Metrics (empty) → 0.1.1

raw patch · 4 files changed

+131/−0 lines, 4 filesdep +basedep +hstatssetup-changed

Dependencies added: base, hstats

Files

LICENSE +23/−0
Metrics.cabal +17/−0
Metrics.hs +89/−0
Setup.hs +2/−0

+ LICENSE view

@@ -0,0 +1,23 @@+Copyright (c) 2012, Kaggle+Author: Ben Hamner (ben@benhamner.com)+All rights reserved.++Redistribution and use in source and binary forms, with or without+modification, are permitted provided that the following conditions are met: ++1. Redistributions of source code must retain the above copyright notice, this+   list of conditions and the following disclaimer. +2. Redistributions in binary form must reproduce the above copyright notice,+   this list of conditions and the following disclaimer in the documentation+   and/or other materials provided with the distribution. ++THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND+ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

+ Metrics.cabal view

@@ -0,0 +1,17 @@+Name:           Metrics+Version:        0.1.1+Cabal-Version:  >= 1.2+Build-Type:     Simple+License:        BSD3+License-File:   LICENSE+Author:         Ben Hamner (ben@benhamner.com)+Maintainer:     Ben Hamner (ben@benhamner.com)+Homepage:       http://github.com/benhamner/Metrics/+Category:       Statistics+Synopsis:       Evaluation metrics commonly used in supervised machine learning+Description:    Evaluation metrics commonly used in supervised machine learning++Library+    build-depends:      base >= 3 && <= 4+                       ,hstats==0.3+    Exposed-modules:    Metrics

+ Metrics.hs view

@@ -0,0 +1,89 @@++module Metrics+    ( ae+    , se+    , sle+    , mae+    , mse+    , msle+    , rmse+    , rmsle+    , ll+    , logLoss+    , apk+    , mapk+    , auc, fstEqual, auc1, sumRank, sumEl+    , ce+    ) where++import Data.List (groupBy, sort)+import Math.Statistics (mean)++ae :: Num a => a -> a -> a+ae x y = abs (x-y)++se :: Num a => a -> a -> a+se x y = (x-y)^2++sle :: Double -> Double -> Double+sle x y = ((log (x+1)) - (log (y+1)))^2++infinity :: Double+infinity = -(log 0)++meanZipWith :: (a -> b -> Double) -> [a] -> [b] -> Double+meanZipWith = ((mean .) .) . zipWith++meanInfZipWith :: (a -> b -> Double) -> [a] -> [b] -> Double+meanInfZipWith f a1 a2+    | any (==infinity) (zipWith f a1 a2) = infinity+    | otherwise = meanZipWith f a1 a2++mae :: [Double] -> [Double] -> Double+mae = meanZipWith ae++mse :: [Double] -> [Double] -> Double+mse = meanZipWith se++msle :: [Double] -> [Double] -> Double+msle = meanZipWith sle++rmse :: [Double] -> [Double] -> Double+rmse = (sqrt .) . mse++rmsle :: [Double] -> [Double] -> Double+rmsle = (sqrt .) . msle++ll :: Double -> Double -> Double+ll 1 y = -log y+ll 0 y = -log (1-y)++logLoss :: [Double] -> [Double] -> Double+logLoss = meanInfZipWith ll++apk :: Int -> [Integer] -> [Integer] -> Double+apk k actual predicted = (apsum actual (take k predicted) [] 0.0 0.0 0.0) / (fromIntegral (min k (length actual))) where+    apsum _ [] _ acc _ _ = acc+    apsum actual (p:ps) visited acc i j+        | (elem p actual) && (not (elem p visited)) = apsum actual ps (p:visited) (acc+(i+1.0)/(j+1.0)) (i+1.0) (j+1.0) +        | otherwise = apsum actual ps visited acc i (j+1.0)++mapk :: Int -> [[Integer]] -> [[Integer]] -> Double+mapk k = meanZipWith (apk k)++auc :: [Double] -> [Double] -> Double+auc a p = ((sumRank a p) - (sumEl 1 a)*(((sumEl 1 a)+1)/2.0)) / ((sumEl 1 a)*(sumEl 0 a))++fstEqual x y = (fst x) == (fst y)+auc1 actual predicted = groupBy fstEqual (sort (zip predicted actual))+sumRank actual predicted = sumRankAccum (auc1 actual predicted) 0.0 0 where+    sumRankAccum [] rSum r = rSum+    sumRankAccum (x:xs) rSum r = sumRankAccum xs (rSum+curPos*(r+curRank)) (r+curRank) where+        curRank = (fromIntegral (1+(length x))) / 2.0+        curPos = sum [1.0 | el <- x, (snd el)==1]+sumEl el a = fromIntegral (length (filter (==el) a))++ce :: Eq a => [a] -> [a] -> Double+ce actual predicted = ((sum [1.0 | (a,p) <- (zip actual predicted), a/=p]) +                       / (fromIntegral (length actual)))+

+ Setup.hs view

@@ -0,0 +1,2 @@+import Distribution.Simple+main = defaultMain