{- Math.Clustering.Spectral.Sparse
Gregory W. Schwartz

Collects the functions pertaining to sparse spectral clustering.
-}

{-# LANGUAGE BangPatterns #-}

module Math.Clustering.Spectral.Sparse
    ( B (..)
    , B1 (..)
    , B2 (..)
    , AdjacencyMatrix (..)
    , LabelVector (..)
    , spectral
    , spectralCluster
    , spectralClusterK
    , spectralNorm
    , spectralClusterNorm
    , spectralClusterKNorm
    , getB
    , b1ToB2
    , getSimilarityFromB2
    ) where

-- Remote
import Data.Bool (bool)
import Data.Maybe (fromMaybe)
import Data.Function (on)
import Data.List (sortBy, foldl1')
import Safe (headMay)
import qualified AI.Clustering.KMeans as K
import qualified Data.Sparse.Common as S
import qualified Data.Vector as V
import qualified Data.Vector.Unboxed as U
import qualified Numeric.LinearAlgebra as H
import qualified Numeric.LinearAlgebra.Devel as H
import qualified Numeric.LinearAlgebra.SVD.SVDLIBC as SVD

-- Local

-- | Output vector containing cluster assignment (0 or 1).
type LabelVector = S.SpVector Double

-- | Adjacency matrix input.
type AdjacencyMatrix = S.SpMatrix Double

-- | B1 observation by feature matrix.
newtype B1 = B1 { unB1 :: S.SpMatrix Double } deriving (Show)
-- | B2 term frequency-inverse document frequency matrix of B1.
newtype B2 = B2 { unB2 :: S.SpMatrix Double } deriving (Show)
-- | Diagonal matrix from \(diag(B(B^{T}1))\).
newtype D  = D { unD :: S.SpMatrix Double } deriving (Show)
-- | Matrix from \(D^{-1/2}B}\).
newtype C  = C { unC :: S.SpMatrix Double } deriving (Show)
-- | Normed rows of B2. For a complete explanation, see Shu et al., "Efficient
-- Spectral Neighborhood Blocking for Entity Resolution", 2011.
newtype B  = B { unB :: S.SpMatrix Double } deriving (Show)

-- | Normalize the input matrix by column. Here, columns are features.
b1ToB2 :: B1 -> B2
b1ToB2 (B1 b1) =
    B2
        . S.fromListSM (n, m)
        . fmap (\ (!i, !j, !x)
               -> (i, j, (log (fromIntegral n / (S.lookupDenseSV j dVec))) * x)
               )
        . S.toListSM
        $ b1
  where
    dVec :: S.SpVector Double
    dVec = S.vr
         . fmap (sum . fmap (\x -> if x > 0 then 1 else 0))
         . S.toRowsL -- faster than toColsL.
         . S.transposeSM
         $ b1
    n = S.nrows b1
    m = S.ncols b1

-- | Euclidean norm each row.
b2ToB :: B2 -> B
b2ToB (B2 b2) =
    B
        . S.fromListSM (n, m)
        . fmap (\(!i, !j, !x) -> (i, j, x / (S.lookupDenseSV i eVec)))
        . S.toListSM
        $ b2
  where
    eVec :: S.SpVector Double
    eVec = S.vr . fmap S.norm2 . S.toRowsL $ b2
    n = S.nrows b2
    m = S.ncols b2

-- | Find the Euclidean norm of a vector.
norm2 :: S.SpVector Double -> Double
norm2 = sqrt . sum . fmap (** 2)

-- | Get the signed diagonal transformed B matrix.
bToD :: B -> D
bToD (B b) = D
           . S.diagonalSM
           . flip S.extractCol 0
           $ (fmap abs b)
       S.#~# ((fmap abs $ S.transposeSM b) S.#~# (S.fromColsL [S.onesSV n]))
  where
    n = S.nrows b

-- | Get the matrix C as input for SVD.
bdToC :: B -> D -> C
bdToC (B b) (D d) = C $ (fmap (\x -> x ** (- 1 / 2)) d) S.#~# b

-- | Obtain the second left singular vector (or N earlier) and E on of a sparse
-- matrix.
secondLeft :: Int -> Int -> S.SpMatrix Double -> [S.SpVector Double]
secondLeft n e m =
  fmap (S.sparsifySV . S.fromListDenseSV e . drop (n - 1) . H.toList)
    . H.toColumns
    . (\(!x, _, _) -> x)
    . SVD.sparseSvd (e + (n - 1))
    . H.mkCSR
    . fmap (\(!i, !j, !x) -> ((i, j), x))
    . S.toListSM
    $ m

-- | Get the normalized matrix B from an input matrix where the features are
-- columns and rows are observations. Optionally, do not normalize.
getB :: Bool -> S.SpMatrix Double -> B
getB True = b2ToB . b1ToB2 . B1
getB False = b2ToB . B2

-- | Returns the second left singular vector (or from N) and E on of a sparse
-- spectral process. Assumes the columns are features and rows are observations.
-- B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral
-- Neighborhood Blocking for Entity Resolution", 2011.
spectral :: Int -> Int -> B -> [S.SpVector Double]
spectral n e b
    | e < 1     = error "Less than 1 eigenvector chosen for clustering."
    | n < 1 = error "N < 1, cannot go before first eigenvector."
    | otherwise = secondLeft n e . unC . bdToC b . bToD $ b

-- | Returns a vector of cluster labels for two groups by finding the second
-- left singular vector of a special normalized matrix. Assumes the columns are
-- features and rows are observations. B is the normalized matrix (from getB).
-- See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity
-- Resolution", 2011.
spectralCluster :: B -> LabelVector
spectralCluster (B b)
  | S.nrows b < 1  = S.zeroSV 0
  | S.nrows b == 1 = S.zeroSV 1
  | otherwise      = S.sparsifySV
                   . S.vr
                   . fmap (bool 0 1 . (>= 0))
                   . S.toDenseListSV
                   . foldl1' S.concatSV
                   . spectral 2 1
                   $ B b

-- | Returns a vector of cluster labels for two groups by finding the second
-- left singular vector and on of a special normalized matrix and running kmeans.
-- Assumes the columns are features and rows are observations. B is the
-- normalized matrix (from getB). See Shu et al., "Efficient Spectral
-- Neighborhood Blocking for Entity Resolution", 2011.
spectralClusterK :: Int -> Int -> B -> LabelVector
spectralClusterK e k (B b)
  | S.nrows b < 1  = S.zeroSV 0
  | S.nrows b == 1 = S.zeroSV 1
  | otherwise      = kmeansVec k . spectral 1 e $ B b

-- | Executes kmeans to cluster a vector.
kmeansVec :: Int -> [S.SpVector Double] -> LabelVector
kmeansVec k = S.sparsifySV
            . S.vr
            . fmap fromIntegral
            . U.toList
            . K.membership
            . (\x -> K.kmeansBy k x id K.defaultKMeansOpts)
            . V.fromList
            . fmap (U.fromList . S.toDenseListSV)
            . S.toRowsL
            . S.fromColsL
            . fmap S.normalize2
            . S.toColsL
            . S.transpose
            . S.fromColsL

-- | Get the cosine similarity between two rows using B2.
getSimilarityFromB2 :: B2 -> Int -> Int -> Double
getSimilarityFromB2 (B2 b2) i j =
    S.dot (S.extractRow b2 i) (S.extractRow b2 j)
        / (S.norm2 (S.extractRow b2 i) * S.norm2 (S.extractRow b2 j))

-- | Returns the eigenvector with the second smallest eigenvalue (or N start)
-- and E on of the symmetric normalized Laplacian L. Computes real symmetric
-- part of L, so ensure the input is real and symmetric. Diagonal should be 0s
-- for adjacency matrix. Uses I + Lnorm instead of I - Lnorm to find second
-- largest singular value instead of second smallest for Lnorm.
spectralNorm :: Int -> Int -> AdjacencyMatrix -> [S.SpVector Double]
spectralNorm n e mat = secondLeft n e lNorm
  where
    lNorm    = i S.^+^ (S.transpose invRootD S.#~# (mat S.#~# invRootD))
    invRootD = S.diagonalSM
             . S.vr
             . fmap ((\x -> if x == 0 then x else x ** (- 1 / 2)) . sum)
             . S.toRowsL
             . fmap abs -- signed diagonal
             $ mat
    i        = S.eye . S.nrows $ mat

-- | Returns the eigenvector with the second smallest eigenvalue and on of the
-- symmetric normalized Laplacian L. Computes real symmetric part of L, so
-- ensure the input is real and symmetric. Diagonal should be 0s for adjacency
-- matrix. Clusters the eigenvector using kmeans into k groups.
spectralClusterKNorm :: Int -> Int -> AdjacencyMatrix -> LabelVector
spectralClusterKNorm e k = kmeansVec k . spectralNorm 1 e

-- | Returns the eigenvector with the second smallest eigenvalue of the
-- symmetric normalized Laplacian L. Computes real symmetric part of L, so
-- ensure the input is real and symmetric. Diagonal should be 0s for adjacency
-- matrix. Clusters the eigenvector by sign.
spectralClusterNorm :: AdjacencyMatrix -> LabelVector
spectralClusterNorm = S.sparsifySV
                    . S.vr
                    . fmap (bool 0 1 . (>= 0))
                    . S.toDenseListSV
                    . foldl1' S.concatSV
                    . spectralNorm 2 1