src/Gargantext/Viz/Graph/Distances/Conditional.hs

   1 {-|
   2 Module      : Gargantext.Graph.Distances.Conditional
   3 Description :
   4 Copyright   : (c) CNRS, 2017-Present
   5 License     : AGPL + CECILL v3
   6 Maintainer  : team@gargantext.org
   7 Stability   : experimental
   8 Portability : POSIX
   9
  10 Motivation and definition of the @Conditional@ distance.
  11 -}
  12
  13 {-# LANGUAGE BangPatterns      #-}
  14 {-# LANGUAGE NoImplicitPrelude #-}
  15 {-# LANGUAGE FlexibleContexts  #-}
  16 {-# LANGUAGE Strict            #-}
  17 module Gargantext.Viz.Graph.Distances.Conditional
  18   where
  19
  20 import Data.Matrix hiding (identity)
  21
  22 import Data.List (sortOn)
  23
  24 import Data.Map (Map)
  25 import qualified Data.Map as M
  26
  27 import qualified Data.Set as S
  28
  29 import qualified Data.Vector as V
  30
  31 import Gargantext.Prelude
  32 import Gargantext.Viz.Graph.Utils
  33
  34 ------------------------------------------------------------------------
  35 ------------------------------------------------------------------------
  36 -- | Optimisation issue
  37
  38 toBeOptimized :: (Num a, Fractional a, Ord a) => Matrix a -> Matrix a
  39 toBeOptimized m = proba Col m
  40
  41 ------------------------------------------------------------------------
  42 -- | Main Functions
  43 -- Compute the probability from axis
  44 -- x' = x / (sum Col x)
  45 proba :: (Num a, Fractional a) => Axis -> Matrix a -> Matrix a
  46 proba a m = mapOn a (\c x -> x / V.sum (axis a c m)) m
  47
  48
  49 mapOn :: Axis -> (AxisId -> a -> a) -> Matrix a -> Matrix a
  50 mapOn a f m = V.foldl' f'  m (V.enumFromTo 1 (nOf a m))
  51   where
  52     f' m' c = mapOnly a f c m'
  53
  54 mapOnly :: Axis -> (AxisId -> a -> a) -> AxisId -> Matrix a -> Matrix a
  55 mapOnly Col = mapCol
  56 mapOnly Row = mapRow
  57
  58 mapAll :: (a -> a) -> Matrix a -> Matrix a
  59 mapAll f m = mapOn Col (\_ -> f) m
  60
  61
  62 ---------------------------------------------------------------
  63 -- | Compute a distance from axis
  64 -- xs = (sum Col x') - x'
  65 distFromSum :: (Num a, Fractional a)
  66          => Axis -> Matrix a -> Matrix a
  67 distFromSum  a m = mapOn a (\c x -> V.sum (axis a c m) - x) m
  68 ---------------------------------------------------------------
  69 ---------------------------------------------------------------
  70 -- | To compute included/excluded or specific/generic scores
  71 opWith  :: (Fractional a1, Num a1)
  72          => (Matrix a2 -> t -> Matrix a1) -> Matrix a2 -> t -> Matrix a1
  73 opWith op xs ys = mapAll (\x -> x / (2*n -1)) (xs `op` ys)
  74   where
  75     n = fromIntegral $ nOf Col xs
  76 ---------------------------------------------------------------
  77
  78
  79 -------------------------------------------------------
  80 conditional :: (Num a, Fractional a, Ord a) => Matrix a -> Matrix a
  81 conditional m = filterMat (threshold m') m'
  82   where
  83 ------------------------------------------------------------------------
  84     -- | Main Operations
  85     -- x' = x / (sum Col x)
  86     x' = proba Col m
  87
  88 ------------------------------------------------------------------------
  89     -- xs = (sum Col x') - x'
  90     xs = distFromSum Col x'
  91     -- ys = (sum Row x') - x'
  92     ys = distFromSum Row x'
  93
  94 ------------------------------------------------------------------------
  95 --  | Top included or excluded
  96     ie = opWith (+) xs ys
  97 --  ie = ( xs + ys) / (2 * (x.shape[0] - 1))
  98
  99 --  | Top specific or generic
 100     sg = opWith (-) xs ys
 101 --  sg = ( xs - ys) / (2 * (x.shape[0] - 1))
 102
 103     nodes_kept :: [Int]
 104     nodes_kept = take k' $ S.toList
 105                          $ foldl' (\s (n1,n2) -> insert [n1,n2] s) S.empty
 106                          $ map fst
 107                          $ nodes_included k <> nodes_specific k
 108
 109     nodes_included n = take n $ sortOn snd $ toListsWithIndex ie
 110     nodes_specific n = take n $ sortOn snd $ toListsWithIndex sg
 111     insert as s = foldl' (\s' a -> S.insert a s') s as
 112     k' = 2*k
 113     k = 10
 114
 115     dico_nodes :: Map Int Int
 116     dico_nodes     = M.fromList $ zip ([1..] :: [Int]) nodes_kept
 117     --dico_nodes_rev = M.fromList $ zip nodes_kept [1..]
 118
 119     m' = matrix (length nodes_kept)
 120                 (length nodes_kept)
 121                 (\(i,j) -> getElem ((M.!) dico_nodes i) ((M.!) dico_nodes j) x')
 122
 123     threshold m'' = V.minimum $ V.map (\cId -> V.maximum $ getCol cId m'') (V.enumFromTo 1 (nOf Col m''))
 124
 125     filterMat t m''  = mapAll (\x -> filter' t x) m''
 126       where
 127         filter' t' x = case (x >= t') of
 128                         True  -> x
 129                         False -> 0
 130
 131 ------------------------------------------------------------------------