feat(stopwords): removeWords and removeStopwords functions as pre-processors
feat(confidence, WIP): calculate confidence of each classification
This commit is contained in:
Mahdi Dibaiee
parent
ea1f05f001
commit
099c25e166
@ -14,26 +14,20 @@ module Main
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test <- readFile "examples/doc-classifier-data/data-reuters-test"
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classes <- map (filter (/= ' ')) . lines <$> readFile "examples/doc-classifier-data/data-classes"
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sws <- lines <$> readFile "examples/stopwords"
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let intClasses = [0..length classes - 1]
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-- let intClasses = [0, 1]
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documents = cleanDocuments $ createDocuments classes dataset
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-- documents = [Document "Chinese Beijing Chinese" 0,
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-- Document "Chinese Chinese Shanghai" 0,
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-- Document "Chinese Macao" 0,
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-- Document "Japan Tokyo Chinese" 1]
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-- testDocuments = [Document "Chinese Chinese Chinese Japan Tokyo" 0]
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documents = cleanDocuments $ removeWords sws $ createDocuments classes dataset
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testDocuments = cleanDocuments $ createDocuments classes test
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devTestDocuments = take 30 testDocuments
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-- devTestDocuments = [Document "Chinese Chinese Chinese Tokyo Japan" 0]
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nb = train documents intClasses
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results = map (\(Document text c) -> (c, run text nb)) testDocuments
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-- results = map (\(Document text c) -> (c, run text nb)) devTestDocuments
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-- print (text $ head documents)
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print (text $ head documents)
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let showResults (c, r) = putStrLn (classes !! c ++ " ~ " ++ classes !! r)
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let showResults (c, (r, confidence)) = putStrLn (classes !! c ++ " ~ " ++ classes !! r)
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mapM_ showResults results
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putStrLn $ "Recall: " ++ show (recall results)
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665
examples/stopwords
Normal file
665
examples/stopwords
Normal file
@ -0,0 +1,665 @@
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a
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able
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about
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above
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abst
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accordance
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according
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accordingly
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across
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act
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actually
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added
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adj
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affected
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affecting
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affects
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after
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afterwards
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again
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against
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ah
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all
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almost
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alone
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along
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already
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also
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although
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always
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am
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among
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amongst
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an
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and
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announce
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another
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any
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anybody
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anyhow
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anymore
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anyone
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anything
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anyway
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anyways
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anywhere
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apparently
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approximately
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are
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aren
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arent
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arise
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around
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as
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aside
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ask
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asking
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at
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auth
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available
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away
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awfully
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b
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back
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be
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became
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because
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become
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becomes
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becoming
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been
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before
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beforehand
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begin
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beginning
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beginnings
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begins
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behind
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being
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believe
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below
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beside
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besides
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between
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beyond
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biol
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both
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brief
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briefly
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but
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by
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c
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ca
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came
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can
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cannot
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can't
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cause
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causes
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certain
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certainly
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co
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com
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come
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comes
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contain
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containing
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contains
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could
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couldnt
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d
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date
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did
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didn't
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different
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do
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does
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doesn't
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doing
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done
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don't
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down
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downwards
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due
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during
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e
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each
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ed
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edu
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effect
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eg
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eight
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eighty
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either
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else
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elsewhere
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end
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ending
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enough
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especially
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et
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et-al
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etc
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even
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ever
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every
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everybody
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everyone
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everything
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everywhere
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ex
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except
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f
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far
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few
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ff
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fifth
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first
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five
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fix
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followed
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following
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follows
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for
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former
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formerly
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forth
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found
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four
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from
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further
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furthermore
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g
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gave
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get
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gets
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getting
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give
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given
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gives
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giving
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go
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goes
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gone
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got
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gotten
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h
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had
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happens
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hardly
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has
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hasn't
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have
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haven't
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having
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he
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hed
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hence
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her
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here
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hereafter
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hereby
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herein
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heres
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hereupon
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hers
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herself
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hes
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hi
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hid
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him
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himself
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his
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hither
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home
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how
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howbeit
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however
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hundred
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i
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id
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ie
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if
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i'll
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im
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immediate
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immediately
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importance
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important
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in
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inc
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indeed
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index
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information
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instead
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into
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invention
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inward
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is
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isn't
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it
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itd
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it'll
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its
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itself
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i've
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j
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just
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k
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keep keeps
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kept
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kg
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km
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know
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known
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knows
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l
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largely
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last
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lately
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later
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latter
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latterly
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least
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less
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lest
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let
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lets
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like
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liked
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likely
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line
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little
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'll
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look
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looking
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looks
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ltd
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m
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made
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mainly
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make
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makes
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many
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may
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maybe
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me
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mean
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means
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meantime
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meanwhile
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merely
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mg
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might
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million
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miss
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ml
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more
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moreover
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most
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mostly
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mr
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mrs
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much
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mug
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must
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my
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myself
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n
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na
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name
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namely
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nay
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nd
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near
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nearly
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necessarily
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necessary
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need
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needs
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neither
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never
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nevertheless
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new
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next
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nine
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ninety
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no
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nobody
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non
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none
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nonetheless
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noone
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nor
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normally
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nos
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not
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noted
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nothing
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now
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nowhere
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o
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obtain
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obtained
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obviously
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of
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off
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often
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oh
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ok
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okay
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old
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omitted
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on
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once
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one
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ones
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only
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onto
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or
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ord
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other
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others
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otherwise
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ought
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our
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ours
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ourselves
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out
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outside
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over
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overall
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owing
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own
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p
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page
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pages
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part
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particular
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particularly
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past
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per
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perhaps
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placed
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please
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plus
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poorly
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possible
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possibly
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potentially
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pp
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predominantly
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present
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previously
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primarily
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probably
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promptly
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proud
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provides
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put
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q
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que
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quickly
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quite
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qv
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r
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ran
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rather
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rd
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re
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readily
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really
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recent
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recently
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ref
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refs
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regarding
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regardless
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regards
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related
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relatively
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research
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respectively
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resulted
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resulting
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results
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right
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run
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s
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said
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same
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saw
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say
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saying
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says
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sec
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section
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see
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seeing
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seem
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seemed
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seeming
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seems
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seen
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self
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selves
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sent
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seven
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several
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shall
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she
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shed
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she'll
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shes
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should
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shouldn't
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show
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showed
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shown
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showns
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shows
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significant
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significantly
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similar
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similarly
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since
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six
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slightly
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so
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some
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somebody
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somehow
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someone
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somethan
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something
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sometime
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sometimes
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somewhat
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somewhere
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soon
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sorry
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specifically
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specified
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specify
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specifying
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still
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stop
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strongly
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sub
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substantially
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successfully
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such
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sufficiently
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suggest
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sup
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sure t
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take
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taken
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taking
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tell
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tends
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th
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than
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thank
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thanks
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thanx
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that
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that'll
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thats
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that've
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the
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their
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theirs
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them
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themselves
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then
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thence
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there
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thereafter
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thereby
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thered
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therefore
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therein
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there'll
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thereof
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therere
|
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theres
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thereto
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thereupon
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there've
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these
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they
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theyd
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they'll
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theyre
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they've
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think
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this
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those
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thou
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though
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thoughh
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thousand
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throug
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through
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throughout
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thru
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thus
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til
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tip
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to
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together
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too
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took
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toward
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towards
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tried
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tries
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truly
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try
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trying
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ts
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twice
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two
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u
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un
|
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under
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unfortunately
|
||||
unless
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unlike
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unlikely
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until
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unto
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up
|
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upon
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ups
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us
|
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use
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used
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useful
|
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usefully
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usefulness
|
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uses
|
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using
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usually
|
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v
|
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value
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various
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've
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very
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via
|
||||
viz
|
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vol
|
||||
vols
|
||||
vs
|
||||
w
|
||||
want
|
||||
wants
|
||||
was
|
||||
wasnt
|
||||
way
|
||||
we
|
||||
wed
|
||||
welcome
|
||||
we'll
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||||
went
|
||||
were
|
||||
werent
|
||||
we've
|
||||
what
|
||||
whatever
|
||||
what'll
|
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whats
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when
|
||||
whence
|
||||
whenever
|
||||
where
|
||||
whereafter
|
||||
whereas
|
||||
whereby
|
||||
wherein
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||||
wheres
|
||||
whereupon
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||||
wherever
|
||||
whether
|
||||
which
|
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while
|
||||
whim
|
||||
whither
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who
|
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whod
|
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whoever
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whole
|
||||
who'll
|
||||
whom
|
||||
whomever
|
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whos
|
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whose
|
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why
|
||||
widely
|
||||
willing
|
||||
wish
|
||||
with
|
||||
within
|
||||
without
|
||||
wont
|
||||
words
|
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world
|
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would
|
||||
wouldnt
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||||
www
|
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x
|
||||
y
|
||||
yes
|
||||
yet
|
||||
you
|
||||
youd
|
||||
you'll
|
||||
your
|
||||
youre
|
||||
yours
|
||||
yourself
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||||
yourselves
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you've
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z
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zero
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@ -12,6 +12,9 @@ module Sibe.NaiveBayes
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stdev,
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cleanText,
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cleanDocuments,
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ngram,
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removeWords,
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removeStopwords,
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)
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where
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import Data.List
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@ -21,7 +24,7 @@ module Sibe.NaiveBayes
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import Data.Maybe
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import Control.Arrow ((&&&))
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import Text.Regex.PCRE
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import Data.Char (isSpace)
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import Data.Char (isSpace, isNumber)
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import NLP.Stemmer
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type Class = Int;
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@ -36,6 +39,7 @@ module Sibe.NaiveBayes
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, megadoc :: String
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, cd :: [(Class, [Document])]
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, cw :: [(Class, [(String, Int)])]
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, cgram :: [(Class, [(String, Int)])]
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} deriving (Eq, Show, Read)
|
||||
|
||||
train :: [Document] -> [Class] -> NB
|
||||
@ -49,7 +53,9 @@ module Sibe.NaiveBayes
|
||||
cd = zip classes (map classDocs classes)
|
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|
||||
-- (class, [(word, count)])
|
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cw = zip classes $ l (map classWordsCounts classes)
|
||||
cw = zip classes $ map classWordsCounts classes
|
||||
|
||||
cgram = zip classes $ map classNGramCounts classes
|
||||
|
||||
in NB { documents = documents
|
||||
, classes = cls
|
||||
@ -57,44 +63,60 @@ module Sibe.NaiveBayes
|
||||
, megadoc = megadoc
|
||||
, cd = cd
|
||||
, cw = cw
|
||||
, cgram = cgram
|
||||
}
|
||||
where
|
||||
concatDocs = concatMap (\(Document text _) -> text ++ " ")
|
||||
|
||||
classDocs x = filter ((==x) . c) documents
|
||||
classMegadoc x = concatMap (\(Document text _) -> text ++ " ") (classDocs x)
|
||||
classWords x = words (classMegadoc x)
|
||||
classNGram n = ngram n . classMegadoc
|
||||
classVocabulary x = ordNub (classWords x)
|
||||
classMegadoc = concatMap (\(Document text _) -> text ++ " ") . classDocs
|
||||
classWords = words . classMegadoc
|
||||
classNGram = concatMap (\(Document text _) -> text ++ " ") . ngram 2 . classDocs
|
||||
classNGramWords = words . classNGram
|
||||
classVocabulary = ordNub . classWords
|
||||
classPrior x = genericLength (classDocs x) / genericLength documents
|
||||
countWordInDoc d w = genericLength (filter (==w) d)
|
||||
classWordsCounts x =
|
||||
let voc = classVocabulary x
|
||||
in zip voc $ map (countWordInDoc (classWords x)) voc
|
||||
wordsCount ws voc =
|
||||
zip voc $ map (countWordInDoc ws) voc
|
||||
classWordsCounts x = wordsCount (classWords x) (classVocabulary x)
|
||||
classNGramCounts x = wordsCount (classNGramWords x) (ordNub $ classNGramWords x)
|
||||
|
||||
ngram :: Int -> String -> [String]
|
||||
ngram n text =
|
||||
ngram :: Int -> [Document] -> [Document]
|
||||
ngram n documents =
|
||||
map (\(Document text c) -> Document (helper text) c) documents
|
||||
where
|
||||
helper text =
|
||||
let ws = words text
|
||||
in map (\(i, w) -> unwords $ w:((take (n - 1) . drop (i+1)) ws)) (zip [0..] ws)
|
||||
pairs = zip [0..] ws
|
||||
grams = map (\(i, w) -> concat . intersperse "_" $ w:((take (n - 1) . drop (i+1)) ws)) pairs
|
||||
in unwords ("<b>":grams)
|
||||
|
||||
run :: String -> NB -> Class
|
||||
run text (NB documents classes vocabulary megadoc cd cw) =
|
||||
run :: String -> NB -> (Class, Double)
|
||||
run txt (NB documents classes vocabulary megadoc cd cw cgram) =
|
||||
let scores = map (score . fst) classes
|
||||
in argmax scores
|
||||
index = argmax scores
|
||||
m = maximum scores
|
||||
confidence = m / sum scores
|
||||
in (index, 0)
|
||||
where
|
||||
score c =
|
||||
let prior = snd (classes !! c)
|
||||
in prior * product (map (prob c) (words text))
|
||||
|
||||
-- below is the formula according to Multinominal Naive Bayes, but it seems
|
||||
-- using a uniform prior probability seems to work better
|
||||
-- in prior * product (map (prob c) (words txt))
|
||||
|
||||
in product (map (prob c) (words txt))
|
||||
|
||||
prob c w =
|
||||
let tctM = find ((== w) . fst) (snd (cw !! c))
|
||||
tct = (snd . fromJust) tctM
|
||||
cvoc = (genericLength . snd) (cw !! c)
|
||||
tct = if isJust tctM then (snd . fromJust) tctM else 0
|
||||
cvoc = sum $ map snd (snd (cw !! c))
|
||||
voc = vocabulary
|
||||
in
|
||||
if isJust tctM then
|
||||
realToFrac (tct + 1) / realToFrac (cvoc + voc)
|
||||
else
|
||||
1 / realToFrac (cvoc + voc)
|
||||
gram = find ((==w) . last . splitOn "_" . fst) (snd (cgram !! c))
|
||||
pg = if isJust gram then (snd . fromJust) gram else 0
|
||||
-- in realToFrac (tct * pg + 1) / realToFrac (cvoc + voc) -- uncomment to enable ngrams
|
||||
in realToFrac (tct + 1) / realToFrac (cvoc + voc)
|
||||
|
||||
argmax :: (Ord a) => [a] -> Int
|
||||
argmax x = fst $ maximumBy (\(_, a) (_, b) -> a `compare` b) (zip [0..] x)
|
||||
@ -113,7 +135,8 @@ module Sibe.NaiveBayes
|
||||
let puncs = filter (`notElem` ['!', '"', '#', '$', '%', '(', ')', '.', '?']) (trim string)
|
||||
spacify = foldl (\acc x -> replace x ' ' acc) puncs [',', '/', '-', '\n', '\r']
|
||||
stemmed = unwords $ map (stem Porter) (words spacify)
|
||||
in stemmed
|
||||
nonumber = filter (not . isNumber) stemmed
|
||||
in (unwords . words) nonumber
|
||||
where
|
||||
trim = f . f
|
||||
where
|
||||
@ -124,11 +147,21 @@ module Sibe.NaiveBayes
|
||||
cleanDocuments :: [Document] -> [Document]
|
||||
cleanDocuments documents =
|
||||
let cleaned = map (\(Document text c) -> Document (cleanText text) c) documents
|
||||
wc = wordCounts (concatDocs cleaned)
|
||||
in cleaned
|
||||
|
||||
removeWords :: [String] -> [Document] -> [Document]
|
||||
removeWords ws documents =
|
||||
map (\(Document text c) -> Document (rm ws text) c) documents
|
||||
where
|
||||
rm list text =
|
||||
unwords $ filter (`notElem` list) (words text)
|
||||
|
||||
removeStopwords :: Int -> [Document] -> [Document]
|
||||
removeStopwords i documents =
|
||||
let wc = wordCounts (concatDocs documents)
|
||||
wlist = sortBy (\(_, a) (_, b) -> b `compare` a) wc
|
||||
stopwords = l $ map fst (take 30 wlist)
|
||||
wstopwords = map (\(Document text c) -> Document (removeWords stopwords text) c) cleaned
|
||||
in wstopwords
|
||||
stopwords = map fst (take i wlist)
|
||||
in removeWords stopwords documents
|
||||
where
|
||||
vocabulary x = ordNub (words x)
|
||||
countWordInDoc d w = genericLength (filter (==w) d)
|
||||
@ -136,9 +169,6 @@ module Sibe.NaiveBayes
|
||||
let voc = vocabulary x
|
||||
in zip voc $ map (countWordInDoc (words x)) voc
|
||||
|
||||
removeWords list text =
|
||||
unwords $ filter (`notElem` list) (words text)
|
||||
|
||||
concatDocs = concatMap (\(Document text _) -> text ++ " ")
|
||||
|
||||
l :: (Show a) => a -> a
|
||||
@ -151,37 +181,38 @@ module Sibe.NaiveBayes
|
||||
go s (x:xs) = if x `Set.member` s then go s xs
|
||||
else x : go (Set.insert x s) xs
|
||||
|
||||
accuracy :: [(Int, Int)] -> Double
|
||||
accuracy :: [(Int, (Int, Double))] -> Double
|
||||
accuracy results =
|
||||
let correct = filter (uncurry (==)) results
|
||||
let pairs = map (\(a, b) -> (a, fst b)) results
|
||||
correct = filter (uncurry (==)) pairs
|
||||
in genericLength correct / genericLength results
|
||||
|
||||
recall :: [(Int, Int)] -> Double
|
||||
recall :: [(Int, (Int, Double))] -> Double
|
||||
recall results =
|
||||
let classes = ordNub (map fst results)
|
||||
s = sum (map rec classes) / genericLength classes
|
||||
in s
|
||||
where
|
||||
rec a =
|
||||
let t = genericLength $ filter (\(c, r) -> c == r && c == a) results
|
||||
y = genericLength $ filter (\(c, r) -> c == a) results
|
||||
let t = genericLength $ filter (\(c, (r, _)) -> c == r && c == a) results
|
||||
y = genericLength $ filter (\(c, (r, _)) -> c == a) results
|
||||
in t / y
|
||||
|
||||
precision :: [(Int, Int)] -> Double
|
||||
precision :: [(Int, (Int, Double))] -> Double
|
||||
precision results =
|
||||
let classes = ordNub (map fst results)
|
||||
s = sum (map prec classes) / genericLength classes
|
||||
in s
|
||||
where
|
||||
prec a =
|
||||
let t = genericLength $ filter (\(c, r) -> c == r && c == a) results
|
||||
y = genericLength $ filter (\(c, r) -> r == a) results
|
||||
let t = genericLength $ filter (\(c, (r, _)) -> c == r && c == a) results
|
||||
y = genericLength $ filter (\(c, (r, _)) -> r == a) results
|
||||
in
|
||||
if y == 0
|
||||
then 0
|
||||
else t / y
|
||||
|
||||
fmeasure :: [(Int, Int)] -> Double
|
||||
fmeasure :: [(Int, (Int, Double))] -> Double
|
||||
fmeasure results =
|
||||
let r = recall results
|
||||
p = precision results
|
||||
|
||||
Loading…
Reference in New Issue
Block a user