# Selection functions are interesting notion with tricky implementation

“Sequential Games and Optimal Strategies” by Martín Escardó and Paulo Oliva has the best possible introduction:

Life is the sum of all your choices, so said Albert Camus. But what does “choice” mean? One could say that to choose is to select one element x out of a set X of possible candidates.

The note is devoted to selection functions. They are all about choices that have underlying sets of options.

Selection functions are not something we can leverage in daily programming. Notwithstanding, playing around with selection functions was fun for me. I stuck a couple of times because of approaches that were not obvious to me. And adopting new thinking patterns is something why we all love programming.

I hope you’ll enjoy some pieces too.

There is a decent amount of good papers on the topic.

Few to name:

What I attempt to do is to make those papers a little bit closer to Scala practitioners.

## Selection function

The signature of selection functions is simple:

``````type J[R, A] = (A => R) => A
``````

The main notion there is that function has a set of objects of type `A` and provides a way to select one of those objects. Formally, the selection function embodies the collection of objects, judges them according to provided criteria, and returns the best option. What is unusual here is that the collection is not a parameter or a part of the context. The selection function hides it under the hood.

Let’s come up with some toy examples. The simplest case is:

• Having a list of entities.
• Maximizing some property.

So, for abstract `Entity` and `Property` we’ll need a collection `List[Entity]` itself and ordering `Order[Property]`.

``````def maxWith[Entity, Property](
entities: List[Entity],
order: cats.Order[Property]
): J[Property, Entity] =
(evaluate: Entity => Property) =>
entities.maxBy(evaluate)(order.toOrdering)
``````

Now we can pick a brand-new BMW!

``````case class BMW(
model: String,
power: hp,
msrp: USD
)

val bmws =
List(
BMW("230i", 255, 38395),
BMW("330e", 288, 46295),
BMW("M550i", 523, 60945)
)

def bmwSelection[Property](
implicit order: Order[Property]
): J[Property, BMW] =
maxWith[BMW, Property](bmws, order)

``````

The particular selection function is going to look like this:

``````val bmwSelectionByHp: J[hp, BMW] = bmwSelection[hp]
``````

The last step is to let the function know which property to use.

``````val powerfulBmw: BMW =
bmwSelectionByHp(_.power) /// BMW(M550i,523,60945)
``````

## Quantifier functions

There are also quantifier functions that provide not a selected object but a property that “causes” a choice.

``````type K[R, A] = (A => R) => R
``````

For sure, we can derive quantifiers from selection functions.

``````def overline[R, A]: J[R, A] => K[R, A] =
(select: J[R, A]) =>
(eval: A => R) => {
val selected: A  = select(eval)
val evaluated: R = eval.apply(selected)
evaluated
}
``````
``````val maxPowerOfBmw = overline(bmwSelectionByHp)(_.power) // 523
``````

# Combining two selections

Things become much less obvious once we try to “glue” a couple of functions together. Selection functions should help with choices. And choices should be made in combination with other choices. Let’s start with a pair of selections.

## Signature of the pairing function

First of all, we need to agree on the signature.

Let us have two functions: `J[R1, A]` and `J[R2, B]`. Both type parameters are different.

We want to combine choices. Choices could have different nature. Hence, distinguishing `A` and `B` is desired. And the combination is literally two objects, so a tuple (`(A, B)`) works well.

And what about the type of “truth values”?
Let’s imagine we have different `R1` and `R2`. And we need to evaluate `(A, B)` somehow. We ought to interpret that tuple as new type. And we need to judge objects of that type. Whatever we want to calculate, single value is required in the end.

So, pairing of `J[R, A]` and `J[R, B]` leads to a selection `J[R, (A, B)]`. We had individual `A => R` and `B => R`, but to judge pair we’ll need `(A, B) => R`.

Finalized signature is following:

``````def pair[R, A, B]: J[R, A] => J[R, B] => J[R, (A, B)]
``````

## Enabling predicates

The selection function does full iteration over all underlying elements. It applies `X => R` transformation to choose the best available option.

That means we can tune our evaluation function to search for an exact match. It would cover the demand of the “brute-forcing” selection function. We turn the evaluation function into the strict predicate.

``````val bmwPredicateSelection = bmwSelection[Boolean]
val matched = bmwPredicateSelection(_.model == "M550i") // BMW(M550i,523,60945)
``````

We don’t need to do much to enable predicates. `Boolean` is a completely valid “truth value” type. Standard ordering makes `true` values larger than `false` ones.

## Implementation of the pairing function

Well, it is time to return to the `pair` function itself.

We have separated selections for `A` and `B`. And also evaluation for a tuple of them.

``````def pair[R, A, B]: J[R, A] => J[R, B] => J[R, (A, B)] =
(selectA: J[R, A]) =>
(selectB: J[R, B]) =>
(evaluateAB: ((A, B)) => R) => ???
``````

The main trick here is to realize that there are no independent judgments yet. So, we need to build tuples and judge them. Once we construct the evaluation function (argument for `selectA`) it is necessary to also select the best possible instance of `B`.

``````def pickUpB(candidateA: A): B => R = ???

val a: A =
selectA(
candidateA =>
//key -> value is sugar to create tuple2
evaluateAB(candidateA -> selectB(pickUpB(candidateA)))
)
)
``````

And “the best possible instance of B” means the best combination with a preliminary instance of A.

``````def pickUpB(candidateA: A): B => R =
(candidateB: B) =>
evaluateAB((candidateA, candidateB))
``````

The final touch is the re-selection of `B` after having selected `A`.

``````def pair[R, A, B]: J[R, A] => J[R, B] => J[R, (A, B)] =
(selectA: J[R, A]) =>
(selectB: J[R, B]) =>
(evaluateAB: ((A, B)) => R) => {
def pickUpB(candidateA: A): B => R =
(candidateB: B) => evaluateAB((candidateA, candidateB))

val a: A =
selectA(
candidateA =>
evaluateAB(
candidateA -> selectB(pickUpB(candidateA)
)
)
)

val b: B =
selectB(candidateB => evaluateAB(a, candidateB))

(a, b)
}
``````

Now we can play around with predicates for combinations. Let’s brute-force a two-char password with the help of two selection functions that formerly worked independently.

``````type Password = (Int, Char)

{ case (a, b) => a == 7 && b == 'p' }

def bruteforceInt[T](implicit order: Order[T]): J[T, Int] =
maxWith(order, (1 to 9).toList)
def bruteforceChar[T](implicit order: Order[T]): J[T, Char] =
maxWith(order, ('a' to 'z').toList)

pair[Boolean, Int, Char]

``````

# More functions combined together

Now we are closer to useful applications of selection functions.

The core notion here is representing our problems (like playing games or graph searching) as a series of choices. Previously we combined two selections into one. In the same manner, we can transform a sequence of `A`-selections into a single selection that will select the best option from the sequence of potential `A`-objects: `List[J[R, A]] => J[R, List[A]]`.

At this step we need the function `List[A] => R` that will judge the whole sequence.

The approach is the same. We just iteratively produce selections with all possible candidates.

``````def product[R, A]: List[J[R, A]] => J[R, List[A]] =
(functions: List[J[R, A]]) =>
(listEval: List[A] => R) =>
functions match {
case evalFun :: restEvalFunctions =>
val a: A =
evalFun((candidate: A) =>
listEval(
candidate ::
product
.apply(restEvalFunctions)
.apply(
restCandidates =>
listEval(candidate :: restCandidates)
)
)
)

val as: List[A] =
product(restEvalFunctions)(rest => listEval(a :: rest))

a :: as

case Nil => Nil
}
``````

The code there is highly non-obvious.

1. On a high level, we iterate over the list of selection functions.
2. For every element, we consider another one function (standard `head :: tail` pattern matching).
3. For every element, we calculate the candidate `val a: A` in terms of both current `evalFun` and the rest of the functions (`restEvalFunctions`) together. Candidate `(candidate: A)` is judged as part of the whole solution (list of `A`-objects) by `listEval`.
4. Recursively, we re-evaluate logic and calculate the “tail.” We already know the best candidate for the current iteration and pass it as part of the known part of the solution: `listEval(a :: rest)`.
Let’s add more logging and counters
``````var n = 0
def productWLogs[R, A]: List[J[R, A]] => J[R, List[A]] =
(functions: List[J[R, A]]) => {
n = n + 1
(evalList: List[A] => R) =>
functions match {
case e :: es =>
val a: A =
e { (candidate: A) =>
evalList
.compose { (list: List[A]) =>
println(s"1. Considering \$list for \$candidate on \${n}th iteration"); list
}
.apply(
candidate :: productWLogs
.apply(es)
.apply(arg =>
evalList
.compose { (list: List[A]) =>
println(s"2. Considering \$list for \$candidate on \${n}th iteration");
list
}
.apply(candidate :: arg)
)
)
}

val as: List[A] =
productWLogs(es)(arg =>
evalList
.compose { (list: List[A]) =>
println(s"3. Considering \$list for \$a on \${n}th iteration");
list
}
.apply(a :: arg)
)
a :: as

case Nil => Nil
}
}
``````

The function does even more than n² iterations! 757 is the actual counter value, while 26² is equal to 676.

So, solutions are being generated multiple times. We have no “caching” mechanism. We re-generate the same combinations to pick up the best solutions during sequential iterations.

There are some feasible optimizations but I suggest switching to the opposite approach.

We can just be as greedy as possible. But we need a slightly different evaluation function to tackle partial solutions.

# Greedy product

To reduce complexity, we can use a greedy approach. That means just picking the best solution for each step. And these per-step solutions are not full-featured combinations but the best options for particular iterations.

``````def greedyProduct[R, A]: List[J[R, A]] => J[R, List[A]] =
(selections: List[J[R, A]]) => { (evalList: List[A] => R) =>
selections match {
case eval :: es =>
val candidate: A  = eval((candidate: A) => evalList(candidate :: List()))
val rest: List[A] = greedyProduct(es)(arg => evalList(candidate :: arg))

candidate :: rest

case Nil => Nil
}
}
``````

So, we use the `evalList` function to evaluate the singleton list:

``````val candidate: A =
eval(
(candidate: A) =>
evalList(candidate :: List())
)
``````

After it, we proceed with the rest of the functions.

To leverage the greedy approach, we need appropriate evaluation functions. Functions should be able to cope with partial solutions. In the case of passwords, we can measure how many characters are equal to the target.

``````val stringEquality: String => (List[Char] => Int) =
target =>
guessed =>
target
.zip(guessed.mkString)
.count { case (c1, c2) => c1 == c2 }
``````
``````val result = // List(p, a, s, s, w, o, r, d)
greedyProduct(
List.fill(8)(bruteforceChar[Int])
``````

Please note that greedy algorithms, by their nature, can provide non-optimal solutions.

First, let’s pack the function into convenient container.

``````case class Selection[R, A](e: J[R, A])
``````

`flip` from Haskell will be useful.

``````def flip[A, B, C](f: A => B => C)(x: B)(y: A) = f(y)(x)
``````

Well, `Monad` instance implements chaining of selections. We fix `R` type (return type of evaluation functions).

``````implicit def makeMonad[R] = new Monad[Selection[R, *]] {
override def flatMap[A, B](fa: Selection[R, A])(f: A => Selection[R, B]): Selection[R, B] =
Selection((p: B => R) => f(fa.e(a => p(flip(f.andThen(_.e))(p)(a)))).e(p))

override def map[A, B](fa: Selection[R, A])(f: A => B): Selection[R, B] =
Selection((p: B => R) => f(fa.e(a => p(f(a)))))

override def pure[A](x: A): Selection[R, A] = Selection(_ => x)

override def tailRecM[A, B](a: A)(f: A => Selection[R, Either[A, B]]): Selection[R, B] = ???
}
``````

As artificial example, I can suggest to pick up the most expensive BMW, and for rest of money look for EV car. We use same `USD` property to judge different entities. And we can take into account previously selected object.

``````case class EvCar(name: String, price: USD)
val evCars: List[EvCar] = List(...)

def evSelectionWithLimit(limit: USD) =
Selection[USD, EvCar]((eval: EvCar => USD) =>
evCars.filter(_.price < limit).maxBy(eval)
)

val evCarSelection: Selection[USD, EvCar] = usdSelectionMonad.flatMap(bmwSelectionByPrice)((bmw: BMW) =>
evSelectionWithLimit(100000 - bmw.msrp)
)
``````

# Summary

I attempted to build a short but meaningful introduction to selection functions.

Selection functions are the topic that is being actively researched. And research papers can quickly dive into the topic. And they could imply that readers have some related background.

I intended to give practical guidance for the initial steps. I hope it can help to gain acknowledgment and not lose the narrative thread (in those papers).

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