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Merge branch 'develop' into multiple-charts

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This commit is contained in:
Sam Nolan 2022-07-19 11:40:59 +10:00
commit 7e33ea1fe7
35 changed files with 744 additions and 475 deletions
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12
packages/components/src/components/SquigglePlayground.tsx
View File

@ -215,31 +215,31 @@ const ViewSettings: React.FC<{ register: UseFormRegister<FormFields> }> = ({
name="minX"
type="number"
register={register}
label="The minimum of the charted distribution domain"
label="Min X Value"
/>
<InputItem
name="maxX"
type="number"
register={register}
label="The maximum of the charted distribution domain"
label="Max X Value"
/>
<InputItem
name="title"
type="text"
register={register}
label="The title shown on the distribution"
label="Title"
/>
<InputItem
name="tickFormat"
type="text"
register={register}
label="The format that the ticks are rendered"
label="Tick Format"
/>
<InputItem
name="color"
type="color"
register={register}
label="The color of the charted distribution"
label="Color"
/>
</div>
</HeadedSection>
@ -521,7 +521,7 @@ export const SquigglePlayground: FC<PlaygroundProps> = ({
);
const withEditor = (
<div className="flex mt-1">
<div className="flex mt-2">
<div className="w-1/2">{tabs}</div>
<div className="w-1/2 p-2 pl-4">{squiggleChart}</div>
</div>

4
packages/components/src/components/ui/Toggle.tsx
View File

@ -23,8 +23,8 @@ export const Toggle: React.FC<Props> = ({
layout
transition={{ duration: 0.2 }}
className={clsx(
"rounded-full py-1 bg-indigo-500 text-white text-xs font-semibold flex items-center space-x-1",
status ? "bg-indigo-500" : "bg-gray-400",
"rounded-md py-0.5 bg-slate-500 text-white text-xs font-semibold flex items-center space-x-1",
status ? "bg-slate-500" : "bg-gray-400",
status ? "pl-1 pr-3" : "pl-3 pr-1",
!status && "flex-row-reverse space-x-reverse"
)}

16
packages/squiggle-lang/__tests__/Distributions/DistributionOperation_test.res
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@ -1,7 +1,7 @@
open Jest
open Expect
let env: DistributionOperation.env = {
let env: GenericDist.env = {
sampleCount: 100,
xyPointLength: 100,
}
@ -34,7 +34,7 @@ describe("sparkline", () => {
expected: DistributionOperation.outputType,
) => {
test(name, () => {
let result = DistributionOperation.run(~env, FromDist(ToString(ToSparkline(20)), dist))
let result = DistributionOperation.run(~env, FromDist(#ToString(ToSparkline(20)), dist))
expect(result)->toEqual(expected)
})
}
@ -81,8 +81,8 @@ describe("sparkline", () => {
describe("toPointSet", () => {
test("on symbolic normal distribution", () => {
let result =
run(FromDist(ToDist(ToPointSet), normalDist5))
->outputMap(FromDist(ToFloat(#Mean)))
run(FromDist(#ToDist(ToPointSet), normalDist5))
->outputMap(FromDist(#ToFloat(#Mean)))
->toFloat
->toExt
expect(result)->toBeSoCloseTo(5.0, ~digits=0)
@ -90,10 +90,10 @@ describe("toPointSet", () => {
test("on sample set", () => {
let result =
run(FromDist(ToDist(ToPointSet), normalDist5))
->outputMap(FromDist(ToDist(ToSampleSet(1000))))
->outputMap(FromDist(ToDist(ToPointSet)))
->outputMap(FromDist(ToFloat(#Mean)))
run(FromDist(#ToDist(ToPointSet), normalDist5))
->outputMap(FromDist(#ToDist(ToSampleSet(1000))))
->outputMap(FromDist(#ToDist(ToPointSet)))
->outputMap(FromDist(#ToFloat(#Mean)))
->toFloat
->toExt
expect(result)->toBeSoCloseTo(5.0, ~digits=-1)

7
packages/squiggle-lang/__tests__/Distributions/GenericDist_Fixtures.res
View File

@ -19,7 +19,6 @@ exception MixtureFailed
let float1 = 1.0
let float2 = 2.0
let float3 = 3.0
let {mkDelta} = module(TestHelpers)
let point1 = mkDelta(float1)
let point2 = mkDelta(float2)
let point3 = mkDelta(float3)
let point1 = TestHelpers.mkDelta(float1)
let point2 = TestHelpers.mkDelta(float2)
let point3 = TestHelpers.mkDelta(float3)

6
packages/squiggle-lang/__tests__/Distributions/Mixture_test.res
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@ -11,7 +11,7 @@ describe("mixture", () => {
let (mean1, mean2) = tup
let meanValue = {
run(Mixture([(mkNormal(mean1, 9e-1), 0.5), (mkNormal(mean2, 9e-1), 0.5)]))->outputMap(
FromDist(ToFloat(#Mean)),
FromDist(#ToFloat(#Mean)),
)
}
meanValue->unpackFloat->expect->toBeSoCloseTo((mean1 +. mean2) /. 2.0, ~digits=-1)
@ -28,7 +28,7 @@ describe("mixture", () => {
let meanValue = {
run(
Mixture([(mkBeta(alpha, beta), betaWeight), (mkExponential(rate), exponentialWeight)]),
)->outputMap(FromDist(ToFloat(#Mean)))
)->outputMap(FromDist(#ToFloat(#Mean)))
}
let betaMean = 1.0 /. (1.0 +. beta /. alpha)
let exponentialMean = 1.0 /. rate
@ -52,7 +52,7 @@ describe("mixture", () => {
(mkUniform(low, high), uniformWeight),
(mkLognormal(mu, sigma), lognormalWeight),
]),
)->outputMap(FromDist(ToFloat(#Mean)))
)->outputMap(FromDist(#ToFloat(#Mean)))
}
let uniformMean = (low +. high) /. 2.0
let lognormalMean = mu +. sigma ** 2.0 /. 2.0

15
packages/squiggle-lang/__tests__/Distributions/KlDivergence_test.res → packages/squiggle-lang/__tests__/Distributions/Scoring/KlDivergence_test.res
View File

@ -3,6 +3,7 @@ open Expect
open TestHelpers
open GenericDist_Fixtures
let klDivergence = DistributionOperation.Constructors.LogScore.distEstimateDistAnswer(~env)
// integral from low to high of 1 / (high - low) log(normal(mean, stdev)(x) / (1 / (high - low))) dx
let klNormalUniform = (mean, stdev, low, high): float =>
-.Js.Math.log((high -. low) /. Js.Math.sqrt(2.0 *. MagicNumbers.Math.pi *. stdev ** 2.0)) +.
@ -11,8 +12,6 @@ let klNormalUniform = (mean, stdev, low, high): float =>
(mean ** 2.0 -. (high +. low) *. mean +. (low ** 2.0 +. high *. low +. high ** 2.0) /. 3.0)
describe("klDivergence: continuous -> continuous -> float", () => {
let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
let testUniform = (lowAnswer, highAnswer, lowPrediction, highPrediction) => {
test("of two uniforms is equal to the analytic expression", () => {
let answer =
@ -58,7 +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)
@ -82,7 +81,6 @@ describe("klDivergence: continuous -> continuous -> float", () => {
})
describe("klDivergence: discrete -> discrete -> float", () => {
let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
let mixture = a => DistributionTypes.DistributionOperation.Mixture(a)
let a' = [(point1, 1e0), (point2, 1e0)]->mixture->run
let b' = [(point1, 1e0), (point2, 1e0), (point3, 1e0)]->mixture->run
@ -117,7 +115,6 @@ describe("klDivergence: discrete -> discrete -> float", () => {
})
describe("klDivergence: mixed -> mixed -> float", () => {
let klDivergence = DistributionOperation.Constructors.klDivergence(~env)
let mixture' = a => DistributionTypes.DistributionOperation.Mixture(a)
let mixture = a => {
let dist' = a->mixture'->run
@ -189,15 +186,15 @@ describe("combineAlongSupportOfSecondArgument0", () => {
uniformMakeR(lowPrediction, highPrediction)->E.R2.errMap(s => DistributionTypes.ArgumentError(
s,
))
let answerWrapped = E.R.fmap(a => run(FromDist(ToDist(ToPointSet), a)), answer)
let predictionWrapped = E.R.fmap(a => run(FromDist(ToDist(ToPointSet), a)), prediction)
let answerWrapped = E.R.fmap(a => run(FromDist(#ToDist(ToPointSet), a)), answer)
let predictionWrapped = E.R.fmap(a => run(FromDist(#ToDist(ToPointSet), a)), prediction)
let interpolator = XYShape.XtoY.continuousInterpolator(#Stepwise, #UseZero)
let integrand = PointSetDist_Scoring.KLDivergence.integrand
let integrand = PointSetDist_Scoring.WithDistAnswer.integrand
let result = switch (answerWrapped, predictionWrapped) {
| (Ok(Dist(PointSet(Continuous(a)))), Ok(Dist(PointSet(Continuous(b))))) =>
Some(combineAlongSupportOfSecondArgument(integrand, interpolator, a.xyShape, b.xyShape))
Some(combineAlongSupportOfSecondArgument(interpolator, integrand, a.xyShape, b.xyShape))
| _ => None
}
result

68
packages/squiggle-lang/__tests__/Distributions/Scoring/WithScalarAnswer_test.res Normal file
View File

@ -0,0 +1,68 @@
open Jest
open Expect
open TestHelpers
open GenericDist_Fixtures
exception ScoreFailed
describe("WithScalarAnswer: discrete -> scalar -> score", () => {
let mixture = a => DistributionTypes.DistributionOperation.Mixture(a)
let pointA = mkDelta(3.0)
let pointB = mkDelta(2.0)
let pointC = mkDelta(1.0)
let pointD = mkDelta(0.0)
test("score: agrees with analytical answer when finite", () => {
let prediction' = [(pointA, 0.25), (pointB, 0.25), (pointC, 0.25), (pointD, 0.25)]->mixture->run
let prediction = switch prediction' {
| Dist(PointSet(p)) => p
| _ => raise(MixtureFailed)
}
let answer = 2.0 // So this is: assigning 100% probability to 2.0
let result = PointSetDist_Scoring.WithScalarAnswer.score(~estimate=prediction, ~answer)
switch result {
| Ok(x) => x->expect->toEqual(-.Js.Math.log(0.25 /. 1.0))
| _ => raise(ScoreFailed)
}
})
test("score: agrees with analytical answer when finite", () => {
let prediction' = [(pointA, 0.75), (pointB, 0.25)]->mixture->run
let prediction = switch prediction' {
| Dist(PointSet(p)) => p
| _ => raise(MixtureFailed)
}
let answer = 3.0 // So this is: assigning 100% probability to 2.0
let result = PointSetDist_Scoring.WithScalarAnswer.score(~estimate=prediction, ~answer)
switch result {
| Ok(x) => x->expect->toEqual(-.Js.Math.log(0.75 /. 1.0))
| _ => raise(ScoreFailed)
}
})
test("scoreWithPrior: agrees with analytical answer when finite", () => {
let prior' = [(pointA, 0.5), (pointB, 0.5)]->mixture->run
let prediction' = [(pointA, 0.75), (pointB, 0.25)]->mixture->run
let prediction = switch prediction' {
| Dist(PointSet(p)) => p
| _ => raise(MixtureFailed)
}
let prior = switch prior' {
| Dist(PointSet(p)) => p
| _ => raise(MixtureFailed)
}
let answer = 3.0 // So this is: assigning 100% probability to 2.0
let result = PointSetDist_Scoring.WithScalarAnswer.scoreWithPrior(
~estimate=prediction,
~answer,
~prior,
)
switch result {
| Ok(x) => x->expect->toEqual(-.Js.Math.log(0.75 /. 1.0) -. -.Js.Math.log(0.5 /. 1.0))
| _ => raise(ScoreFailed)
}
})
})

26
packages/squiggle-lang/__tests__/Distributions/Symbolic_test.res
View File

@ -8,34 +8,34 @@ let mkNormal = (mean, stdev) => DistributionTypes.Symbolic(#Normal({mean: mean,
describe("(Symbolic) normalize", () => {
testAll("has no impact on normal distributions", list{-1e8, -1e-2, 0.0, 1e-4, 1e16}, mean => {
let normalValue = mkNormal(mean, 2.0)
let normalizedValue = run(FromDist(ToDist(Normalize), normalValue))
let normalizedValue = run(FromDist(#ToDist(Normalize), normalValue))
normalizedValue->unpackDist->expect->toEqual(normalValue)
})
})
describe("(Symbolic) mean", () => {
testAll("of normal distributions", list{-1e8, -16.0, -1e-2, 0.0, 1e-4, 32.0, 1e16}, mean => {
run(FromDist(ToFloat(#Mean), mkNormal(mean, 4.0)))->unpackFloat->expect->toBeCloseTo(mean)
run(FromDist(#ToFloat(#Mean), mkNormal(mean, 4.0)))->unpackFloat->expect->toBeCloseTo(mean)
})
Skip.test("of normal(0, -1) (it NaNs out)", () => {
run(FromDist(ToFloat(#Mean), mkNormal(1e1, -1e0)))->unpackFloat->expect->ExpectJs.toBeFalsy
run(FromDist(#ToFloat(#Mean), mkNormal(1e1, -1e0)))->unpackFloat->expect->ExpectJs.toBeFalsy
})
test("of normal(0, 1e-8) (it doesn't freak out at tiny stdev)", () => {
run(FromDist(ToFloat(#Mean), mkNormal(0.0, 1e-8)))->unpackFloat->expect->toBeCloseTo(0.0)
run(FromDist(#ToFloat(#Mean), mkNormal(0.0, 1e-8)))->unpackFloat->expect->toBeCloseTo(0.0)
})
testAll("of exponential distributions", list{1e-7, 2.0, 10.0, 100.0}, rate => {
let meanValue = run(
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Exponential({rate: rate}))),
FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Exponential({rate: rate}))),
)
meanValue->unpackFloat->expect->toBeCloseTo(1.0 /. rate) // https://en.wikipedia.org/wiki/Exponential_distribution#Mean,_variance,_moments,_and_median
})
test("of a cauchy distribution", () => {
let meanValue = run(
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))),
FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Cauchy({local: 1.0, scale: 1.0}))),
)
meanValue->unpackFloat->expect->toBeSoCloseTo(1.0098094001641797, ~digits=5)
//-> toBe(GenDistError(Other("Cauchy distributions may have no mean value.")))
@ -48,7 +48,7 @@ describe("(Symbolic) mean", () => {
let (low, medium, high) = tup
let meanValue = run(
FromDist(
ToFloat(#Mean),
#ToFloat(#Mean),
DistributionTypes.Symbolic(#Triangular({low: low, medium: medium, high: high})),
),
)
@ -63,7 +63,7 @@ describe("(Symbolic) mean", () => {
tup => {
let (alpha, beta) = tup
let meanValue = run(
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: alpha, beta: beta}))),
FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: alpha, beta: beta}))),
)
meanValue->unpackFloat->expect->toBeCloseTo(1.0 /. (1.0 +. beta /. alpha)) // https://en.wikipedia.org/wiki/Beta_distribution#Mean
},
@ -72,7 +72,7 @@ describe("(Symbolic) mean", () => {
// TODO: When we have our theory of validators we won't want this to be NaN but to be an error.
test("of beta(0, 0)", () => {
let meanValue = run(
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))),
FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Beta({alpha: 0.0, beta: 0.0}))),
)
meanValue->unpackFloat->expect->ExpectJs.toBeFalsy
})
@ -85,7 +85,7 @@ describe("(Symbolic) mean", () => {
let betaDistribution = SymbolicDist.Beta.fromMeanAndStdev(mean, stdev)
let meanValue =
betaDistribution->E.R2.fmap(d =>
run(FromDist(ToFloat(#Mean), d->DistributionTypes.Symbolic))
run(FromDist(#ToFloat(#Mean), d->DistributionTypes.Symbolic))
)
switch meanValue {
| Ok(value) => value->unpackFloat->expect->toBeCloseTo(mean)
@ -100,7 +100,7 @@ describe("(Symbolic) mean", () => {
tup => {
let (mu, sigma) = tup
let meanValue = run(
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Lognormal({mu: mu, sigma: sigma}))),
FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Lognormal({mu: mu, sigma: sigma}))),
)
meanValue->unpackFloat->expect->toBeCloseTo(Js.Math.exp(mu +. sigma ** 2.0 /. 2.0)) // https://brilliant.org/wiki/log-normal-distribution/
},
@ -112,14 +112,14 @@ describe("(Symbolic) mean", () => {
tup => {
let (low, high) = tup
let meanValue = run(
FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Uniform({low: low, high: high}))),
FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Uniform({low: low, high: high}))),
)
meanValue->unpackFloat->expect->toBeCloseTo((low +. high) /. 2.0) // https://en.wikipedia.org/wiki/Continuous_uniform_distribution#Moments
},
)
test("of a float", () => {
let meanValue = run(FromDist(ToFloat(#Mean), DistributionTypes.Symbolic(#Float(7.7))))
let meanValue = run(FromDist(#ToFloat(#Mean), DistributionTypes.Symbolic(#Float(7.7))))
meanValue->unpackFloat->expect->toBeCloseTo(7.7)
})
})

2
packages/squiggle-lang/__tests__/TestHelpers.res
View File

@ -29,7 +29,7 @@ let {toFloat, toDist, toString, toError, fmap} = module(DistributionOperation.Ou
let fnImage = (theFn, inps) => Js.Array.map(theFn, inps)
let env: DistributionOperation.env = {
let env: GenericDist.env = {
sampleCount: MagicNumbers.Environment.defaultSampleCount,
xyPointLength: MagicNumbers.Environment.defaultXYPointLength,
}

86
packages/squiggle-lang/src/rescript/Distributions/DistributionOperation.res
View File

@ -4,12 +4,9 @@ type error = DistributionTypes.error
// TODO: It could be great to use a cache for some calculations (basically, do memoization). Also, better analytics/tracking could go a long way.
type env = {
sampleCount: int,
xyPointLength: int,
}
type env = GenericDist.env
let defaultEnv = {
let defaultEnv: env = {
sampleCount: MagicNumbers.Environment.defaultSampleCount,
xyPointLength: MagicNumbers.Environment.defaultXYPointLength,
}
@ -93,7 +90,7 @@ module OutputLocal = {
}
}
let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
let rec run = (~env: env, functionCallInfo: functionCallInfo): outputType => {
let {sampleCount, xyPointLength} = env
let reCall = (~env=env, ~functionCallInfo=functionCallInfo, ()) => {
@ -101,14 +98,14 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
}
let toPointSetFn = r => {
switch reCall(~functionCallInfo=FromDist(ToDist(ToPointSet), r), ()) {
switch reCall(~functionCallInfo=FromDist(#ToDist(ToPointSet), r), ()) {
| Dist(PointSet(p)) => Ok(p)
| e => Error(OutputLocal.toErrorOrUnreachable(e))
}
}
let toSampleSetFn = r => {
switch reCall(~functionCallInfo=FromDist(ToDist(ToSampleSet(sampleCount)), r), ()) {
switch reCall(~functionCallInfo=FromDist(#ToDist(ToSampleSet(sampleCount)), r), ()) {
| Dist(SampleSet(p)) => Ok(p)
| e => Error(OutputLocal.toErrorOrUnreachable(e))
}
@ -116,13 +113,13 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
let scaleMultiply = (r, weight) =>
reCall(
~functionCallInfo=FromDist(ToDistCombination(Pointwise, #Multiply, #Float(weight)), r),
~functionCallInfo=FromDist(#ToDistCombination(Pointwise, #Multiply, #Float(weight)), r),
(),
)->OutputLocal.toDistR
let pointwiseAdd = (r1, r2) =>
reCall(
~functionCallInfo=FromDist(ToDistCombination(Pointwise, #Add, #Dist(r2)), r1),
~functionCallInfo=FromDist(#ToDistCombination(Pointwise, #Add, #Dist(r2)), r1),
(),
)->OutputLocal.toDistR
@ -131,49 +128,40 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
dist: genericDist,
): outputType => {
let response = switch subFnName {
| ToFloat(distToFloatOperation) =>
| #ToFloat(distToFloatOperation) =>
GenericDist.toFloatOperation(dist, ~toPointSetFn, ~distToFloatOperation)
->E.R2.fmap(r => Float(r))
->OutputLocal.fromResult
| ToString(ToString) => dist->GenericDist.toString->String
| ToString(ToSparkline(bucketCount)) =>
| #ToString(ToString) => dist->GenericDist.toString->String
| #ToString(ToSparkline(bucketCount)) =>
GenericDist.toSparkline(dist, ~sampleCount, ~bucketCount, ())
->E.R2.fmap(r => String(r))
->OutputLocal.fromResult
| ToDist(Inspect) => {
| #ToDist(Inspect) => {
Js.log2("Console log requested: ", dist)
Dist(dist)
}
| ToDist(Normalize) => dist->GenericDist.normalize->Dist
| ToScore(KLDivergence(t2)) =>
GenericDist.Score.klDivergence(dist, t2, ~toPointSetFn)
->E.R2.fmap(r => Float(r))
| #ToDist(Normalize) => dist->GenericDist.normalize->Dist
| #ToScore(LogScore(answer, prior)) =>
GenericDist.Score.logScore(~estimate=dist, ~answer, ~prior, ~env)
->E.R2.fmap(s => Float(s))
->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
| ToDist(Truncate(leftCutoff, rightCutoff)) =>
| #ToBool(IsNormalized) => dist->GenericDist.isNormalized->Bool
| #ToDist(Truncate(leftCutoff, rightCutoff)) =>
GenericDist.truncate(~toPointSetFn, ~leftCutoff, ~rightCutoff, dist, ())
->E.R2.fmap(r => Dist(r))
->OutputLocal.fromResult
| ToDist(ToSampleSet(n)) =>
| #ToDist(ToSampleSet(n)) =>
dist
->GenericDist.toSampleSetDist(n)
->E.R2.fmap(r => Dist(SampleSet(r)))
->OutputLocal.fromResult
| ToDist(ToPointSet) =>
| #ToDist(ToPointSet) =>
dist
->GenericDist.toPointSet(~xyPointLength, ~sampleCount, ())
->E.R2.fmap(r => Dist(PointSet(r)))
->OutputLocal.fromResult
| ToDist(Scale(#LogarithmWithThreshold(eps), f)) =>
| #ToDist(Scale(#LogarithmWithThreshold(eps), f)) =>
dist
->GenericDist.pointwiseCombinationFloat(
~toPointSetFn,
@ -182,23 +170,23 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
)
->E.R2.fmap(r => Dist(r))
->OutputLocal.fromResult
| ToDist(Scale(#Multiply, f)) =>
| #ToDist(Scale(#Multiply, f)) =>
dist
->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Multiply, ~f)
->E.R2.fmap(r => Dist(r))
->OutputLocal.fromResult
| ToDist(Scale(#Logarithm, f)) =>
| #ToDist(Scale(#Logarithm, f)) =>
dist
->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Logarithm, ~f)
->E.R2.fmap(r => Dist(r))
->OutputLocal.fromResult
| ToDist(Scale(#Power, f)) =>
| #ToDist(Scale(#Power, f)) =>
dist
->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination=#Power, ~f)
->E.R2.fmap(r => Dist(r))
->OutputLocal.fromResult
| ToDistCombination(Algebraic(_), _, #Float(_)) => GenDistError(NotYetImplemented)
| ToDistCombination(Algebraic(strategy), arithmeticOperation, #Dist(t2)) =>
| #ToDistCombination(Algebraic(_), _, #Float(_)) => GenDistError(NotYetImplemented)
| #ToDistCombination(Algebraic(strategy), arithmeticOperation, #Dist(t2)) =>
dist
->GenericDist.algebraicCombination(
~strategy,
@ -209,12 +197,12 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
)
->E.R2.fmap(r => Dist(r))
->OutputLocal.fromResult
| ToDistCombination(Pointwise, algebraicCombination, #Dist(t2)) =>
| #ToDistCombination(Pointwise, algebraicCombination, #Dist(t2)) =>
dist
->GenericDist.pointwiseCombination(~toPointSetFn, ~algebraicCombination, ~t2)
->E.R2.fmap(r => Dist(r))
->OutputLocal.fromResult
| ToDistCombination(Pointwise, algebraicCombination, #Float(f)) =>
| #ToDistCombination(Pointwise, algebraicCombination, #Float(f)) =>
dist
->GenericDist.pointwiseCombinationFloat(~toPointSetFn, ~algebraicCombination, ~f)
->E.R2.fmap(r => Dist(r))
@ -225,8 +213,7 @@ let rec run = (~env, functionCallInfo: functionCallInfo): outputType => {
switch functionCallInfo {
| FromDist(subFnName, dist) => fromDistFn(subFnName, dist)
| FromFloat(subFnName, float) =>
reCall(~functionCallInfo=FromDist(subFnName, GenericDist.fromFloat(float)), ())
| FromFloat(subFnName, x) => reCall(~functionCallInfo=FromFloat(subFnName, x), ())
| Mixture(dists) =>
dists
->GenericDist.mixture(~scaleMultiplyFn=scaleMultiply, ~pointwiseAddFn=pointwiseAdd)
@ -278,13 +265,16 @@ module Constructors = {
let pdf = (~env, dist, f) => C.pdf(dist, f)->run(~env)->toFloatR
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
module LogScore = {
let distEstimateDistAnswer = (~env, estimate, answer) =>
C.LogScore.distEstimateDistAnswer(estimate, answer)->run(~env)->toFloatR
let distEstimateDistAnswerWithPrior = (~env, estimate, answer, prior) =>
C.LogScore.distEstimateDistAnswerWithPrior(estimate, answer, prior)->run(~env)->toFloatR
let distEstimateScalarAnswer = (~env, estimate, answer) =>
C.LogScore.distEstimateScalarAnswer(estimate, answer)->run(~env)->toFloatR
let distEstimateScalarAnswerWithPrior = (~env, estimate, answer, prior) =>
C.LogScore.distEstimateScalarAnswerWithPrior(estimate, 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

165
packages/squiggle-lang/src/rescript/Distributions/DistributionOperation.resi
View File

@ -1,11 +1,5 @@
@genType
type env = {
sampleCount: int,
xyPointLength: int,
}
@genType
let defaultEnv: env
let defaultEnv: GenericDist.env
open DistributionTypes
@ -19,15 +13,18 @@ type outputType =
| GenDistError(error)
@genType
let run: (~env: env, DistributionTypes.DistributionOperation.genericFunctionCallInfo) => outputType
let run: (
~env: GenericDist.env,
DistributionTypes.DistributionOperation.genericFunctionCallInfo,
) => outputType
let runFromDist: (
~env: env,
~env: GenericDist.env,
~functionCallInfo: DistributionTypes.DistributionOperation.fromDist,
genericDist,
) => outputType
let runFromFloat: (
~env: env,
~functionCallInfo: DistributionTypes.DistributionOperation.fromDist,
~env: GenericDist.env,
~functionCallInfo: DistributionTypes.DistributionOperation.fromFloat,
float,
) => outputType
@ -42,79 +39,147 @@ module Output: {
let toBool: t => option<bool>
let toBoolR: t => result<bool, error>
let toError: t => option<error>
let fmap: (~env: env, t, DistributionTypes.DistributionOperation.singleParamaterFunction) => t
let fmap: (
~env: GenericDist.env,
t,
DistributionTypes.DistributionOperation.singleParamaterFunction,
) => t
}
module Constructors: {
@genType
let mean: (~env: env, genericDist) => result<float, error>
let mean: (~env: GenericDist.env, genericDist) => result<float, error>
@genType
let stdev: (~env: env, genericDist) => result<float, error>
let stdev: (~env: GenericDist.env, genericDist) => result<float, error>
@genType
let variance: (~env: env, genericDist) => result<float, error>
let variance: (~env: GenericDist.env, genericDist) => result<float, error>
@genType
let sample: (~env: env, genericDist) => result<float, error>
let sample: (~env: GenericDist.env, genericDist) => result<float, error>
@genType
let cdf: (~env: env, genericDist, float) => result<float, error>
let cdf: (~env: GenericDist.env, genericDist, float) => result<float, error>
@genType
let inv: (~env: env, genericDist, float) => result<float, error>
let inv: (~env: GenericDist.env, genericDist, float) => result<float, error>
@genType
let pdf: (~env: env, genericDist, float) => result<float, error>
let pdf: (~env: GenericDist.env, genericDist, float) => result<float, error>
@genType
let normalize: (~env: env, genericDist) => result<genericDist, error>
let normalize: (~env: GenericDist.env, genericDist) => result<genericDist, error>
@genType
let isNormalized: (~env: env, genericDist) => result<bool, error>
let isNormalized: (~env: GenericDist.env, genericDist) => result<bool, error>
module LogScore: {
@genType
let klDivergence: (~env: env, genericDist, genericDist) => result<float, error>
@genType
let logScoreWithPointResolution: (
~env: env,
~prediction: genericDist,
~answer: float,
~prior: option<genericDist>,
let distEstimateDistAnswer: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<float, error>
@genType
let toPointSet: (~env: env, genericDist) => result<genericDist, error>
let distEstimateDistAnswerWithPrior: (
~env: GenericDist.env,
genericDist,
genericDist,
genericDist,
) => result<float, error>
@genType
let toSampleSet: (~env: env, genericDist, int) => result<genericDist, error>
let distEstimateScalarAnswer: (
~env: GenericDist.env,
genericDist,
float,
) => result<float, error>
@genType
let fromSamples: (~env: env, SampleSetDist.t) => result<genericDist, error>
let distEstimateScalarAnswerWithPrior: (
~env: GenericDist.env,
genericDist,
float,
genericDist,
) => result<float, error>
}
@genType
let truncate: (~env: env, genericDist, option<float>, option<float>) => result<genericDist, error>
let toPointSet: (~env: GenericDist.env, genericDist) => result<genericDist, error>
@genType
let inspect: (~env: env, genericDist) => result<genericDist, error>
let toSampleSet: (~env: GenericDist.env, genericDist, int) => result<genericDist, error>
@genType
let toString: (~env: env, genericDist) => result<string, error>
let fromSamples: (~env: GenericDist.env, SampleSetDist.t) => result<genericDist, error>
@genType
let toSparkline: (~env: env, genericDist, int) => result<string, error>
let truncate: (
~env: GenericDist.env,
genericDist,
option<float>,
option<float>,
) => result<genericDist, error>
@genType
let algebraicAdd: (~env: env, genericDist, genericDist) => result<genericDist, error>
let inspect: (~env: GenericDist.env, genericDist) => result<genericDist, error>
@genType
let algebraicMultiply: (~env: env, genericDist, genericDist) => result<genericDist, error>
let toString: (~env: GenericDist.env, genericDist) => result<string, error>
@genType
let algebraicDivide: (~env: env, genericDist, genericDist) => result<genericDist, error>
let toSparkline: (~env: GenericDist.env, genericDist, int) => result<string, error>
@genType
let algebraicSubtract: (~env: env, genericDist, genericDist) => result<genericDist, error>
let algebraicAdd: (~env: GenericDist.env, genericDist, genericDist) => result<genericDist, error>
@genType
let algebraicLogarithm: (~env: env, genericDist, genericDist) => result<genericDist, error>
let algebraicMultiply: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let algebraicPower: (~env: env, genericDist, genericDist) => result<genericDist, error>
let algebraicDivide: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let scaleLogarithm: (~env: env, genericDist, float) => result<genericDist, error>
let algebraicSubtract: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let scaleMultiply: (~env: env, genericDist, float) => result<genericDist, error>
let algebraicLogarithm: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let scalePower: (~env: env, genericDist, float) => result<genericDist, error>
let algebraicPower: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let pointwiseAdd: (~env: env, genericDist, genericDist) => result<genericDist, error>
let scaleLogarithm: (~env: GenericDist.env, genericDist, float) => result<genericDist, error>
@genType
let pointwiseMultiply: (~env: env, genericDist, genericDist) => result<genericDist, error>
let scaleMultiply: (~env: GenericDist.env, genericDist, float) => result<genericDist, error>
@genType
let pointwiseDivide: (~env: env, genericDist, genericDist) => result<genericDist, error>
let scalePower: (~env: GenericDist.env, genericDist, float) => result<genericDist, error>
@genType
let pointwiseSubtract: (~env: env, genericDist, genericDist) => result<genericDist, error>
let pointwiseAdd: (~env: GenericDist.env, genericDist, genericDist) => result<genericDist, error>
@genType
let pointwiseLogarithm: (~env: env, genericDist, genericDist) => result<genericDist, error>
let pointwiseMultiply: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let pointwisePower: (~env: env, genericDist, genericDist) => result<genericDist, error>
let pointwiseDivide: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let pointwiseSubtract: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let pointwiseLogarithm: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
@genType
let pointwisePower: (
~env: GenericDist.env,
genericDist,
genericDist,
) => result<genericDist, error>
}

188
packages/squiggle-lang/src/rescript/Distributions/DistributionTypes.res
View File

@ -98,61 +98,86 @@ module DistributionOperation = {
| ToString
| ToSparkline(int)
type toScore = KLDivergence(genericDist) | LogScore(float, option<genericDist>)
type genericDistOrScalar = Score_Dist(genericDist) | Score_Scalar(float)
type fromDist =
| ToFloat(toFloat)
| ToDist(toDist)
| ToScore(toScore)
| ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
| ToString(toString)
| ToBool(toBool)
type toScore = LogScore(genericDistOrScalar, option<genericDist>)
type fromFloat = [
| #ToFloat(toFloat)
| #ToDist(toDist)
| #ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
| #ToString(toString)
| #ToBool(toBool)
]
type fromDist = [
| fromFloat
| #ToScore(toScore)
]
type singleParamaterFunction =
| FromDist(fromDist)
| FromFloat(fromDist)
| FromFloat(fromFloat)
type genericFunctionCallInfo =
| FromDist(fromDist, genericDist)
| FromFloat(fromDist, float)
| FromFloat(fromFloat, float)
| FromSamples(array<float>)
| Mixture(array<(genericDist, float)>)
let distCallToString = (distFunction: fromDist): string =>
switch distFunction {
| ToFloat(#Cdf(r)) => `cdf(${E.Float.toFixed(r)})`
| ToFloat(#Inv(r)) => `inv(${E.Float.toFixed(r)})`
| ToFloat(#Mean) => `mean`
| ToFloat(#Min) => `min`
| ToFloat(#Max) => `max`
| ToFloat(#Stdev) => `stdev`
| ToFloat(#Variance) => `variance`
| ToFloat(#Mode) => `mode`
| ToFloat(#Pdf(r)) => `pdf(${E.Float.toFixed(r)})`
| 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)})`
| ToDist(Truncate(_, _)) => `truncate`
| ToDist(Inspect) => `inspect`
| ToDist(Scale(#Power, r)) => `scalePower(${E.Float.toFixed(r)})`
| ToDist(Scale(#Multiply, r)) => `scaleMultiply(${E.Float.toFixed(r)})`
| ToDist(Scale(#Logarithm, r)) => `scaleLog(${E.Float.toFixed(r)})`
| ToDist(Scale(#LogarithmWithThreshold(eps), r)) =>
let floatCallToString = (floatFunction: fromFloat): string =>
switch floatFunction {
| #ToFloat(#Cdf(r)) => `cdf(${E.Float.toFixed(r)})`
| #ToFloat(#Inv(r)) => `inv(${E.Float.toFixed(r)})`
| #ToFloat(#Mean) => `mean`
| #ToFloat(#Min) => `min`
| #ToFloat(#Max) => `max`
| #ToFloat(#Stdev) => `stdev`
| #ToFloat(#Variance) => `variance`
| #ToFloat(#Mode) => `mode`
| #ToFloat(#Pdf(r)) => `pdf(${E.Float.toFixed(r)})`
| #ToFloat(#Sample) => `sample`
| #ToFloat(#IntegralSum) => `integralSum`
| #ToDist(Normalize) => `normalize`
| #ToDist(ToPointSet) => `toPointSet`
| #ToDist(ToSampleSet(r)) => `toSampleSet(${E.I.toString(r)})`
| #ToDist(Truncate(_, _)) => `truncate`
| #ToDist(Inspect) => `inspect`
| #ToDist(Scale(#Power, r)) => `scalePower(${E.Float.toFixed(r)})`
| #ToDist(Scale(#Multiply, r)) => `scaleMultiply(${E.Float.toFixed(r)})`
| #ToDist(Scale(#Logarithm, r)) => `scaleLog(${E.Float.toFixed(r)})`
| #ToDist(Scale(#LogarithmWithThreshold(eps), r)) =>
`scaleLogWithThreshold(${E.Float.toFixed(r)}, epsilon=${E.Float.toFixed(eps)})`
| ToString(ToString) => `toString`
| ToString(ToSparkline(n)) => `sparkline(${E.I.toString(n)})`
| ToBool(IsNormalized) => `isNormalized`
| ToDistCombination(Algebraic(_), _, _) => `algebraic`
| ToDistCombination(Pointwise, _, _) => `pointwise`
| #ToString(ToString) => `toString`
| #ToString(ToSparkline(n)) => `sparkline(${E.I.toString(n)})`
| #ToBool(IsNormalized) => `isNormalized`
| #ToDistCombination(Algebraic(_), _, _) => `algebraic`
| #ToDistCombination(Pointwise, _, _) => `pointwise`
}
let distCallToString = (
distFunction: [
| #ToFloat(toFloat)
| #ToDist(toDist)
| #ToDistCombination(direction, Operation.Algebraic.t, [#Dist(genericDist) | #Float(float)])
| #ToString(toString)
| #ToBool(toBool)
| #ToScore(toScore)
],
): string =>
switch distFunction {
| #ToScore(_) => `logScore`
| #ToFloat(x) => floatCallToString(#ToFloat(x))
| #ToDist(x) => floatCallToString(#ToDist(x))
| #ToString(x) => floatCallToString(#ToString(x))
| #ToBool(x) => floatCallToString(#ToBool(x))
| #ToDistCombination(x, y, z) => floatCallToString(#ToDistCombination(x, y, z))
}
let toString = (d: genericFunctionCallInfo): string =>
switch d {
| FromDist(f, _) | FromFloat(f, _) => distCallToString(f)
| FromDist(f, _) => distCallToString(f)
| FromFloat(f, _) => floatCallToString(f)
| Mixture(_) => `mixture`
| FromSamples(_) => `fromSamples`
}
@ -162,80 +187,93 @@ module Constructors = {
module UsingDists = {
@genType
let mean = (dist): t => FromDist(ToFloat(#Mean), dist)
let stdev = (dist): t => FromDist(ToFloat(#Stdev), dist)
let variance = (dist): t => FromDist(ToFloat(#Variance), dist)
let sample = (dist): t => FromDist(ToFloat(#Sample), dist)
let cdf = (dist, x): t => FromDist(ToFloat(#Cdf(x)), dist)
let inv = (dist, x): t => FromDist(ToFloat(#Inv(x)), dist)
let pdf = (dist, x): t => FromDist(ToFloat(#Pdf(x)), dist)
let normalize = (dist): t => FromDist(ToDist(Normalize), dist)
let isNormalized = (dist): t => FromDist(ToBool(IsNormalized), dist)
let toPointSet = (dist): t => FromDist(ToDist(ToPointSet), dist)
let toSampleSet = (dist, r): t => FromDist(ToDist(ToSampleSet(r)), dist)
let mean = (dist): t => FromDist(#ToFloat(#Mean), dist)
let stdev = (dist): t => FromDist(#ToFloat(#Stdev), dist)
let variance = (dist): t => FromDist(#ToFloat(#Variance), dist)
let sample = (dist): t => FromDist(#ToFloat(#Sample), dist)
let cdf = (dist, x): t => FromDist(#ToFloat(#Cdf(x)), dist)
let inv = (dist, x): t => FromDist(#ToFloat(#Inv(x)), dist)
let pdf = (dist, x): t => FromDist(#ToFloat(#Pdf(x)), dist)
let normalize = (dist): t => FromDist(#ToDist(Normalize), dist)
let isNormalized = (dist): t => FromDist(#ToBool(IsNormalized), dist)
let toPointSet = (dist): t => FromDist(#ToDist(ToPointSet), dist)
let toSampleSet = (dist, r): t => FromDist(#ToDist(ToSampleSet(r)), dist)
let fromSamples = (xs): t => FromSamples(xs)
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 truncate = (dist, left, right): t => FromDist(#ToDist(Truncate(left, right)), dist)
let inspect = (dist): t => FromDist(#ToDist(Inspect), dist)
module LogScore = {
let distEstimateDistAnswer = (estimate, answer): t => FromDist(
#ToScore(LogScore(Score_Dist(answer), None)),
estimate,
)
let scaleMultiply = (dist, n): t => FromDist(ToDist(Scale(#Multiply, n)), dist)
let scalePower = (dist, n): t => FromDist(ToDist(Scale(#Power, n)), dist)
let scaleLogarithm = (dist, n): t => FromDist(ToDist(Scale(#Logarithm, n)), dist)
let distEstimateDistAnswerWithPrior = (estimate, answer, prior): t => FromDist(
#ToScore(LogScore(Score_Dist(answer), Some(prior))),
estimate,
)
let distEstimateScalarAnswer = (estimate, answer): t => FromDist(
#ToScore(LogScore(Score_Scalar(answer), None)),
estimate,
)
let distEstimateScalarAnswerWithPrior = (estimate, answer, prior): t => FromDist(
#ToScore(LogScore(Score_Scalar(answer), Some(prior))),
estimate,
)
}
let scaleMultiply = (dist, n): t => FromDist(#ToDist(Scale(#Multiply, n)), dist)
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(
ToDist(Scale(#LogarithmWithThreshold(eps), n)),
#ToDist(Scale(#LogarithmWithThreshold(eps), n)),
dist,
)
let toString = (dist): t => FromDist(ToString(ToString), dist)
let toSparkline = (dist, n): t => FromDist(ToString(ToSparkline(n)), dist)
let toString = (dist): t => FromDist(#ToString(ToString), dist)
let toSparkline = (dist, n): t => FromDist(#ToString(ToSparkline(n)), dist)
let algebraicAdd = (dist1, dist2: genericDist): t => FromDist(
ToDistCombination(Algebraic(AsDefault), #Add, #Dist(dist2)),
#ToDistCombination(Algebraic(AsDefault), #Add, #Dist(dist2)),
dist1,
)
let algebraicMultiply = (dist1, dist2): t => FromDist(
ToDistCombination(Algebraic(AsDefault), #Multiply, #Dist(dist2)),
#ToDistCombination(Algebraic(AsDefault), #Multiply, #Dist(dist2)),
dist1,
)
let algebraicDivide = (dist1, dist2): t => FromDist(
ToDistCombination(Algebraic(AsDefault), #Divide, #Dist(dist2)),
#ToDistCombination(Algebraic(AsDefault), #Divide, #Dist(dist2)),
dist1,
)
let algebraicSubtract = (dist1, dist2): t => FromDist(
ToDistCombination(Algebraic(AsDefault), #Subtract, #Dist(dist2)),
#ToDistCombination(Algebraic(AsDefault), #Subtract, #Dist(dist2)),
dist1,
)
let algebraicLogarithm = (dist1, dist2): t => FromDist(
ToDistCombination(Algebraic(AsDefault), #Logarithm, #Dist(dist2)),
#ToDistCombination(Algebraic(AsDefault), #Logarithm, #Dist(dist2)),
dist1,
)
let algebraicPower = (dist1, dist2): t => FromDist(
ToDistCombination(Algebraic(AsDefault), #Power, #Dist(dist2)),
#ToDistCombination(Algebraic(AsDefault), #Power, #Dist(dist2)),
dist1,
)
let pointwiseAdd = (dist1, dist2): t => FromDist(
ToDistCombination(Pointwise, #Add, #Dist(dist2)),
#ToDistCombination(Pointwise, #Add, #Dist(dist2)),
dist1,
)
let pointwiseMultiply = (dist1, dist2): t => FromDist(
ToDistCombination(Pointwise, #Multiply, #Dist(dist2)),
#ToDistCombination(Pointwise, #Multiply, #Dist(dist2)),
dist1,
)
let pointwiseDivide = (dist1, dist2): t => FromDist(
ToDistCombination(Pointwise, #Divide, #Dist(dist2)),
#ToDistCombination(Pointwise, #Divide, #Dist(dist2)),
dist1,
)
let pointwiseSubtract = (dist1, dist2): t => FromDist(
ToDistCombination(Pointwise, #Subtract, #Dist(dist2)),
#ToDistCombination(Pointwise, #Subtract, #Dist(dist2)),
dist1,
)
let pointwiseLogarithm = (dist1, dist2): t => FromDist(
ToDistCombination(Pointwise, #Logarithm, #Dist(dist2)),
#ToDistCombination(Pointwise, #Logarithm, #Dist(dist2)),
dist1,
)
let pointwisePower = (dist1, dist2): t => FromDist(
ToDistCombination(Pointwise, #Power, #Dist(dist2)),
#ToDistCombination(Pointwise, #Power, #Dist(dist2)),
dist1,
)
}

109
packages/squiggle-lang/src/rescript/Distributions/GenericDist.res
View File

@ -6,6 +6,11 @@ type toSampleSetFn = t => result<SampleSetDist.t, error>
type scaleMultiplyFn = (t, float) => result<t, error>
type pointwiseAddFn = (t, t) => result<t, error>
type env = {
sampleCount: int,
xyPointLength: int,
}
let isPointSet = (t: t) =>
switch t {
| PointSet(_) => true
@ -61,46 +66,6 @@ let integralEndY = (t: t): float =>
let isNormalized = (t: t): bool => Js.Math.abs_float(integralEndY(t) -. 1.0) < 1e-7
module Score = {
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))
)
}
}
}
let toFloatOperation = (
t,
~toPointSetFn: toPointSetFn,
@ -171,6 +136,70 @@ let toPointSet = (
}
}
module Score = {
type genericDistOrScalar = DistributionTypes.DistributionOperation.genericDistOrScalar
let argsMake = (~esti: t, ~answ: genericDistOrScalar, ~prior: option<t>, ~env: env): result<
PointSetDist_Scoring.scoreArgs,
error,
> => {
let toPointSetFn = t =>
toPointSet(
t,
~xyPointLength=env.xyPointLength,
~sampleCount=env.sampleCount,
~xSelection=#ByWeight,
(),
)
let prior': option<result<PointSetTypes.pointSetDist, error>> = switch prior {
| None => None
| Some(d) => toPointSetFn(d)->Some
}
let twoDists = (~toPointSetFn, esti': t, answ': t): result<
(PointSetTypes.pointSetDist, PointSetTypes.pointSetDist),
error,
> => E.R.merge(toPointSetFn(esti'), toPointSetFn(answ'))
switch (esti, answ, prior') {
| (esti', Score_Dist(answ'), None) =>
twoDists(~toPointSetFn, esti', answ')->E.R2.fmap(((esti'', answ'')) =>
{estimate: esti'', answer: answ'', prior: None}->PointSetDist_Scoring.DistAnswer
)
| (esti', Score_Dist(answ'), Some(Ok(prior''))) =>
twoDists(~toPointSetFn, esti', answ')->E.R2.fmap(((esti'', answ'')) =>
{
estimate: esti'',
answer: answ'',
prior: Some(prior''),
}->PointSetDist_Scoring.DistAnswer
)
| (esti', Score_Scalar(answ'), None) =>
toPointSetFn(esti')->E.R2.fmap(esti'' =>
{
estimate: esti'',
answer: answ',
prior: None,
}->PointSetDist_Scoring.ScalarAnswer
)
| (esti', Score_Scalar(answ'), Some(Ok(prior''))) =>
toPointSetFn(esti')->E.R2.fmap(esti'' =>
{
estimate: esti'',
answer: answ',
prior: Some(prior''),
}->PointSetDist_Scoring.ScalarAnswer
)
| (_, _, Some(Error(err))) => err->Error
}
}
let logScore = (~estimate: t, ~answer: genericDistOrScalar, ~prior: option<t>, ~env: env): result<
float,
error,
> =>
argsMake(~esti=estimate, ~answ=answer, ~prior, ~env)->E.R.bind(x =>
x->PointSetDist.logScore->E.R2.errMap(y => DistributionTypes.OperationError(y))
)
}
/*
PointSetDist.toSparkline calls "downsampleEquallyOverX", which downsamples it to n=bucketCount.
It first needs a pointSetDist, so we convert to a pointSetDist. In this process we want the

12
packages/squiggle-lang/src/rescript/Distributions/GenericDist.resi
View File

@ -5,6 +5,9 @@ type toSampleSetFn = t => result<SampleSetDist.t, error>
type scaleMultiplyFn = (t, float) => result<t, error>
type pointwiseAddFn = (t, t) => result<t, error>
@genType
type env = {sampleCount: int, xyPointLength: int}
let sampleN: (t, int) => array<float>
let sample: t => float
@ -25,12 +28,11 @@ let toFloatOperation: (
) => result<float, error>
module Score: {
let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
let logScoreWithPointResolution: (
~prediction: t,
~answer: float,
let logScore: (
~estimate: t,
~answer: DistributionTypes.DistributionOperation.genericDistOrScalar,
~prior: option<t>,
~toPointSetFn: toPointSetFn,
~env: env,
) => result<float, error>
}

16
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Continuous.res
View File

@ -120,7 +120,7 @@ let combinePointwise = (
let interpolator = XYShape.XtoY.continuousInterpolator(t1.interpolation, extrapolation)
combiner(fn, interpolator, t1.xyShape, t2.xyShape)->E.R2.fmap(x =>
combiner(interpolator, fn, t1.xyShape, t2.xyShape)->E.R2.fmap(x =>
make(~integralSumCache=combinedIntegralSum, x)
)
}
@ -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 => {

13
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Discrete.res
View File

@ -49,7 +49,7 @@ let combinePointwise = (
// TODO: does it ever make sense to pointwise combine the integrals here?
// It could be done for pointwise additions, but is that ever needed?
combiner(fn, XYShape.XtoY.discreteInterpolator, t1.xyShape, t2.xyShape)->E.R2.fmap(make)
combiner(XYShape.XtoY.discreteInterpolator, fn, t1.xyShape, t2.xyShape)->E.R2.fmap(make)
}
let reduce = (
@ -222,15 +222,4 @@ module T = Dist({
let getMeanOfSquares = t => t |> shapeMap(XYShape.T.square) |> mean
XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
}
let klDivergence = (prediction: t, answer: t) => {
combinePointwise(
~fn=PointSetDist_Scoring.KLDivergence.integrand,
prediction,
answer,
)->E.R2.fmap(integralEndY)
}
let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
Error(Operation.NotYetImplemented)
}
})

9
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Distributions.res
View File

@ -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 = {

9
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Mixed.res
View File

@ -302,15 +302,6 @@ module T = Dist({
| _ => XYShape.Analysis.getVarianceDangerously(t, mean, getMeanOfSquares)
}
}
let klDivergence = (prediction: t, answer: t) => {
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))
}
let logScoreWithPointResolution = (~prediction: t, ~answer: float, ~prior: option<t>) => {
Error(Operation.NotYetImplemented)
}
})
let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t => {

28
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist.res
View File

@ -66,6 +66,7 @@ let combineAlgebraically = (op: Operation.convolutionOperation, t1: t, t2: t): t
}
let combinePointwise = (
~combiner=XYShape.PointwiseCombination.combine,
~integralSumCachesFn: (float, float) => option<float>=(_, _) => None,
~integralCachesFn: (
PointSetTypes.continuousShape,
@ -78,6 +79,7 @@ let combinePointwise = (
switch (t1, t2) {
| (Continuous(m1), Continuous(m2)) =>
Continuous.combinePointwise(
~combiner,
~integralSumCachesFn,
fn,
m1,
@ -85,6 +87,7 @@ let combinePointwise = (
)->E.R2.fmap(x => PointSetTypes.Continuous(x))
| (Discrete(m1), Discrete(m2)) =>
Discrete.combinePointwise(
~combiner,
~integralSumCachesFn,
~fn,
m1,
@ -195,25 +198,16 @@ 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.Integral.sum,
~toMixedFn=toMixed,
)
let pdf = (f: float, t: t) => {
let mixedPoint: PointSetTypes.mixedPoint = T.xToY(f, t)
mixedPoint.continuous +. mixedPoint.discrete

157
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist_Scoring.res
View File

@ -1,46 +1,149 @@
module KLDivergence = {
let logFn = Js.Math.log // base e
let integrand = (predictionElement: float, answerElement: float): result<
type pointSetDist = PointSetTypes.pointSetDist
type scalar = float
type score = float
type abstractScoreArgs<'a, 'b> = {estimate: 'a, answer: 'b, prior: option<'a>}
type scoreArgs =
| DistAnswer(abstractScoreArgs<pointSetDist, pointSetDist>)
| ScalarAnswer(abstractScoreArgs<pointSetDist, scalar>)
let logFn = Js.Math.log // base e
let minusScaledLogOfQuotient = (~esti, ~answ): result<float, Operation.Error.t> => {
let quot = esti /. answ
quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answ *. logFn(quot))
}
module WithDistAnswer = {
// The Kullback-Leibler divergence
let integrand = (estimateElement: float, answerElement: float): result<
float,
Operation.Error.t,
> =>
// We decided that negative infinity, not an error at answerElement = 0.0, is a desirable value.
// We decided that 0.0, not an error at answerElement = 0.0, is a desirable value.
if answerElement == 0.0 {
Ok(0.0)
} else if predictionElement == 0.0 {
} else if estimateElement == 0.0 {
Ok(infinity)
} else {
let quot = predictionElement /. answerElement
quot < 0.0 ? Error(Operation.ComplexNumberError) : Ok(-.answerElement *. logFn(quot))
minusScaledLogOfQuotient(~esti=estimateElement, ~answ=answerElement)
}
let sum = (
~estimate: pointSetDist,
~answer: pointSetDist,
~combineFn,
~integrateFn,
~toMixedFn,
): result<score, Operation.Error.t> => {
let combineAndIntegrate = (estimate, answer) =>
combineFn(integrand, estimate, answer)->E.R2.fmap(integrateFn)
let getMixedSums = (estimate: pointSetDist, answer: pointSetDist) => {
let esti = estimate->toMixedFn
let answ = answer->toMixedFn
switch (
Mixed.T.toContinuous(esti),
Mixed.T.toDiscrete(esti),
Mixed.T.toContinuous(answ),
Mixed.T.toDiscrete(answ),
) {
| (
Some(estiContinuousPart),
Some(estiDiscretePart),
Some(answContinuousPart),
Some(answDiscretePart),
) =>
E.R.merge(
combineAndIntegrate(
PointSetTypes.Discrete(estiDiscretePart),
PointSetTypes.Discrete(answDiscretePart),
),
combineAndIntegrate(Continuous(estiContinuousPart), Continuous(answContinuousPart)),
)
| (_, _, _, _) => `unreachable state`->Operation.Other->Error
}
}
switch (estimate, answer) {
| (Continuous(_), Continuous(_))
| (Discrete(_), Discrete(_)) =>
combineAndIntegrate(estimate, answer)
| (_, _) =>
getMixedSums(estimate, answer)->E.R2.fmap(((discretePart, continuousPart)) =>
discretePart +. continuousPart
)
}
}
let sumWithPrior = (
~estimate: pointSetDist,
~answer: pointSetDist,
~prior: pointSetDist,
~combineFn,
~integrateFn,
~toMixedFn,
): result<score, Operation.Error.t> => {
let kl1 = sum(~estimate, ~answer, ~combineFn, ~integrateFn, ~toMixedFn)
let kl2 = sum(~estimate=prior, ~answer, ~combineFn, ~integrateFn, ~toMixedFn)
E.R.merge(kl1, kl2)->E.R2.fmap(((kl1', kl2')) => kl1' -. kl2')
}
}
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 {
module WithScalarAnswer = {
let sum = (mp: PointSetTypes.MixedPoint.t): float => mp.continuous +. mp.discrete
let score = (~estimate: pointSetDist, ~answer: scalar): result<score, Operation.Error.t> => {
let _score = (~estimatePdf: float => option<float>, ~answer: float): result<
score,
Operation.Error.t,
> => {
let density = answer->estimatePdf
switch density {
| None => Operation.PdfInvalidError->Error
| Some(density') =>
if density' < 0.0 {
Operation.PdfInvalidError->Error
} else if numerator == 0.0 {
} else if density' == 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
density'->logFn->(x => -.x)->Ok
}
}
}
)->Ok
let estimatePdf = x =>
switch estimate {
| Continuous(esti) => Continuous.T.xToY(x, esti)->sum->Some
| Discrete(esti) => Discrete.T.xToY(x, esti)->sum->Some
| Mixed(_) => None
}
_score(~estimatePdf, ~answer)
}
let scoreWithPrior = (~estimate: pointSetDist, ~answer: scalar, ~prior: pointSetDist): result<
score,
Operation.Error.t,
> => {
E.R.merge(score(~estimate, ~answer), score(~estimate=prior, ~answer))->E.R2.fmap(((s1, s2)) =>
s1 -. s2
)
}
}
let twoGenericDistsToTwoPointSetDists = (~toPointSetFn, estimate, answer): result<
(pointSetDist, pointSetDist),
'e,
> => E.R.merge(toPointSetFn(estimate, ()), toPointSetFn(answer, ()))
let logScore = (args: scoreArgs, ~combineFn, ~integrateFn, ~toMixedFn): result<
score,
Operation.Error.t,
> =>
switch args {
| DistAnswer({estimate, answer, prior: None}) =>
WithDistAnswer.sum(~estimate, ~answer, ~integrateFn, ~combineFn, ~toMixedFn)
| DistAnswer({estimate, answer, prior: Some(prior)}) =>
WithDistAnswer.sumWithPrior(~estimate, ~answer, ~prior, ~integrateFn, ~combineFn, ~toMixedFn)
| ScalarAnswer({estimate, answer, prior: None}) => WithScalarAnswer.score(~estimate, ~answer)
| ScalarAnswer({estimate, answer, prior: Some(prior)}) =>
WithScalarAnswer.scoreWithPrior(~estimate, ~answer, ~prior)
}

9
packages/squiggle-lang/src/rescript/FunctionRegistry/FunctionRegistry_Core.res
View File

@ -8,6 +8,7 @@ type rec frType =
| FRTypeNumber
| FRTypeNumeric
| FRTypeDistOrNumber
| FRTypeDist
| FRTypeLambda
| FRTypeRecord(frTypeRecord)
| FRTypeDict(frType)
@ -41,7 +42,7 @@ and frValueDistOrNumber = FRValueNumber(float) | FRValueDist(DistributionTypes.g
type fnDefinition = {
name: string,
inputs: array<frType>,
run: (array<frValue>, DistributionOperation.env) => result<internalExpressionValue, string>,
run: (array<frValue>, GenericDist.env) => result<internalExpressionValue, string>,
}
type function = {
@ -60,6 +61,7 @@ module FRType = {
switch t {
| FRTypeNumber => "number"
| FRTypeNumeric => "numeric"
| FRTypeDist => "distribution"
| FRTypeDistOrNumber => "distribution|number"
| FRTypeRecord(r) => {
let input = ((name, frType): frTypeRecordParam) => `${name}: ${toString(frType)}`
@ -98,6 +100,7 @@ module FRType = {
| (FRTypeDistOrNumber, IEvDistribution(Symbolic(#Float(f)))) =>
Some(FRValueDistOrNumber(FRValueNumber(f)))
| (FRTypeDistOrNumber, IEvDistribution(f)) => Some(FRValueDistOrNumber(FRValueDist(f)))
| (FRTypeDist, IEvDistribution(f)) => Some(FRValueDist(f))
| (FRTypeNumeric, IEvNumber(f)) => Some(FRValueNumber(f))
| (FRTypeNumeric, IEvDistribution(Symbolic(#Float(f)))) => Some(FRValueNumber(f))
| (FRTypeLambda, IEvLambda(f)) => Some(FRValueLambda(f))
@ -319,7 +322,7 @@ module FnDefinition = {
t.name ++ `(${inputs})`
}
let run = (t: t, args: array<internalExpressionValue>, env: DistributionOperation.env) => {
let run = (t: t, args: array<internalExpressionValue>, env: GenericDist.env) => {
let argValues = FRType.matchWithExpressionValueArray(t.inputs, args)
switch argValues {
| Some(values) => t.run(values, env)
@ -374,7 +377,7 @@ module Registry = {
~registry: registry,
~fnName: string,
~args: array<internalExpressionValue>,
~env: DistributionOperation.env,
~env: GenericDist.env,
) => {
let matchToDef = m => Matcher.Registry.matchToDef(registry, m)
//Js.log(toSimple(registry))

28
packages/squiggle-lang/src/rescript/FunctionRegistry/FunctionRegistry_Helpers.res
View File

@ -27,6 +27,12 @@ module Prepare = {
| _ => Error(impossibleError)
}
let threeArgs = (inputs: ts): result<ts, err> =>
switch inputs {
| [FRValueRecord([(_, n1), (_, n2), (_, n3)])] => Ok([n1, n2, n3])
| _ => Error(impossibleError)
}
let toArgs = (inputs: ts): result<ts, err> =>
switch inputs {
| [FRValueRecord(args)] => args->E.A2.fmap(((_, b)) => b)->Ok
@ -57,6 +63,16 @@ module Prepare = {
}
}
let twoDist = (values: ts): result<
(DistributionTypes.genericDist, DistributionTypes.genericDist),
err,
> => {
switch values {
| [FRValueDist(a1), FRValueDist(a2)] => Ok(a1, a2)
| _ => Error(impossibleError)
}
}
let twoNumbers = (values: ts): result<(float, float), err> => {
switch values {
| [FRValueNumber(a1), FRValueNumber(a2)] => Ok(a1, a2)
@ -81,6 +97,11 @@ module Prepare = {
module Record = {
let twoDistOrNumber = (values: ts): result<(frValueDistOrNumber, frValueDistOrNumber), err> =>
values->ToValueArray.Record.twoArgs->E.R.bind(twoDistOrNumber)
let twoDist = (values: ts): result<
(DistributionTypes.genericDist, DistributionTypes.genericDist),
err,
> => values->ToValueArray.Record.twoArgs->E.R.bind(twoDist)
}
}
@ -128,8 +149,7 @@ module Prepare = {
module Process = {
module DistOrNumberToDist = {
module Helpers = {
let toSampleSet = (r, env: DistributionOperation.env) =>
GenericDist.toSampleSetDist(r, env.sampleCount)
let toSampleSet = (r, env: GenericDist.env) => GenericDist.toSampleSetDist(r, env.sampleCount)
let mapFnResult = r =>
switch r {
@ -166,7 +186,7 @@ module Process = {
let oneValue = (
~fn: float => result<DistributionTypes.genericDist, string>,
~value: frValueDistOrNumber,
~env: DistributionOperation.env,
~env: GenericDist.env,
): result<DistributionTypes.genericDist, string> => {
switch value {
| FRValueNumber(a1) => fn(a1)
@ -179,7 +199,7 @@ module Process = {
let twoValues = (
~fn: ((float, float)) => result<DistributionTypes.genericDist, string>,
~values: (frValueDistOrNumber, frValueDistOrNumber),
~env: DistributionOperation.env,
~env: GenericDist.env,
): result<DistributionTypes.genericDist, string> => {
switch values {
| (FRValueNumber(a1), FRValueNumber(a2)) => fn((a1, a2))

66
packages/squiggle-lang/src/rescript/FunctionRegistry/FunctionRegistry_Library.res
View File

@ -49,7 +49,7 @@ let inputsTodist = (inputs: array<FunctionRegistry_Core.frValue>, makeDist) => {
expressionValue
}
let registry = [
let registryStart = [
Function.make(
~name="toContinuousPointSet",
~definitions=[
@ -510,3 +510,67 @@ to(5,10)
(),
),
]
let runScoring = (estimate, answer, prior, env) => {
GenericDist.Score.logScore(~estimate, ~answer, ~prior, ~env)
->E.R2.fmap(FunctionRegistry_Helpers.Wrappers.evNumber)
->E.R2.errMap(DistributionTypes.Error.toString)
}
let scoreFunctions = [
Function.make(
~name="Score",
~definitions=[
FnDefinition.make(
~name="logScore",
~inputs=[
FRTypeRecord([
("estimate", FRTypeDist),
("answer", FRTypeDistOrNumber),
("prior", FRTypeDist),
]),
],
~run=(inputs, env) => {
switch FunctionRegistry_Helpers.Prepare.ToValueArray.Record.threeArgs(inputs) {
| Ok([FRValueDist(estimate), FRValueDistOrNumber(FRValueDist(d)), FRValueDist(prior)]) =>
runScoring(estimate, Score_Dist(d), Some(prior), env)
| Ok([
FRValueDist(estimate),
FRValueDistOrNumber(FRValueNumber(d)),
FRValueDist(prior),
]) =>
runScoring(estimate, Score_Scalar(d), Some(prior), env)
| Error(e) => Error(e)
| _ => Error(FunctionRegistry_Helpers.impossibleError)
}
},
),
FnDefinition.make(
~name="logScore",
~inputs=[FRTypeRecord([("estimate", FRTypeDist), ("answer", FRTypeDistOrNumber)])],
~run=(inputs, env) => {
switch FunctionRegistry_Helpers.Prepare.ToValueArray.Record.twoArgs(inputs) {
| Ok([FRValueDist(estimate), FRValueDistOrNumber(FRValueDist(d))]) =>
runScoring(estimate, Score_Dist(d), None, env)
| Ok([FRValueDist(estimate), FRValueDistOrNumber(FRValueNumber(d))]) =>
runScoring(estimate, Score_Scalar(d), None, env)
| Error(e) => Error(e)
| _ => Error(FunctionRegistry_Helpers.impossibleError)
}
},
),
FnDefinition.make(~name="klDivergence", ~inputs=[FRTypeDist, FRTypeDist], ~run=(
inputs,
env,
) => {
switch inputs {
| [FRValueDist(estimate), FRValueDist(d)] => runScoring(estimate, Score_Dist(d), None, env)
| _ => Error(FunctionRegistry_Helpers.impossibleError)
}
}),
],
(),
),
]
let registry = E.A.append(registryStart, scoreFunctions)

2
packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_Date.res
View File

@ -1,7 +1,7 @@
module IEV = ReducerInterface_InternalExpressionValue
type internalExpressionValue = IEV.t
let dispatch = (call: IEV.functionCall, _: DistributionOperation.env): option<
let dispatch = (call: IEV.functionCall, _: GenericDist.env): option<
result<internalExpressionValue, QuriSquiggleLang.Reducer_ErrorValue.errorValue>,
> => {
switch call {

2
packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_Duration.res
View File

@ -1,7 +1,7 @@
module IEV = ReducerInterface_InternalExpressionValue
type internalExpressionValue = IEV.t
let dispatch = (call: IEV.functionCall, _: DistributionOperation.env): option<
let dispatch = (call: IEV.functionCall, _: GenericDist.env): option<
result<internalExpressionValue, QuriSquiggleLang.Reducer_ErrorValue.errorValue>,
> => {
switch call {

2
packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_ExternalExpressionValue.res
View File

@ -86,7 +86,7 @@ let toStringResult = x =>
}
@genType
type environment = DistributionOperation.env
type environment = GenericDist.env
@genType
let defaultEnvironment: environment = DistributionOperation.defaultEnv

83
packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_GenericDistribution.res
View File

@ -32,50 +32,38 @@ module Helpers = {
let toFloatFn = (
fnCall: DistributionTypes.DistributionOperation.toFloat,
dist: DistributionTypes.genericDist,
~env: DistributionOperation.env,
~env: GenericDist.env,
) => {
FromDist(DistributionTypes.DistributionOperation.ToFloat(fnCall), dist)
->DistributionOperation.run(~env)
->Some
FromDist(#ToFloat(fnCall), dist)->DistributionOperation.run(~env)->Some
}
let toStringFn = (
fnCall: DistributionTypes.DistributionOperation.toString,
dist: DistributionTypes.genericDist,
~env: DistributionOperation.env,
~env: GenericDist.env,
) => {
FromDist(DistributionTypes.DistributionOperation.ToString(fnCall), dist)
->DistributionOperation.run(~env)
->Some
FromDist(#ToString(fnCall), dist)->DistributionOperation.run(~env)->Some
}
let toBoolFn = (
fnCall: DistributionTypes.DistributionOperation.toBool,
dist: DistributionTypes.genericDist,
~env: DistributionOperation.env,
~env: GenericDist.env,
) => {
FromDist(DistributionTypes.DistributionOperation.ToBool(fnCall), dist)
->DistributionOperation.run(~env)
->Some
FromDist(#ToBool(fnCall), dist)->DistributionOperation.run(~env)->Some
}
let toDistFn = (
fnCall: DistributionTypes.DistributionOperation.toDist,
dist,
~env: DistributionOperation.env,
~env: GenericDist.env,
) => {
FromDist(DistributionTypes.DistributionOperation.ToDist(fnCall), dist)
->DistributionOperation.run(~env)
->Some
FromDist(#ToDist(fnCall), dist)->DistributionOperation.run(~env)->Some
}
let twoDiststoDistFn = (direction, arithmetic, dist1, dist2, ~env: DistributionOperation.env) => {
let twoDiststoDistFn = (direction, arithmetic, dist1, dist2, ~env: GenericDist.env) => {
FromDist(
DistributionTypes.DistributionOperation.ToDistCombination(
direction,
arithmeticMap(arithmetic),
#Dist(dist2),
),
#ToDistCombination(direction, arithmeticMap(arithmetic), #Dist(dist2)),
dist1,
)->DistributionOperation.run(~env)
}
@ -109,7 +97,7 @@ module Helpers = {
let mixtureWithGivenWeights = (
distributions: array<DistributionTypes.genericDist>,
weights: array<float>,
~env: DistributionOperation.env,
~env: GenericDist.env,
): DistributionOperation.outputType =>
E.A.length(distributions) == E.A.length(weights)
? Mixture(Belt.Array.zip(distributions, weights))->DistributionOperation.run(~env)
@ -119,7 +107,7 @@ module Helpers = {
let mixtureWithDefaultWeights = (
distributions: array<DistributionTypes.genericDist>,
~env: DistributionOperation.env,
~env: GenericDist.env,
): DistributionOperation.outputType => {
let length = E.A.length(distributions)
let weights = Belt.Array.make(length, 1.0 /. Belt.Int.toFloat(length))
@ -128,7 +116,7 @@ module Helpers = {
let mixture = (
args: array<internalExpressionValue>,
~env: DistributionOperation.env,
~env: GenericDist.env,
): DistributionOperation.outputType => {
let error = (err: string): DistributionOperation.outputType =>
err->DistributionTypes.ArgumentError->GenDistError
@ -167,20 +155,6 @@ 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 = {
@ -199,7 +173,7 @@ module SymbolicConstructors = {
}
}
let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperation.env): option<
let dispatchToGenericOutput = (call: IEV.functionCall, env: GenericDist.env): option<
DistributionOperation.outputType,
> => {
let (fnName, args) = call
@ -239,35 +213,6 @@ let dispatchToGenericOutput = (call: IEV.functionCall, env: DistributionOperatio
~env,
)->Some
| ("normalize", [IEvDistribution(dist)]) => Helpers.toDistFn(Normalize, dist, ~env)
| ("klDivergence", [IEvDistribution(prediction), IEvDistribution(answer)]) =>
Some(DistributionOperation.run(FromDist(ToScore(KLDivergence(answer)), prediction), ~env))
| (
"klDivergence",
[IEvDistribution(prediction), IEvDistribution(answer), IEvDistribution(prior)],
) =>
Helpers.klDivergenceWithPrior(prediction, answer, prior, env)
| (
"logScoreWithPointAnswer",
[IEvDistribution(prediction), IEvNumber(answer), IEvDistribution(prior)],
)
| (
"logScoreWithPointAnswer",
[
IEvDistribution(prediction),
IEvDistribution(Symbolic(#Float(answer))),
IEvDistribution(prior),
],
) =>
DistributionOperation.run(
FromDist(ToScore(LogScore(answer, prior->Some)), prediction),
~env,
)->Some
| ("logScoreWithPointAnswer", [IEvDistribution(prediction), IEvNumber(answer)])
| (
"logScoreWithPointAnswer",
[IEvDistribution(prediction), IEvDistribution(Symbolic(#Float(answer)))],
) =>
DistributionOperation.run(FromDist(ToScore(LogScore(answer, None)), prediction), ~env)->Some
| ("isNormalized", [IEvDistribution(dist)]) => Helpers.toBoolFn(IsNormalized, dist, ~env)
| ("toPointSet", [IEvDistribution(dist)]) => Helpers.toDistFn(ToPointSet, dist, ~env)
| ("scaleLog", [IEvDistribution(dist)]) =>

2
packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_Number.res
View File

@ -24,7 +24,7 @@ module ScientificUnit = {
}
}
let dispatch = (call: IEV.functionCall, _: DistributionOperation.env): option<
let dispatch = (call: IEV.functionCall, _: GenericDist.env): option<
result<internalExpressionValue, QuriSquiggleLang.Reducer_ErrorValue.errorValue>,
> => {
switch call {

2
packages/squiggle-lang/src/rescript/TypescriptInterface.res
View File

@ -8,7 +8,7 @@ The below few seem to work fine. In the future there's definitely more work to d
*/
@genType
type samplingParams = DistributionOperation.env
type samplingParams = GenericDist.env
@genType
type genericDist = DistributionTypes.genericDist

1
packages/squiggle-lang/src/rescript/Utility/E.res
View File

@ -547,6 +547,7 @@ 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)

30
packages/squiggle-lang/src/rescript/Utility/XYShape.res
View File

@ -327,8 +327,8 @@ module Zipped = {
module PointwiseCombination = {
// t1Interpolator and t2Interpolator are functions from XYShape.XtoY, e.g. linearBetweenPointsExtrapolateFlat.
let combine: (
(float, float) => result<float, Operation.Error.t>,
interpolator,
(float, float) => result<float, Operation.Error.t>,
T.t,
T.t,
) => result<T.t, Operation.Error.t> = %raw(`
@ -337,7 +337,7 @@ module PointwiseCombination = {
// and interpolates the value on the other side, thus accumulating xs and ys.
// This is written in raw JS because this can still be a bottleneck, and using refs for the i and j indices is quite painful.
function(fn, interpolator, t1, t2) {
function(interpolator, fn, t1, t2) {
let t1n = t1.xs.length;
let t2n = t2.xs.length;
let outX = [];
@ -399,11 +399,11 @@ module PointwiseCombination = {
This is from an approach to kl divergence that was ultimately rejected. Leaving it in for now because it may help us factor `combine` out of raw javascript soon.
*/
let combineAlongSupportOfSecondArgument0: (
(float, float) => result<float, Operation.Error.t>,
interpolator,
(float, float) => result<float, Operation.Error.t>,
T.t,
T.t,
) => result<T.t, Operation.Error.t> = (fn, interpolator, t1, t2) => {
) => result<T.t, Operation.Error.t> = (interpolator, fn, t1, t2) => {
let newYs = []
let newXs = []
let (l1, l2) = (E.A.length(t1.xs), E.A.length(t2.xs))
@ -496,29 +496,9 @@ module PointwiseCombination = {
let newYs = E.A.fmap(x => XtoY.linear(x, t), newXs)
{xs: newXs, ys: newYs}
}
// This function is used for klDivergence
let combineAlongSupportOfSecondArgument: (
(float, float) => result<float, Operation.Error.t>,
T.t,
T.t,
) => result<T.t, Operation.Error.t> = (fn, prediction, answer) => {
let combineWithFn = (answerX: float, i: int) => {
let answerY = answer.ys[i]
let predictionY = XtoY.linear(answerX, prediction)
fn(predictionY, answerY)
}
let newYsWithError = Js.Array.mapi((x, i) => combineWithFn(x, i), answer.xs)
let newYsOrError = E.A.R.firstErrorOrOpen(newYsWithError)
let result = switch newYsOrError {
| Ok(a) => Ok({xs: answer.xs, ys: a})
| Error(b) => Error(b)
}
result
}
let addCombine = (interpolator: interpolator, t1: T.t, t2: T.t): T.t =>
combine((a, b) => Ok(a +. b), interpolator, t1, t2)->E.R.toExn(
combine(interpolator, (a, b) => Ok(a +. b), t1, t2)->E.R.toExn(
"Add operation should never fail",
_,
)

24
packages/vscode-ext/README.md
View File

@ -1,21 +1,35 @@
# Squiggle For VS Code
_[marketplace](https://marketplace.visualstudio.com/items?itemName=QURI.vscode-squiggle)_
## About
This extension provides support for [Squiggle](https://www.squiggle-language.com/) in VS Code.
This extension provides support for [Squiggle](https://www.squiggle-language.com/) in VS Code. It can be found in the VS code _[marketplace](https://marketplace.visualstudio.com/items?itemName=QURI.vscode-squiggle)_
Features:
- Preview `.squiggle` files in a preview pane
- Syntax highlighting for `.squiggle` and `.squiggleU` files
# Configuration
## Installation
Some preview settings, e.g. whether to show the summary table or types of outputs, can be configurable on in the VS Code settings and persist between different preview sessions.
You can install this extension by going to the "extensions" tab, searching for "Squiggle", and then installing it.
![](./images/vs-code-install.png)
## Usage
After loading a `.squiggle` file, an "Open Preview" button will appear. If you click it, the squiggle model will be shown, and updated as you edit and save you file.
![](./images/extension-screenshot.png)
### Configuration (optional)
Some preview settings, e.g. whether to show the summary table or types of outputs, can be configurable on in the VS Code settings and persist between different preview sessions. The VS Code settings can be accessed with the shortcut `Ctrl+,` with `Ctrl+Shift+P` + searching "Open Settings", or by accessing a file like `$HOME/.config/Code/User/settings.json` in Linux (see [here](https://stackoverflow.com/questions/65908987/how-can-i-open-visual-studio-codes-settings-json-file)) for other operating systems.
![](./images/vs-code-settings.png)
Check out the full list of Squiggle settings in the main VS Code settings.
# Build locally
## Build locally
We assume you ran `yarn` at the monorepo level for all dependencies.

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