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haskell-gargantext
Commit 0ce69ad8 authored by Alexandre Delanoë's avatar Alexandre Delanoë
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[FIX] Node size back

parent 92951126
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Showing with 110 additions and 48 deletions
bin/build
View file @ 0ce69ad8
#!/bin/bash
stack build --profile # --test # --haddock
stack build # --profile # --test # --haddock
bin/install
View file @ 0ce69ad8
#!/bin/bash
stack install --profile # --test --haddock
stack install #--profile # --test --haddock
src/Gargantext/Viz/Graph.hs
View file @ 0ce69ad8
......@@ -116,8 +116,8 @@ instance ToSchema GraphMetadata where
makeLenses ''GraphMetadata
data Graph = Graph { _graph_nodes :: [Node]
, _graph_edges :: [Edge]
data Graph = Graph { _graph_nodes :: [Node]
, _graph_edges :: [Edge]
, _graph_metadata :: Maybe GraphMetadata
}
deriving (Show, Generic)
......
src/Gargantext/Viz/Graph/API.hs
View file @ 0ce69ad8
......@@ -149,7 +149,7 @@ computeGraph cId d nt repo = do
-- TODO split diagonal
myCooc <- Map.filter (>1)
<$> getCoocByNgrams (Diagonal False)
<$> getCoocByNgrams (Diagonal True)
<$> groupNodesByNgrams ngs
<$> getNodesByNgramsOnlyUser cId (lIds <> [lId]) nt (Map.keys ngs)
......
src/Gargantext/Viz/Graph/Distances/Matrice.hs
View file @ 0ce69ad8
......@@ -30,6 +30,7 @@ Implementation use Accelerate library which enables GPU and CPU computation:
module Gargantext.Viz.Graph.Distances.Matrice
where
import qualified Data.Foldable as P (foldl1)
import Debug.Trace (trace)
import Data.Array.Accelerate
import Data.Array.Accelerate.Interpreter (run)
......@@ -137,11 +138,10 @@ divByDiag d mat = zipWith (/) mat (replicate (constant (Z :. (d :: Int) :. All))
-- [ 0.0, 4.0, 7.0,
-- 0.0, 5.0, 8.0,
-- 0.0, 6.0, 9.0]
matMiniMax :: Acc (Matrix Double) -> Acc (Matrix Double)
matMiniMax m = map (\x -> ifThenElse (x > miniMax') x 0) (transpose m)
matMiniMax :: (Elt a, Ord a, P.Num a) => Acc (Matrix a) -> Acc (Matrix a)
matMiniMax m = filterWith' miniMax' (constant 0) m
where
miniMax' = (the $ minimum $ maximum m)
miniMax' = the $ minimum $ maximum m
-- | Filters the matrix with a constant
......@@ -157,10 +157,14 @@ filter' t m = filterWith t 0 m
filterWith :: Double -> Double -> Acc (Matrix Double) -> Acc (Matrix Double)
filterWith t v m = map (\x -> ifThenElse (x > (constant t)) x (constant v)) (transpose m)
filterWith' :: (Elt a, Ord a) => Exp a -> Exp a -> Acc (Matrix a) -> Acc (Matrix a)
filterWith' t v m = map (\x -> ifThenElse (x > t) x v) m
-----------------------------------------------------------------------
-- * Measures of proximity
-- * Metrics of proximity
-----------------------------------------------------------------------
-- ** Conditional distance
......@@ -168,10 +172,10 @@ filterWith t v m = map (\x -> ifThenElse (x > (constant t)) x (constant v)) (tra
-- | Conditional distance (basic version)
--
-- 2 main measures are actually implemented in order to compute the
-- 2 main metrics are actually implemented in order to compute the
-- proximity of two terms: conditional and distributional
--
-- Conditional measure is an absolute measure which reflects
-- Conditional metric is an absolute metric which reflects
-- interactions of 2 terms in the corpus.
measureConditional :: Matrix Int -> Matrix Double
--measureConditional m = run (matMiniMax $ matProba (dim m) $ map fromIntegral $ use m)
......@@ -184,7 +188,7 @@ measureConditional m = run $ matProba (dim m)
-- | Conditional distance (advanced version)
--
-- The conditional measure P(i|j) of 2 terms @i@ and @j@, also called
-- The conditional metric P(i|j) of 2 terms @i@ and @j@, also called
-- "confidence" , is the maximum probability between @i@ and @j@ to see
-- @i@ in the same context of @j@ knowing @j@.
--
......@@ -216,12 +220,12 @@ conditional' m = ( run $ ie $ map fromIntegral $ use m
-----------------------------------------------------------------------
-- ** Distributional Distance
-- | Distributional Distance Measure
-- | Distributional Distance metric
--
-- Distributional measure is a relative measure which depends on the
-- Distributional metric is a relative metric which depends on the
-- selected list, it represents structural equivalence of mutual information.
--
-- The distributional measure P(c) of @i@ and @j@ terms is: \[
-- The distributional metric P(c) of @i@ and @j@ terms is: \[
-- S_{MI} = \frac {\sum_{k \neq i,j ; MI_{ik} >0}^{} \min(MI_{ik},
-- MI_{jk})}{\sum_{k \neq i,j ; MI_{ik}>0}^{}} \]
--
......@@ -242,8 +246,8 @@ conditional' m = ( run $ ie $ map fromIntegral $ use m
--
distributional :: Matrix Int -> Matrix Double
distributional m = run -- $ matMiniMax
-- $ diagNull n
$ ri
$ diagNull n
$ rIJ n
$ filterWith 0 100
$ filter' 0
$ s_mi
......@@ -253,14 +257,14 @@ distributional m = run -- $ matMiniMax
{- push matrix in Accelerate type -}
where
ri :: Acc (Matrix Double) -> Acc (Matrix Double)
ri mat = mat1 -- zipWith (/) mat1 mat2
_ri :: Acc (Matrix Double) -> Acc (Matrix Double)
_ri mat = mat1 -- zipWith (/) mat1 mat2
where
mat1 = matSumCol n $ zipWith min (myMin mat) (myMin $ filterWith 0 100 $ diagNull n $ transpose mat)
mat2 = total mat
mat1 = matSumCol n $ zipWith min (_myMin mat) (_myMin $ filterWith 0 100 $ diagNull n $ transpose mat)
_mat2 = total mat
myMin :: Acc (Matrix Double) -> Acc (Matrix Double)
myMin = replicate (constant (Z :. n :. All)) . minimum
_myMin :: Acc (Matrix Double) -> Acc (Matrix Double)
_myMin = replicate (constant (Z :. n :. All)) . minimum
-- TODO fix NaN
......@@ -303,21 +307,66 @@ nullOf n' dir =
zeros = fill (index2 n n) 0
n = constant n'
in
permute const ones -- (\(unindex2 -> i) -> let Exp (x,y) = i in index2 x y)
( lift1 ( \(Z :. (i :: Exp Int) :. (_j:: Exp Int))
permute const ones ( lift1 ( \(Z :. (i :: Exp Int) :. (_j:: Exp Int))
-> case dir of
MatCol m -> (Z :. i :. m)
MatRow m -> (Z :. m :. i)
Diag -> (Z :. i :. i)
))
)
)
zeros
nullOfWithDiag :: Num a => Dim -> Direction -> Acc (Matrix a)
nullOfWithDiag n dir = zipWith (*) (nullOf n dir) (nullOf n Diag)
rIJ' :: Matrix Int -> Matrix Double
rIJ' m = run $ sumRowMin (dim m) m'
where
m' = (map fromIntegral $ use m)
rIJ :: (Elt a, Ord a, P.Fractional (Exp a), P.Num a)
=> Dim -> Acc (Matrix a) -> Acc (Matrix a)
rIJ n m = matMiniMax $ divide a b
where
a = sumRowMin n m
b = sumColMin n m
divide :: (Elt a, Ord a, P.Fractional (Exp a), P.Num a)
=> Acc (Matrix a) -> Acc (Matrix a) -> Acc (Matrix a)
divide = zipWith divide'
where
divide' a b = ifThenElse (b > (constant 0))
(a / b)
(constant 0)
-- | Nominator
sumRowMin :: (Num a, Ord a) => Dim -> Acc (Matrix a) -> Acc (Matrix a)
sumRowMin n m = {-trace (P.show $ run m') $-} m'
where
m' = reshape (shape m) vs
vs = P.foldl1 (++)
$ P.map (\z -> sumRowMin1 n (constant z) m) [0..n-1]
sumRowMin1 :: (Num a, Ord a) => Dim -> Exp Int -> Acc (Matrix a) -> Acc (Vector a)
sumRowMin1 n x m = trace (P.show (run m,run $ transpose m)) $ m''
where
m'' = sum $ zipWith min (transpose m) m
_m' = zipWith (*) (zipWith (*) (nullOf n (MatCol x)) $ nullOfWithDiag n (MatRow x)) m
-- | Denominator
sumColMin :: (Num a, Ord a) => Dim -> Acc (Matrix a) -> Acc (Matrix a)
sumColMin n m = reshape (shape m) vs
where
vs = P.foldl1 (++)
$ P.map (\z -> sumColMin1 n (constant z) m) [0..n-1]
sumColMin1 :: (Num a) => Dim -> Exp Int -> Acc (Matrix a) -> Acc (Matrix a)
sumColMin1 n x m = zipWith (*) (nullOfWithDiag n (MatCol x)) m
{- | WIP fun with indexes
selfMatrix :: Num a => Dim -> Acc (Matrix a)
......@@ -478,28 +527,39 @@ p_ m = zipWith (/) m (n_ m)
-- | Test perfermance with this matrix
-- TODO : add this in a benchmark folder
distriTest :: Int -> Matrix Double
distriTest n = distributional (matrix n theMatrix)
distriTest n = distributional (theMatrix n)
theMatrix :: Int -> Matrix Int
theMatrix n = matrix n (dataMatrix n)
where
theMatrix | (P.==) n 2 = [ 1, 1
, 1, 2
]
| (P.==) n 3 = [ 1, 1, 2
, 1, 2, 3
, 2, 3, 4
]
| (P.==) n 4 = [ 1, 1, 2, 3
, 1, 2, 3, 4
, 2, 3, 4, 5
, 3, 4, 5, 6
]
| P.otherwise = P.undefined
theResult | (P.==) n 2 = let r = 1.6094379124341003 in [ 0, r, r, 0]
| P.otherwise = [ 1, 1 ]
dataMatrix :: Int -> [Int]
dataMatrix x | (P.==) x 2 = [ 1, 1
, 1, 2
]
| (P.==) x 3 = [ 1, 1, 2
, 1, 2, 3
, 2, 3, 4
]
| (P.==) x 4 = [ 1, 1, 2, 3
, 1, 2, 3, 4
, 2, 3, 4, 5
, 3, 4, 5, 6
]
| P.otherwise = P.undefined
{-
theResult :: Int -> Matrix Double
theResult n | (P.==) n 2 = let r = 1.6094379124341003 in [ 0, r, r, 0]
| P.otherwise = [ 1, 1 ]
-}
colMatrix :: Elt e
=> Int -> [e] -> Acc (Array ((Z :. Int) :. Int) e)
colMatrix n ns = replicate (constant (Z :. (n :: Int) :. All)) v
where
v = use $ vector (P.length ns) ns
-----------------------------------------------------------------------
src/Gargantext/Viz/Graph/Tools.hs
View file @ 0ce69ad8
......@@ -60,7 +60,9 @@ cooc2graph distance threshold myCooc = do
let
(ti, _) = createIndices myCooc
myCooc' = toIndex ti myCooc
matCooc = map2mat 0 (Map.size ti) $ Map.filter (> 1) myCooc'
matCooc = map2mat 0 (Map.size ti)
$ Map.filterWithKey (\(a,b) _ -> a /= b)
$ Map.filter (> 1) myCooc'
distanceMat = measure distance matCooc
distanceMap = Map.filter (> threshold) $ mat2map distanceMat
......
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