colada-0.5.5: colada.hs

{-# LANGUAGE FlexibleInstances , DeriveDataTypeable 
 , TemplateHaskell , OverloadedStrings , NoMonomorphismRestriction
 , FlexibleContexts
 #-}
module Main
where       
import qualified Data.Text.Lazy.IO          as Text
import qualified Data.Text.Lazy             as Text
import qualified Data.Text.Lazy.Builder     as Text
import qualified Data.Text.Lazy.Builder.Int as Text
import qualified Data.ByteString            as BS
import qualified Data.Serialize             as Serialize
import qualified Data.List                  as List
import qualified Data.Vector.Generic        as V
import qualified Data.Vector.Unboxed        as U
import qualified System.Environment         as Env
import qualified Data.Label                 as L
import qualified Data.Label.Maybe           as M
import qualified NLP.CoNLL                  as CoNLL
import qualified Colada.WordClass           as C
import qualified Text.Printf                as Printf

import System.Console.CmdArgs.Explicit
import Prelude hiding ((.))
import Control.Category ((.))



-- Command line parsing

data Program = Help 
             | Learn { _options :: C.Options 
                     , _modelPath :: FilePath }
             | Predict { _topn :: Int 
                       , _modelPath :: FilePath }
             | Label { _modelPath :: FilePath 
                     , _noContext :: Bool }
             | Summary { _modelPath :: FilePath 
                       ,_harden :: Bool }

             deriving (Show)
$(L.mkLabels [''Program])                                       

help :: Mode Program
help = 
  mode "help" Help "Display help" 
  (flagArg (\ x _ -> 
             Left $ "Unexpected argument " ++ x) "")
  []

predict :: Mode Program
predict = 
  mode "predict" Predict { _topn = maxBound 
                         , _modelPath = "model" } "Predict words"
  (flagArg (\x p -> Right $ maybe p id (M.set modelPath x p)) "FILE")
  [ flagReq ["topn"] (\x p -> 
                       case safeRead x of
                         Right n -> Right $ maybe p id (M.set topn n p)
                         Left err -> Left err 
                     )
    
    "NAT" "Number of most probable words to show"
  ]

summary :: Mode Program  
summary =
  mode "summary" Summary { _modelPath = "model" , _harden = False } 
        "Display summary of word classes"
  (flagArg (\x p -> Right $ maybe p id (M.set modelPath x p)) "FILE")  
  [ flagNone ["harden"] (\p -> p { _harden = True })
    "Harden class assignments for summary"
  ]  

label :: Mode Program
label =
  mode "label" Label { _modelPath = "model" , _noContext = False } 
       "Label words with classes"
  (flagArg (\x p -> Right $ maybe p id (M.set modelPath x p)) "FILE")
  [ flagNone ["no-context"] (\p -> p { _noContext = True }) 
    "Ignore context while labeling"  
  ]
  
learn :: Mode Program
learn = 
  let setOption = setOptionWith id   
      setOptionWith f field x p =   
          fmap (maybe p id . flip (M.set (field . options)) p)
        . fmap f 
        . safeRead 
        $ x 
  in mode "learn" Learn { _options = C.defaultOptions 
                        , _modelPath = "model" } "Learn word classes"
     (flagArg (\x p -> Right $ maybe p id (M.set modelPath x p))
      "FILE")
        [ flagReq ["features"]  
            (\x p -> case x of 
                "unigram" -> Right . maybe p id 
                             $ M.set (C.featIds . options) [-1,1] p
                "bigram"  -> Right . maybe p id 
                             $ M.set (C.featIds . options) [-12,12] p
                _         -> Left $ "Unknown feature specification " ++ x)
            "(unigram|bigram)" "Feature specification"
          
        , flagReq ["topic-num"] (setOption C.topicNum)
            "NAT" "Number of topics K" 
        
        , flagReq ["alphasum"] (setOption C.alphasum)
            "FLOAT" "Parameter alpha * K"
        
        , flagReq ["beta"] (setOption C.beta)  
            "FLOAT" "Parameter beta"
          
        , flagReq ["passes"] (setOption C.passes)
            "NAT" "Passes per batch"
          
        , flagReq ["repeats"] (setOption C.repeats)
            "NAT" "Repeats per sentence"
          
        , flagReq ["batch-size"] (setOption C.batchSize)
            "NAT" "Sentences per batch"
          
        , flagReq ["seed"] (setOption C.seed)
            "NAT" "Random seed"
          
        , flagNone ["progressive"] 
           (\p -> 
             maybe p id . M.set (C.progressive . options) True $ p)
                  "Label progressively"  
        
        , flagNone ["pre"]  
            (\p -> 
              maybe p id . M.set (C.pre . options) True $ p)
                  "Progressive labeling done before running a pass"
          
        , flagReq ["lambda"] (setOption C.lambda)
             "FLOAT" "Interpolation parameter for progressive labeling"
          
        , flagReq ["init-size"] (setOption C.initSize) 
            "NAT" "Data prefix size for batch initialization"
          
        , flagReq ["init-passes"] (setOption C.initPasses)
            "NAT" "Number of passes for initialization"
          
        , flagReq ["exponent"] (setOptionWith Just C.exponent)            
            "FLOAT" "Exponent for learning rate"
        ]
        
  
program :: Mode Program                     
program = modes "colada" Help "Word class learning" 
          [learn, predict, label, summary, help] 


-- Run the program

main :: IO ()
main = do
  args <- Env.getArgs
  let opts = processValue program args
  case opts of
    Help -> print $ helpText [] HelpFormatDefault program
    Predict { _topn = n, _modelPath = p } -> do
      -- FIXME: use Data.Text.Builder instead of converting to Lists
      let format s = {-# SCC "format" #-}
                   Text.unlines 
                     [ Text.concat . List.intersperse "," . map snd . V.toList 
                       $ ws 
                     | ws <-  s ]
      m <- L.set (C.topn . C.options) n `fmap` parseModel p
      ss <- CoNLL.parse `fmap` Text.getContents
      Text.putStr . Text.unlines . map (format . C.predict m) $ ss
    Label { _modelPath = p , _noContext = noctx } -> do
      m <- parseModel p
      ss <- CoNLL.parse `fmap` Text.getContents
      Text.putStr . Text.unlines 
        . map (formatLabeling 
               . V.map V.maxIndex . C.label noctx m) 
        $ ss  
    Learn { _options = o , _modelPath = p } -> do
      ss <- CoNLL.parse `fmap` Text.getContents
      let (m, ls) = C.learn o ss
      if (L.get C.progressive o)     
        then do Text.putStr . Text.unlines . map formatFullLabeling $ ls
        else do Text.putStr . C.summary $ m    
      BS.writeFile p . Serialize.encode $ m      
    Summary { _modelPath = p , _harden = h } -> do  
      m <- parseModel p
      if h
        then do Text.putStr . C.summarize True  $ m   
        else do Text.putStr . C.summarize False $ m
      
formatLabeling :: (V.Vector v Int, V.Vector v Text.Text) =>
     v Int -> Text.Text
formatLabeling = Text.unlines . V.toList 
                 . V.map (Text.toLazyText . Text.decimal)


formatFullLabeling = 
    Text.unlines 
  . map (Text.unwords . map (Text.pack . Printf.printf "%.3f") . U.toList)
  . V.toList
  
  

parseModel :: FilePath -> IO C.WordClass
parseModel p = do
  (either (\err -> error $ "Error reading model " ++ err) id
   . Serialize.decode)
  `fmap` BS.readFile p
       
safeRead :: Read b => String -> Either String b
safeRead x = 
  case reads x of
    [(a,"")] -> Right a
    _        -> Left $ "Couldn't parse " ++ show x