FeatureMatrix.hs

{- Math.Clustering.Hierarchical.Spectral.Eigen.FeatureMatrix
Gregory W. Schwartz

Collects the functions pertaining to hierarchical spectral clustering for
feature matrices.
-}

{-# LANGUAGE BangPatterns #-}

module Math.Clustering.Hierarchical.Spectral.Eigen.FeatureMatrix
    ( hierarchicalSpectralCluster
    , FeatureMatrix (..)
    , B (..)
    , Items (..)
    , ShowB (..)
    ) where

-- Remote
import Data.Bool (bool)
import Data.Clustering.Hierarchical (Dendrogram (..))
import Data.Maybe (fromMaybe)
import Data.Tree (Tree (..))
import Math.Clustering.Spectral.Eigen.FeatureMatrix (B (..), getB, spectralCluster, spectralClusterK)
import Math.Modularity.Eigen.Sparse (getBModularity)
import Math.Modularity.Types (Q (..))
import qualified Data.Foldable as F
import qualified Data.Set as Set
import qualified Data.Eigen.SparseMatrix as S
import qualified Data.Vector as V
import qualified Data.Vector.Storable as VS

-- Local
import Math.Clustering.Hierarchical.Spectral.Types
import Math.Clustering.Hierarchical.Spectral.Utility

type FeatureMatrix   = S.SparseMatrixXd
type Items a         = V.Vector a
type ShowB           = ((Int, Int), [(Int, Int, Double)])
type NormalizeFlag   = Bool

-- | Check if there is more than one cluster.
hasMultipleClusters :: S.SparseMatrixXd -> Bool
hasMultipleClusters = (> 1)
                    . Set.size
                    . Set.fromList
                    . concat
                    . S.toDenseList

-- | Generates a tree through divisive hierarchical clustering using
-- Newman-Girvan modularity as a stopping criteria. Can use minimum number of
-- observations in a cluster as a stopping criteria. Assumes the feature matrix
-- has column features and row observations. Items correspond to rows. Can
-- use FeatureMatrix or a pre-generated B matrix. See Shu et al., "Efficient
-- Spectral Neighborhood Blocking for Entity Resolution", 2011.
hierarchicalSpectralCluster :: EigenGroup
                            -> NormalizeFlag
                            -> Maybe NumEigen
                            -> Maybe Int
                            -> Maybe Q
                            -> Items a
                            -> Either FeatureMatrix B
                            -> ClusteringTree a
hierarchicalSpectralCluster eigenGroup normFlag numEigenMay minSizeMay minModMay initItems initMat =
    go initItems initB
  where
    initB = either (getB normFlag) id $ initMat
    minMod      = fromMaybe (Q 0) minModMay
    minSize     = fromMaybe 1 minSizeMay
    numEigen    = fromMaybe 1 numEigenMay
    go :: Items a -> B -> ClusteringTree a
    go !items !b =
        if (S.rows $ unB b) > 1
            && hasMultipleClusters clusters
            && ngMod > minMod
            && S.rows (unB left) >= minSize
            && S.rows (unB right) >= minSize
            then
                Node { rootLabel = vertex
                     , subForest = [ go (subsetVector items leftIdxs) left
                                   , go (subsetVector items rightIdxs) right
                                   ]
                     }

            else
                Node {rootLabel = vertex, subForest = []}
      where
        vertex      = ClusteringVertex
                        { _clusteringItems = items
                        , _ngMod = ngMod
                        }
        clusters :: S.SparseMatrixXd
        clusters = spectralClustering eigenGroup b
        spectralClustering :: EigenGroup -> B -> S.SparseMatrixXd
        spectralClustering SignGroup   = spectralCluster
        spectralClustering KMeansGroup = spectralClusterK numEigen 2
        ngMod :: Q
        ngMod = getBModularity clusters b
        getSortedIdxs :: Double -> S.SparseMatrixXd -> [Int]
        getSortedIdxs val = VS.ifoldr' (\ !i !v !acc -> bool acc (i:acc) $ v == val) []
                          . VS.fromList
                          . concat
                          . S.toDenseList
        leftIdxs :: [Int]
        leftIdxs    = getSortedIdxs 0 clusters
        rightIdxs :: [Int]
        rightIdxs   = getSortedIdxs 1 clusters
        left :: B
        left        = B $ extractRows (unB b) leftIdxs
        right :: B
        right       = B $ extractRows (unB b) rightIdxs
        extractRows :: S.SparseMatrixXd -> [Int] -> S.SparseMatrixXd
        extractRows mat [] = S.fromList 0 0 []
        extractRows mat xs = S.fromRows . fmap (flip S.getRow mat) $ xs