Homework 5.0

Monads

This homework is written in literate Haskell; you can download the raw source to fill in yourself. You’re welcome to submit literate Haskell yourself, or to start fresh in a new file, literate or not.

Please submit homeworks via the new submission page.

In this homework, you’ll be working with monads in a variety of ways: to speed up a shuffling algorithm; to create a generic framework for MapReduce; and to generate random values for testing.

I’ve imported the libraries you’ll need. Look at them before you start! You’ll want to use both the Haskell language documentation as well as Hackage and Hoogle.

You are of course allowed to import other libraries. It may even make your solutions easier!

module Hw05 where

import Control.Monad

import Data.Array.IO

import qualified Data.Map as Map
import Data.Map (Map(..),(!))

import System.Environment
import System.Exit
import System.IO
import System.Random

import Test.QuickCheck

Problem (1): shuffling

In this problem, we’re going to “shuffle” a list, reordering it randomly.

To start, let’s get familiar with System.Random.

Write a function that takes two numbers, low and high, and returns a random number n such that low <= n <= high, i.e., inclusively within the range.

rand :: Int -> Int -> IO Int
rand low high = undefined

Now write a function that takes a list and shuffles it. The straightforward algorithm is O(n²):

Given a non-empty list xs,
randomly split the list into an element y and the rest of the list ys,
cons y onto the shuffling of ys.

Don’t worry, we’ll speed it up in a minute.

shuffleList :: [a] -> IO [a]
shuffleList xs = undefined

Don’t forget that you can run :set +s to get timing information in GHCi. My implementation on my computer runs sum <$> shuffleList [0..10000] 3.26 seconds.

It turns out that there’s a much faster, O(n) algorithm for shuffling: the Fisher–Yates shuffle. It works on arrays, not linked lists, so we’ll have to use Haskell’s arrays.

Haskell’s arrays are a little funny: arrays are parameterized by two things: the type of their index and the monad in which they’ll be used. We’ll work with IOArrays. The IOArray type represents arrays that can be used in the IO monad. We’ll interact with these arrays using the MArray interface.

Let’s take a brief look at IOArray. It has kind * -> * -> *. The first type it needs is the type of its indices… we can just use Int for that, but it’s interesting that we can use any type in the Ix class. The second type it needs is the type of its contents. Shuffling won’t care about that, so we’ll end up working with IOArray Int a.

As a warmup, write a function that takes a list and generates a corresponding array. It’s worth noting that the bounds that Haskell uses in, e.g., newListArray are inclusive, per Data.Ix.

listToArray :: [a] -> IO (IOArray Int a)
listToArray x = undefined

Okay: let’s do it. Implement the Fisher–Yates shuffling algorithm that takes a given array and shuffles it.

shuffle :: IOArray Int a -> IO ()
shuffle arr = undefined

Now use your array-based function shuffle to work on lists.

fastShuffle :: [a] -> IO [a]
fastShuffle l = undefined

My version of fastShuffle runs much more quickly than the naive one: sum <$> fastShuffle [0..10000] runs in 0.04 seconds!

Finally, write a function that reads in a file and shuffles its lines.

shuffleFile :: FilePath -> IO String
shuffleFile f = undefined

Finally, this shuffling program is useful enough that we should make it a command. To do so, you’ll need to write a main function, which should have type IO ().

First and foremost, to compile your program, you’ll run a command like: ghc Hw05.lhs -main-is Hw05.main -o shuffle. This will compile your program to an executable called shuffle. (The -main-is flag is necessary because the default in Haskell is to have a file named Main.hs.)

How should your program behave? It should take a single, optional argument indicating a filename to shuffle. Without an argument (or with an argument of -), it should read from standard input. If more than one argument is given, you should print a “usage” message on standard error and exit with a non-zero exit code.

Either way, your program should read the lines of the input (file or standard input), shuffle them, and then print out the shuffled input.

Look at the various System modules to find out how to parse command-line arguments.

main :: IO ()
main = undefined

Problem (2): monadic MapReduce

MapReduce is a model for data parallel computation. We’ll look at data parallel programming after Thanksgiving, but for now let’s try to understand MapReduce as is.

Our mappers will take an input of type a and produce a list of key-value pairs, where keys have type k and values have type v.

type Mapper a k v = a -> [(k,v)]

Our reducers take a key and list of values and produces a new (ideally shorter!) list of values.

type Reducer k v = k -> [v] -> [v]

The actual MapReduce implementaiton has three real phases: mapping, shuffling, and reducing. Here’s a simple implementation:

mapReduce :: Ord k => Mapper a k v -> Reducer k v -> [a] -> [(k,[v])]
mapReduce m r = reduce r . shuffleKeys . concatMap (map listifyVal . m)
  where listifyVal (k,v) = (k,[v])
        shuffleKeys = Map.fromListWith (++)
        reduce r = Map.toList . Map.mapWithKey r

The canonical MapReduce example is word count. (Riveting, isn’t it?) Here’s how to implement word count in MapReduce: given a list of documents, a mapper breaks a given document into its consituent words, where appearance of a word maps to 1. After shuffling, identical words will be grouped together; the reducer sums up each token.

wordCount = mapReduce countWords sumCounts
 where countWords = map (\w -> (w,1)) . words
       sumCounts _ cs = [sum cs]

I’m not sure why Google is so proud of what amounts to eight lines of code and a slow way to count words.

Let’s modify the MapReduce paradigm to allow for monadic computations, where our mappers and reducers are monadic computations, like so:

type MapperM m a k v = a -> m [(k,v)]
type ReducerM m k v = k -> [v] -> m [v]

Note that a MapperM returns its list of key-value pairs inside of some monad m; ReducerM is similar.

Adapt mapReduce above to define mapReduceM:

mapReduceM :: (Ord k, Monad m) => MapperM m a k v -> ReducerM m k v -> [a] -> m [(k,[v])]
mapReduceM m r input = undefined

To test, here’s an adaptation of the wordCount example above.

wordCountM = mapReduceM countWords sumCounts
 where countWords = return . map (\w -> (w,1)) . words
       sumCounts w cs = do
         when (length cs > 1) $ putStrLn $ "Lots of " ++ w ++ "!"
         return [sum cs]

Problem (3): QuickCheck

We’ll be using QuickCheck to write some tests.

Write a QuickCheck property to check that reverse is involutive, i.e., that reversing a reversed list yields the original list.

prop_rev_involutive l = undefined

Write a QuickCheck property to check that checks the Collatz conjecture for a given number greater than 0.

prop_Collatz = undefined

data ArithExp =
    Num Int
  | Plus ArithExp ArithExp
  | Times ArithExp ArithExp
  | Neg ArithExp
  deriving Show

eval :: ArithExp -> Int
eval (Num i) = i
eval (Plus e1 e2) = eval e1 + eval e2
eval (Times e1 e2) = eval e1 * eval e2
eval (Neg e) = 0 - eval e

Write a generator that generates arbitrary ArithExps. Use it to define an Arbitrary instance for ArithExp… keep in mind that we don’t want to generate giant data structures, so you may need to keep track of sizes.

instance Arbitrary ArithExp where
  arbitrary = undefined

Write a test to ensure that Plus e e behaves the same as Times 2 e for all expressions e.

prop_double = undefined

CSCI 131 Fall '16

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Homework 5.0

Monads