{-| Module : Gargantext.Core.Viz.Phylo.TemporalMatching Description : Module dedicated to the adaptative temporal matching of a Phylo. Copyright : (c) CNRS, 2017-Present License : AGPL + CECILL v3 Maintainer : team@gargantext.org Stability : experimental Portability : POSIX Reference : Chavalarias, D., Lobbé, Q. & Delanoë, A. Draw me Science. Scientometrics 127, 545–575 (2022). https://doi.org/10.1007/s11192-021-04186-5 -} module Gargantext.Core.Viz.Phylo.TemporalMatching where import Control.Lens hiding (Level) import Control.Parallel.Strategies (parList, rdeepseq, using) import Data.Ord import Data.List (concat, splitAt, tail, sortOn, sortBy, (++), intersect, null, inits, groupBy, scanl, nub, nubBy, union, dropWhile, partition, or) import Data.Map (Map, fromList, elems, restrictKeys, unionWith, findWithDefault, keys, (!), empty, mapKeys, adjust) import Debug.Trace (trace) import Gargantext.Core.Viz.Phylo import Gargantext.Core.Viz.Phylo.PhyloTools import Gargantext.Prelude import Prelude (tan,pi) import Text.Printf import qualified Data.Map as Map import qualified Data.List as List import qualified Data.Set as Set import qualified Data.Vector as Vector type Branch = [PhyloGroup] type FinalQuality = Double type LocalQuality = Double type ShouldTry = Bool ---------------------------- -- | Similarity Measure | -- ---------------------------- {- -- compute a jaccard similarity between two lists -} jaccard :: [Int] -> [Int] -> Double jaccard inter' union' = ((fromIntegral . length) $ inter') / ((fromIntegral . length) $ union') {- -- process the inverse sumLog -} sumInvLog' :: Double -> Double -> [Double] -> Double sumInvLog' s nb diago = foldl (\mem occ -> mem + (1 / (log (occ + 1/ tan (s * pi / 2)) / log (nb + 1/ tan (s * pi / 2))))) 0 diago {- -- process the sumLog -} sumLog' :: Double -> Double -> [Double] -> Double sumLog' s nb diago = foldl (\mem occ -> mem + (log (occ + 1/ tan (s * pi / 2)) / log (nb + 1/ tan (s * pi / 2)))) 0 diago {- -- compute the weightedLogJaccard -} weightedLogJaccard' :: Double -> Double -> Map Int Double -> [Int] -> [Int] -> Double weightedLogJaccard' sens nbDocs diago ngrams ngrams' | null ngramsInter = 0 | ngramsInter == ngramsUnion = 1 | sens == 0 = jaccard ngramsInter ngramsUnion | sens > 0 = (sumInvLog' sens nbDocs diagoInter) / (sumInvLog' sens nbDocs diagoUnion) | otherwise = (sumLog' sens nbDocs diagoInter) / (sumLog' sens nbDocs diagoUnion) where -------------------------------------- ngramsInter :: [Int] ngramsInter = intersect ngrams ngrams' -------------------------------------- ngramsUnion :: [Int] ngramsUnion = union ngrams ngrams' -------------------------------------- diagoInter :: [Double] diagoInter = elems $ restrictKeys diago (Set.fromList ngramsInter) -------------------------------------- diagoUnion :: [Double] diagoUnion = elems $ restrictKeys diago (Set.fromList ngramsUnion) -------------------------------------- {- -- compute the weightedLogSim -- 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) -- tests not conclusive -} weightedLogSim' :: Double -> Double -> Map Int Double -> [Int] -> [Int] -> Double weightedLogSim' sens nbDocs diago ego_ngrams target_ngrams | null ngramsInter = 0 | ngramsInter == ngramsUnion = 1 | sens == 0 = jaccard ngramsInter ngramsUnion | sens > 0 = (sumInvLog' sens nbDocs diagoInter) / minimum [(sumInvLog' sens nbDocs diagoEgo),(sumInvLog' sens nbDocs diagoTarget)] | otherwise = (sumLog' sens nbDocs diagoInter) / minimum [(sumLog' sens nbDocs diagoEgo),(sumLog' sens nbDocs diagoTarget)] where -------------------------------------- ngramsInter :: [Int] ngramsInter = intersect ego_ngrams target_ngrams -------------------------------------- ngramsUnion :: [Int] ngramsUnion = union ego_ngrams target_ngrams -------------------------------------- diagoInter :: [Double] diagoInter = elems $ restrictKeys diago (Set.fromList ngramsInter) -------------------------------------- diagoEgo :: [Double] diagoEgo = elems $ restrictKeys diago (Set.fromList ego_ngrams) -------------------------------------- diagoTarget :: [Double] diagoTarget = elems $ restrictKeys diago (Set.fromList target_ngrams) -------------------------------------- {- -- perform a seamilarity measure between a given group and a pair of targeted groups -} toProximity :: Double -> Map Int Double -> Proximity -> [Int] -> [Int] -> [Int] -> Double toProximity nbDocs diago proximity egoNgrams targetNgrams targetNgrams' = case proximity of WeightedLogJaccard sens _ -> let pairNgrams = if targetNgrams == targetNgrams' then targetNgrams else union targetNgrams targetNgrams' in weightedLogJaccard' sens nbDocs diago egoNgrams pairNgrams WeightedLogSim sens _ -> let pairNgrams = if targetNgrams == targetNgrams' then targetNgrams else union targetNgrams targetNgrams' in weightedLogSim' sens nbDocs diago egoNgrams pairNgrams Hamming _ _ -> undefined ----------------------------- -- | Pointers & Matrices | -- ----------------------------- findLastPeriod :: Filiation -> [Period] -> Period findLastPeriod fil periods = case fil of ToParents -> head' "findLastPeriod" (sortOn fst periods) ToChilds -> last' "findLastPeriod" (sortOn fst periods) ToChildsMemory -> undefined ToParentsMemory -> undefined removeOldPointers :: [Pointer] -> Filiation -> Double -> Proximity -> Period -> [((PhyloGroupId,[Int]),(PhyloGroupId,[Int]))] -> [((PhyloGroupId,[Int]),(PhyloGroupId,[Int]))] removeOldPointers oldPointers fil thr prox prd pairs | null oldPointers = pairs | null (filterPointers prox thr oldPointers) = let lastMatchedPrd = findLastPeriod fil (map (fst . fst . fst) oldPointers) in if lastMatchedPrd == prd then [] else filter (\((id,_),(id',_)) -> case fil of ToChildsMemory -> undefined ToParentsMemory -> undefined ToParents -> (((fst . fst . fst) id ) < (fst lastMatchedPrd)) || (((fst . fst . fst) id') < (fst lastMatchedPrd)) ToChilds -> (((fst . fst . fst) id ) > (fst lastMatchedPrd)) || (((fst . fst . fst) id') > (fst lastMatchedPrd))) pairs | otherwise = [] filterPointers :: Proximity -> Double -> [Pointer] -> [Pointer] filterPointers proxi thr pts = filter (\(_,w) -> filterProximity proxi thr w) pts filterPointers' :: Proximity -> Double -> [(Pointer,[Int])] -> [(Pointer,[Int])] filterPointers' proxi thr pts = filter (\((_,w),_) -> filterProximity proxi thr w) pts reduceDiagos :: Map Date Cooc -> Map Int Double reduceDiagos diagos = mapKeys (\(k,_) -> k) $ foldl (\acc diago -> unionWith (+) acc diago) empty (elems diagos) filterPointersByPeriod :: Filiation -> [(Pointer,[Int])] -> [Pointer] filterPointersByPeriod fil pts = let pts' = sortOn (fst . fst . fst . fst) pts inf = (fst . fst . fst . fst) $ head' "filterPointersByPeriod" pts' sup = (fst . fst . fst . fst) $ last' "filterPointersByPeriod" pts' in map fst $ nubBy (\pt pt' -> snd pt == snd pt') $ filter (\pt -> ((fst . fst . fst . fst) pt == inf) || ((fst . fst . fst . fst) pt == sup)) $ case fil of ToParents -> reverse pts' ToChilds -> pts' ToChildsMemory -> undefined ToParentsMemory -> undefined filterDocs :: Map Date Double -> [Period] -> Map Date Double filterDocs d pds = restrictKeys d $ periodsToYears pds filterDiago :: Map Date Cooc -> [Period] -> Map Date Cooc filterDiago diago pds = restrictKeys diago $ periodsToYears pds --------------------------------- -- | Inter-temporal matching | -- --------------------------------- {- -- perform the related component algorithm, construct the resulting branch id and update the corresponding group's branch id -} groupsToBranches :: Map PhyloGroupId PhyloGroup -> [Branch] groupsToBranches groups = {- run the related component algorithm -} let egos = groupBy (\gs gs' -> (fst $ fst $ head' "egos" gs) == (fst $ fst $ head' "egos" gs')) $ sortOn (\gs -> fst $ fst $ head' "egos" gs) $ map (\group -> [getGroupId group] ++ (map fst $ group ^. phylo_groupPeriodParents) ++ (map fst $ group ^. phylo_groupPeriodChilds) ) $ elems groups -- first find the related components by inside each ego's period -- a supprimer graph' = map relatedComponents egos -- then run it for the all the periods branches = zip [1..] $ relatedComponents $ concat (graph' `using` parList rdeepseq) -- update each group's branch id in map (\(bId,branch) -> let groups' = map (\group -> group & phylo_groupBranchId %~ (\(lvl,lst) -> (lvl,lst ++ [bId]))) $ elems $ restrictKeys groups (Set.fromList branch) in groups' `using` parList rdeepseq ) branches `using` parList rdeepseq {- -- find the best pair/singleton of parents/childs for a given group -} makePairs :: (PhyloGroupId,[Int]) -> [(PhyloGroupId,[Int])] -> [Period] -> [Pointer] -> Filiation -> Double -> Proximity -> Map Date Double -> Map Date Cooc -> [((PhyloGroupId,[Int]),(PhyloGroupId,[Int]))] makePairs (egoId, egoNgrams) candidates periods oldPointers fil thr prox docs diagos = if (null periods) then [] else removeOldPointers oldPointers fil thr prox lastPrd {- at least on of the pair candidates should be from the last added period -} $ filter (\((id,_),(id',_)) -> ((fst . fst) id == lastPrd) || ((fst . fst) id' == lastPrd)) $ filter (\((id,_),(id',_)) -> (elem id inPairs) || (elem id' inPairs)) $ listToCombi' candidates where -------------------------------------- inPairs :: [PhyloGroupId] inPairs = map fst $ filter (\(id,ngrams) -> let nbDocs = (sum . elems) $ filterDocs docs ([(fst . fst) egoId, (fst . fst) id]) diago = reduceDiagos $ filterDiago diagos ([(fst . fst) egoId, (fst . fst) id]) in (toProximity nbDocs diago prox egoNgrams egoNgrams ngrams) >= thr ) candidates -------------------------------------- lastPrd :: Period lastPrd = findLastPeriod fil periods -------------------------------------- {- -- find the best temporal links between a given group and its parents/childs -} phyloGroupMatching :: [[(PhyloGroupId,[Int])]] -> Filiation -> Proximity -> Map Date Double -> Map Date Cooc -> Double -> [Pointer] -> (PhyloGroupId,[Int]) -> [Pointer] phyloGroupMatching candidates filiation proxi docs diagos thr oldPointers (id,ngrams) = if (null $ filterPointers proxi thr oldPointers) -- if no previous pointers satisfy the current threshold then let's find new pointers then if null nextPointers then [] else filterPointersByPeriod filiation -- 2) keep only the best set of pointers grouped by proximity $ head' "phyloGroupMatching" $ groupBy (\pt pt' -> (snd . fst) pt == (snd . fst) pt') -- 1) find the first time frame where at leats one pointer satisfies the proximity threshold $ sortBy (comparing (Down . snd . fst)) $ head' "pointers" nextPointers else oldPointers where nextPointers :: [[(Pointer,[Int])]] nextPointers = take 1 -- stop as soon as we find a time frame where at least one singleton / pair satisfies the threshold $ dropWhile (null) -- for each time frame, process the proximity on relevant pairs of targeted groups $ scanl (\acc targets -> let periods = nub $ map (fst . fst . fst) targets lastPrd = findLastPeriod filiation periods nbdocs = sum $ elems $ (filterDocs docs ([(fst . fst) id] ++ periods)) diago = reduceDiagos $ filterDiago diagos ([(fst . fst) id] ++ periods) singletons = processProximity nbdocs diago $ map (\g -> (g,g)) $ filter (\g -> (fst . fst . fst) g == lastPrd) targets pairs = makePairs (id,ngrams) targets periods oldPointers filiation thr proxi docs diagos in if (null singletons) then acc ++ ( processProximity nbdocs diago pairs ) else acc ++ singletons ) [] $ map concat $ inits candidates -- groups from [[1900],[1900,1901],[1900,1901,1902],...] ----------------------------- processProximity :: Double -> Map Int Double -> [((PhyloGroupId,[Int]),(PhyloGroupId,[Int]))] -> [(Pointer,[Int])] processProximity nbdocs diago targets = filterPointers' proxi thr $ concat $ map (\(c,c') -> let proximity = toProximity nbdocs diago proxi ngrams (snd c) (snd c') in if ((c == c') || (snd c == snd c')) then [((fst c,proximity),snd c)] else [((fst c,proximity),snd c),((fst c',proximity),snd c')] ) targets {- -- get the upstream/downstream timescale of a given period -} getNextPeriods :: Filiation -> Int -> Period -> [Period] -> [Period] getNextPeriods fil max' pId pIds = case fil of ToChilds -> take max' $ (tail . snd) $ splitAt (elemIndex' pId pIds) pIds ToParents -> take max' $ (reverse . fst) $ splitAt (elemIndex' pId pIds) pIds ToChildsMemory -> undefined ToParentsMemory -> undefined {- -- find all the candidates parents/childs of ego -} getCandidates :: Int -> PhyloGroup -> [[(PhyloGroupId,[Int])]] -> [[(PhyloGroupId,[Int])]] getCandidates minNgrams ego targets = if (length (ego ^. phylo_groupNgrams)) > 1 then map (\groups' -> filter (\g' -> (> minNgrams) $ length $ intersect (ego ^. phylo_groupNgrams) (snd g')) groups') targets else map (\groups' -> filter (\g' -> (not . null) $ intersect (ego ^. phylo_groupNgrams) (snd g')) groups') targets {- -- set up and start performing the upstream/downstream inter‐temporal matching period by period -} reconstructTemporalLinks :: Int -> [Period] -> Proximity -> Double -> Map Date Double -> Map Date Cooc -> [PhyloGroup] -> [PhyloGroup] reconstructTemporalLinks frame periods proximity thr docs coocs groups = let groups' = groupByField _phylo_groupPeriod groups in foldl' (\acc prd -> let -- 1) find the parents/childs matching periods periodsPar = getNextPeriods ToParents frame prd periods periodsChi = getNextPeriods ToChilds frame prd periods -- 2) find the parents/childs matching candidates candidatesPar = map (\prd' -> map (\g -> (getGroupId g, g ^. phylo_groupNgrams)) $ findWithDefault [] prd' groups') periodsPar candidatesChi = map (\prd' -> map (\g -> (getGroupId g, g ^. phylo_groupNgrams)) $ findWithDefault [] prd' groups') periodsChi -- 3) find the parents/childs number of docs by years docsPar = filterDocs docs ([prd] ++ periodsPar) docsChi = filterDocs docs ([prd] ++ periodsChi) -- 4) find the parents/child diago by years diagoPar = filterDiago (map coocToDiago coocs) ([prd] ++ periodsPar) diagoChi = filterDiago (map coocToDiago coocs) ([prd] ++ periodsPar) -- 5) match in parallel all the groups (egos) to their possible candidates egos = map (\ego -> let pointersPar = phyloGroupMatching (getCandidates (getMinSharedNgrams proximity) ego candidatesPar) ToParents proximity docsPar diagoPar thr (getPeriodPointers ToParents ego) (getGroupId ego, ego ^. phylo_groupNgrams) pointersChi = phyloGroupMatching (getCandidates (getMinSharedNgrams proximity) ego candidatesChi) ToChilds proximity docsChi diagoChi thr (getPeriodPointers ToChilds ego) (getGroupId ego, ego ^. phylo_groupNgrams) in addPointers ToChilds TemporalPointer pointersChi $ addPointers ToParents TemporalPointer pointersPar $ addMemoryPointers ToChildsMemory TemporalPointer thr pointersChi $ addMemoryPointers ToParentsMemory TemporalPointer thr pointersPar ego) $ findWithDefault [] prd groups' egos' = egos `using` parList rdeepseq in acc ++ egos' ) [] periods {- -- reconstruct a phylomemetic network from a list of groups and from a given threshold -} toPhylomemeticNetwork :: Int -> [Period] -> Proximity -> Double -> Map Date Double -> Map Date Cooc -> [PhyloGroup] -> [Branch] toPhylomemeticNetwork timescale periods similarity thr docs coocs groups = groupsToBranches $ fromList $ map (\g -> (getGroupId g, g)) $ reconstructTemporalLinks timescale periods similarity thr docs coocs groups ---------------------------- -- | Quality Assessment | -- ---------------------------- {- -- filter the branches containing x -} relevantBranches :: Int -> [Branch] -> [Branch] relevantBranches x branches = filter (\groups -> (any (\group -> elem x $ group ^. phylo_groupNgrams) groups)) branches {- -- compute the accuracy ξ -- the accuracy of a branch relatively to a root x is computed only over the periods where clusters mentionning x in the phylo do exist -} accuracy :: Int -> [(Date,Date)] -> Branch -> Double accuracy x periods bk = ((fromIntegral $ length $ filter (\g -> elem x $ g ^. phylo_groupNgrams) bk') / (fromIntegral $ length bk')) where --- bk' :: [PhyloGroup] bk' = filter (\g -> elem (g ^. phylo_groupPeriod) periods) bk {- -- compute the recall ρ -} recall :: Int -> Branch -> [Branch] -> Double recall x bk bx = ((fromIntegral $ length $ filter (\g -> elem x $ g ^. phylo_groupNgrams) bk) / (fromIntegral $ length $ filter (\g -> elem x $ g ^. phylo_groupNgrams) $ concat bx)) {- -- compute the F-score function -} fScore :: Double -> Int -> [(Date,Date)] -> [PhyloGroup] -> [[PhyloGroup]] -> Double fScore lambda x periods bk bx = let rec = recall x bk bx acc = accuracy x periods bk in ((1 + lambda ** 2) * acc * rec) / (((lambda ** 2) * acc + rec)) {- -- compute the number of groups -} wk :: [PhyloGroup] -> Double wk bk = fromIntegral $ length bk {- -- compute the recall ρ for all the branches -} globalRecall :: Map Int Double -> [Branch] -> Double globalRecall freq branches = if (null branches) then 0 else sum $ map (\x -> let px = freq ! x bx = relevantBranches x branches wks = sum $ map wk bx in (px / pys) * (sum $ map (\bk -> ((wk bk) / wks) * (recall x bk bx)) bx)) $ keys freq where pys :: Double pys = sum (elems freq) {- -- compute the accuracy ξ for all the branches -} globalAccuracy :: Map Int Double -> [Branch] -> Double globalAccuracy freq branches = if (null branches) then 0 else sum $ map (\x -> let px = freq ! x bx = relevantBranches x branches -- | periods containing x periods = nub $ map _phylo_groupPeriod $ filter (\g -> elem x $ g ^. phylo_groupNgrams) $ concat bx wks = sum $ map wk bx in (px / pys) * (sum $ map (\bk -> ((wk bk) / wks) * (accuracy x periods bk)) bx)) $ keys freq where pys :: Double pys = sum (elems freq) {- -- compute the quality score F(λ) -} toPhyloQuality :: Double -> Double -> Map Int Double -> [[PhyloGroup]] -> Double toPhyloQuality fdt lambda freq branches = if (null branches) then 0 else sum $ map (\x -> -- let px = freq ! x let bx = relevantBranches x branches -- | periods containing x periods = nub $ map _phylo_groupPeriod $ filter (\g -> elem x $ g ^. phylo_groupNgrams) $ concat bx wks = sum $ map wk bx -- in (px / pys) * (sum $ map (\bk -> ((wk bk) / wks) * (fScore beta x bk bx)) bx)) -- in (1 / fdt) * (sum $ map (\bk -> ((wk bk) / wks) * (fScore beta x periods bk bx)) bx)) in (1 / fdt) * (sum $ map (\bk -> ((wk bk) / wks) * (fScore (tan (lambda * pi / 2)) x periods bk bx)) bx)) $ keys freq -- where -- pys :: Double -- pys = sum (elems freq) ------------------------- -- | Sea-level Rise | -- ------------------------- {- -- attach a rise value to branches & groups metadata -} riseToMeta :: Double -> [Branch] -> [Branch] riseToMeta rise branches = let break = length branches > 1 in map (\b -> map (\g -> if break then g & phylo_groupMeta .~ (adjust (\lst -> lst ++ [rise]) "breaks"(g ^. phylo_groupMeta)) else g) b) branches {- -- attach a thr value to branches & groups metadata -} thrToMeta :: Double -> [Branch] -> [Branch] thrToMeta thr branches = map (\b -> map (\g -> g & phylo_groupMeta .~ (adjust (\lst -> lst ++ [thr]) "seaLevels" (g ^. phylo_groupMeta))) b) branches {- -- TODO -- 1) try the zipper structure https://wiki.haskell.org/Zipper to performe the sea-level rise algorithme -- 2) investigate how the branches order influences the 'separateBranches' function -} {- -- sequentially separate each branch for a given threshold and check if it locally increases the quality score -- sequence = [done] | currentBranch | [rest] -- done = all the already separated branches -- rest = all the branches we still have to separate -} separateBranches :: Double -> Proximity -> Double -> Map Int Double -> Int -> Double -> Double -> Int -> Map Date Double -> Map Date Cooc -> [Period] -> [(Branch,ShouldTry)] -> (Branch,ShouldTry) -> [(Branch,ShouldTry)] -> [(Branch,ShouldTry)] separateBranches fdt similarity lambda frequency minBranch thr rise timescale docs coocs periods done currentBranch rest = let done' = done ++ (if snd currentBranch then (if ((null (fst branches')) || (quality > quality')) ---- 5) if the quality is not increased by the new branches or if the new branches are all small ---- then undo the separation and localy stop the sea rise ---- else validate the separation and authorise next sea rise in the long new branches then -- trace (" ✗ F(λ) = " <> show(quality) <> " (vs) " <> show(quality') -- <> " | " <> show(length $ fst ego) <> " groups : " -- <> " |✓ " <> show(length $ fst ego') <> show(map length $ fst ego') -- <> " |✗ " <> show(length $ snd ego') <> "[" <> show(length $ concat $ snd ego') <> "]") [(fst currentBranch,False)] else -- trace (" ✓ F(λ) = " <> show(quality) <> " (vs) " <> show(quality') -- <> " | " <> show(length $ fst ego) <> " groups : " -- <> " |✓ " <> show(length $ fst ego') <> show(map length $ fst ego') -- <> " |✗ " <> show(length $ snd ego') <> "[" <> show(length $ concat $ snd ego') <> "]") ((map (\e -> (e,True)) (fst branches')) ++ (map (\e -> (e,False)) (snd branches')))) else [currentBranch]) in -- 6) if there is no more branch to separate tne return [done'] else continue with [rest] if null rest then done' else separateBranches fdt similarity lambda frequency minBranch thr rise timescale docs coocs periods done' (List.head rest) (List.tail rest) where ------- 1) compute the quality before splitting any branch quality :: LocalQuality quality = toPhyloQuality fdt lambda frequency ((map fst done) ++ [fst currentBranch] ++ (map fst rest)) ------------------- 2) split the current branch and create a new phylomemetic network phylomemeticNetwork :: [Branch] phylomemeticNetwork = toPhylomemeticNetwork timescale periods similarity thr docs coocs (fst currentBranch) --------- 3) change the new phylomemetic network into a tuple of new branches --------- on the left : the long branches, on the right : the small ones branches' :: ([Branch],[Branch]) branches' = partition (\b -> (length $ nub $ map _phylo_groupPeriod b) >= minBranch) $ thrToMeta thr $ riseToMeta rise phylomemeticNetwork -------- 4) compute again the quality by considering the new branches quality' :: LocalQuality quality' = toPhyloQuality fdt lambda frequency ((map fst done) ++ (fst branches') ++ (snd branches') ++ (map fst rest)) {- -- perform the sea-level rise algorithm, browse the similarity ladder and check that we can try out the next step -} seaLevelRise :: Double -> Proximity -> Double -> Int -> Map Int Double -> [Double] -> Double -> Int -> [Period] -> Map Date Double -> Map Date Cooc -> [(Branch,ShouldTry)] -> ([(Branch,ShouldTry)],FinalQuality) seaLevelRise fdt proximity lambda minBranch frequency ladder rise frame periods docs coocs branches = -- if the ladder is empty or thr > 1 or there is no branch to break then stop if (null ladder) || ((List.head ladder) > 1) || (stopRise branches) then (branches, toPhyloQuality fdt lambda frequency (map fst branches)) else -- start breaking up all the possible branches for the current similarity threshold let thr = List.head ladder branches' = trace ("threshold = " <> printf "%.3f" thr <> " F(λ) = " <> printf "%.5f" (toPhyloQuality fdt lambda frequency (map fst branches)) <> " ξ = " <> printf "%.5f" (globalAccuracy frequency (map fst branches)) <> " ρ = " <> printf "%.5f" (globalRecall frequency (map fst branches)) <> " branches = " <> show(length branches)) $ separateBranches fdt proximity lambda frequency minBranch thr rise frame docs coocs periods [] (List.head branches) (List.tail branches) in seaLevelRise fdt proximity lambda minBranch frequency (List.tail ladder) (rise + 1) frame periods docs coocs branches' where -------- stopRise :: [(Branch,ShouldTry)] -> Bool stopRise bs = ((not . or) $ map snd bs) {- -- start the temporal matching process up, recover the resulting branches and update the groups (at scale 1) consequently -} temporalMatching :: [Double] -> Phylo -> Phylo temporalMatching ladder phylo = updatePhyloGroups 1 (Map.fromList $ map (\g -> (getGroupId g,g)) $ traceMatchEnd $ concat branches) (updateQuality quality phylo) where ------- quality :: FinalQuality quality = snd sea -------- branches :: [Branch] branches = map fst $ fst sea --- 2) process the temporal matching by elevating the similarity ladder sea :: ([(Branch,ShouldTry)],FinalQuality) sea = seaLevelRise (fromIntegral $ Vector.length $ getRoots phylo) (phyloProximity $ getConfig phylo) (_qua_granularity $ phyloQuality $ getConfig phylo) (_qua_minBranch $ phyloQuality $ getConfig phylo) (phylo ^. phylo_termFreq) ladder 1 (getTimeFrame $ timeUnit $ getConfig phylo) (getPeriodIds phylo) (phylo ^. phylo_timeDocs) (phylo ^. phylo_timeCooc) (reverse $ sortOn (length . fst) seabed) ------ 1) for each group, process an initial temporal Matching and create a 'seabed' ------ ShouldTry determines if you should apply the seaLevelRise function again within each branch seabed :: [(Branch,ShouldTry)] seabed = map (\b -> (b,(length $ nub $ map _phylo_groupPeriod b) >= (_qua_minBranch $ phyloQuality $ getConfig phylo))) $ toPhylomemeticNetwork (getTimeFrame $ timeUnit $ getConfig phylo) (getPeriodIds phylo) (phyloProximity $ getConfig phylo) (List.head ladder) (phylo ^. phylo_timeDocs) (phylo ^. phylo_timeCooc) (traceTemporalMatching $ getGroupsFromScale 1 phylo)