Moving Average Test: Uses Conduits

conduit-test.cabal

name:                conduit-test
version:             0.1.0.0
synopsis:            Initial project template from stack
description:         Please see README.md
homepage:            https://gist.github.com/pellagic-puffbomb/9239cb57834789886f5c
license:             BSD3
license-file:        LICENSE
author:              Erik Aker
maintainer:          [email protected]
-- copyright:
category:            Web
build-type:          Simple
-- extra-source-files:
cabal-version:       >=1.10

executable conduit-test
  hs-source-dirs:      src
  main-is:             Main.hs
  ghc-options:         -threaded -rtsopts -with-rtsopts=-N
  build-depends:       base
                     , transformers
                     , mtl
                     , conduit
                     , conduit-combinators
                     , resourcet
                     , containers
  default-language:    Haskell2010

test-suite new-template-test
  type:                exitcode-stdio-1.0
  hs-source-dirs:      test
  main-is:             Spec.hs

  build-depends:       base
                     , conduit
                     , conduit-combinators
                     , transformers
                     , mtl
                     , resourcet
                     , containers
  ghc-options:         -threaded -rtsopts -with-rtsopts=-N
  default-language:    Haskell2010

source-repository head
  type:     git
  location: https://gist.github.com/pellagic-puffbomb/9239cb57834789886f5c

Main.hs

{-# LANGUAGE MultiWayIf #-}

module Main where

import Control.Monad.IO.Class (liftIO)
import Data.Conduit (($$), (=$=), (=$)
                    , Conduit, Sink, Source
                    , await, yield)
import qualified Data.Conduit.List as CL
import Data.Sequence
import Prelude hiding (head, length, drop)


averageLen :: Int
averageLen = 10

type Size = Int
type RunningSum = Int


source :: Source IO Int
source = CL.sourceList [1..1000]


conduit :: RunningSum -> Size -> Seq Int -> Conduit Int IO Int
conduit total len sequence = do
  val <- await
  case val of
    Nothing -> return ()
    Just n -> do
      let newtotal = total + n
      let newsequence = (|>) sequence n
      if | length newsequence < len -> conduit newtotal len newsequence
         | length newsequence == len -> do
             yield $ newtotal `div` (fromIntegral len)
             conduit newtotal len newsequence
         | otherwise -> do
            let dropFrontTotal = fromIntegral (newtotal - index newsequence 0)
            let newsequence' = drop 1 newsequence
            yield $ dropFrontTotal `div` (fromIntegral len)
            conduit dropFrontTotal len newsequence'

-- Using views: to be benchmarked
conduitV :: RunningSum -> Size -> Seq Int -> Conduit Int IO Int
conduitV total len sequence = do
  val <- await
  case val of
    Nothing -> return ()
    Just n -> do
      let newtotal = total + n
      let newsequence = (|>) sequence n
      if | length newsequence < len -> conduitV newtotal len newsequence
         | length newsequence == len -> do
             yield $ newtotal `div` (fromIntegral len)
             conduitV newtotal len newsequence
         | otherwise -> do
             let newSeq = viewl newsequence
             case newSeq of
               EmptyL -> return () -- shouldn't happen: we just appended to it above?
               (a :< seq) -> do
                 let dropFrontTotal = newtotal - a
                 yield $ dropFrontTotal `div` (fromIntegral len)
                 conduitV dropFrontTotal len seq

sink :: Sink Int IO ()
sink = do
  val <- await
  case val of
    Nothing -> return ()
    Just n -> do
      liftIO $ print n
      sink

main :: IO ()
main = do
  let initial = fromList []
  source $$ conduitV 0 averageLen initial =$ sink

Benchmarked with criterion (whnfIO?):

Using conduit on 1..10,000

type	lower bound	estimate	upper bound
OLS regression	51.4 ms	52.2 ms	53.0 ms
R² goodness-of-fit	0.998	0.999	1.000
Mean execution time	51.2 ms	51.7 ms	52.3 ms
Standard deviation	667 μs	954 μs	1.21 ms

Outlying measurements have slight (7.1%) effect on estimated standard deviation.

Using conduitV on 1..10,000

type	lower bound	estimate	upper bound
OLS regression	50.9 ms	51.3 ms	51.7 ms
R² goodness-of-fit	0.998	0.999	1.000
Mean execution time	50.7 ms	51.2 ms	51.5 ms
Standard deviation	318 μs	797 μs	1.10 ms

Outlying measurements have slight (7.1%) effect on estimated standard deviation.

Using conduit on 1..100,000

type	lower bound	estimate	upper bound
OLS regression	531 ms	540 ms	552 ms
R² goodness-of-fit	1.000	1.000	1.000
Mean execution time	538 ms	539 ms	542 ms
Standard deviation	0 s	2.01 ms	2.02 ms

Outlying measurements have moderate (18.8%) effect on estimated standard deviation.

Using conduitV on 1..100,000

type	lower bound	estimate	upper bound
OLS regression	525 ms	528 ms	535 ms
R² goodness-of-fit	1.000	1.000	1.000
Mean execution time	527 ms	528 ms	528 ms
Standard deviation	0 s	959 μs	1.11 ms

Outlying measurements have moderate (18.7%) effect on estimated standard deviation.