src/Gargantext/Core/Viz/Phylo/TemporalMatching.hs

   1 {-|
   2 Module      : Gargantext.Core.Viz.Phylo.TemporalMatching
   3 Description : Module dedicated to the adaptative temporal matching of a Phylo.
   4 Copyright   : (c) CNRS, 2017-Present
   5 License     : AGPL + CECILL v3
   6 Maintainer  : team@gargantext.org
   7 Stability   : experimental
   8 Portability : POSIX
   9 -}
  10
  11
  12 module Gargantext.Core.Viz.Phylo.TemporalMatching where
  13
  14 import Data.List (concat, splitAt, tail, sortOn, (++), intersect, null, inits, groupBy, scanl, nub, nubBy, union, dropWhile, partition, or, sort, (!!))
  15 import Data.Map  (Map, fromList, elems, restrictKeys, unionWith, findWithDefault, keys, (!), (!?), filterWithKey, singleton, empty, mapKeys, adjust)
  16
  17 import Gargantext.Prelude
  18 import Gargantext.Core.Viz.AdaptativePhylo
  19 import Gargantext.Core.Viz.Phylo.PhyloTools
  20
  21 import Prelude (floor,tan,pi)
  22 import Control.Lens hiding (Level)
  23 import Control.Parallel.Strategies (parList, rdeepseq, using)
  24 import Debug.Trace (trace)
  25
  26 import Text.Printf
  27
  28 import qualified Data.Map as Map
  29 import qualified Data.Set as Set
  30 import qualified Data.Vector as Vector
  31
  32
  33
  34 -------------------
  35 -- | Proximity | --
  36 -------------------
  37
  38
  39 -- | To compute a jaccard similarity between two lists
  40 jaccard :: [Int] -> [Int] -> Double
  41 jaccard inter' union' = ((fromIntegral . length) $ inter') / ((fromIntegral . length) $ union')
  42
  43
  44 -- | Process the inverse sumLog
  45 sumInvLog' :: Double -> Double -> [Double] -> Double
  46 sumInvLog' s nb diago = foldl (\mem occ -> mem + (1 / (log (occ + 1/s) / log (nb + 1/s)))) 0 diago
  47
  48
  49 -- | Process the sumLog
  50 sumLog' :: Double -> Double -> [Double] -> Double
  51 sumLog' s nb diago = foldl (\mem occ -> mem + (log (occ + 1/s) / log (nb + 1/s))) 0 diago
  52
  53
  54 weightedLogJaccard' :: Double -> Double -> Map Int Double -> [Int] -> [Int] -> Double
  55 weightedLogJaccard' sens nbDocs diago ngrams ngrams'
  56   | null ngramsInter           = 0
  57   | ngramsInter == ngramsUnion = 1
  58   | sens == 0    = jaccard ngramsInter ngramsUnion
  59   | sens > 0     = (sumInvLog' sens nbDocs diagoInter) / (sumInvLog' sens nbDocs diagoUnion)
  60   | otherwise    = (sumLog' sens nbDocs diagoInter) / (sumLog' sens nbDocs diagoUnion)
  61   where
  62     --------------------------------------
  63     ngramsInter :: [Int]
  64     ngramsInter = intersect ngrams ngrams'
  65     --------------------------------------
  66     ngramsUnion :: [Int]
  67     ngramsUnion = union ngrams ngrams'
  68     --------------------------------------
  69     diagoInter :: [Double]
  70     diagoInter =  elems $ restrictKeys diago (Set.fromList ngramsInter)
  71     --------------------------------------
  72     diagoUnion :: [Double]
  73     diagoUnion =  elems $ restrictKeys diago (Set.fromList ngramsUnion)
  74     --------------------------------------
  75
  76 -- | Process the weighted similarity between clusters. Adapted from Wang, X., Cheng, Q., Lu, W., 2014. Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks. Scientometrics 101, 1253–1271. https://doi.org/10.1007/s11192-014-1347-y (log added in the formula + pair comparison)
  77 -- tests not conclusive
  78 weightedLogSim' :: Double -> Double -> Map Int Double -> [Int] -> [Int] -> Double
  79 weightedLogSim' sens nbDocs diago ego_ngrams target_ngrams
  80   | null ngramsInter           = 0
  81   | ngramsInter == ngramsUnion = 1
  82   | sens == 0    = jaccard ngramsInter ngramsUnion
  83   | sens > 0     = (sumInvLog' sens nbDocs diagoInter) / minimum [(sumInvLog' sens nbDocs diagoEgo),(sumInvLog' sens nbDocs diagoTarget)]
  84   | otherwise    = (sumLog' sens nbDocs diagoInter) / minimum [(sumLog' sens nbDocs diagoEgo),(sumLog' sens nbDocs diagoTarget)]
  85   where
  86     --------------------------------------
  87     ngramsInter :: [Int]
  88     ngramsInter = intersect ego_ngrams target_ngrams
  89     --------------------------------------
  90     ngramsUnion :: [Int]
  91     ngramsUnion = union ego_ngrams target_ngrams
  92     --------------------------------------
  93     diagoInter :: [Double]
  94     diagoInter =  elems $ restrictKeys diago (Set.fromList ngramsInter)
  95     --------------------------------------
  96     diagoEgo :: [Double]
  97     diagoEgo =  elems $ restrictKeys diago (Set.fromList ego_ngrams)
  98     --------------------------------------
  99     diagoTarget :: [Double]
 100     diagoTarget =  elems $ restrictKeys diago (Set.fromList target_ngrams)
 101     --------------------------------------
 102
 103 toProximity :: Double -> Map Int Double -> Proximity -> [Int] -> [Int] -> [Int] -> Double
 104 -- | To process the proximity between a current group and a pair of targets group using the adapted Wang et al. Similarity
 105 toProximity nbDocs diago proximity egoNgrams targetNgrams targetNgrams' =
 106   case proximity of
 107     WeightedLogJaccard sens ->
 108       let pairNgrams = if targetNgrams == targetNgrams'
 109                           then targetNgrams
 110                           else union targetNgrams targetNgrams'
 111        in weightedLogJaccard' sens nbDocs diago egoNgrams pairNgrams
 112     WeightedLogSim sens ->
 113       let pairNgrams = if targetNgrams == targetNgrams'
 114                           then targetNgrams
 115                           else union targetNgrams targetNgrams'
 116        in weightedLogSim' sens nbDocs diago egoNgrams pairNgrams
 117     Hamming -> undefined
 118
 119 ------------------------
 120 -- | Local Matching | --
 121 ------------------------
 122
 123 findLastPeriod :: Filiation -> [PhyloPeriodId] -> PhyloPeriodId
 124 findLastPeriod fil periods = case fil of
 125     ToParents -> head' "findLastPeriod" (sortOn fst periods)
 126     ToChilds  -> last' "findLastPeriod" (sortOn fst periods)
 127
 128
 129 -- | To filter pairs of candidates related to old pointers periods
 130 removeOldPointers :: [Pointer] -> Filiation -> Double -> Proximity -> PhyloPeriodId
 131                   -> [((PhyloGroupId,[Int]),(PhyloGroupId,[Int]))]
 132                   -> [((PhyloGroupId,[Int]),(PhyloGroupId,[Int]))]
 133 removeOldPointers oldPointers fil thr prox prd pairs
 134   | null oldPointers = pairs
 135   | null (filterPointers prox thr oldPointers) =
 136     let lastMatchedPrd = findLastPeriod fil (map (fst . fst . fst) oldPointers)
 137      in if lastMatchedPrd == prd
 138         then []
 139         else filter (\((id,_),(id',_)) ->
 140                 case fil of
 141                      ToParents -> (((fst . fst . fst) id ) < (fst lastMatchedPrd))
 142                                || (((fst . fst . fst) id') < (fst lastMatchedPrd))
 143                      ToChilds  -> (((fst . fst . fst) id ) > (fst lastMatchedPrd))
 144                                || (((fst . fst . fst) id') > (fst lastMatchedPrd))) pairs
 145   | otherwise = []
 146
 147
 148 makePairs' :: (PhyloGroupId,[Int]) -> [(PhyloGroupId,[Int])] -> [PhyloPeriodId] -> [Pointer] -> Filiation -> Double -> Proximity
 149            -> Map Date Double -> Map Date Cooc -> [((PhyloGroupId,[Int]),(PhyloGroupId,[Int]))]
 150 makePairs' (egoId, egoNgrams) candidates periods oldPointers fil thr prox docs diagos =
 151     if (null periods)
 152         then []
 153         else removeOldPointers oldPointers fil thr prox lastPrd
 154            {- at least on of the pair candidates should be from the last added period -}
 155            $ filter (\((id,_),(id',_)) -> ((fst . fst) id == lastPrd) || ((fst . fst) id' == lastPrd))
 156            $ listToKeys
 157            $ filter (\(id,ngrams) ->
 158                 let nbDocs = (sum . elems) $ filterDocs docs    ([(fst . fst) egoId, (fst . fst) id])
 159                     diago  = reduceDiagos  $ filterDiago diagos ([(fst . fst) egoId, (fst . fst) id])
 160                  in (toProximity nbDocs diago prox egoNgrams egoNgrams ngrams) >= thr
 161             ) candidates
 162     where
 163       lastPrd :: PhyloPeriodId
 164       lastPrd = findLastPeriod fil periods
 165
 166
 167 filterPointers :: Proximity -> Double -> [Pointer] -> [Pointer]
 168 filterPointers proxi thr pts = filter (\(_,w) -> filterProximity proxi thr w) pts
 169
 170 filterPointers' :: Proximity -> Double -> [(Pointer,[Int])] -> [(Pointer,[Int])]
 171 filterPointers' proxi thr pts = filter (\((_,w),_) -> filterProximity proxi thr w) pts
 172
 173
 174 reduceDiagos :: Map Date Cooc -> Map Int Double
 175 reduceDiagos diagos = mapKeys (\(k,_) -> k)
 176                     $ foldl (\acc diago -> unionWith (+) acc diago) empty (elems diagos)
 177
 178 filterPointersByPeriod :: Filiation -> [(Pointer,[Int])] -> [Pointer]
 179 filterPointersByPeriod fil pts =
 180   let pts' = sortOn (fst . fst . fst . fst) pts
 181       inf  = (fst . fst . fst . fst) $ head' "filterPointersByPeriod" pts'
 182       sup  = (fst . fst . fst . fst) $ last' "filterPointersByPeriod" pts'
 183    in map fst
 184     $ nubBy (\pt pt' -> snd pt == snd pt')
 185     $ filter (\pt -> ((fst . fst . fst . fst) pt == inf) || ((fst . fst . fst . fst) pt == sup))
 186     $ case fil of
 187         ToParents -> reverse pts'
 188         ToChilds  -> pts'
 189
 190 phyloGroupMatching :: [[(PhyloGroupId,[Int])]] -> Filiation -> Proximity -> Map Date Double -> Map Date Cooc
 191                    -> Double -> [Pointer] -> (PhyloGroupId,[Int]) -> [Pointer]
 192 phyloGroupMatching candidates fil proxi docs diagos thr oldPointers (id,ngrams) =
 193         if (null $ filterPointers proxi thr oldPointers)
 194           {- let's find new pointers -}
 195           then if null nextPointers
 196             then []
 197             else filterPointersByPeriod fil
 198                $ head' "phyloGroupMatching"
 199                -- Keep only the best set of pointers grouped by proximity
 200                $ groupBy (\pt pt' -> (snd . fst) pt == (snd . fst) pt')
 201                $ reverse $ sortOn (snd . fst) $ head' "pointers" nextPointers
 202                -- Find the first time frame where at leats one pointer satisfies the proximity threshold
 203           else oldPointers
 204     where
 205         nextPointers :: [[(Pointer,[Int])]]
 206         nextPointers = take 1
 207                  $ dropWhile (null)
 208                  {- for each time frame, process the proximity on relevant pairs of targeted groups -}
 209                  $ scanl (\acc groups ->
 210                             let periods = nub $ map (fst . fst . fst) $ concat groups
 211                                 nbdocs  = sum $ elems $ (filterDocs docs ([(fst . fst) id] ++ periods))
 212                                 diago   = reduceDiagos
 213                                         $ filterDiago diagos ([(fst . fst) id] ++ periods)
 214                                         {- important resize nbdocs et diago dans le make pairs -}
 215                                 pairs = makePairs' (id,ngrams) (concat groups) periods oldPointers fil thr proxi docs diagos
 216                             in acc ++ ( filterPointers' proxi thr
 217                                         $ concat
 218                                         $ map (\(c,c') ->
 219                                             {- process the proximity between the current group and a pair of candidates -}
 220                                             let proximity = toProximity nbdocs diago proxi ngrams (snd c) (snd c')
 221                                             in if ((c == c') || (snd c == snd c'))
 222                                                then [((fst c,proximity),snd c)]
 223                                                else [((fst c,proximity),snd c),((fst c',proximity),snd c')] ) pairs )) []
 224                  $ inits candidates -- groups from [[1900],[1900,1901],[1900,1901,1902],...]
 225
 226
 227 filterDocs :: Map Date Double -> [PhyloPeriodId] -> Map Date Double
 228 filterDocs d pds = restrictKeys d $ periodsToYears pds
 229
 230 filterDiago :: Map Date Cooc -> [PhyloPeriodId] -> Map Date Cooc
 231 filterDiago diago pds = restrictKeys diago $ periodsToYears pds
 232
 233
 234 -----------------------------
 235 -- | Matching Processing | --
 236 -----------------------------
 237
 238
 239 getNextPeriods :: Filiation -> Int -> PhyloPeriodId -> [PhyloPeriodId] -> [PhyloPeriodId]
 240 getNextPeriods fil max' pId pIds =
 241     case fil of
 242         ToChilds  -> take max' $ (tail . snd) $ splitAt (elemIndex' pId pIds) pIds
 243         ToParents -> take max' $ (reverse . fst) $ splitAt (elemIndex' pId pIds) pIds
 244
 245
 246 getCandidates :: PhyloGroup -> [[(PhyloGroupId,[Int])]] -> [[(PhyloGroupId,[Int])]]
 247 getCandidates ego targets =
 248   map (\groups' ->
 249     filter (\g' -> (not . null) $ intersect (ego ^. phylo_groupNgrams) (snd g')
 250   ) groups') targets
 251
 252
 253 matchGroupsToGroups :: Int -> [PhyloPeriodId] -> Proximity -> Double -> Map Date Double -> Map Date Cooc -> [PhyloGroup] -> [PhyloGroup]
 254 matchGroupsToGroups frame periods proximity thr docs coocs groups =
 255   let groups' = groupByField _phylo_groupPeriod groups
 256    in foldl' (\acc prd ->
 257         let -- 1) find the parents/childs matching periods
 258             periodsPar = getNextPeriods ToParents frame prd periods
 259             periodsChi = getNextPeriods ToChilds  frame prd periods
 260             --  2) find the parents/childs matching candidates
 261             candidatesPar = map (\prd' -> map (\g -> (getGroupId g, g ^. phylo_groupNgrams)) $ findWithDefault [] prd' groups') periodsPar
 262             candidatesChi = map (\prd' -> map (\g -> (getGroupId g, g ^. phylo_groupNgrams)) $ findWithDefault [] prd' groups') periodsChi
 263             --  3) find the parents/child number of docs by years
 264             docsPar = filterDocs docs ([prd] ++ periodsPar)
 265             docsChi = filterDocs docs ([prd] ++ periodsChi)
 266             --  4) find the parents/child diago by years
 267             diagoPar = filterDiago (map coocToDiago coocs) ([prd] ++ periodsPar)
 268             diagoChi = filterDiago (map coocToDiago coocs) ([prd] ++ periodsPar)
 269             --  5) match in parallel all the groups (egos) to their possible candidates
 270             egos  = map (\ego ->
 271                       let pointersPar = phyloGroupMatching (getCandidates ego candidatesPar) ToParents proximity docsPar diagoPar
 272                                         thr (getPeriodPointers ToParents ego) (getGroupId ego, ego ^. phylo_groupNgrams)
 273                           pointersChi = phyloGroupMatching (getCandidates ego candidatesChi) ToChilds  proximity docsChi diagoChi
 274                                         thr (getPeriodPointers ToChilds  ego) (getGroupId ego, ego ^. phylo_groupNgrams)
 275                        in addPointers ToChilds  TemporalPointer pointersChi
 276                         $ addPointers ToParents TemporalPointer pointersPar ego)
 277                   $ findWithDefault [] prd groups'
 278             egos' = egos `using` parList rdeepseq
 279          in acc ++ egos'
 280     ) [] periods
 281
 282
 283 -----------------------
 284 -- | Phylo Quality | --
 285 -----------------------
 286
 287
 288 relevantBranches :: Int -> [[PhyloGroup]] -> [[PhyloGroup]]
 289 relevantBranches term branches =
 290     filter (\groups -> (any (\group -> elem term $ group ^. phylo_groupNgrams) groups)) branches
 291
 292 accuracy :: Int -> [(Date,Date)] -> [PhyloGroup] -> Double
 293 -- The accuracy of a branch relatively to a term x is computed only over the periods there exist some cluster mentionning x in the phylomemy
 294 accuracy x periods bk  = ((fromIntegral $ length $ filter (\g -> elem x $ g ^. phylo_groupNgrams) bk')
 295                /  (fromIntegral $ length bk'))
 296   where
 297     bk' :: [PhyloGroup]
 298     bk' = filter (\g -> elem (g ^. phylo_groupPeriod) periods) bk
 299
 300 recall :: Int -> [PhyloGroup] -> [[PhyloGroup]] -> Double
 301 recall x bk bx = ((fromIntegral $ length $ filter (\g -> elem x $ g ^. phylo_groupNgrams) bk)
 302                /  (fromIntegral $ length $ filter (\g -> elem x $ g ^. phylo_groupNgrams) $ concat bx))
 303
 304 fScore :: Double -> Int -> [(Date,Date)] -> [PhyloGroup] -> [[PhyloGroup]] -> Double
 305 fScore lambda x periods bk bx =
 306   let rec = recall x bk bx
 307       acc = accuracy x periods bk
 308    in ((1 + lambda ** 2) * acc * rec)
 309     / (((lambda ** 2) * acc  + rec))
 310
 311
 312 wk :: [PhyloGroup] -> Double
 313 wk bk = fromIntegral $ length bk
 314
 315
 316 toPhyloQuality' :: Double -> Map Int Double -> [[PhyloGroup]] -> Double
 317 toPhyloQuality' lambda freq branches =
 318   if (null branches)
 319     then 0
 320     else sum
 321        $ map (\i ->
 322           let bks = relevantBranches i branches
 323               periods = nub $ map _phylo_groupPeriod $ filter (\g -> elem i $ g ^. phylo_groupNgrams) $ concat bks
 324            in (freq ! i) * (sum $ map (\bk -> ((wk bk) / (sum $ map wk bks)) * (fScore lambda i periods bk bks)) bks))
 325        $ keys freq
 326
 327 toRecall :: Map Int Double -> [[PhyloGroup]] -> Double
 328 toRecall freq branches =
 329   if (null branches)
 330     then 0
 331     else sum
 332        $ map (\x ->
 333           let px = freq ! x
 334               bx = relevantBranches x branches
 335               wks = sum $ map wk bx
 336            in (px / pys) * (sum $ map (\bk -> ((wk bk) / wks) * (recall x bk bx)) bx))
 337        $ keys freq
 338   where
 339       pys :: Double
 340       pys = sum (elems freq)
 341
 342
 343 toAccuracy :: Map Int Double -> [[PhyloGroup]] -> Double
 344 toAccuracy freq branches =
 345   if (null branches)
 346     then 0
 347     else sum
 348        $ map (\x ->
 349           let px = freq ! x
 350               bx = relevantBranches x branches
 351               -- | periods containing x
 352               periods = nub $ map _phylo_groupPeriod $ filter (\g -> elem x $ g ^. phylo_groupNgrams) $ concat bx
 353               wks = sum $ map wk bx
 354            in (px / pys) * (sum $ map (\bk -> ((wk bk) / wks) * (accuracy x periods bk)) bx))
 355        $ keys freq
 356   where
 357       pys :: Double
 358       pys = sum (elems freq)
 359
 360
 361 -- | here we do the average of all the local f_scores
 362 toPhyloQuality :: Double -> Double -> Map Int Double -> [[PhyloGroup]] -> Double
 363 toPhyloQuality fdt lambda freq branches =
 364   if (null branches)
 365     then 0
 366     else sum
 367        $ map (\x ->
 368         -- let px = freq ! x
 369         let bx = relevantBranches x branches
 370             -- | periods containing x
 371             periods = nub $ map _phylo_groupPeriod $ filter (\g -> elem x $ g ^. phylo_groupNgrams) $ concat bx
 372             wks = sum $ map wk bx
 373               -- in (px / pys) * (sum $ map (\bk -> ((wk bk) / wks) * (fScore beta x bk bx)) bx))
 374               -- in (1 / fdt) *  (sum $ map (\bk -> ((wk bk) / wks) * (fScore beta x periods bk bx)) bx))
 375          in (1 / fdt) *  (sum $ map (\bk -> ((wk bk) / wks) * (fScore (tan (lambda * pi / 2)) x periods bk bx)) bx))
 376        $ keys freq
 377   -- where
 378     --  pys :: Double
 379     --  pys = sum (elems freq)
 380
 381 -- 1 / nb de foundation
 382
 383 ------------------------------------
 384 -- | Constant Temporal Matching | --
 385 ------------------------------------
 386
 387
 388 groupsToBranches' :: Map PhyloGroupId PhyloGroup -> [[PhyloGroup]]
 389 groupsToBranches' groups =
 390     {- run the related component algorithm -}
 391     let egos = groupBy (\gs gs' -> (fst $ fst $ head' "egos" gs) == (fst $ fst $ head' "egos" gs'))
 392              $ sortOn  (\gs -> fst $ fst $ head' "egos" gs)
 393              $ map (\group -> [getGroupId group]
 394                             ++ (map fst $ group ^. phylo_groupPeriodParents)
 395                             ++ (map fst $ group ^. phylo_groupPeriodChilds) ) $ elems groups
 396         --  first find the related components by inside each ego's period
 397         --  a supprimer
 398         graph' = map relatedComponents egos
 399         --  then run it for the all the periods
 400         graph  = zip [1..]
 401                $ relatedComponents $ concat (graph' `using` parList rdeepseq)
 402     --  update each group's branch id
 403     in map (\(bId,ids) ->
 404         let groups'  = map (\group -> group & phylo_groupBranchId %~ (\(lvl,lst) -> (lvl,lst ++ [bId])))
 405                      $ elems $ restrictKeys groups (Set.fromList ids)
 406          in groups' `using` parList rdeepseq ) graph
 407
 408
 409 reduceFrequency :: Map Int Double -> [[PhyloGroup]] -> Map Int Double
 410 reduceFrequency frequency branches =
 411   restrictKeys frequency (Set.fromList $ (nub . concat) $ map _phylo_groupNgrams $ concat branches)
 412
 413 updateThr :: Double -> [[PhyloGroup]] -> [[PhyloGroup]]
 414 updateThr thr branches = map (\b -> map (\g ->
 415   g & phylo_groupMeta .~ (singleton "seaLevels" (((g ^. phylo_groupMeta) ! "seaLevels") ++ [thr]))) b) branches
 416
 417
 418 --  Sequentially break each branch of a phylo where
 419 -- done = all the allready broken branches
 420 -- ego  = the current branch we want to break
 421 -- rest = the branches we still have to break
 422 breakBranches :: Double -> Proximity -> Double -> Map Int Double -> Int -> Double -> Double -> Double
 423               -> Int -> Map Date Double -> Map Date Cooc -> [PhyloPeriodId] -> [([PhyloGroup],Bool)] -> ([PhyloGroup],Bool) -> [([PhyloGroup],Bool)] -> [([PhyloGroup],Bool)]
 424 breakBranches fdt proximity lambda frequency minBranch thr depth elevation frame docs coocs periods done ego rest =
 425   --  1) keep or not the new division of ego
 426   let done' = done ++ (if snd ego
 427                         then
 428                             (if ((null (fst ego')) || (quality > quality'))
 429                                then
 430                                 -- trace ("  ✗ F(β) = " <> show(quality) <> " (vs) " <> show(quality')
 431                                 --         <> "  | "  <> show(length $ fst ego) <> " groups : "
 432                                 --         <> "  |✓ " <> show(length $ fst ego') <> show(map length $ fst ego')
 433                                 --         <> "  |✗ " <> show(length $ snd ego') <> "[" <> show(length $ concat $ snd ego') <> "]")
 434                                   [(fst ego,False)]
 435                                else
 436                                 -- trace ("  ✓ level = " <> printf "%.1f" thr <> "")
 437                                 -- trace ("  ✓ F(β) = " <> show(quality) <> " (vs) " <> show(quality')
 438                                 --         <> "  | "  <> show(length $ fst ego) <> " groups : "
 439                                 --         <> "  |✓ " <> show(length $ fst ego') <> show(map length $ fst ego')
 440                                 --         <> "  |✗ " <> show(length $ snd ego') <> "[" <> show(length $ concat $ snd ego') <> "]")
 441                                   ((map (\e -> (e,True)) (fst ego')) ++ (map (\e -> (e,False)) (snd ego'))))
 442                         else [ego])
 443   in
 444     --  2) if there is no more branches in rest then return else continue
 445     if null rest
 446       then done'
 447       else breakBranches fdt proximity lambda frequency minBranch thr depth elevation frame docs coocs periods
 448                        done' (head' "breakBranches" rest) (tail' "breakBranches" rest)
 449   where
 450     --------------------------------------
 451     quality :: Double
 452     quality = toPhyloQuality fdt lambda frequency ((map fst done) ++ [fst ego] ++ (map fst rest))
 453     --------------------------------------
 454     ego' :: ([[PhyloGroup]],[[PhyloGroup]])
 455     ego' =
 456       let branches  = groupsToBranches' $ fromList $ map (\g -> (getGroupId g, g))
 457                     $ matchGroupsToGroups frame periods proximity thr docs coocs (fst ego)
 458           branches' = branches `using` parList rdeepseq
 459        in partition (\b -> (length $ nub $ map _phylo_groupPeriod b) >= minBranch)
 460         $ thrToMeta thr
 461         $ depthToMeta (elevation - depth) branches'
 462     --------------------------------------
 463     quality' :: Double
 464     quality' = toPhyloQuality fdt lambda frequency
 465                                     ((map fst done) ++ (fst ego') ++ (snd ego') ++ (map fst rest))
 466
 467
 468 seaLevelMatching :: Double -> Proximity -> Double -> Int -> Map Int Double -> Double -> Double -> Double -> Double
 469                  -> Int -> [PhyloPeriodId] -> Map Date Double -> Map Date Cooc -> [([PhyloGroup],Bool)] -> [([PhyloGroup],Bool)]
 470 seaLevelMatching fdt proximity lambda minBranch frequency thr step depth elevation frame periods docs coocs branches =
 471   --  if there is no branch to break or if seaLvl level > 1 then end
 472   if (thr >= 1) || ((not . or) $ map snd branches)
 473     then branches
 474     else
 475       -- break all the possible branches at the current seaLvl level
 476       let quality    = toPhyloQuality fdt lambda frequency (map fst branches)
 477           acc        = toAccuracy frequency (map fst branches)
 478           rec        = toRecall frequency (map fst branches)
 479           branches'  = trace ("↑ level = " <> printf "%.3f" thr <> " F(λ) = " <> printf "%.5f" quality
 480                                                                 <> " ξ = " <> printf "%.5f" acc
 481                                                                 <> " ρ = " <> printf "%.5f" rec
 482                                                                 <> " branches = " <> show(length branches) <> " ↴")
 483                      $ breakBranches fdt proximity lambda frequency minBranch thr depth elevation frame docs coocs periods
 484                                      [] (head' "seaLevelMatching" branches) (tail' "seaLevelMatching" branches)
 485           frequency' = reduceFrequency frequency (map fst branches')
 486        in seaLevelMatching fdt proximity lambda minBranch frequency' (thr + step) step (depth - 1) elevation frame periods docs coocs branches'
 487
 488
 489 constanteTemporalMatching :: Double -> Double -> Phylo -> Phylo
 490 constanteTemporalMatching start step phylo = updatePhyloGroups 1
 491                          (fromList $ map (\g -> (getGroupId g,g)) $ traceMatchEnd $ concat branches)
 492                          (toPhyloHorizon phylo)
 493   where
 494     --  2) process the temporal matching by elevating seaLvl level
 495     branches :: [[PhyloGroup]]
 496     branches = map fst
 497              $ seaLevelMatching (fromIntegral $ Vector.length $ getRoots phylo)
 498                                 (phyloProximity $ getConfig phylo)
 499                                 (_qua_granularity $ phyloQuality $ getConfig phylo)
 500                                 (_qua_minBranch $ phyloQuality $ getConfig phylo)
 501                                 (phylo ^. phylo_termFreq)
 502                                 start step
 503                                 ((((1 - start) / step) - 1))
 504                                 (((1 - start) / step))
 505                                 (getTimeFrame $ timeUnit $ getConfig phylo)
 506                                 (getPeriodIds phylo)
 507                                 (phylo ^. phylo_timeDocs)
 508                                 (phylo ^. phylo_timeCooc)
 509                                 (reverse $ sortOn (length . fst) groups)
 510     --  1) for each group process an initial temporal Matching
 511     --  here we suppose that all the groups of level 1 are part of the same big branch
 512     groups :: [([PhyloGroup],Bool)]
 513     groups = map (\b -> (b,(length $ nub $ map _phylo_groupPeriod b) >= (_qua_minBranch $ phyloQuality $ getConfig phylo)))
 514            $ groupsToBranches' $ fromList $ map (\g -> (getGroupId g, g))
 515            $ matchGroupsToGroups (getTimeFrame $ timeUnit $ getConfig phylo)
 516                          (getPeriodIds phylo) (phyloProximity $ getConfig phylo)
 517                          start
 518                          (phylo ^. phylo_timeDocs)
 519                          (phylo ^. phylo_timeCooc)
 520                          (traceTemporalMatching $ getGroupsFromLevel 1 phylo)
 521
 522 -----------------
 523 -- | Horizon | --
 524 -----------------
 525
 526 toPhyloHorizon :: Phylo -> Phylo
 527 toPhyloHorizon phylo =
 528   let t0 = take 1 (getPeriodIds phylo)
 529       groups = getGroupsFromLevelPeriods 1 t0 phylo
 530       sens = getSensibility (phyloProximity $ getConfig phylo)
 531       nbDocs = sum $ elems $ filterDocs (phylo ^. phylo_timeDocs) t0
 532       diago = reduceDiagos $ filterDiago (phylo ^. phylo_timeCooc) t0
 533    in phylo & phylo_horizon .~ (fromList $ map (\(g,g') ->
 534         ((getGroupId g,getGroupId g'),weightedLogJaccard' sens nbDocs diago (g ^. phylo_groupNgrams) (g' ^. phylo_groupNgrams))) $ listToCombi' groups)
 535
 536
 537 --------------------------------------
 538 -- | Adaptative Temporal Matching | --
 539 --------------------------------------
 540
 541
 542 thrToMeta :: Double -> [[PhyloGroup]] -> [[PhyloGroup]]
 543 thrToMeta thr branches =
 544   map (\b ->
 545     map (\g -> g & phylo_groupMeta .~ (adjust (\lst -> lst ++ [thr]) "seaLevels" (g ^. phylo_groupMeta))) b) branches
 546
 547 depthToMeta :: Double -> [[PhyloGroup]] -> [[PhyloGroup]]
 548 depthToMeta depth branches =
 549   let break = length branches > 1
 550    in map (\b ->
 551         map (\g ->
 552           if break then g & phylo_groupMeta .~ (adjust (\lst -> lst ++ [depth]) "breaks"(g ^. phylo_groupMeta))
 553                    else g) b) branches
 554
 555 reduceTupleMapByKeys :: Eq a => [a] -> Map (a,a) Double -> Map (a,a) Double
 556 reduceTupleMapByKeys ks m = filterWithKey (\(k,k') _ -> (elem k ks) && (elem k' ks)) m
 557
 558
 559 getInTupleMap :: Ord a => Map (a,a) Double -> a -> a -> Double
 560 getInTupleMap m k k'
 561   | isJust (m !? ( k ,k')) = m ! ( k ,k')
 562   | isJust (m !? ( k',k )) = m ! ( k',k )
 563   | otherwise = 0
 564
 565
 566 toThreshold :: Double -> Map (PhyloGroupId,PhyloGroupId) Double -> Double
 567 toThreshold lvl proxiGroups =
 568   let idx = ((Map.size proxiGroups) `div` (floor lvl)) - 1
 569    in if idx >= 0
 570         then (sort $ elems proxiGroups) !! idx
 571         else 1
 572
 573
 574 -- done = all the allready broken branches
 575 -- ego  = the current branch we want to break
 576 -- rest = the branches we still have to break
 577 adaptativeBreakBranches :: Double -> Proximity -> Double -> Double -> Map (PhyloGroupId,PhyloGroupId) Double
 578                -> Double -> Map Int Double -> Int -> Int -> Map Date Double -> Map Date Cooc
 579                -> [PhyloPeriodId] -> [([PhyloGroup],(Bool,[Double]))] -> ([PhyloGroup],(Bool,[Double])) -> [([PhyloGroup],(Bool,[Double]))]
 580                -> [([PhyloGroup],(Bool,[Double]))]
 581 adaptativeBreakBranches fdt  proxiConf depth elevation groupsProxi lambda frequency minBranch frame docs coocs periods done ego rest =
 582   --  1) keep or not the new division of ego
 583   let done' = done ++ (if (fst . snd) ego
 584                         then (if ((null (fst ego')) || (quality > quality'))
 585                                then
 586                                   [(concat $ thrToMeta thr $ [fst ego],(False, ((snd . snd) ego)))]
 587                                else
 588                                   (  (map (\e -> (e,(True,  ((snd . snd) ego) ++ [thr]))) (fst ego'))
 589                                   ++ (map (\e -> (e,(False, ((snd . snd) ego)))) (snd ego'))))
 590                         else [(concat $ thrToMeta thr $ [fst ego], snd ego)])
 591   in
 592     --  uncomment let .. in for debugging
 593     -- let part1 = partition (snd) done'
 594     --     part2 = partition (snd) rest
 595     --  in trace ( "[✓ " <> show(length $ fst part1) <> "(" <> show(length $ concat $ map (fst) $ fst part1) <> ")|✗ " <> show(length $ snd part1) <> "(" <> show(length $ concat $ map (fst) $ snd part1) <> ")] "
 596     --          <> "[✓ " <> show(length $ fst part2) <> "(" <> show(length $ concat $ map (fst) $ fst part2) <> ")|✗ " <> show(length $ snd part2) <> "(" <> show(length $ concat $ map (fst) $ snd part2) <> ")]"
 597     --            ) $
 598     --  2) if there is no more branches in rest then return else continue
 599     if null rest
 600       then done'
 601       else adaptativeBreakBranches fdt proxiConf depth elevation groupsProxi lambda frequency minBranch frame docs coocs periods
 602                        done' (head' "breakBranches" rest) (tail' "breakBranches" rest)
 603   where
 604     --------------------------------------
 605     thr :: Double
 606     thr = toThreshold depth $ Map.filter (\v -> v > (last' "breakBranches" $ (snd . snd) ego)) $ reduceTupleMapByKeys (map getGroupId $ fst ego) groupsProxi
 607     --------------------------------------
 608     quality :: Double
 609     quality = toPhyloQuality fdt lambda frequency ((map fst done) ++ [fst ego] ++ (map fst rest))
 610     --------------------------------------
 611     ego' :: ([[PhyloGroup]],[[PhyloGroup]])
 612     ego' =
 613       let branches  = groupsToBranches' $ fromList $ map (\g -> (getGroupId g, g))
 614                     $ matchGroupsToGroups frame periods proxiConf thr docs coocs (fst ego)
 615           branches' = branches `using` parList rdeepseq
 616        in partition (\b -> (length $ nub $ map _phylo_groupPeriod b) > minBranch)
 617         $ thrToMeta thr
 618         $ depthToMeta (elevation - depth) branches'
 619     --------------------------------------
 620     quality' :: Double
 621     quality' = toPhyloQuality fdt lambda frequency
 622                                     ((map fst done) ++ (fst ego') ++ (snd ego') ++ (map fst rest))
 623
 624
 625 adaptativeSeaLevelMatching :: Double -> Proximity -> Double -> Double -> Map (PhyloGroupId, PhyloGroupId) Double
 626                   -> Double -> Int -> Map Int Double
 627                   -> Int -> [PhyloPeriodId] -> Map Date Double -> Map Date Cooc
 628                   -> [([PhyloGroup],(Bool,[Double]))] -> [([PhyloGroup],(Bool,[Double]))]
 629 adaptativeSeaLevelMatching fdt proxiConf depth elevation groupsProxi lambda minBranch frequency frame periods docs coocs branches =
 630   --  if there is no branch to break or if seaLvl level >= depth then end
 631   if (Map.null groupsProxi) || (depth <= 0) || ((not . or) $ map (fst . snd) branches)
 632     then branches
 633     else
 634       --  break all the possible branches at the current seaLvl level
 635       let branches'  = adaptativeBreakBranches fdt proxiConf depth elevation groupsProxi lambda frequency minBranch frame docs coocs periods
 636                                       [] (head' "seaLevelMatching" branches) (tail' "seaLevelMatching" branches)
 637           frequency' = reduceFrequency frequency (map fst branches')
 638           groupsProxi' = reduceTupleMapByKeys (map (getGroupId) $ concat $ map (fst) $ filter (fst . snd) branches') groupsProxi
 639           -- thr = toThreshold depth groupsProxi
 640        in trace("\n  " <> foldl (\acc _ -> acc <> "🌊 ") "" [0..(elevation - depth)]
 641                        <> " [✓ " <> show(length $ filter (fst . snd) branches') <> "(" <> show(length $ concat $ map (fst) $ filter (fst . snd) branches')
 642                        <> ")|✗ " <> show(length $ filter (not . fst . snd) branches') <> "(" <> show(length $ concat $ map (fst) $ filter (not . fst . snd) branches') <> ")]"
 643                        <> " thr = ")
 644         $ adaptativeSeaLevelMatching fdt proxiConf (depth - 1) elevation groupsProxi' lambda minBranch frequency' frame periods docs coocs branches'
 645
 646
 647 adaptativeTemporalMatching :: Double -> Phylo -> Phylo
 648 adaptativeTemporalMatching elevation phylo = updatePhyloGroups 1
 649                           (fromList $ map (\g -> (getGroupId g,g)) $ traceMatchEnd $ concat branches)
 650                           (toPhyloHorizon phylo)
 651   where
 652     --  2) process the temporal matching by elevating seaLvl level
 653     branches :: [[PhyloGroup]]
 654     branches = map fst
 655              $ adaptativeSeaLevelMatching (fromIntegral $ Vector.length $ getRoots phylo)
 656                                  (phyloProximity $ getConfig phylo)
 657                                  (elevation - 1)
 658                                  elevation
 659                                  (phylo ^. phylo_groupsProxi)
 660                                  (_qua_granularity $ phyloQuality $ getConfig phylo)
 661                                  (_qua_minBranch $ phyloQuality $ getConfig phylo)
 662                                  (phylo ^. phylo_termFreq)
 663                                  (getTimeFrame $ timeUnit $ getConfig phylo)
 664                                  (getPeriodIds phylo)
 665                                  (phylo ^. phylo_timeDocs)
 666                                  (phylo ^. phylo_timeCooc)
 667                                  groups
 668     --  1) for each group process an initial temporal Matching
 669     --  here we suppose that all the groups of level 1 are part of the same big branch
 670     groups :: [([PhyloGroup],(Bool,[Double]))]
 671     groups = map (\b -> (b,((length $ nub $ map _phylo_groupPeriod b) >= (_qua_minBranch $ phyloQuality $ getConfig phylo),[thr])))
 672            $ groupsToBranches' $ fromList $ map (\g -> (getGroupId g, g))
 673            $ matchGroupsToGroups (getTimeFrame $ timeUnit $ getConfig phylo)
 674                          (getPeriodIds phylo) (phyloProximity $ getConfig phylo)
 675                          thr
 676                          (phylo ^. phylo_timeDocs)
 677                          (phylo ^. phylo_timeCooc)
 678                          (traceTemporalMatching $ getGroupsFromLevel 1 phylo)
 679     --------------------------------------
 680     thr :: Double
 681     thr = toThreshold elevation (phylo ^. phylo_groupsProxi)