sibe/examples/naivebayes-doc-classifier.hs
2016-10-17 01:54:35 +03:30

82 lines
3.5 KiB
Haskell

module Main
where
import Numeric.Sibe.NLP
import Numeric.Sibe.NaiveBayes
import Text.Printf
import Data.List
import Data.Maybe
import Debug.Trace
import Data.List.Split
import Control.Arrow ((&&&))
import Control.Monad (when, unless)
import Data.Function (on)
import System.Environment
main = do
args <- getArgs
dataset <- readFile "examples/doc-classifier-data/data-reuters"
test <- readFile "examples/doc-classifier-data/data-reuters-test"
classes <- map (filter (/= ' ')) . lines <$> readFile "examples/doc-classifier-data/data-classes"
sws <- lines <$> readFile "examples/stopwords"
let verbose = elem "-v" args || elem "--verbose" args
topten = elem "-10" args || elem "--top-ten" args
unless verbose $ putStrLn "use --verbose to print more information"
let intClasses = [0..length classes - 1]
documents = cleanDocuments . removeWords sws $ createDocuments classes dataset
testDocuments = cleanDocuments $ createDocuments classes test
nb = initialize documents intClasses
-- top-ten
topClasses = take 10 . reverse $ sortBy (compare `on` (length . snd)) (cd nb)
filtered = map (\(c, ds) -> (c, take 100 ds)) topClasses
filteredClasses = map fst filtered
ttDocs = concatMap snd filtered
ttNB = initialize ttDocs filteredClasses
ttTestDocuments = filter ((`elem` filteredClasses) . c) . cleanDocuments $ createDocuments classes test
ttResults = session ttTestDocuments ttNB
normalResults = session testDocuments nb
results = if topten then ttResults else normalResults
iClasses = if topten then filteredClasses else intClasses
-- results = session devTestDocuments nb
when verbose . putStrLn $ "# Example of cleaned document:\n" ++ (show . text $ head documents)
let showResults (c, (r, confidence)) = putStrLn (classes !! c ++ " ~ " ++ classes !! r)
when verbose $ mapM_ showResults results
when (verbose && not topten) .
putStrLn $ "The training data is imbalanced which causes the classifier to be biased towards\n"
++ "some classes, `earn` is an example, the class alone has around 90% accuracy while\n"
++ "the rest of classes have a much lower accuracy and it's commonly seen that most inputs\n"
++ "are incorrectly classified as `earn`.\n"
++ "Try running with --top-ten to classify top 10 classes by using evenly split documents\n"
let
accuracies =
let as = zip iClasses $ map (\c -> filter ((==c) . fst) results) iClasses
av = filter (not . null . snd) as
calculated = map (fst &&& accuracy . snd) av
in sortBy (\(_, a) (_, b) -> b `compare` a) calculated
when verbose $
mapM_ (\(c, a) -> putStrLn $ "Accuracy(" ++ classes !! c ++ ") = " ++ show a) accuracies
putStrLn $ "\nAverages: "
putStrLn $ "Recall = " ++ show (recall results)
putStrLn $ "Precision = " ++ show (precision results)
putStrLn $ "F Measure = " ++ show (fmeasure results)
putStrLn $ "Accuracy = " ++ show (accuracy results)
createDocuments classes content =
let splitted = splitOn (replicate 10 '-' ++ "\n") content
pairs = map ((head . lines) &&& (unwords . tail . lines)) splitted
documents = map (\(topic, text) -> Document text (fromJust $ elemIndex topic classes)) pairs
in documents