[FIX] graph generation

package.yaml

@@ -158,7 +158,7 @@ library:
   - full-text-search
   - fullstop
   - gargantext-prelude
-  - gargantext-graph >= 0.1.0.0
+    #  - gargantext-graph >= 0.1.0.0
   - graphviz
   - hashable
   - haskell-igraph

src/Gargantext/Core/Viz/Graph/API.hs

@@ -189,8 +189,10 @@ computeGraph cId d nt repo = do
 
   listNgrams <- getListNgrams [lId] nt
 
+  -- graph <- liftBase $ cooc2graphWith Bac d 0 myCooc
   graph <- liftBase $ cooc2graphWith Spinglass d 0 myCooc
   -- saveAsFileDebug "debug/graph" graph
+
   pure $ mergeGraphNgrams graph (Just listNgrams)
 
 

src/Gargantext/Core/Viz/Graph/Bridgeness.hs

@@ -24,7 +24,9 @@ import Data.Maybe (catMaybes)
 import Data.Ord (Down(..))
 import Gargantext.Prelude
 import qualified Data.Map as DM
-import Graph.Types (ClusterNode(..))
+import Gargantext.Core.Viz.Graph.Types (ClusterNode(..))
+
+
 
 ----------------------------------------------------------------------
 type Partitions a = Map (Int, Int) Double -> IO [a]

src/Gargantext/Core/Viz/Graph/Tools.hs

@@ -28,9 +28,9 @@ import Gargantext.Core.Viz.Graph
 import Gargantext.Core.Viz.Graph.Bridgeness (bridgeness, Partitions, ToComId(..))
 import Gargantext.Core.Viz.Graph.Index (createIndices, toIndex, map2mat, mat2map, Index, MatrixShape(..))
 import Gargantext.Core.Viz.Graph.Tools.IGraph (mkGraphUfromEdges, spinglass)
+import Gargantext.Core.Viz.Graph.Types (ClusterNode)
 import Gargantext.Prelude
-import Graph.Types (ClusterNode)
-import qualified Graph.BAC.ProxemyOptim as BAC
+-- import qualified Graph.BAC.ProxemyOptim as BAC
 import IGraph.Random -- (Gen(..))
 import qualified Data.HashMap.Strict      as HashMap
 import qualified Data.List                as List
@@ -43,7 +43,8 @@ import qualified IGraph.Algorithms.Layout as Layout
 
 -------------------------------------------------------------
 defaultClustering :: Map (Int, Int) Double -> IO [ClusterNode]
-defaultClustering x = pure $ BAC.defaultClustering x
+-- defaultClustering x = pure $ BAC.defaultClustering x
+defaultClustering x = spinglass 1 x
 
 -------------------------------------------------------------
 type Threshold = Double
@@ -67,7 +68,7 @@ cooc2graph' distance threshold myCooc
         myCooc' = toIndex ti myCooc
 
 
-data PartitionMethod = Louvain | Spinglass | Bac
+data PartitionMethod = Louvain | Spinglass -- | Bac
 
 -- | coocurrences graph computation
 cooc2graphWith :: PartitionMethod
@@ -75,9 +76,9 @@ cooc2graphWith :: PartitionMethod
                -> Threshold
                -> HashMap (NgramsTerm, NgramsTerm) Int
                -> IO Graph
-cooc2graphWith Louvain   = undefined -- TODO use IGraph bindings
+cooc2graphWith Louvain   = undefined
 cooc2graphWith Spinglass = cooc2graphWith' (spinglass 1)
-cooc2graphWith Bac       = undefined -- cooc2graphWith' BAC.defaultClustering
+-- cooc2graphWith Bac       = cooc2graphWith' (\x -> pure $ BAC.defaultClustering x)
 
 cooc2graph'' :: Ord t => Distance
                       -> Double
@@ -113,9 +114,12 @@ filterByNeighbours threshold distanceMap = filteredMap
 
 
 doDistanceMap :: Distance
-               -> Threshold
-               -> HashMap (NgramsTerm, NgramsTerm) Int
-               -> (Map (Int,Int) Double, Map (Index, Index) Int, Map NgramsTerm Index)
+              -> Threshold
+              -> HashMap (NgramsTerm, NgramsTerm) Int
+              -> ( Map (Int,Int) Double
+                 , Map (Index, Index) Int
+                 , Map NgramsTerm Index
+                 )
 doDistanceMap distance threshold myCooc = (distanceMap, myCooc', ti)
   where
     -- TODO remove below
@@ -125,9 +129,10 @@ doDistanceMap distance threshold myCooc = (distanceMap, myCooc', ti)
     (ti, _) = createIndices theMatrix
     tiSize  = Map.size ti
     myCooc' = toIndex ti theMatrix
+
     matCooc = case distance of  -- Shape of the Matrix
                 Conditional    -> map2mat Triangle 0 tiSize
-                Distributional -> map2mat Square     0 tiSize
+                Distributional -> map2mat Square   0 tiSize
             $ case distance of   -- Removing the Diagonal ?
                 Conditional     -> Map.filterWithKey (\(a,b) _ -> a /= b)
                 Distributional  -> identity
@@ -136,7 +141,8 @@ doDistanceMap distance threshold myCooc = (distanceMap, myCooc', ti)
     similarities = measure distance matCooc
     links = round (let n :: Double = fromIntegral tiSize in n * log n)
 
-    distanceMap = Map.fromList  $ List.take links
+    distanceMap = Map.fromList
+                $ List.take links
                 $ List.sortOn snd
                 $ Map.toList
                 $ case distance of

src/Gargantext/Core/Viz/Graph/Tools/IGraph.hs

@@ -20,7 +20,8 @@ import Data.Singletons (SingI)
 import IGraph hiding (mkGraph, neighbors, edges, nodes, Node, Graph)
 import Protolude
 import Gargantext.Core.Viz.Graph.Index
-import Graph.Types
+-- import Graph.Types
+import Gargantext.Core.Viz.Graph.Types
 import qualified Data.List                   as List
 import qualified IGraph                      as IG
 import qualified IGraph.Algorithms.Clique    as IG

src/Gargantext/Core/Viz/Graph/Types.hs

+{-| Module      : Graph.Types
+Description :
+Copyright   : (c) CNRS, Alexandre Delanoë
+License     : AGPL + CECILL v3
+Maintainer  : contact@gargantext.org
+Stability   : experimental
+Portability : POSIX
+
+-}
+
+module Gargantext.Core.Viz.Graph.Types where
+
+import qualified Data.Graph.Inductive.PatriciaTree as DGIP
+-- import Data.IntMap (IntMap)
+-- import qualified Eigen.Matrix as DenseMatrix
+-- import Eigen.SparseMatrix (SparseMatrix)
+
+--import qualified Data.Matrix.Sparse.Static    as Sparse
+-- import qualified Data.Vector.Unboxed          as VU
+-- import qualified Numeric.LinearAlgebra.Static as Dense
+import Protolude hiding (sum, natVal)
+
+-- | Main Types use in this libray
+
+type Dict = IntMap
+
+-- | Use the optimized version of Graph
+type Graph a b = DGIP.Gr a b
+
+-- | Type for Matrix computation optimizations (with Eigen)
+-- type MatrixD          n = Dense.L n n
+-- type MatrixS          n = Sparse.Matrix n n Double
+
+
+data ClusterNode = ClusterNode
+  { cl_node_id      :: Int
+  , cl_community_id :: Int
+  } deriving Show
+
+