src/Gargantext/Core/Methods/Distances/Accelerate/Conditional.hs

   1 {-|
   2 Module      : Gargantext.Core.Methods.Distances.Accelerate.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 This module aims at implementig distances of terms context by context is
  11 the same referential of corpus.
  12
  13 Implementation use Accelerate library which enables GPU and CPU computation
  14 See Gargantext.Core.Methods.Graph.Accelerate)
  15
  16 -}
  17
  18 {-# LANGUAGE TypeFamilies        #-}
  19 {-# LANGUAGE TypeOperators       #-}
  20 {-# LANGUAGE ScopedTypeVariables #-}
  21 {-# LANGUAGE ViewPatterns        #-}
  22
  23 module Gargantext.Core.Methods.Distances.Accelerate.Conditional
  24   where
  25
  26 -- import qualified Data.Foldable as P (foldl1)
  27 -- import Debug.Trace (trace)
  28 import Data.Array.Accelerate
  29 import Data.Array.Accelerate.Interpreter (run)
  30 import Gargantext.Core.Methods.Matrix.Accelerate.Utils
  31 import Gargantext.Core.Methods.Distances.Accelerate.SpeGen
  32 import qualified Gargantext.Prelude as P
  33
  34
  35 -- * Metrics of proximity
  36 -----------------------------------------------------------------------
  37 -- ** Conditional distance
  38
  39 -- *** Conditional distance (basic)
  40
  41 -- | Conditional distance (basic version)
  42 --
  43 -- 2 main metrics are actually implemented in order to compute the
  44 -- proximity of two terms: conditional and distributional
  45 --
  46 -- Conditional metric is an absolute metric which reflects
  47 -- interactions of 2 terms in the corpus.
  48 measureConditional :: Matrix Int -> Matrix Double
  49 measureConditional m = run $ zipWith (/) m' (matSumCol d m')
  50   where
  51     m' = map fromIntegral (use m)
  52     d  = dim m
  53
  54
  55 -- *** Conditional distance (advanced)
  56
  57 -- | Conditional distance (advanced version)
  58 --
  59 -- The conditional metric P(i|j) of 2 terms @i@ and @j@, also called
  60 -- "confidence" , is the maximum probability between @i@ and @j@ to see
  61 -- @i@ in the same context of @j@ knowing @j@.
  62 --
  63 -- If N(i) (resp. N(j)) is the number of occurrences of @i@ (resp. @j@)
  64 -- in the corpus and _[n_{ij}\] the number of its occurrences we get:
  65 --
  66 -- \[P_c=max(\frac{n_i}{n_{ij}},\frac{n_j}{n_{ij}} )\]
  67 conditional' :: Matrix Int -> (Matrix GenericityInclusion, Matrix SpecificityExclusion)
  68 conditional' m = ( run $ ie $ map fromIntegral $ use m
  69                  , run $ sg $ map fromIntegral $ use m
  70                  )
  71   where
  72     ie :: Acc (Matrix Double) -> Acc (Matrix Double)
  73     ie mat = map (\x -> x / (2*n-1)) $ zipWith (+) (xs mat) (ys mat)
  74     sg :: Acc (Matrix Double) -> Acc (Matrix Double)
  75     sg mat = map (\x -> x / (2*n-1)) $ zipWith (-) (xs mat) (ys mat)
  76
  77     n :: Exp Double
  78     n = P.fromIntegral r
  79
  80     r :: Dim
  81     r = dim m
  82
  83     xs :: Acc (Matrix Double) -> Acc (Matrix Double)
  84     xs mat = zipWith (-) (matSumCol r $ matProba r mat) (matProba r mat)
  85     ys :: Acc (Matrix Double) -> Acc (Matrix Double)
  86     ys mat = zipWith (-) (matSumCol r $ transpose $ matProba r mat) (matProba r mat)
  87