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The Maybe and List Monads

> module Monads where
> import Prelude hiding ((>>))
> import Control.Monad (guard)
> import qualified Data.Maybe as Maybe
> import qualified Data.List as List

Warm-up exercise

Consider the definition of trees with values at their leaves.

> data Tree a = Leaf a | Branch (Tree a) (Tree a)
>    deriving (Eq, Show)

Define the following function that combines together the data stored in the tree.

> zipTree :: Tree a -> Tree b -> Tree (a,b)
> zipTree = undefined

    o                     o                      o
   / \                   / \                   /   \
 "a"  o        ===>     0   o        ===>  ("a",0)  o
     / \                   / \                     / \
   "b" "c"                1   2             ("b",1)  ("c", 2)

> testZip0 :: Bool
> testZip0 =
>   zipTree (Branch (Leaf "a") (Branch (Leaf "b") (Leaf "c")))
>           (Branch (Leaf (0 :: Int)) (Branch (Leaf 1) (Leaf 2)))
>   ==
>   Branch (Leaf ("a",0)) (Branch (Leaf ("b",1)) (Leaf ("c",2)))

> -- >>> zipTree (Branch (Leaf "a") (Branch (Leaf "b") (Leaf "c"))) (Branch (Leaf 0) (Branch (Leaf 1) (Leaf 2)))

Keeping track of errors

What is tricky about this problem? The trees must have the same shape in order to be zipped together. There is no sensible result when the first argument is Leaf x and the second is Branch t1 t2. This function is inherently partial.

How could we define zipTree so that we can recover from failure? That's right, we'll have it return a Maybe!

Let's rewrite it so that the partiality is explicit in the type.

> zipTree1 :: Tree a -> Tree b -> Maybe (Tree (a,b))
> zipTree1 = undefined

This function is going to be our inspiration for the following development. We are going to factor out the repeated code in its definition. Therefore, as we do this refactoring we will want to do regression tests.

> testZip :: (Tree String -> Tree Int -> Maybe (Tree (String, Int))) -> Bool
> testZip zt =
>   zt (Branch (Leaf "a") (Branch (Leaf "b") (Leaf "c")))
>      (Branch (Leaf 0  ) (Branch (Leaf 1  ) (Leaf 2  )))
>       ==
>       Just (Branch (Leaf ("a",0)) (Branch (Leaf ("b",1)) (Leaf ("c",2))))
>   &&
>   Maybe.isNothing (zt (Branch (Leaf "a") (Leaf "b")) (Leaf 0))

We can test it interactively or in ghci:

> -- >>> testZip zipTree1
> -- True

> --       SPOILER SPACE BELOW. Don't look until you finish zipTree1.

> --        |
> --        |
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> --        |
> --        |
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> --        |

Abstracting programming patterns

Here's what my solution to the zipTree problem looks like. It's not terribly beautiful.

> zipTree2 :: Tree a -> Tree b -> Maybe (Tree (a,b))
> zipTree2 = go where
>   go (Leaf a)     (Leaf b)       = Just (Leaf (a,b))
>   go (Branch l r) (Branch l' r') =
>     case go l l' of
>        Nothing -> Nothing
>        Just x  -> case go r r' of
>                   Nothing -> Nothing
>                   Just y  -> Just (Branch x y)
>   go _            _              = Nothing

> -- >>> testZip zipTree2

All that nested pattern matching! This version looks so much worse than the original (partial) version. It's hard to see the overall structure of the code.

And, as you might guess, anytime we use a Maybe type this sort of pattern is going to show up.

So let's try to do better!

Can you identify any patterns in the code?

• How do we return a value? Is there a common pattern?

• How do we use a value? Is there a common pattern? A helper function that we can define to factor out the pattern matching?

Looking closely for patterns

• We return a value in two places in this function. See the lines marked (*) below.

      zipTree2 :: Tree a -> Tree b -> Maybe (Tree (a,b))
      zipTree2 = go where
        go (Leaf a)     (Leaf b)       = Just (Leaf (a,b))         (*)
        go (Branch l r) (Branch l' r') =
          case go l l' of
            Nothing -> Nothing
            Just x  -> case go r r' of
                   Nothing -> Nothing
                   Just y  -> Just (Branch x y)                    (*)
        go _            _              = Nothing

In both cases we have a successful answer and we mark this success using the Just data constructor.

As a reminder here is the type of Just

   Just :: a -> Maybe a

Let's give this part of the program a good name, so that we can make it clear that we are returning a successful value. (The spelling below is intentional..)

> retrn :: a -> Maybe a
> retrn = Just

• We also use a pattern in two cases in the code. In two cases, we pattern match the result of a recursive call, and if it is successful, we use that successful result later on in the computation. See the parts marked (*) below.

      zipTree2 :: Tree a -> Tree b -> Maybe (Tree (a,b))
      zipTree2 = go where
        go (Leaf a)     (Leaf b)       = Just (Leaf (a,b))      
        go (Branch l r) (Branch l' r') =
          case go l l' of   <----------------------------- (*)
            Nothing -> Nothing
            Just x  -> case go r r' of   <---------------- (*)
                   Nothing -> Nothing
                   Just y  -> Just (Branch x y)               
        go _            _              = Nothing

i.e. the two parts of the code we are focusing are look like this

 case go l l' of
   Nothing -> Nothing
   Just x -> ...
       do something with x

 case go r r' of
   Nothing -> Nothing
   Just y -> ...
      do something with y

We can see the general pattern if we abstract both the scrutinee of the case analysis (let's call it x) and the part in the Just branch, "do something with ..." (let call it f). In other words, the general pattern is

 case x of
   Nothing -> Nothing
   Just y -> f y

where x has some sort of Maybe _ type

 x :: Maybe (Tree (a,b))

and f is a function that says what we will do with this intermediate result.

 f :: Tree (a,b) -> Maybe (Tree (a,b))

We'll give this pattern a name ("bind")

> bind :: Maybe t -> (t -> Maybe a) -> Maybe a
> bind x f = case x of
>    Nothing -> Nothing
>    Just y  -> f y

and take a note of its type.

With these two new functions (retrn and bind) we can refactor the code. Let's rewrite zipTree using them in place of Just and explicit case analysis.

> zipTree3 :: Tree a -> Tree b -> Maybe (Tree (a,b))
> zipTree3 = go where
>   go (Leaf a)     (Leaf b)       = retrn (Leaf (a,b))
>   go (Branch l r) (Branch l' r') =
>     go l l' `bind` (\x -> 
>       go r r' `bind` (\y -> retrn (Branch x y)))
>   go _            _              = Nothing

> -- >>> testZip zipTree3

Do the names retrn and bind sound familiar to you? Do their types look familiar?

Monads in Haskell

These operations are the two components of the Monad type class. The general concept of a monad comes from a branch of mathematics called category theory. In Haskell, however, a monad is simply a parameterized type m, together with two functions of the following types:

return :: a -> m a
(>>=)  :: m a -> (a -> m b) -> m b

The notion of a monad can now be captured as follows:

class Monad (m :: Type -> Type) where
  return :: a -> m a
  (>>=)  :: m a -> (a -> m b) -> m b

That is, a monad is a parameterized type m that supports return and >>= (i.e. bind) functions of the specified types. The fact that m must be a parameterized type, rather than just a type, is inferred from its use in the types of return and >>=.

(To form a well-defined monad, the two functions are also required to satisfy some simple properties; we will return to these later.)

The two operations we derived above are exactly what makes the parameterized type Maybe a monadic type.

instance Monad Maybe where
   return      :: a -> Maybe a
   return x    =  Just x

   (>>=)       :: Maybe a -> (a -> Maybe b) -> Maybe b
   Nothing  >>= _ =  Nothing
   (Just x) >>= f =  f x

Note that the definitions of return and >>= in this instance are exactly the same ones that we derived for retrn and bind!

So we can rewrite the example to use the monadic functions!

> zipTree4 :: Tree a -> Tree b -> Maybe (Tree (a,b))
> zipTree4 = go where
>   go (Leaf a)     (Leaf b)       = return (Leaf (a,b))
>   go (Branch l r) (Branch l' r') =
>     go l l' >>= (\x -> 
>       go r r' >>= (\y -> return (Branch x y)))
>   go _            _              = Nothing

> -- >>> testZip zipTree4

What is the benefit to writing the code this way?

• First, using return and >>= encapsulates a common pattern of plumbing when working with Maybes. Note that zipTree4 does not need to do any of the case analysis. That is handled automatically by >>=. We've saved a little effort because it is common that we will need to combine several Maybe computations into one.

• The second benefit is that we get to use the do notation to make our code even prettier.

Monads and the do notation

So what does the Monad type class have to do with the do notation? Well, remember that Haskell automatically transforms code of this form

do x1 <- m1
   x2 <- m2
   ...
   xn <- mn
   f x1 x2 ... xn

Into the following sequence of nested binds.

m1 >>= (\x1 ->
  m2 >>= (\x2 ->
     ...
      mn >>= (\xn ->
        f x1 x2 ... xn)...))

So much easier to read with the arrows flipped around, no?

With this translation, we can put the notation to use. Try rewriting the zipTree function using the above do notation for the >>= operator.

> zipTree5 :: Tree a -> Tree b -> Maybe (Tree (a,b))
> zipTree5 = go where
>   go (Leaf a)     (Leaf b)       = return (Leaf (a,b))
>   go (Branch l r) (Branch l' r') = do
>       undefined
>   go _            _              = Nothing

> -- >>> testZip zipTree5

Nice!

But wait, there's more. Sometimes with do notation, we see a line in the middle that doesn't bind a variable.

        main = do
          x <- doSomething
          doSomethingElse     -- what is going on here?
          y <- andSoOn
          f x y

In fact, we saw this sort of thing when using the IO monad at the beginning of the semester. (Yep, the IO type constructor is an instance of the Monad type class. Under the covers, it is all binds and returns.)

> main :: IO ()
> main = do
>    putStrLn "Hello. What is your name?"
>    inpStr <- getLine
>    putStrLn $ "Welcome to Haskell, " ++ inpStr ++ "!"

Sometimes we don't need to use the result of a computation -- in particular, if the computation has type IO () then the result is trivial. However, we still want to use bind for it's sequencing capabilities...

That brings us to a derived monad operator, called "sequence":

> (>>)  :: Monad m => m a -> m b -> m b
> m1 >> m2 = m1 >>= const m2

So the do notation above transforms to:

       doSomething >>= ( \x ->
         doSomethingElse >>         -- it was just a sequence
           (andSoOn >>= ( \y ->
             f x y)))

Applicative Functors (preview)

There is yet another way to implement zipTree.

Let's take a look at the documentation for the Monad Class.

We haven't talked about it yet, but the class Applicative is a superclass of Monad. Any type constructor, like Maybe that is an instance of Monad must also be an instance of Applicative.

      class Applicative m => Monad m where
          ...

The name Applicative is short for "applicative functor". What are these things? They are functors with extra features.

      class Functor f => Applicative f where
          pure   :: a -> f a
          (<*>)  :: f (a -> b) -> f a -> f b

In other words, an Applicative is a functor with application. It provides operations to embed pure expressions (pure), and sequence computations while combining their results (<*>). (This operation is called "zap".)

Like Functor, this class captures useful functions for working with data structures. Let's look at the Applicative instance for Maybe.

   instance Applicative Maybe where
      pure :: a -> Maybe a
      pure = Just

      (<*>) :: Maybe (a -> b) -> Maybe a -> Maybe b
      Just f  <*> Just x = Just (f x)
      _       <*> _      = Nothing

For Maybe, the <*> operation applies a function to an argument, provided that they are both defined.

Here's how we could use these operations in zipTree :

> zipTree6 :: Tree a -> Tree b -> Maybe (Tree (a,b))
> zipTree6 = go where
>   go (Leaf a)     (Leaf b)       = return (Leaf (a,b))
>   go (Branch l r) (Branch l' r') = do
>     pure Branch <*> go l l' <*> go r r'
>   go _            _              = Nothing

> -- >>> testZip zipTree6

The <*> operator lets us "lift" the Branch data constructor to the partial results of the left and right recursive calls.

Furthermore, it is a required law of applicative functors that the following relationship must hold (recall that <$> is an infix operator for fmap):

     pure f <*> x  ==  f <$> x 

That gives us one more tweak:

> zipTree7 :: Tree a -> Tree b -> Maybe (Tree (a,b))
> zipTree7 = go where
>   go (Leaf a)     (Leaf b)       = return (Leaf (a,b))
>   go (Branch l r) (Branch l' r') = do
>     Branch <$> go l l' <*> go r r'
>   go _            _              = Nothing

Compare this version to our initial (unsafe) one. Making this code safe comes at very little cost.

> -- >>> testZip zipTree7

Functors, Applicatives and Monads

Note that the superclass constraint of the Monad type class means that all monads that you define must also have Applicative and Functor instances. However, this is not much of a burden: there is a straightforward definition of Functor and Applicative functions in terms of return and (>>=).

See if you can define functions with the correct types below only using return and >>=. (We will justify these definitions later, when we talk about the general properties that all functors, applicatives and monads must satisfy. However, if you can define something that type checks, it is probably right. )

> fmapMonad :: (Monad m) => (a -> b) -> m a -> m b
> fmapMonad = undefined

> pureMonad :: (Monad m) => a -> m a
> pureMonad = undefined

> zapMonad  :: (Monad m) => m (a -> b) -> m a -> m b
> zapMonad = undefined

Note that fmapMonad is called liftM and zapMonad is called ap in the Control.Monad library.

With these functions it is trivial to construct instances of Functor and Applicative given an instance of Monad. Of course, you may not want to always do that: sometimes a specialized definition will be more efficient. (Note: This lecture is only a preview to get you started thinking about the abstract relationships between these concepts. We will cover Applicatives in more depth in two weeks.)

The List Monad

The Maybe monad provides a simple model of computations that can fail, in the sense that a value of type Maybe a is either Nothing, which we can think of as representing failure, or has the form Just x for some x of type a, which we can think of as success.

The list monad generalizes this notion by permitting multiple results in the case of success. More precisely, a value of [a] is either the empty list [], which we can think of as failure, or a non-empty list [x1,x2,...,xn], which we can think of as a list of successes.

A challenge

Write the function that takes each possible value x from the list xs, and each possible value y from the list ys and returns a list of the (x,y) pairs.

Hint: you should use the functions concat :: [[a]] -> [a] and map :: (a -> b) -> [a] -> [b] in your solution. Your solution itself should not be recursive.

> -- >>> pairs0 [1,2,3] [5,6,7]
> -- [(1,5),(1,6),(1,7),(2,5),(2,6),(2,7),(3,5),(3,6),(3,7)]

> pairs0 :: [a] -> [b] -> [(a,b)]
> pairs0 xs ys = undefined

> testPairs :: ([Int] -> [Int] -> [(Int,Int)]) -> Bool
> testPairs ps = ps [1,2,3,4] [5,6,7,8] ==
>   [(1,5),(1,6),(1,7),(1,8),(2,5),(2,6),(2,7),(2,8),
>    (3,5),(3,6),(3,7),(3,8),(4,5),(4,6),(4,7),(4,8)]

> --      SPOILER SPACE BELOW

> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |
> --       |

Here's my version:

> -- >>> pairs1 [1,2,3] [5,6,7]
> -- [(1,5),(1,6),(1,7),(2,5),(2,6),(2,7),(3,5),(3,6),(3,7)]

> pairs1 :: [a] -> [b] -> [(a,b)]
> pairs1 xs ys = concat (map (\x ->
>                  concat (map (\y ->
>                    [(x,y)]) ys))
>                   xs)

Can we divide this up? What are the patterns here?

We have

 concat (map (\x ->  do something with x) xs)

 concat (map (\y ->  do something with y) ys)

Generalize:

 concatMap f xs

where

 concatMap = concat . map

What is the type of concatMap

 concatMap :: (a -> [b]) -> [a] -> [b]

AHA!! That type looks familiar. We can define

 (>>=) :: [a] -> (a -> [b]) -> [b]
 xs >>= f = concatMap f xs

What could return be?

 return :: a -> [a]
 return x = [x]

This is how lists can be monadic.

    instance Monad [] where
       return :: a -> [a]
       return x  =  [x]

       (>>=)  :: [a] -> (a -> [b]) -> [b]
       xs >>= f  =  concatMap f xs

(Aside: in this context, [] denotes the list type [a] without its parameter.)

So return simply converts a value into a (singleton) successful result, while >>= provides a means of sequencing computations that may produce multiple results: xs >>= f applies the function f to each of the values in xs to give a list of lists of results, which is then concatenated to give a single list of results.

Rewrite pairs using >>= and return

> -- >>> pairs2 [1,2,3] [5,6,7]
> -- [(1,5),(1,6),(1,7),(2,5),(2,6),(2,7),(3,5),(3,6),(3,7)]
> pairs2 :: [a] -> [b] -> [(a,b)]
> pairs2 xs ys = undefined

Rewrite again using do notation

> -- >>> pairs3 [1,2,3] [5,6,7]
> -- [(1,5),(1,6),(1,7),(2,5),(2,6),(2,7),(3,5),(3,6),(3,7)]
> pairs3 :: [a] -> [b] -> [(a,b)]
> pairs3 xs ys = undefined

Make sure that it still works.

> testPairs2 :: Bool
> testPairs2 = testPairs pairs2
> testPairs3 :: Bool
> testPairs3 = testPairs pairs3

List comprehensions

It is interesting to note the similarity to how this function would be defined using list comprehension notation:

> -- >>> pairs4 [1,2,3] [1,2,3]
> pairs4 :: [Int] -> [Int] -> [(Int,Int)]
> pairs4 xs ys = [ (x,y) | x <- xs, y <- ys ]

The reason is that the list comprehension syntax in Haskell is defined in terms of >>= and return. Each x <- xs corresponds to a use of >>=, just as in do-notation. The final list is constructed with return.

List comprehensions can also include guards, i.e. boolean expressions that filter out some of the results

> -- >>> pairs5 [1,2,3] [1,2,3]
> pairs5 :: [Int] -> [Int] -> [(Int,Int)]
> pairs5 xs ys = [ (x,y) | x <- xs, y <- ys, x /= y ]

Compare the results of the versions with and without the guard.

ghci> pairs4 [1,2,3] [1,2,3]
ghci> pairs5 [1,2,3] [1,2,3]

We can also rewrite the guard version with do notation using the following guard function:

     guard :: Bool -> [()]
     guard True      =  [()]
     guard False     =  []

> -- >>> pairs5' [1,2,3] [1,2,3]
> pairs5' :: [Int] -> [Int] -> [(Int,Int)]
> pairs5' xs ys = do
>     x <- xs
>     y <- ys
>     guard (x /= y)    --- remember that no `<-` means `>>`, i.e. `>>=` ignoring the argument
>     return (x,y)

What is going on with this definition of guard? In the list monad, an empty list signals failure. That means that if the guard fails, then pairs5' immediately returns the empty list. Otherwise, to signal success, guard returns a nonempty list. However, we don't really care what is in this list so we use the () value.

In fact, there is a formal connection between the do notation and the comprehension notation. Both are simply different shorthands for repeated use of the >>= operator for lists. Indeed, the language Gofer, one of the precursors to Haskell, permitted the comprehension notation to be used with any monad. For simplicity, Haskell only allows comprehension to be used with lists.

Let's play around with list comprehensions in case you have not seen them in other contexts.

What are some other examples that can be written using list comprehension?

• We can find out the smallest number colors that can color a few southern states so that neighboring states are not the same color.

Here are some colors

> data Color = Red | Green | Blue | Yellow | Orange | Violet deriving (Show, Enum, Eq)

And, given a list of colors, this function determines all of the ways the five states can be colored with them:

> stateColors :: [Color] -> [(Color, Color, Color, Color, Color)]
> stateColors colors =
>   [(tennessee, mississippi, alabama, georgia, florida) |
>    tennessee   <- colors,
>    mississippi <- colors,
>    alabama     <- colors,
>    georgia     <- colors,
>    florida     <- colors,
>    tennessee   /= mississippi,   -- ensure neighboring states have different colors
>    tennessee   /= alabama,
>    tennessee   /= georgia,
>    mississippi /= alabama,
>    alabama     /= georgia,
>    florida     /= alabama,
>    florida     /= georgia]

And this code finds the smallest list of colors that can do so:

> -- >>> colorsNeeded
> colorsNeeded :: Maybe [Color]
> colorsNeeded = List.find (not . null . stateColors) cs where
>     cs :: [[Color]]  -- i.e. [[Red], [Red,Green], [Red,Green,Blue], ...] 
>     cs = zipWith take [1..] (replicate 6 [Red ..])

Other examples

• Rewrite the map function using a list comprehension.

> -- >>> map' (+1) [1,2,3]
> map' :: (a -> b) -> [a] -> [b]
> map' f xs = undefined

• Create a list of all pairs where the first component is from the first list, the second component is from the second list, and where the first component is strictly less than the second.

> -- >>> firstLess [1,2,3] [1,2,3]
> firstLess  :: Ord a => [a] -> [a] -> [(a,a)]
> firstLess = undefined

Now rewrite map' and firstLess using do notation (don't forget guard above)

> map1 :: (a -> b) -> [a] -> [b]
> map1 = undefined

> firstLess1 :: Ord a => [a] -> [a] -> [(a,a)]
> firstLess1 xs ys = undefined

• Rewrite filter, using a guarded list comprehension.

> filter' :: (a -> Bool) -> [a] -> [a]
> filter' f xs = undefined

The List Applicative

Like Maybe, there is also an instance of the 'Applicative' type class for lists.

   instance Applicative [] where
      pure :: a -> [a]
      pure x = [x]

      (<*>) :: [a -> b] -> [a] -> [b]
      fs <*> xs = [f x | f <- fs, x <- xs]

Can we rewrite the pair example using Applicative instance instead? Take a look at the definition above to see what could makes sense.

> -- >>> pairs6 [1,2,3] [1,2,3]
> pairs6 :: [a] -> [b] -> [(a,b)]
> pairs6 xs ys = undefined

Once you get pairs6, try inlining the definitions of pure and <*> to see how they relate.