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logScore now in interface.

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Value: [1e-4 to 1e-1]
This commit is contained in:
Quinn Dougherty 2022-05-12 14:16:52 -04:00
parent 978e149913
commit 51310819a1
11 changed files with 65 additions and 21 deletions
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8
packages/squiggle-lang/src/rescript/Distributions/DistributionOperation.res
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@ -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

2
packages/squiggle-lang/src/rescript/Distributions/DistributionOperation.resi
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@ -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>

7
packages/squiggle-lang/src/rescript/Distributions/DistributionTypes.res
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@ -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(

11
packages/squiggle-lang/src/rescript/Distributions/GenericDist.res
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@ -59,11 +59,20 @@ let integralEndY = (t: t): float =>
let isNormalized = (t: t): bool => Js.Math.abs_float(integralEndY(t) -. 1.0) < 1e-7
let klDivergence = (t1, t2, ~toPointSetFn: toPointSetFn): result<float, error> => {
module Score = {
let klDivergence = (t1, t2, ~toPointSetFn: toPointSetFn): result<float, error> => {
let pointSets = E.R.merge(toPointSetFn(t1), toPointSetFn(t2))
pointSets |> E.R2.bind(((a, b)) =>
PointSetDist.T.klDivergence(a, b)->E.R2.errMap(x => DistributionTypes.OperationError(x))
)
}
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 = (

5
packages/squiggle-lang/src/rescript/Distributions/GenericDist.resi
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@ -23,7 +23,10 @@ let toFloatOperation: (
~distToFloatOperation: DistributionTypes.DistributionOperation.toFloat,
) => result<float, error>
let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
module Score: {
let klDivergence: (t, t, ~toPointSetFn: toPointSetFn) => result<float, error>
let logScore: (t, t, float, ~toPointSetFn: toPointSetFn) => result<float, error>
}
@genType
let toPointSet: (

4
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Continuous.res
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@ -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 => {

3
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Discrete.res
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@ -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)
}
})

2
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Distributions.res
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@ -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

3
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/Mixed.res
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@ -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 => {

1
packages/squiggle-lang/src/rescript/MagicNumbers.res
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@ -12,6 +12,7 @@ module Epsilon = {
module Environment = {
let defaultXYPointLength = 1000
let defaultSampleCount = 10000
let sparklineLength = 20
}
module OpCost = {

32
packages/squiggle-lang/src/rescript/ReducerInterface/ReducerInterface_GenericDistribution.res
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@ -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)]) =>