[Haskell-cafe] Optimizing nearest-k code
Mark Wotton
mwotton at gmail.com
Fri May 25 14:04:56 UTC 2018
Yes. Showed up in testing - if you're using a decent chunk of the subsets,
you can avoid computing twice. If the pressure is actually memory rather
than cpu and you can consume lazily, you are quite right that the original
could be better.
On Fri, May 25, 2018, 10:00 AM Vanessa McHale <vanessa.mchale at iohk.io>
wrote:
> Also: are you sure
>
> let rs = subsets xs in rs ++ map (x:) rs
>
> is more efficient than
>
> subsets xs ++ map (x:) (subsets xs)
>
>
> I would have assumed these would be the same due to laziness, etc.
>
> On 05/24/2018 12:36 PM, Mark Wotton wrote:
> > there are a few things you can do.
> >
> > I ran it locally and got 3s cpu time and 3gb of allocation, more or less.
> >
> > first off, your subset implementation computes the subsets twice.
> > taking that off took it down to 2s.
> >
> > After that it's mostly in the Set implementation. Using Map a ()
> > instead gives you access to Data.Map.Merge.Strict.merge, which is a
> > bit more efficient (going over the two data structures once only)
> >
> > ```
> > module Main where
> >
> > import Data.List
> > import Data.Map.Strict (Map)
> > import qualified Data.Map.Merge.Strict as Map
> > import qualified Data.Map.Strict as Map
> >
> > nearest_k :: (Ord a) => Int -> [(Map a (), v)] -> Map a () -> [(Map a
> (), v)]
> > nearest_k k bs b = take k bs' where
> > bs' = sortOn (hamming b) bs
> >
> > hamming :: (Ord a)=> Map a () -> (Map a (), v) -> Int
> > hamming x (y, _) = hamming_distance x y
> >
> > hamming_distance :: (Ord a)=> Map a () -> Map a () -> Int
> > hamming_distance xs ys = Map.size (Map.merge Map.preserveMissing
> > Map.preserveMissing (Map.zipWithMaybeMatched (\_ _ _ -> Nothing)) xs
> > ys)
> >
> > subsets :: [a] -> [[a]]
> > subsets [] = [[]]
> > subsets (x:xs) = let rs = subsets xs in rs ++ map (x:) rs
> >
> > int_lists :: [[Int]]
> > int_lists = subsets [1..20]
> >
> > values :: [(Map Int (), Int)]
> > values = map f (zip [1..] int_lists) where
> > f (i, x) = (Map.fromList(zip x (repeat ())), i)
> >
> > test = nearest_k 8 values (Map.fromList (zip [1,2,3] (repeat ())))
> >
> >
> > main = print test
> > ```
> >
> > that took it to 1s, and now profiling indicates more than half the
> > time is spent in generation of the test values, so I'll leave it
> > there.
> >
> > I think if you wanted to do better than this you'd have to do some
> > algorithmic changes - for instance, once your worst candidate is n
> > steps away, you can stop calculating the hamming distance for anything
> > else once it's > n, as it can't contribute usefully to the nearest
> > neighbours.
> >
> > cheers,
> > Mark
> >
> > On Thu, May 24, 2018 at 12:15 PM, Richard Evans
> > <richardprideauxevans at gmail.com> wrote:
> >> Dear Haskell Cafe,
> >>
> >> Given a set of sets, and a particular target set, I want to find the
> sets
> >> that are nearest (in terms of Hamming distance) to the target set.
> >>
> >> I am using the following code:
> >>
> >> import Data.List
> >> import qualified Data.Set as Set
> >>
> >> nearest_k :: Ord a => Int -> [(Set.Set a, v)] -> Set.Set a -> [(Set.Set
> a,
> >> v)]
> >> nearest_k k bs b = take k bs' where
> >> bs' = sortOn (hamming b) bs
> >>
> >> hamming :: Ord a => Set.Set a -> (Set.Set a, v) -> Int
> >> hamming x (y, _) = hamming_distance x y
> >>
> >> hamming_distance :: Ord a => Set.Set a -> Set.Set a -> Int
> >> hamming_distance xs ys = Set.size (Set.difference xs ys) + Set.size
> >> (Set.difference ys xs)
> >>
> >>
> >>
> >> subsets :: [a] -> [[a]]
> >> subsets [] = [[]]
> >> subsets (x:xs) = subsets xs ++ map (x:) (subsets xs)
> >>
> >> int_lists :: [[Int]]
> >> int_lists = subsets [1..20]
> >>
> >> values :: [(Set.Set Int, Int)]
> >> values = map f (zip [1..] int_lists) where
> >> f (i, x) = (Set.fromList x, i)
> >>
> >> test = nearest_k 8 values (Set.fromList [1,2,3])
> >>
> >> ----
> >>
> >> This works ok for the test above (with sets of ints), but is rather
> slow in
> >> my actual application (in which the sets are large sets of ground atoms
> of
> >> first-order logic). Is there some major optimization I should be doing
> here?
> >>
> >> thanks,
> >> Richard
> >>
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> >
>
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