diff --git a/LICENSE b/LICENSE new file mode 100644 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. By contrast, +the GNU General Public License is intended to guarantee your freedom to +share and change all versions of a program--to make sure it remains free +software for all its users. We, the Free Software Foundation, use the +GNU General Public License for most of our software; it applies also to +any other work released this way by its authors. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. 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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Sibe Copyright (C) 2016 Mahdi Dibaiee + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/Setup.hs b/Setup.hs new file mode 100644 --- /dev/null +++ b/Setup.hs @@ -0,0 +1,2 @@ +import Distribution.Simple +main = defaultMain diff --git a/examples/424encoder.hs b/examples/424encoder.hs new file mode 100644 --- /dev/null +++ b/examples/424encoder.hs @@ -0,0 +1,49 @@ +module Main where + import Sibe + import Numeric.LinearAlgebra + import Data.List + import Debug.Trace + import Data.Default.Class + + main = do + let alpha = 0.5 + epochs = 1000 + a = (sigmoid, sigmoid') + rnetwork = randomNetwork 0 (-0.1, 0.1) 4 [(2, a)] (4, a) + + inputs = [vector [1, 0, 0, 0], + vector [0, 1, 0, 0], + vector [0, 0, 1, 0], + vector [0, 0, 0, 1]] + + labels = [vector [1, 0, 0, 0], + vector [0, 1, 0, 0], + vector [0, 0, 1, 0], + vector [0, 0, 0, 1]] + + session = def { network = rnetwork + , learningRate = 0.5 + , epochs = 1000 + , training = zip inputs labels + , test = zip inputs labels + } :: Session + + let initialCost = crossEntropy session + + newsession <- run gd session + + let results = map (`forward` newsession) inputs + rounded = map (map round . toList) results + + cost = crossEntropy newsession + + putStrLn "parameters: " + putStrLn $ "- inputs: " ++ show inputs + putStrLn $ "- labels: " ++ show labels + putStrLn $ "- learning rate: " ++ show alpha + putStrLn $ "- epochs: " ++ show epochs + putStrLn $ "- initial cost (cross-entropy): " ++ show initialCost + putStrLn "results: " + putStrLn $ "- actual result: " ++ show results + putStrLn $ "- rounded result: " ++ show rounded + putStrLn $ "- cost (cross-entropy): " ++ show cost diff --git a/examples/naivebayes-doc-classifier.hs b/examples/naivebayes-doc-classifier.hs new file mode 100644 --- /dev/null +++ b/examples/naivebayes-doc-classifier.hs @@ -0,0 +1,82 @@ +module Main + where + -- import Sibe + import Sibe.NLP + import Sibe.NaiveBayes + import Text.Printf + import Data.List + import Data.Maybe + import Debug.Trace + import Data.List.Split + import Control.Arrow ((&&&)) + import Control.Monad (when, unless) + import Data.Function (on) + import System.Environment + + main = do + args <- getArgs + dataset <- readFile "examples/doc-classifier-data/data-reuters" + test <- readFile "examples/doc-classifier-data/data-reuters-test" + + classes <- map (filter (/= ' ')) . lines <$> readFile "examples/doc-classifier-data/data-classes" + sws <- lines <$> readFile "examples/stopwords" + + let verbose = elem "-v" args || elem "--verbose" args + topten = elem "-10" args || elem "--top-ten" args + unless verbose $ putStrLn "use --verbose to print more information" + + let intClasses = [0..length classes - 1] + documents = cleanDocuments . removeWords sws $ createDocuments classes dataset + testDocuments = cleanDocuments $ createDocuments classes test + + nb = initialize documents intClasses + + -- top-ten + topClasses = take 10 . reverse $ sortBy (compare `on` (length . snd)) (cd nb) + filtered = map (\(c, ds) -> (c, take 100 ds)) topClasses + filteredClasses = map fst filtered + ttDocs = concatMap snd filtered + ttNB = initialize ttDocs filteredClasses + + ttTestDocuments = filter ((`elem` filteredClasses) . c) . cleanDocuments $ createDocuments classes test + + ttResults = session ttTestDocuments ttNB + normalResults = session testDocuments nb + results = if topten then ttResults else normalResults + + iClasses = if topten then filteredClasses else intClasses + -- results = session devTestDocuments nb + + when verbose . putStrLn $ "# Example of cleaned document:\n" ++ (show . text $ head documents) + + let showResults (c, (r, confidence)) = putStrLn (classes !! c ++ " ~ " ++ classes !! r) + when verbose $ mapM_ showResults results + + when (verbose && not topten) . + putStrLn $ "The training data is imbalanced which causes the classifier to be biased towards\n" + ++ "some classes, `earn` is an example, the class alone has around 90% accuracy while\n" + ++ "the rest of classes have a much lower accuracy and it's commonly seen that most inputs\n" + ++ "are incorrectly classified as `earn`.\n" + ++ "Try running with --top-ten to classify top 10 classes by using evenly split documents\n" + + let + accuracies = + let as = zip iClasses $ map (\c -> filter ((==c) . fst) results) iClasses + av = filter (not . null . snd) as + calculated = map (fst &&& accuracy . snd) av + in sortBy (\(_, a) (_, b) -> b `compare` a) calculated + + when verbose $ + mapM_ (\(c, a) -> putStrLn $ "Accuracy(" ++ classes !! c ++ ") = " ++ show a) accuracies + + putStrLn $ "\nAverages: " + putStrLn $ "Recall = " ++ show (recall results) + putStrLn $ "Precision = " ++ show (precision results) + putStrLn $ "F Measure = " ++ show (fmeasure results) + putStrLn $ "Accuracy = " ++ show (accuracy results) + + createDocuments classes content = + let splitted = splitOn (replicate 10 '-' ++ "\n") content + pairs = map ((head . lines) &&& (unwords . tail . lines)) splitted + documents = map (\(topic, text) -> Document text (fromJust $ elemIndex topic classes)) pairs + in documents diff --git a/examples/notmnist.hs b/examples/notmnist.hs new file mode 100644 --- /dev/null +++ b/examples/notmnist.hs @@ -0,0 +1,103 @@ +{-# LANGUAGE RecordWildCards #-} +{-# LANGUAGE FlexibleContexts #-} +{-# LANGUAGE ScopedTypeVariables #-} + +module Main where + import Sibe + import Numeric.LinearAlgebra + import Data.List + import Debug.Trace + import System.IO + import System.Directory + import Codec.Picture + import Codec.Picture.Types + import qualified Data.Vector.Storable as V + import Data.Either + import System.Random + import System.Random.Shuffle + import Data.Default.Class + + main = do + -- random seed, you might comment this line to get real random results + setStdGen (mkStdGen 100) + + let a = (sigmoid, sigmoid') + o = (softmax, crossEntropy') + rnetwork = randomNetwork 0 (-1, 1) (28*28) [(100, a)] (10, o) + + (inputs, labels) <- dataset + + let trp = length inputs * 70 `div` 100 + tep = length inputs * 30 `div` 100 + + -- training data + trinputs = take trp inputs + trlabels = take trp labels + + -- test data + teinputs = take tep . drop trp $ inputs + telabels = take tep . drop trp $ labels + + let session = def { learningRate = 0.5 + , batchSize = 32 + , epochs = 10 + , network = rnetwork + , training = zip trinputs trlabels + , test = zip teinputs telabels + , drawChart = True + , chartName = "notmnist.png" + } :: Session + + let initialCost = crossEntropy session + + newsession <- run (sgd . learningRateDecay (1.1, 5e-2)) session + + let cost = crossEntropy newsession + + putStrLn "parameters: " + putStrLn $ "- batch size: " ++ show (batchSize session) + putStrLn $ "- learning rate: " ++ show (learningRate session) + putStrLn $ "- epochs: " ++ show (epochs session) + putStrLn $ "- initial cost (cross-entropy): " ++ show initialCost + putStrLn "results: " + putStrLn $ "- accuracy: " ++ show (accuracy newsession) + putStrLn $ "- cost (cross-entropy): " ++ show cost + + dataset :: IO ([Vector Double], [Vector Double]) + dataset = do + let dir = "examples/notMNIST/" + + groups <- filter ((/= '.') . head) <$> listDirectory dir + + inputFiles <- mapM (listDirectory . (dir ++)) groups + + let n = 512 {-- minimum (map length inputFiles) --} + numbers = map (`div` n) [0..n * length groups - 1] + inputFilesFull = map (\(i, g) -> map ((dir ++ i ++ "/") ++) g) (zip groups inputFiles) + + + inputImages <- mapM (mapM readImage . take n) inputFilesFull + + let names = map (take n) inputFilesFull + + let (l, r) = partitionEithers $ concat inputImages + inputs = map (fromPixels . convertRGB8) r + labels = map (\i -> V.replicate i 0 `V.snoc` 1 V.++ V.replicate (9 - i) 0) numbers + + pairs = zip inputs labels + + shuffled <- shuffleM pairs + return (map fst shuffled, map snd shuffled) + + where + fromPixels :: Image PixelRGB8 -> Vector Double + fromPixels img@Image { .. } = + let pairs = [(x, y) | x <- [0..imageWidth - 1], y <- [0..imageHeight - 1]] + in V.fromList $ map iter pairs + where + iter (x, y) = + let (PixelRGB8 r g b) = convertPixel $ pixelAt img x y + in + if r == 0 && g == 0 && b == 0 then 0 else 1 + + diff --git a/examples/word2vec.hs b/examples/word2vec.hs new file mode 100644 --- /dev/null +++ b/examples/word2vec.hs @@ -0,0 +1,88 @@ +{-# LANGUAGE RecordWildCards #-} +{-# LANGUAGE FlexibleContexts #-} +{-# LANGUAGE ScopedTypeVariables #-} + +module Main where + import Sibe + import Sibe.Word2Vec + import Sibe.Utils + import Data.Default.Class + import qualified Data.Vector.Storable as V + import Data.List (sortBy) + import Data.Function (on) + import Numeric.LinearAlgebra + import System.IO + import System.Directory + import Data.List.Split + import Control.Exception (evaluate) + import Debug.Trace + import Data.Char + import System.Random + + rf :: FilePath -> IO String + rf p = do + hs <- openFile p ReadMode + hSetEncoding hs latin1 + content <- evaluate =<< hGetContents hs + length content `seq` hClose hs + return content + + main = do + setStdGen (mkStdGen 100) + sws <- lines <$> readFile "examples/stopwords" + + -- real data, takes a lot of time to train + ds <- do + files <- filter ((/= "xml") . take 1 . reverse) <$> listDirectory "examples/blogs-corpus/" + contents <- mapM (rf . ("examples/blogs-corpus/" ++)) files + + let texts = map (unwords . splitOn " ") contents + let tags = ["", "", "", "", "", "", " "] + return $ map cleanText $ removeWords (sws ++ tags) texts + + {-let ds = ["the king loves the queen", "the queen loves the king",-} + {-"the dwarf hates the king", "the queen hates the dwarf",-} + {-"the dwarf poisons the king", "the dwarf poisons the queen",-} + {-"the man loves the woman", "the woman loves the man",-} + {-"the thief hates the man", "the woman hates the thief",-} + {-"the thief robs the man", "the thief robs the woman"]-} + + let session = def { learningRate = 5e-1 + , batchSize = 1 + , epochs = 200 + , debug = True + } :: Session + w2v = def { docs = ds + , dimensions = 300 + , method = SkipGram + , window = 2 + , w2vDrawChart = True + , w2vChartName = "w2v-big-data.png" + } :: Word2Vec + + (computed, vocvec) <- word2vec w2v session + + return () + + cleanText :: String -> String + cleanText string = + let notag = unwords $ filter ((/= "") . take 6) (words string) + ws = unwords $ filter (`notElem` ["urlLink"]) (words notag) + spacify = foldl (\acc x -> replace x ' ' acc) (trim ws) [',', '/', '-', '\n', '\r', '?', '.', '(', ')', '%', '$', '"', ';', ':', '!', '\''] + nonumber = filter (not . isNumber) spacify + lower = map toLower nonumber + in unwords . words $ lower + where + trim = f . f + where + f = reverse . dropWhile isSpace + replace needle replacement = + map (\c -> if c == needle then replacement else c) + + removeWords :: [String] -> [String] -> [String] + removeWords ws documents = + map rm documents + where + rm text = + unwords $ filter (`notElem` ws) (words text) + diff --git a/examples/xor.hs b/examples/xor.hs new file mode 100644 --- /dev/null +++ b/examples/xor.hs @@ -0,0 +1,40 @@ +module Main where + import Sibe + import Numeric.LinearAlgebra + import Data.List + import Debug.Trace + import Data.Default.Class + + main = do + let a = (sigmoid, sigmoid') + rnetwork = randomNetwork 0 (-1, 1) 2 [(2, a)] (1, a) -- two inputs, 8 nodes in a single hidden layer, 1 output + + inputs = [vector [0, 1], vector [1, 0], vector [1, 1], vector [0, 0]] + labels = [vector [1], vector [1], vector [0], vector [0]] + + session = def { network = rnetwork + , learningRate = 0.8 + , epochs = 1000 + , training = zip inputs labels + , test = zip inputs labels + } :: Session + + initialCost = crossEntropy session + + newsession <- run gd session + + let results = map (`forward` newsession) inputs + rounded = map (map round . toList) results + + cost = crossEntropy newsession + + putStrLn "parameters: " + putStrLn $ "- inputs: " ++ show inputs + putStrLn $ "- labels: " ++ show labels + putStrLn $ "- learning rate: " ++ show (learningRate session) + putStrLn $ "- epochs: " ++ show (epochs session) + putStrLn $ "- initial cost (cross-entropy): " ++ show initialCost + putStrLn "results: " + putStrLn $ "- actual result: " ++ show results + putStrLn $ "- rounded result: " ++ show rounded + putStrLn $ "- cost (cross-entropy): " ++ show cost diff --git a/sibe.cabal b/sibe.cabal new file mode 100644 --- /dev/null +++ b/sibe.cabal @@ -0,0 +1,111 @@ +name: sibe +version: 0.1.0.0 +synopsis: Initial project template from stack +description: Haskell Machine Learning +homepage: https://github.com/mdibaiee/sibe +license: GPL-3 +license-file: LICENSE +author: Mahdi Dibaiee +maintainer: mdibaiee@aol.com +copyright: 2016 Mahdi Dibaiee +category: Web, Machine Learning, Data Science +build-type: Simple +-- extra-source-files: +cabal-version: >=1.10 + +library + hs-source-dirs: src + exposed-modules: Sibe, Sibe.NaiveBayes, Sibe.NLP, Sibe.Word2Vec, Sibe.Utils + build-depends: base >= 4.7 && < 5 + , hmatrix + , random + , deepseq + , containers + , split + , regex-base + , regex-pcre + , text + , stemmer + , vector + , random-shuffle + , data-default-class + , Chart + , Chart-cairo + , lens + default-language: Haskell2010 + +executable example-xor + hs-source-dirs: examples + main-is: xor.hs + ghc-options: -threaded -rtsopts -with-rtsopts=-N + build-depends: base + , sibe + , hmatrix + , data-default-class + default-language: Haskell2010 + +executable example-word2vec + hs-source-dirs: examples + main-is: word2vec.hs + ghc-options: -threaded -rtsopts -with-rtsopts=-N + build-depends: base + , sibe + , hmatrix + , data-default-class + , split + , vector + , directory + , random + default-language: Haskell2010 + +executable example-424 + hs-source-dirs: examples + main-is: 424encoder.hs + ghc-options: -threaded -rtsopts -with-rtsopts=-N + build-depends: base + , sibe + , hmatrix + , data-default-class + default-language: Haskell2010 + +executable example-notmnist + hs-source-dirs: examples + main-is: notmnist.hs + ghc-options: -threaded -rtsopts -with-rtsopts=-N + build-depends: base + , sibe + , hmatrix + , directory >= 1.2.5.0 + , JuicyPixels == 3.2.7.2 + , vector == 0.11.0.0 + , random + , random-shuffle + , data-default-class + , Chart + , Chart-cairo + default-language: Haskell2010 + +executable example-naivebayes-doc-classifier + hs-source-dirs: examples + main-is: naivebayes-doc-classifier.hs + ghc-options: -threaded -rtsopts -with-rtsopts=-N + build-depends: base + , sibe + , hmatrix + , containers + , split + default-language: Haskell2010 + +test-suite sibe-test + type: exitcode-stdio-1.0 + hs-source-dirs: test + main-is: Spec.hs + build-depends: base + , sibe + , hmatrix + ghc-options: -threaded -rtsopts -with-rtsopts=-N + default-language: Haskell2010 + +source-repository head + type: git + location: https://github.com/mdibaiee/sibe diff --git a/src/Sibe.hs b/src/Sibe.hs new file mode 100644 --- /dev/null +++ b/src/Sibe.hs @@ -0,0 +1,362 @@ +{-# LANGUAGE GADTs #-} +{-# LANGUAGE BangPatterns #-} +{-# LANGUAGE DataKinds #-} +{-# LANGUAGE TypeOperators #-} + +module Sibe + (Network(..), + Layer(..), + Input, + Output, + Activation, + forward, + forward', + runLayer, + runLayer', + randomLayer, + randomNetwork, + buildNetwork, + saveNetwork, + loadNetwork, + train, + gd, + sgd, + run, + sigmoid, + sigmoid', + softmax, + softmax', + relu, + relu', + crossEntropy, + crossEntropy', + genSeed, + replaceVector, + Session(..), + accuracy, + learningRateDecay, + ignoreBiases, + one + ) where + import Numeric.LinearAlgebra + import System.Random + import System.Random.Shuffle + import Debug.Trace + import Data.List (foldl', sortBy, genericLength, permutations) + import System.IO + import Control.DeepSeq + import Control.Monad + import qualified Data.Vector.Storable as V + import Data.Default.Class + import System.Exit + + import qualified Graphics.Rendering.Chart.Easy as Chart + import Graphics.Rendering.Chart.Backend.Cairo + + type LearningRate = Double + type Input = Vector Double + type Output = Vector Double + type Activation = (Vector Double -> Vector Double, Vector Double -> Vector Double) + + data Layer = Layer { biases :: !(Vector Double) + , nodes :: !(Matrix Double) + , activation :: Activation + } + + instance Show Layer where + show (Layer biases nodes _) = "(" ++ show biases ++ "," ++ show nodes ++ ")" + + data Network = O Layer + | Layer :- Network + + instance Show Network where + show (Layer biases nodes _ :- n) = + (show . length $ toLists nodes) ++ "x" ++ (show . length . head . toLists $ nodes) ++ " " ++ (show . length . toList $ biases) ++ " :- " ++ show n + show (O (Layer biases nodes _)) = + (show . length $ toLists nodes) ++ "x" ++ (show . length . head . toLists $ nodes) ++ " " ++ (show . length . toList $ biases) + + infixr 5 :- + + data Session = Session { network :: Network + , training :: [(Vector Double, Vector Double)] + , test :: [(Vector Double, Vector Double)] + , learningRate :: Double + , epochs :: Int + , epoch :: Int + , batchSize :: Int + , chart :: [(Int, Double, Double)] + , drawChart :: Bool + , chartName :: String + , momentum :: Double + , debug :: Bool + } deriving (Show) + + emptyNetwork = randomNetwork 0 (0, 0) 0 [] (0, (id, id)) + instance Default Session where + def = Session { network = seq (die "You have not specified a network parameter") emptyNetwork + , training = seq (die "You have not specified training data") [] + , test = seq (die "You have not specified test data") [] + , learningRate = 0.5 + , epochs = 35 + , epoch = 0 + , batchSize = 0 + , chart = [] + , drawChart = False + , chartName = "chart.png" + , momentum = 0 + , debug = False + } + + saveNetwork :: Network -> String -> IO () + saveNetwork network file = + writeFile file ((show . reverse) (gen network [])) + where + gen (O (Layer biases nodes _)) list = (biases, nodes) : list + gen (Layer biases nodes _ :- n) list = gen n $ (biases, nodes) : list + + loadNetwork :: [Activation] -> String -> IO Network + loadNetwork activations file = do + handle <- openFile file ReadMode + content <- hGetContents handle + let list = read content :: [(Vector Double, Matrix Double)] + network = gen list activations + content `deepseq` hClose handle + return network + + where + gen [(biases, nodes)] [a] = O (Layer biases nodes a) + gen ((biases, nodes):hs) (a:as) = Layer biases nodes a :- gen hs as + + runLayer :: Input -> Layer -> Output + runLayer input (Layer !biases !weights _) = input <# weights + biases + + runLayer' :: Input -> Layer -> Output + runLayer' input (Layer !biases !weights _) = input <# weights + + forward :: Input -> Session -> Output + forward input session = compute input (network session) + where + compute input (O l@(Layer _ _ (fn, _))) = fn $ runLayer input l + compute input (l@(Layer _ _ (fn, _)) :- n) = compute ((fst . activation $ l) $ runLayer input l) n + + forward' :: Input -> Session -> Output + forward' input session = compute input (network session) + where + compute input (O l@(Layer _ _ (fn, _))) = fn $ runLayer' input l + compute input (l@(Layer _ _ (fn, _)) :- n) = compute ((fst . activation $ l) $ runLayer' input l) n + + randomLayer :: Seed -> (Int, Int) -> (Double, Double) -> Activation -> Layer + randomLayer seed (wr, wc) (l, u) = + let weights = uniformSample seed wr $ replicate wc (l, u) + biases = randomVector seed Uniform wc * realToFrac u - realToFrac l + in Layer biases weights + + randomNetwork :: Seed -> (Double, Double) -> Int -> [(Int, Activation)] -> (Int, Activation) -> Network + randomNetwork seed bound input [] (output, a) = + O $ randomLayer seed (input, output) bound a + randomNetwork seed bound input ((h, a):hs) output = + randomLayer seed (input, h) bound a :- + randomNetwork (seed + 1) bound h hs output + + buildNetwork :: Seed -> (Double, Double) -> Int -> [(Int, Int, Activation)] -> (Int, Int, Activation) -> Network + buildNetwork seed bound input [] (outputRows, outputColumns, a) = + O $ randomLayer seed (input, outputColumns) bound a + buildNetwork seed bound input ((rows, columns, a):hs) output = + randomLayer seed (input, columns) bound a :- + buildNetwork (seed + 1) bound columns hs output + + sigmoid :: Vector Double -> Vector Double + sigmoid x = 1 / max (1 + exp (-x)) 1e-10 + + sigmoid' :: Vector Double -> Vector Double + sigmoid' x = sigmoid x * (1 - sigmoid x) + + softmax :: Vector Double -> Vector Double + softmax x = cmap (\a -> exp a / s) x + where + s = V.sum $ exp x + + softmax' :: Vector Double -> Vector Double + softmax' = cmap (\a -> sig a * (1 - sig a)) + where + sig x = 1 / max (1 + exp (-x)) 1e-10 + + -- used for negative sampling + {-sampledSoftmax :: Vector Double -> Vector Double-} + {-sampledSoftmax x = cmap (\a -> exp a / s) x-} + {-where-} + {-s = V.sum . exp $ x-} + + relu :: Vector Double -> Vector Double + relu = cmap (max 0.1) + + relu' :: Vector Double -> Vector Double + relu' = cmap dev + where dev x + | x < 0 = 0 + | otherwise = 1 + + crossEntropy :: Session -> Double + crossEntropy session = + let inputs = map fst (test session) + labels = map (toList . snd) (test session) + outputs = map (toList . (`forward` session)) inputs + pairs = zip outputs labels + n = genericLength pairs + in sum (map set pairs) / n + where + set (os, ls) = (-1 / genericLength os) * sum (zipWith f os ls) + f a y = y * log (max 1e-10 a) + + crossEntropy' :: Vector Double -> Vector Double + crossEntropy' x = 1 / fromIntegral (V.length x) + + one :: Vector Double -> Vector Double + one v = vector $ replicate (V.length v) 1 + + train :: Input + -> Network + -> Output -- target + -> Double -- learning rate + -> Network -- network's output + train input network target alpha = fst $ run input network + where + run :: Input -> Network -> (Network, Vector Double) + run input (O l@(Layer biases weights (fn, fn'))) = + let y = runLayer input l + o = fn y + delta = o - target + de = delta * fn' y + + biases' = biases - scale alpha de + weights' = weights - scale alpha (input `outer` de) -- small inputs learn slowly + layer = Layer biases' weights' (fn, fn') -- updated layer + + pass = weights #> de + + in (O layer, pass) + run input (l@(Layer biases weights (fn, fn')) :- n) = + let y = runLayer input l + o = fn y + (n', delta) = run o n + + de = delta * fn' y + + biases' = biases - scale alpha de + weights' = weights - scale alpha (input `outer` de) + layer = Layer biases' weights' (fn, fn') + + pass = weights #> de + in (layer :- n', pass) + + gd :: Session -> IO Session + gd session = do + seed <- newStdGen + + let pairs = training session + alpha = learningRate session + net = network session + + let n = length pairs + + shuffled <- shuffleM pairs + + let newnet = foldl' (\n (input, label) -> train input n label alpha) net pairs + cost = crossEntropy (session { network = newnet }) + + let el = map (\(e, l, _) -> (e, l)) (chart session) + ea = map (\(e, _, a) -> (e, a)) (chart session) + + when (drawChart session) $ do + toFile Chart.def (chartName session) $ do + Chart.layoutlr_title Chart..= "loss over time" + Chart.plotLeft (Chart.line "loss" [el]) + Chart.plotRight (Chart.line "learningRate" [ea]) + + return session { network = newnet + , epoch = epoch session + 1 + , chart = (epoch session, cost, learningRate session):chart session + } + + sgd :: Session -> IO Session + sgd session = do + seed <- newStdGen + + let pairs = training session + bsize = batchSize session + alpha = learningRate session + net = network session + + let n = length pairs + iterations = n `div` bsize - 1 + + shuffled <- shuffleM pairs + + let iter net i = + let n = length pairs + batch = take bsize . drop (i * bsize) $ shuffled + batchInputs = map fst batch + batchLabels = map snd batch + batchPair = zip batchInputs batchLabels + in foldl' (\n (input, label) -> train input n label alpha) net batchPair + + let newnet = foldl' iter net [0..iterations] + cost = crossEntropy (session { network = newnet }) + + let el = map (\(e, l, _) -> (e, l)) (chart session) + ea = map (\(e, _, a) -> (e, a)) (chart session) + + when (drawChart session) $ do + toFile Chart.def (chartName session) $ do + Chart.layoutlr_title Chart..= "loss over time" + Chart.plotLeft (Chart.line "loss" [el]) + Chart.plotRight (Chart.line "learningRate" [ea]) + + return session { network = newnet + , epoch = epoch session + 1 + , chart = (epoch session, cost, learningRate session):chart session + } + + + accuracy :: Session -> Double + accuracy session = + let inputs = map fst (test session) + labels = map snd (test session) + + results = map (`forward` session) inputs + rounded = map (map round . toList) results + + equals = zipWith (==) rounded (map (map round . toList) labels) + in genericLength (filter (== True) equals) / genericLength inputs + + learningRateDecay :: (Double, Double) -> Session -> Session + learningRateDecay (step, m) session = + session { learningRate = max m $ learningRate session / step } + + ignoreBiases :: Session -> Session + ignoreBiases session = + session { network = rmbias (network session) } + where + rmbias (O (Layer biases nodes a)) = O $ Layer (biases * 0) nodes a + rmbias ((Layer biases nodes a) :- n) = Layer (biases * 0) nodes a :- rmbias n + + run :: (Session -> IO Session) + -> Session -> IO Session + run fn session = foldM (\s i -> fn s) session [0..epochs session] + + factorial :: Int -> Int + factorial 0 = 1 + factorial x = x * factorial (x - 1) + + genSeed :: IO Seed + genSeed = do + (seed, _) <- random <$> newStdGen :: IO (Int, StdGen) + return seed + + replaceVector :: Vector Double -> Int -> Double -> Vector Double + replaceVector vec index value = + let list = toList vec + in fromList $ take index list ++ value : drop (index + 1) list + + clip :: Double -> (Double, Double) -> Double + clip x (l, u) = min u (max l x) diff --git a/src/Sibe/NLP.hs b/src/Sibe/NLP.hs new file mode 100644 --- /dev/null +++ b/src/Sibe/NLP.hs @@ -0,0 +1,122 @@ +module Sibe.NLP + (Class, + Document(..), + accuracy, + recall, + precision, + fmeasure, + cleanText, + cleanDocuments, + removeWords, + removeStopwords, + ngram, + ngramText, + ) + where + import Sibe.Utils + import Data.List + import Debug.Trace + import Data.List.Split + import Data.Maybe + import Control.Arrow ((&&&)) + import Text.Regex.PCRE + import Data.Char (isSpace, isNumber, toLower) + import NLP.Stemmer + import qualified Data.Set as Set + + type Class = Int; + + data Document = Document { text :: String + , c :: Class + } deriving (Eq, Show, Read) + + + cleanText :: String -> String + cleanText string = + let puncs = filter (`notElem` ['!', '"', '#', '$', '%', '(', ')', '.', '?']) (trim string) + spacify = foldl (\acc x -> replace x ' ' acc) puncs [',', '/', '-', '\n', '\r'] + stemmed = unwords $ map (stem Porter) (words spacify) + nonumber = filter (not . isNumber) stemmed + lower = map toLower nonumber + in (unwords . words) lower -- remove unnecessary spaces + where + trim = f . f + where + f = reverse . dropWhile isSpace + replace needle replacement = + map (\c -> if c == needle then replacement else c) + + cleanDocuments :: [Document] -> [Document] + cleanDocuments documents = + let cleaned = map (\(Document text c) -> Document (cleanText text) c) documents + in cleaned + + removeWords :: [String] -> [Document] -> [Document] + removeWords ws documents = + map (\(Document text c) -> Document (rm ws text) c) documents + where + rm list text = + unwords $ filter (`notElem` list) (words text) + + removeStopwords :: Int -> [Document] -> [Document] + removeStopwords i documents = + let wc = wordCounts (concatDocs documents) + wlist = sortBy (\(_, a) (_, b) -> b `compare` a) wc + stopwords = map fst (take i wlist) + in removeWords stopwords documents + where + vocabulary x = ordNub (words x) + countWordInDoc d w = genericLength (filter (==w) d) + wordCounts x = + let voc = vocabulary x + in zip voc $ map (countWordInDoc (words x)) voc + + concatDocs = concatMap (\(Document text _) -> text ++ " ") + + accuracy :: [(Int, (Int, Double))] -> Double + accuracy results = + let pairs = map (\(a, b) -> (a, fst b)) results + correct = filter (uncurry (==)) pairs + in genericLength correct / genericLength results + + recall :: [(Int, (Int, Double))] -> Double + recall results = + let classes = ordNub (map fst results) + s = sum (map rec classes) / genericLength classes + in s + where + rec a = + let t = genericLength $ filter (\(c, (r, _)) -> c == r && c == a) results + y = genericLength $ filter (\(c, (r, _)) -> c == a) results + in t / y + + precision :: [(Int, (Int, Double))] -> Double + precision results = + let classes = ordNub (map fst results) + s = sum (map prec classes) / genericLength classes + in s + where + prec a = + let t = genericLength $ filter (\(c, (r, _)) -> c == r && c == a) results + y = genericLength $ filter (\(c, (r, _)) -> r == a) results + in + if y == 0 + then 0 + else t / y + + fmeasure :: [(Int, (Int, Double))] -> Double + fmeasure results = + let r = recall results + p = precision results + in (2 * p * r) / (p + r) + + ngram :: Int -> [Document] -> [Document] + ngram n documents = + map (\(Document text c) -> Document (ngramText n text) c) documents + + ngramText :: Int -> String -> String + ngramText n text = + let ws = words text + pairs = zip [0..] ws + grams = map (\(i, w) -> concat . intersperse "_" $ w:((take (n - 1) . drop (i+1)) ws)) pairs + in unwords ("_":grams) diff --git a/src/Sibe/NaiveBayes.hs b/src/Sibe/NaiveBayes.hs new file mode 100644 --- /dev/null +++ b/src/Sibe/NaiveBayes.hs @@ -0,0 +1,128 @@ +module Sibe.NaiveBayes + (Document(..), + NB(..), + initialize, + run, + session, + accuracy, + precision, + recall, + fmeasure, + mean, + stdev, + cleanText, + cleanDocuments, + ngram, + ngramText, + removeWords, + removeStopwords, + ) + where + import Sibe.Utils + import Sibe.NLP + import Data.List + import Debug.Trace + import qualified Data.Set as Set + import Data.List.Split + import Data.Maybe + import Control.Arrow ((&&&)) + + data NB = NB { documents :: [Document] + , classes :: [(Class, Double)] + , vocabulary :: Int + , megadoc :: String + , cd :: [(Class, [Document])] + , cw :: [(Class, [(String, Int)])] + , cgram :: [(Class, [(String, Int)])] + } deriving (Eq, Show, Read) + + initialize :: [Document] -> [Class] -> NB + initialize documents classes = + let megadoc = concatDocs documents + vocabulary = genericLength ((ordNub . words) megadoc) + -- (class, prior probability) + cls = zip classes (map classPrior classes) + + -- (class, [document]) + cd = zip classes (map classDocs classes) + + -- (class, [(word, count)]) + cw = zip classes $ map classWordsCounts classes + + cgram = zip classes $ map classNGramCounts classes + + in NB { documents = documents + , classes = cls + , vocabulary = vocabulary + , megadoc = megadoc + , cd = cd + , cw = cw + , cgram = cgram + } + where + concatDocs = concatMap (\(Document text _) -> text ++ " ") + + classDocs x = filter ((==x) . c) documents + classMegadoc = concatMap (\(Document text _) -> text ++ " ") . classDocs + classWords = words . classMegadoc + classNGram = concatMap (\(Document text _) -> text ++ " ") . ngram 2 . classDocs + classNGramWords = words . classNGram + classVocabulary = ordNub . classWords + classPrior x = genericLength (classDocs x) / genericLength documents + countWordInDoc d w = genericLength (filter (==w) d) + wordsCount ws voc = + zip voc $ map (countWordInDoc ws) voc + classWordsCounts x = wordsCount (classWords x) (classVocabulary x) + classNGramCounts x = wordsCount (classNGramWords x) (ordNub $ classNGramWords x) + + session :: [Document] -> NB -> [(Class, (Class, Double))] + session docs nb = + let results = map (\(Document text c) -> (c, run text nb)) docs + in results + + run :: String -> NB -> (Class, Double) + run txt (NB documents classes vocabulary megadoc cd cw cgram) = + let scores = map (score . fst) classes + index = argmax scores + m = maximum scores + in (fst (classes !! index), m) + where + score c = + let prior = snd (fromJust $ find ((==c) . fst) classes) + + -- below is the formula according to Multinominal Naive Bayes, but it seems + -- using a uniform prior probability seems to work better when working with imbalanced + -- training datasets, instead, we help rare classes get higher scores using + -- alpha = (1 - prior * ALPHA), we use ALPHA = 1 here + -- in prior * product (map (prob c) (words txt)) + + alpha = 1 - prior + + in alpha * product (map (prob c) (words txt)) + + prob c w = + let fcw = fromJust $ find ((==c) . fst) cw + fcg = fromJust $ find ((==c) . fst) cgram + tctM = find ((== w) . fst) (snd fcw) + tct = if isJust tctM then (snd . fromJust) tctM else 0 + cvoc = sum $ map snd (snd fcw) + voc = vocabulary + gram = find ((==w) . last . splitOn "_" . fst) (snd fcg) + pg = if isJust gram then (snd . fromJust) gram else 0 + -- in realToFrac (tct * pg + 1) / realToFrac (cvoc + voc) -- uncomment to enable ngrams + in realToFrac (tct + 1) / realToFrac (cvoc + voc) + + argmax :: (Ord a) => [a] -> Int + argmax x = fst $ maximumBy (\(_, a) (_, b) -> a `compare` b) (zip [0..] x) + + mean :: [Double] -> Double + mean x = sum x / genericLength x + + stdev :: [Double] -> Double + stdev x = + let avg = mean x + variance = sum (map ((^2) . subtract avg) x) / (genericLength x - 1) + in sqrt variance + + l :: (Show a) => a -> a + l a = trace (show a) a diff --git a/src/Sibe/Utils.hs b/src/Sibe/Utils.hs new file mode 100644 --- /dev/null +++ b/src/Sibe/Utils.hs @@ -0,0 +1,28 @@ +module Sibe.Utils + ( similarity + , ordNub + , onehot + , average + ) where + import qualified Data.Vector.Storable as V + import qualified Data.Set as Set + import Numeric.LinearAlgebra + + similarity :: Vector Double -> Vector Double -> Double + similarity a b = (V.sum $ a * b) / (magnitude a * magnitude b) + where + magnitude :: Vector Double -> Double + magnitude v = sqrt $ V.sum (cmap (^2) v) + + onehot :: Int -> Int -> Vector Double + onehot len i = vector $ replicate i 0 ++ [1] ++ replicate (len - i - 1) 0 + + ordNub :: (Ord a) => [a] -> [a] + ordNub = go Set.empty + where + go _ [] = [] + go s (x:xs) = if x `Set.member` s then go s xs + else x : go (Set.insert x s) xs + + average :: Vector Double -> Vector Double + average v = cmap (/ (V.sum v)) v diff --git a/src/Sibe/Word2Vec.hs b/src/Sibe/Word2Vec.hs new file mode 100644 --- /dev/null +++ b/src/Sibe/Word2Vec.hs @@ -0,0 +1,131 @@ +module Sibe.Word2Vec + ( word2vec + , Word2Vec (..) + , W2VMethod (..) + ) where + import Sibe + import Sibe.Utils + import Debug.Trace + import Data.Char + import Data.Maybe + import Data.List + import Numeric.LinearAlgebra hiding (find) + import qualified Data.Vector.Storable as V + import Data.Default.Class + import Data.Function (on) + import Control.Monad + import System.Random + + import Graphics.Rendering.Chart as Chart + import Graphics.Rendering.Chart.Backend.Cairo + import Control.Lens + + data W2VMethod = SkipGram | CBOW + data Word2Vec = Word2Vec { docs :: [String] + , window :: Int + , dimensions :: Int + , method :: W2VMethod + , w2vChartName :: String + , w2vDrawChart :: Bool + } + instance Default Word2Vec where + def = Word2Vec { docs = [] + , window = 2 + , w2vChartName = "w2v.png" + , w2vDrawChart = False + } + + word2vec w2v session = do + seed <- newStdGen + + let s = session { training = trainingData + , network = randomNetwork 0 (-1, 1) v [(dimensions w2v, (id, one))] (v, (softmax, crossEntropy')) + } + + when (debug s) $ do + putStr "vocabulary size: " + print v + + putStr "trainingData length: " + print . length $ trainingData + + -- biases are not used in skipgram/cbow + newses <- run (sgd . ignoreBiases) s + + + -- export the hidden layer + let (hidden@(Layer biases nodes _) :- _) = network newses + -- run words through the hidden layer alone to get the word vector + let computedVocVec = map (\(w, v) -> (w, runLayer' v hidden)) vocvec + + when (w2vDrawChart w2v) $ do + let mat = fromColumns . map snd $ computedVocVec + (u, s, v) = svd mat + cut = subMatrix (0, 0) (2, cols mat) + diagS = diagRect 0 (V.take 2 s) (rows mat) (cols mat) + + twoDimensions = cut $ u <> diagS <> tr v + textData = zipWith (\s l -> (V.head l, V.last l, s)) (map fst computedVocVec) (toColumns twoDimensions) + + chart = toRenderable layout + where + textP = plot_annotation_values .~ textData + $ def + layout = layout_title .~ "word vectors" + $ layout_plots .~ [toPlot textP] + $ def + + renderableToFile def (w2vChartName w2v) chart + return () + + return (computedVocVec, vocvec) + where + -- clean documents + ds = map cleanText (docs w2v) + + -- words of each document + wd = map (words . (++ " ") . (map toLower)) ds + + -- all words together, used to generate the vocabulary + ws = words (concatMap ((++ " ") . map toLower) ds) + vocabulary = ordNub ws + v = length vocabulary + + -- generate one-hot vectors for each word of vocabulary + vocvec = zip vocabulary $ map (onehot v) [0..v - 1] + + -- training data: generate input and output pairs for each word and the words in it's window + trainingData = concatMap (\wds -> concatMap (iter wds) $ zip [0..] wds) wd + where + iter wds (i, w) = + let v = snd . fromJust . find ((==w) . fst) $ vocvec + before = take (window w2v) . drop (i - window w2v) $ wds + after = take (window w2v) . drop (i + 1) $ wds + ns + | i == 0 = after + | i == length vocvec - 1 = before + | otherwise = before ++ after + vectorized = map (\w -> snd . fromJust $ find ((== w) . fst) vocvec) ns + new = foldl1 (+) vectorized + in + if length wds <= 1 + then [] + else + case method w2v of + SkipGram -> [(v, average new)] + CBOW -> [(average new, v)] + _ -> error "unsupported word2vec method" + + cleanText :: String -> String + cleanText string = + let puncs = filter (`notElem` ['!', '"', '#', '$', '%', '(', ')', '.', '?', '\'']) (trim string) + spacify = foldl (\acc x -> replace x ' ' acc) puncs [',', '/', '-', '\n', '\r'] + nonumber = filter (not . isNumber) spacify + lower = map toLower nonumber + in (unwords . words) lower -- remove unnecessary spaces + where + trim = f . f + where + f = reverse . dropWhile isSpace + replace needle replacement = + map (\c -> if c == needle then replacement else c) diff --git a/test/Spec.hs b/test/Spec.hs new file mode 100644 --- /dev/null +++ b/test/Spec.hs @@ -0,0 +1,8 @@ +module Main where + import System.Exit (exitFailure) + + import Sibe + + main = do + putStrLn "Hey" + exitFailure