module Duckling.Ranking.Rank
( rank
) where
import Control.Arrow ((***))
import Control.Monad (join)
import qualified Data.HashMap.Strict as HashMap
import Data.HashSet (HashSet)
import qualified Data.HashSet as HashSet
import Data.Maybe
import qualified Data.Set as Set
import Prelude
import Duckling.Dimensions.Types
import Duckling.Ranking.Extraction
import Duckling.Ranking.Types
import Duckling.Types
classify :: Classifier -> BagOfFeatures -> (Class, Double)
classify Classifier {..} feats = if okScore >= koScore
then (True, okScore)
else (False, koScore)
where
(okScore, koScore) = join (***) (p feats) (okData, koData)
p :: BagOfFeatures -> ClassData -> Double
p feats ClassData{..} =
prior + HashMap.foldrWithKey (\feat x res ->
res + fromIntegral x * HashMap.lookupDefault unseen feat likelihoods
) 0.0 feats
score :: Classifiers -> Node -> Double
score classifiers node@Node {rule = Just rule, ..} =
case HashMap.lookup rule classifiers of
Just c -> let feats = extractFeatures node
in snd (classify c feats) + sum (map (score classifiers) children)
Nothing -> 0.0
score _ Node {rule = Nothing} = 0.0
winners :: Ord a => [a] -> [a]
winners xs = filter (\x -> all ((/=) LT . compare x) xs) xs
rank
:: Classifiers
-> HashSet (Some Dimension)
-> [ResolvedToken]
-> [ResolvedToken]
rank classifiers targets tokens =
Set.toList . Set.fromList
. map (\(Candidate token _ _) -> token)
. winners
$ map makeCandidate tokens
where
makeCandidate :: ResolvedToken -> Candidate
makeCandidate token@Resolved {node = n@Node {token = Token d _}} =
Candidate token (score classifiers n) $ HashSet.member (This d) targets