src/Gargantext/Text/List/Learn.hs

   1 {-|
   2 Module      : Gargantext.Text.List.Learn
   3 Description : Learn to make lists
   4 Copyright   : (c) CNRS, 2018-Present
   5 License     : AGPL + CECILL v3
   6 Maintainer  : team@gargantext.org
   7 Stability   : experimental
   8 Portability : POSIX
   9
  10 CSV parser for Gargantext corpus files.
  11
  12 -}
  13
  14 {-# OPTIONS_GHC -fno-warn-orphans #-}
  15
  16 {-# LANGUAGE NoImplicitPrelude #-}
  17 {-# LANGUAGE OverloadedStrings #-}
  18
  19 module Gargantext.Text.List.Learn
  20   where
  21
  22 import Control.Monad.Reader (MonadReader)
  23 import Control.Monad.IO.Class (MonadIO, liftIO)
  24 import Gargantext.API.Settings
  25 import Data.Map (Map)
  26 import Data.Maybe (maybe)
  27 import Gargantext.Core.Types.Main (ListType(..), listTypeId, fromListTypeId)
  28 import Gargantext.Prelude
  29 import Gargantext.Prelude.Utils
  30 import Gargantext.Text.Metrics.Count (occurrencesWith)
  31 import qualified Data.IntMap as IntMap
  32 import qualified Data.List   as List
  33 import qualified Data.Map    as Map
  34 import qualified Data.SVM    as SVM
  35 import qualified Data.Vector as Vec
  36
  37 ------------------------------------------------------------------------
  38 train :: Double -> Double -> SVM.Problem -> IO SVM.Model
  39 train x y = (SVM.train (SVM.CSvc x) (SVM.RBF y))
  40
  41 predict :: SVM.Model -> [Vec.Vector Double] -> IO [Double]
  42 predict m vs = mapM (predict' m) vs
  43   where
  44     predict' m' vs' = SVM.predict m' (IntMap.fromList $ (zip [1..]) $ Vec.toList vs')
  45
  46 ------------------------------------------------------------------------
  47 trainList :: Double -> Double -> Map ListType [Vec.Vector Double] -> IO SVM.Model
  48 trainList x y = (train x y) . trainList'
  49   where
  50     trainList' :: Map ListType [Vec.Vector Double] -> SVM.Problem
  51     trainList' = mapVec2problem . (Map.mapKeys (fromIntegral . listTypeId))
  52
  53     mapVec2problem :: Map Double [Vec.Vector Double] -> SVM.Problem
  54     mapVec2problem = List.concat . (map (\(a,as) -> zip (repeat a) as)) . Map.toList . (Map.map vecs2maps)
  55
  56     vecs2maps :: [Vec.Vector Double] -> [IntMap.IntMap Double]
  57     vecs2maps = map (IntMap.fromList . (zip [1..]) . Vec.toList)
  58
  59
  60 predictList :: Model -> [Vec.Vector Double] -> IO [Maybe ListType]
  61 predictList (ModelSVM m _ _) vs = map (fromListTypeId . round) <$> predict m vs
  62
  63 ------------------------------------------------------------------------
  64 data Model = ModelSVM { modelSVM :: SVM.Model
  65                       , param1 :: Maybe Double
  66                       , param2 :: Maybe Double
  67                       }
  68 --{-
  69 instance SaveFile Model
  70   where
  71     saveFile' fp (ModelSVM m _ _) = SVM.saveModel m fp
  72
  73 instance ReadFile Model
  74   where
  75     readFile' fp = do
  76       m <- SVM.loadModel fp
  77       pure $ ModelSVM m Nothing Nothing
  78 --}
  79 ------------------------------------------------------------------------
  80 -- | TODO
  81 -- shuffle list
  82 -- split list : train / test
  83 -- grid parameters on best result on test
  84
  85 type Train = Map ListType [Vec.Vector Double]
  86 type Tests = Map ListType [Vec.Vector Double]
  87 type Score = Double
  88 type Param = Double
  89
  90 grid :: (MonadReader env m, MonadIO m, HasSettings env)
  91      => Param -> Param -> Train -> [Tests] -> m (Maybe Model)
  92 grid _ _ _ []  = panic "Gargantext.Text.List.Learn.grid : empty test data"
  93 grid s e tr te = do
  94   let
  95     grid' :: (MonadReader env m, MonadIO m, HasSettings env)
  96           => Double -> Double
  97           -> Train
  98           -> [Tests]
  99           -> m (Score, Model)
 100     grid' x y tr' te' = do
 101       model'' <- liftIO $ trainList x y tr'
 102
 103       let
 104         model' = ModelSVM model'' (Just x) (Just y)
 105
 106         score' :: [(ListType, Maybe ListType)] -> Map (Maybe Bool) Int
 107         score' = occurrencesWith (\(a,b) -> (==) <$> Just a <*> b)
 108
 109         score'' :: Map (Maybe Bool) Int -> Double
 110         score'' m'' = maybe 0 (\t -> (fromIntegral t)/total) (Map.lookup (Just True) m'')
 111           where
 112             total = fromIntegral $ foldl (+) 0 $ Map.elems m''
 113
 114         getScore m t = do
 115           let (res, toGuess) = List.unzip
 116                              $ List.concat
 117                              $ map (\(k,vs) -> zip (repeat k) vs)
 118                              $ Map.toList t
 119
 120           res' <- liftIO $ predictList m toGuess
 121           pure $ score'' $ score' $ List.zip res res'
 122
 123       score <- mapM (getScore model') te'
 124       pure (mean score, model')
 125
 126   r <- head . List.reverse
 127             . (List.sortOn fst)
 128            <$> mapM (\(x,y) -> grid' x y tr te)
 129                          [(x,y) | x <- [s..e], y <- [s..e]]
 130
 131   printDebug "GRID SEARCH" (map fst r)
 132   --printDebug "file" fp
 133   --fp <- saveFile (ModelSVM model')
 134   --save best result
 135   pure $ snd <$> r
 136
 137
 138