Merge pull request #402 from quantified-uncertainty/algebraic-combination-refactor

Algebraic combination refactor

packages/squiggle-lang/__tests__/ReducerInterface/ReducerInterface_Distribution_test.res

@@ -92,11 +92,11 @@ describe("eval on distribution functions", () => {
     testEval("log(2, uniform(5,8))", "Ok(Sample Set Distribution)")
     testEval(
       "log(normal(5,2), 3)",
-      "Error(Distribution Math Error: Logarithm of input error: First input must completely greater than 0)",
+      "Error(Distribution Math Error: Logarithm of input error: First input must be completely greater than 0)",
     )
     testEval(
       "log(normal(5,2), normal(10,1))",
-      "Error(Distribution Math Error: Logarithm of input error: First input must completely greater than 0)",
+      "Error(Distribution Math Error: Logarithm of input error: First input must be completely greater than 0)",
     )
     testEval("log(uniform(5,8))", "Ok(Sample Set Distribution)")
     testEval("log10(uniform(5,8))", "Ok(Sample Set Distribution)")

packages/squiggle-lang/src/rescript/Distributions/GenericDist/GenericDist.res

@@ -150,144 +150,143 @@ let truncate = Truncate.run
    of a new variable that is the result of the operation on A and B.
    For instance, normal(0, 1) + normal(1, 1) -> normal(1, 2).
    In general, this is implemented via convolution.
-
-  TODO: It would be useful to be able to pass in a paramater to get this to run either with convolution or monte carlo.
 */
 module AlgebraicCombination = {
-  let runConvolution = (
+  module InputValidator = {
-    toPointSet: toPointSetFn,
+    /*
-    arithmeticOperation: Operation.convolutionOperation,
-    t1: t,
-    t2: t,
-  ) =>
-    E.R.merge(toPointSet(t1), toPointSet(t2))->E.R2.fmap(((a, b)) =>
-      PointSetDist.combineAlgebraically(arithmeticOperation, a, b)
-    )
-
-  let runMonteCarlo = (
-    toSampleSet: toSampleSetFn,
-    arithmeticOperation: Operation.algebraicOperation,
-    t1: t,
-    t2: t,
-  ): result<t, error> => {
-    let fn = Operation.Algebraic.toFn(arithmeticOperation)
-    E.R.merge(toSampleSet(t1), toSampleSet(t2))
-    ->E.R.bind(((t1, t2)) => {
-      SampleSetDist.map2(~fn, ~t1, ~t2)->E.R2.errMap(x => DistributionTypes.OperationError(x))
-    })
-    ->E.R2.fmap(r => DistributionTypes.SampleSet(r))
-  }
-
-  /*
      It would be good to also do a check to make sure that probability mass for the second
      operand, at value 1.0, is 0 (or approximately 0). However, we'd ideally want to check 
      that both the probability mass and the probability density are greater than zero.
      Right now we don't yet have a way of getting probability mass, so I'll leave this for later.
  */
-  let getLogarithmInputError = (t1: t, t2: t, ~toPointSetFn: toPointSetFn): option<error> => {
+    let getLogarithmInputError = (t1: t, t2: t, ~toPointSetFn: toPointSetFn): option<error> => {
-    let firstOperandIsGreaterThanZero =
+      let firstOperandIsGreaterThanZero =
-      toFloatOperation(
+        toFloatOperation(
-        t1,
+          t1,
-        ~toPointSetFn,
+          ~toPointSetFn,
-        ~distToFloatOperation=#Cdf(MagicNumbers.Epsilon.ten),
+          ~distToFloatOperation=#Cdf(MagicNumbers.Epsilon.ten),
-      ) |> E.R.fmap(r => r > 0.)
+        ) |> E.R.fmap(r => r > 0.)
-    let secondOperandIsGreaterThanZero =
+      let secondOperandIsGreaterThanZero =
-      toFloatOperation(
+        toFloatOperation(
-        t2,
+          t2,
-        ~toPointSetFn,
+          ~toPointSetFn,
-        ~distToFloatOperation=#Cdf(MagicNumbers.Epsilon.ten),
+          ~distToFloatOperation=#Cdf(MagicNumbers.Epsilon.ten),
-      ) |> E.R.fmap(r => r > 0.)
+        ) |> E.R.fmap(r => r > 0.)
-    let items = E.A.R.firstErrorOrOpen([
+      let items = E.A.R.firstErrorOrOpen([
-      firstOperandIsGreaterThanZero,
+        firstOperandIsGreaterThanZero,
-      secondOperandIsGreaterThanZero,
+        secondOperandIsGreaterThanZero,
-    ])
+      ])
-    switch items {
+      switch items {
-    | Error(r) => Some(r)
+      | Error(r) => Some(r)
-    | Ok([true, _]) =>
+      | Ok([true, _]) =>
-      Some(LogarithmOfDistributionError("First input must completely greater than 0"))
+        Some(LogarithmOfDistributionError("First input must be completely greater than 0"))
-    | Ok([false, true]) =>
+      | Ok([false, true]) =>
-      Some(LogarithmOfDistributionError("Second input must completely greater than 0"))
+        Some(LogarithmOfDistributionError("Second input must be completely greater than 0"))
-    | Ok([false, false]) => None
+      | Ok([false, false]) => None
-    | Ok(_) => Some(Unreachable)
+      | Ok(_) => Some(Unreachable)
+      }
+    }
+
+    let run = (t1: t, t2: t, ~toPointSetFn: toPointSetFn, ~arithmeticOperation): option<error> => {
+      if arithmeticOperation == #Logarithm {
+        getLogarithmInputError(t1, t2, ~toPointSetFn)
+      } else {
+        None
+      }
     }
   }
 
-  let getInvalidOperationError = (
+  module StrategyCallOnValidatedInputs = {
-    t1: t,
+    let convolution = (
-    t2: t,
+      toPointSet: toPointSetFn,
-    ~toPointSetFn: toPointSetFn,
+      arithmeticOperation: Operation.convolutionOperation,
+      t1: t,
+      t2: t,
+    ): result<t, error> =>
+      E.R.merge(toPointSet(t1), toPointSet(t2))
+      ->E.R2.fmap(((a, b)) => PointSetDist.combineAlgebraically(arithmeticOperation, a, b))
+      ->E.R2.fmap(r => DistributionTypes.PointSet(r))
+
+    let monteCarlo = (
+      toSampleSet: toSampleSetFn,
+      arithmeticOperation: Operation.algebraicOperation,
+      t1: t,
+      t2: t,
+    ): result<t, error> => {
+      let fn = Operation.Algebraic.toFn(arithmeticOperation)
+      E.R.merge(toSampleSet(t1), toSampleSet(t2))
+      ->E.R.bind(((t1, t2)) => {
+        SampleSetDist.map2(~fn, ~t1, ~t2)->E.R2.errMap(x => DistributionTypes.OperationError(x))
+      })
+      ->E.R2.fmap(r => DistributionTypes.SampleSet(r))
+    }
+
+    let symbolic = (
+      arithmeticOperation: Operation.algebraicOperation,
+      t1: t,
+      t2: t,
+    ): SymbolicDistTypes.analyticalSimplificationResult => {
+      switch (t1, t2) {
+      | (DistributionTypes.Symbolic(d1), DistributionTypes.Symbolic(d2)) =>
+        SymbolicDist.T.tryAnalyticalSimplification(d1, d2, arithmeticOperation)
+      | _ => #NoSolution
+      }
+    }
+  }
+
+  module StrategyChooser = {
+    type specificStrategy = [#AsSymbolic | #AsMonteCarlo | #AsConvolution]
+
+    //I'm (Ozzie) really just guessing here, very little idea what's best
+    let expectedConvolutionCost: t => int = x =>
+      switch x {
+      | Symbolic(#Float(_)) => MagicNumbers.OpCost.floatCost
+      | Symbolic(_) => MagicNumbers.OpCost.symbolicCost
+      | PointSet(Discrete(m)) => m.xyShape->XYShape.T.length
+      | PointSet(Mixed(_)) => MagicNumbers.OpCost.mixedCost
+      | PointSet(Continuous(_)) => MagicNumbers.OpCost.continuousCost
+      | _ => MagicNumbers.OpCost.wildcardCost
+      }
+
+    let run = (~t1: t, ~t2: t, ~arithmeticOperation): specificStrategy => {
+      switch StrategyCallOnValidatedInputs.symbolic(arithmeticOperation, t1, t2) {
+      | #AnalyticalSolution(_)
+      | #Error(_) =>
+        #AsSymbolic
+      | #NoSolution =>
+        if Operation.Convolution.canDoAlgebraicOperation(arithmeticOperation) {
+          expectedConvolutionCost(t1) * expectedConvolutionCost(t2) >
+            MagicNumbers.OpCost.monteCarloCost
+            ? #AsMonteCarlo
+            : #AsConvolution
+        } else {
+          #AsMonteCarlo
+        }
+      }
+    }
+  }
+
+  let runStrategyOnValidatedInputs = (
+    ~t1: t,
+    ~t2: t,
     ~arithmeticOperation,
-  ): option<error> => {
+    ~strategy: StrategyChooser.specificStrategy,
-    if arithmeticOperation == #Logarithm {
-      getLogarithmInputError(t1, t2, ~toPointSetFn)
-    } else {
-      None
-    }
-  }
-
-  //I'm (Ozzie) really just guessing here, very little idea what's best
-  let expectedConvolutionCost: t => int = x =>
-    switch x {
-    | Symbolic(#Float(_)) => MagicNumbers.OpCost.floatCost
-    | Symbolic(_) => MagicNumbers.OpCost.symbolicCost
-    | PointSet(Discrete(m)) => m.xyShape->XYShape.T.length
-    | PointSet(Mixed(_)) => MagicNumbers.OpCost.mixedCost
-    | PointSet(Continuous(_)) => MagicNumbers.OpCost.continuousCost
-    | _ => MagicNumbers.OpCost.wildcardCost
-    }
-
-  type calculationStrategy = MonteCarloStrat | ConvolutionStrat(Operation.convolutionOperation)
-
-  let chooseConvolutionOrMonteCarloDefault = (
-    op: Operation.algebraicOperation,
-    t2: t,
-    t1: t,
-  ): calculationStrategy =>
-    switch op {
-    | #Divide
-    | #Power
-    | #Logarithm =>
-      MonteCarloStrat
-    | (#Add | #Subtract | #Multiply) as convOp =>
-      expectedConvolutionCost(t1) * expectedConvolutionCost(t2) > MagicNumbers.OpCost.monteCarloCost
-        ? MonteCarloStrat
-        : ConvolutionStrat(convOp)
-    }
-
-  let tryAnalyticalSimplification = (
-    arithmeticOperation: Operation.algebraicOperation,
-    t1: t,
-    t2: t,
-  ): option<SymbolicDistTypes.analyticalSimplificationResult> => {
-    switch (t1, t2) {
-    | (DistributionTypes.Symbolic(d1), DistributionTypes.Symbolic(d2)) =>
-      Some(SymbolicDist.T.tryAnalyticalSimplification(d1, d2, arithmeticOperation))
-    | _ => None
-    }
-  }
-
-  let runDefault = (
-    t1: t,
     ~toPointSetFn: toPointSetFn,
     ~toSampleSetFn: toSampleSetFn,
-    ~arithmeticOperation,
-    ~t2: t,
   ): result<t, error> => {
-    switch tryAnalyticalSimplification(arithmeticOperation, t1, t2) {
+    switch strategy {
-    | Some(#AnalyticalSolution(symbolicDist)) => Ok(Symbolic(symbolicDist))
+    | #AsMonteCarlo =>
-    | Some(#Error(e)) => Error(OperationError(e))
+      StrategyCallOnValidatedInputs.monteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
-    | Some(#NoSolution)
+    | #AsSymbolic =>
-    | None =>
+      switch StrategyCallOnValidatedInputs.symbolic(arithmeticOperation, t1, t2) {
-      switch getInvalidOperationError(t1, t2, ~toPointSetFn, ~arithmeticOperation) {
+      | #AnalyticalSolution(symbolicDist) => Ok(Symbolic(symbolicDist))
-      | Some(e) => Error(e)
+      | #Error(e) => Error(OperationError(e))
-      | None =>
+      | #NoSolution => Error(Unreachable)
-        switch chooseConvolutionOrMonteCarloDefault(arithmeticOperation, t1, t2) {
+      }
-        | MonteCarloStrat => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
+    | #AsConvolution =>
-        | ConvolutionStrat(convOp) =>
+      switch Operation.Convolution.fromAlgebraicOperation(arithmeticOperation) {
-          runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(
+      | Some(convOp) => StrategyCallOnValidatedInputs.convolution(toPointSetFn, convOp, t1, t2)
-            r,
+      | None => Error(Unreachable)
-          ))
-        }
       }
     }
   }
@@ -300,27 +299,38 @@ module AlgebraicCombination = {
     ~arithmeticOperation: Operation.algebraicOperation,
     ~t2: t,
   ): result<t, error> => {
-    switch strategy {
+    let invalidOperationError = InputValidator.run(t1, t2, ~arithmeticOperation, ~toPointSetFn)
-    | AsDefault => runDefault(t1, ~toPointSetFn, ~toSampleSetFn, ~arithmeticOperation, ~t2)
+    switch (invalidOperationError, strategy) {
-    | AsSymbolic =>
+    | (Some(e), _) => Error(e)
-      switch tryAnalyticalSimplification(arithmeticOperation, t1, t2) {
+    | (None, AsDefault) => {
-      | Some(#AnalyticalSolution(symbolicDist)) => Ok(Symbolic(symbolicDist))
+        let chooseStrategy = StrategyChooser.run(~arithmeticOperation, ~t1, ~t2)
-      | Some(#NoSolution) => Error(RequestedStrategyInvalidError(`No analytical solution`))
+        runStrategyOnValidatedInputs(
-      | None => Error(RequestedStrategyInvalidError("Inputs were not even symbolic"))
+          ~t1,
-      | Some(#Error(err)) => Error(OperationError(err))
+          ~t2,
+          ~strategy=chooseStrategy,
+          ~arithmeticOperation,
+          ~toPointSetFn,
+          ~toSampleSetFn,
+        )
       }
-    | AsConvolution => {
+    | (None, AsMonteCarlo) =>
-        let errString = opString => `Can't convolve on ${opString}`
+      StrategyCallOnValidatedInputs.monteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
-        switch arithmeticOperation {
+    | (None, AsSymbolic) =>
-        | (#Add | #Subtract | #Multiply) as convOp =>
+      switch StrategyCallOnValidatedInputs.symbolic(arithmeticOperation, t1, t2) {
-          runConvolution(toPointSetFn, convOp, t1, t2)->E.R2.fmap(r => DistributionTypes.PointSet(
+      | #AnalyticalSolution(symbolicDist) => Ok(Symbolic(symbolicDist))
-            r,
+      | #NoSolution => Error(RequestedStrategyInvalidError(`No analytic solution for inputs`))
-          ))
+      | #Error(err) => Error(OperationError(err))
-        | (#Divide | #Power | #Logarithm) as op =>
+      }
-          op->Operation.Algebraic.toString->errString->RequestedStrategyInvalidError->Error
+    | (None, AsConvolution) =>
+      switch Operation.Convolution.fromAlgebraicOperation(arithmeticOperation) {
+      | None => {
+          let errString = `Convolution not supported for ${Operation.Algebraic.toString(
+              arithmeticOperation,
+            )}`
+          Error(RequestedStrategyInvalidError(errString))
         }
+      | Some(convOp) => StrategyCallOnValidatedInputs.convolution(toPointSetFn, convOp, t1, t2)
       }
-    | AsMonteCarlo => runMonteCarlo(toSampleSetFn, arithmeticOperation, t1, t2)
     }
   }
 }

packages/squiggle-lang/src/rescript/Utility/Operation.res

@@ -29,6 +29,18 @@ type distToFloatOperation = [
 
 module Convolution = {
   type t = convolutionOperation
+  //Only a selection of operations are supported by convolution.
+  let fromAlgebraicOperation = (op: algebraicOperation): option<convolutionOperation> =>
+    switch op {
+    | #Add => Some(#Add)
+    | #Subtract => Some(#Subtract)
+    | #Multiply => Some(#Multiply)
+    | #Divide | #Power | #Logarithm => None
+    }
+
+  let canDoAlgebraicOperation = (op: algebraicOperation): bool =>
+    fromAlgebraicOperation(op)->E.O.isSome
+
   let toFn: (t, float, float) => float = x =>
     switch x {
     | #Add => \"+."