 1, index + 1]
-        } |> Belt.Array.map(_, r => sortedArray[r])
-        let hasSimilarElement = Belt.Array.some(possiblySimilarElements, r => r == element)
-        hasSimilarElement
-          ? FloatFloatMap.increment(element, discrete)
-          : {
-              let _ = Js.Array.push(element, continuous)
-            }
+      Samples are thought to be discrete if they have at least `minDiscreteWight` duplicates.
 
-        ()
-      })
-
-      (continuous, discrete)
-    }
-
-    /*
-      This function works very similarly to splitContinuousAndDiscreteForDuplicates. The one major difference
-      is that you can specify a minDiscreteWeight.  If the min discreet weight is 4, that would mean that
-      at least four elements needed from a specific value for that to be kept as discrete. This is important
-      because in some cases, we can expect that some common elements will be generated by regular operations.
-      The final continous array will be sorted.
+      If the min discreet weight is 4, that would mean that at least four elements needed from a specific
+      value for that to be kept as discrete. This is important because in some cases, we can expect that
+      some common elements will be generated by regular operations. The final continous array will be sorted.
  */
     let splitContinuousAndDiscreteForMinWeight = (
       sortedArray: array<float>,
       ~minDiscreteWeight: int,
     ) => {
-      let (continuous, discrete) = _splitContinuousAndDiscreteForDuplicates(sortedArray)
-      let keepFn = v => Belt.Float.toInt(v) >= minDiscreteWeight
-      let (discreteToKeep, discreteToIntegrate) = FloatFloatMap.partition(
-        ((_, v)) => keepFn(v),
-        discrete,
-      )
-      let newContinousSamples =
-        discreteToIntegrate->FloatFloatMap.toArray
-        |> fmap(((k, v)) => Belt.Array.makeBy(Belt.Float.toInt(v), _ => k))
-        |> Belt.Array.concatMany
-      let newContinuous = concat(continuous, newContinousSamples)
-      newContinuous |> Array.fast_sort(floatCompare)
-      (newContinuous, discreteToKeep)
+      let continuous: array<float> = []
+      let discrete = FloatFloatMap.empty()
+
+      let flush = (cnt: int, value: float): unit => {
+        if cnt >= minDiscreteWeight {
+          FloatFloatMap.add(value, cnt->Belt.Int.toFloat, discrete)
+        } else {
+          for _ in 1 to cnt {
+            let _ = continuous->Js.Array2.push(value)
+          }
+        }
+      }
+
+      if sortedArray->Js.Array2.length != 0 {
+        let (finalCnt, finalValue) = sortedArray->Belt.Array.reduce(
+          // initial prev value doesn't matter; if it collides with the first element of the array, flush won't do anything
+          (0, 0.),
+          ((cnt, prev), element) => {
+            if element == prev {
+              (cnt + 1, prev)
+            } else {
+              // new value, process previous ones
+              flush(cnt, prev)
+              (1, element)
+            }
+          }
+        )
+
+        // flush final values
+        flush(finalCnt, finalValue)
+      }
+
+      (continuous, discrete)
     }
   }
 }

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

@@ -16,6 +16,14 @@ let increment = (el, t: t) =>
     }
   )
 
+let add = (el, amount: float, t: t) =>
+  Belt.MutableMap.update(t, el, x =>
+    switch x {
+    | Some(n) => Some(n +. amount)
+    | None => Some(amount)
+    }
+  )
+
 let get = (el, t: t) => Belt.MutableMap.get(t, el)
 let fmap = (fn, t: t) => Belt.MutableMap.map(t, fn)
 let partition = (fn, t: t) => {