Merge pull request #359 from quantified-uncertainty/log-inputs-errors
Show correct errors early on when log(distribution) has bad arguments
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dc127a884a
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@ -23,7 +23,7 @@ describe("eval on distribution functions", () => {
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testEval("-normal(5,2)", "Ok(Normal(-5,2))")
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})
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describe("to", () => {
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testEval("5 to 2", "Error(Math Error: Low value must be less than high value.)")
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testEval("5 to 2", "Error(Distribution Math Error: Low value must be less than high value.)")
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testEval("to(2,5)", "Ok(Lognormal(1.1512925464970227,0.27853260523016377))")
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testEval("to(-2,2)", "Ok(Normal(0,1.2159136638235384))")
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})
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@ -90,10 +90,13 @@ describe("eval on distribution functions", () => {
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describe("log", () => {
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testEval("log(2, uniform(5,8))", "Ok(Sample Set Distribution)")
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testEval("log(normal(5,2), 3)", "Error(Math Error: Operation returned complex result)")
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testEval(
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"log(normal(5,2), 3)",
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"Error(Distribution Math Error: Logarithm of input error: First input must completely greater than 0)",
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)
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testEval(
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"log(normal(5,2), normal(10,1))",
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"Error(Math Error: Operation returned complex result)",
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"Error(Distribution Math Error: Logarithm of input error: First input must completely greater than 0)",
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)
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testEval("log(uniform(5,8))", "Ok(Sample Set Distribution)")
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testEval("log10(uniform(5,8))", "Ok(Sample Set Distribution)")
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@ -12,7 +12,7 @@ describe("Symbolic mean", () => {
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expect(squiggleResult.value).toBeCloseTo((x + y + z) / 3);
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} catch (err) {
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expect((err as Error).message).toEqual(
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"Expected squiggle expression to evaluate but got error: Math Error: Triangular values must be increasing order."
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"Expected squiggle expression to evaluate but got error: Distribution Math Error: Triangular values must be increasing order."
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);
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}
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}
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@ -14,6 +14,7 @@ type error =
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| OperationError(Operation.Error.t)
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| PointSetConversionError(SampleSetDist.pointsetConversionError)
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| SparklineError(PointSetTypes.sparklineError) // This type of error is for when we find a sparkline of a discrete distribution. This should probably at some point be actually implemented
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| LogarithmOfDistributionError(string)
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| OtherError(string)
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@genType
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@ -29,6 +30,7 @@ module Error = {
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| Unreachable => "Unreachable"
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| DistributionVerticalShiftIsInvalid => "Distribution Vertical Shift is Invalid"
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| ArgumentError(s) => `Argument Error ${s}`
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| LogarithmOfDistributionError(s) => `Logarithm of input error: ${s}`
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| TooFewSamples => "Too Few Samples"
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| OperationError(err) => Operation.Error.toString(err)
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| PointSetConversionError(err) => SampleSetDist.pointsetConversionErrorToString(err)
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@ -186,6 +186,49 @@ module AlgebraicCombination = {
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->E.R2.fmap(r => DistributionTypes.SampleSet(r))
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}
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/*
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It would be good to also do a check to make sure that probability mass for the second
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operand, at value 1.0, is 0 (or approximately 0). However, we'd ideally want to check
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that both the probability mass and the probability density are greater than zero.
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Right now we don't yet have a way of getting probability mass, so I'll leave this for later.
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*/
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let getLogarithmInputError = (t1: t, t2: t, ~toPointSetFn: toPointSetFn): option<error> => {
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let firstOperandIsGreaterThanZero =
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toFloatOperation(t1, ~toPointSetFn, ~distToFloatOperation=#Cdf(1e-10)) |> E.R.fmap(r =>
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r > 0.
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)
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let secondOperandIsGreaterThanZero =
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toFloatOperation(t2, ~toPointSetFn, ~distToFloatOperation=#Cdf(1e-10)) |> E.R.fmap(r =>
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r > 0.
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)
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let items = E.A.R.firstErrorOrOpen([
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firstOperandIsGreaterThanZero,
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secondOperandIsGreaterThanZero,
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])
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switch items {
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| Error(r) => Some(r)
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| Ok([true, _]) =>
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Some(LogarithmOfDistributionError("First input must completely greater than 0"))
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| Ok([false, true]) =>
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Some(LogarithmOfDistributionError("Second input must completely greater than 0"))
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| Ok([false, false]) => None
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| Ok(_) => Some(Unreachable)
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}
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}
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let getInvalidOperationError = (
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t1: t,
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t2: t,
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~toPointSetFn: toPointSetFn,
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~arithmeticOperation,
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): option<error> => {
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if arithmeticOperation == #Logarithm {
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getLogarithmInputError(t1, t2, ~toPointSetFn)
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} else {
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None
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}
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}
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//I'm (Ozzie) really just guessing here, very little idea what's best
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let expectedConvolutionCost: t => int = x =>
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switch x {
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@ -226,10 +269,16 @@ module AlgebraicCombination = {
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| Some(Ok(symbolicDist)) => Ok(Symbolic(symbolicDist))
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| Some(Error(e)) => Error(OperationError(e))
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| None =>
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switch chooseConvolutionOrMonteCarlo(arithmeticOperation, t1, t2) {
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| MonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
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| Convolution(convOp) =>
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runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(r))
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switch getInvalidOperationError(t1, t2, ~toPointSetFn, ~arithmeticOperation) {
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| Some(e) => Error(e)
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| None =>
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switch chooseConvolutionOrMonteCarlo(arithmeticOperation, t1, t2) {
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| MonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
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| Convolution(convOp) =>
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runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(
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r,
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))
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}
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}
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}
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}
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@ -21,7 +21,7 @@ let errorToString = err =>
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| REAssignmentExpected => "Assignment expected"
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| REExpressionExpected => "Expression expected"
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| REFunctionExpected(msg) => `Function expected: ${msg}`
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| REDistributionError(err) => `Math Error: ${DistributionTypes.Error.toString(err)}`
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| REDistributionError(err) => `Distribution Math Error: ${DistributionTypes.Error.toString(err)}`
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| REJavaScriptExn(omsg, oname) => {
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let answer = "JS Exception:"
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let answer = switch oname {
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