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removed \TwoScalars\ material

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This commit is contained in:
Quinn Dougherty 2022-06-26 12:28:04 -04:00
parent 29cf1cebb2
commit acb0d3b9ee
3 changed files with 1 additions and 120 deletions
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90
packages/squiggle-lang/__tests__/Distributions/Scoring/TwoScalars_test.res
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@ -1,90 +0,0 @@
open Jest
open Expect
open GenericDist_Fixtures
exception ScoreFailed
describe("TwoScalars: scalar -> scalar -> score", () => {
test("score: infinity", () => {
let scalar1 = 1.0 // 100% of probability mass 1.0
let scalar2 = 2.0 // 100% of probability mass to 2.0
let score = PointSetDist_Scoring.TwoScalars.score(~estimate=scalar1, ~answer=scalar2)
switch score {
| Ok(x) => x->expect->toEqual(infinity)
| _ => raise(MixtureFailed)
}
})
test("score: 0.0", () => {
let scalar1 = 1.5 // 100% of probability mass 1.5
let scalar2 = 1.5 // 100% of probability mass to 1.5
let score = PointSetDist_Scoring.TwoScalars.score(~estimate=scalar1, ~answer=scalar2)
switch score {
| Ok(x) => x->expect->toEqual(0.0)
| _ => raise(MixtureFailed)
}
})
test("scoreWithPrior: minus infinity", () => {
let scalar1 = 1.5 // 100% of probability mass 1.5
let scalar2 = 1.5 // 100% of probability mass to 1.5
let scalar3 = 1.0 // 100% of probability mass to 1.0
let score = PointSetDist_Scoring.TwoScalars.scoreWithPrior(
~estimate=scalar1,
~answer=scalar2,
~prior=scalar3,
)
switch score {
| Ok(x) => x->expect->toEqual(-.infinity)
| _ => raise(MixtureFailed)
}
})
test("scoreWithPrior: 0.0", () => {
let scalar1 = 1.5 // 100% of probability mass 1.5
let scalar2 = 1.5 // 100% of probability mass to 1.5
let scalar3 = 1.5 // 100% of probability mass to 1.5
let score = PointSetDist_Scoring.TwoScalars.scoreWithPrior(
~estimate=scalar1,
~answer=scalar2,
~prior=scalar3,
)
switch score {
| Ok(x) => x->expect->toEqual(0.0)
| _ => raise(ScoreFailed)
}
})
test("scoreWithPrior: really dumb forecasters", () => {
let scalar1 = 1.0 // 100% of probability mass 1.0
let scalar2 = 1.5 // 100% of probability mass to 1.5
let scalar3 = 1.0 // 100% of probability mass to 1.0
let score = PointSetDist_Scoring.TwoScalars.scoreWithPrior(
~estimate=scalar1,
~answer=scalar2,
~prior=scalar3,
)
switch score {
| Ok(x) => x->expect->toEqual(infinity -. infinity) // "Error: Really dumb forecasters"
| _ => raise(ScoreFailed)
}
})
test("scoreWithPrior: 0.0", () => {
let scalar1 = 1.0 // 100% of probability mass 1.0
let scalar2 = 1.0 // 100% of probability mass to 1.0
let scalar3 = 1.0 // 100% of probability mass to 1.0
let score = PointSetDist_Scoring.TwoScalars.scoreWithPrior(
~estimate=scalar1,
~answer=scalar2,
~prior=scalar3,
)
switch score {
| Ok(x) => x->expect->toEqual(0.0)
| _ => raise(ScoreFailed)
}
})
})

9
packages/squiggle-lang/src/rescript/Distributions/GenericDist.res
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@ -190,14 +190,7 @@ module Score = {
| (Score_Scalar(_), Score_Dist(_), None) => NotYetImplemented->Error
| (Score_Scalar(_), Score_Dist(_), Some(Ok(PSScalar(_)))) => NotYetImplemented->Error
| (Score_Scalar(_), _, Some(Ok(PSDist(_)))) => DistributionTypes.Unreachable->Error
| (Score_Scalar(esti'), Score_Scalar(answ'), None) =>
{estimate: esti', answer: answ', prior: None}
->PointSetDist_Scoring.ScalarEstimateScalarAnswer
->Ok
| (Score_Scalar(esti'), Score_Scalar(answ'), Some(Ok(PSScalar(prior'')))) =>
{estimate: esti', answer: answ', prior: prior''->Some}
->PointSetDist_Scoring.ScalarEstimateScalarAnswer
->Ok
| (Score_Scalar(_), Score_Scalar(_), _) => NotYetImplemented->Error
| (_, _, Some(Error(err))) => err->Error
}
}

22
packages/squiggle-lang/src/rescript/Distributions/PointSetDist/PointSetDist_Scoring.res
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@ -6,7 +6,6 @@ type abstractScoreArgs<'a, 'b> = {estimate: 'a, answer: 'b, prior: option<'a>}
type scoreArgs =
| DistEstimateDistAnswer(abstractScoreArgs<pointSetDist, pointSetDist>)
| DistEstimateScalarAnswer(abstractScoreArgs<pointSetDist, scalar>)
| ScalarEstimateScalarAnswer(abstractScoreArgs<scalar, scalar>)
let logFn = Js.Math.log // base e
let minusScaledLogOfQuotient = (~esti, ~answ): result<float, Operation.Error.t> => {
@ -130,23 +129,6 @@ module WithScalarAnswer = {
}
}
module TwoScalars = {
// You will almost never want to use this.
let score = (~estimate: scalar, ~answer: scalar) => {
if Js.Math.abs_float(estimate -. answer) < MagicNumbers.Epsilon.ten {
0.0->Ok
} else {
infinity->Ok // - log(0)
}
}
let scoreWithPrior = (~estimate: scalar, ~answer: scalar, ~prior: scalar) => {
E.R.merge(score(~estimate, ~answer), score(~estimate=prior, ~answer))->E.R2.fmap(((s1, s2)) =>
s1 -. s2
) // This will presently NaN if both are wrong: infinity-infinity.
}
}
let twoGenericDistsToTwoPointSetDists = (~toPointSetFn, estimate, answer): result<
(pointSetDist, pointSetDist),
'e,
@ -165,8 +147,4 @@ let logScore = (args: scoreArgs, ~combineFn, ~integrateFn, ~toMixedFn): result<
WithScalarAnswer.score(~estimate, ~answer)
| DistEstimateScalarAnswer({estimate, answer, prior: Some(prior)}) =>
WithScalarAnswer.scoreWithPrior(~estimate, ~answer, ~prior)
| ScalarEstimateScalarAnswer({estimate, answer, prior: None}) =>
TwoScalars.score(~estimate, ~answer)
| ScalarEstimateScalarAnswer({estimate, answer, prior: Some(prior)}) =>
TwoScalars.scoreWithPrior(~estimate, ~answer, ~prior)
}