The following code is from a university assignment of mine to write a classification algorithm (using nearest neighbour) to classify whether or not a given feature set (each feature is the frequency of words in an email) is spam or not.
We are given a CSV file (the training data) with the frequencies, and an integer (1 or 0) at the end of the row indicating whether or not it is spam. So in this form:
The test data is also in this form (including the SPAM column, so we can verify the accuracy).
My question is, how can I make this code more idiomatic, and what speedups can I make to this code? For example, is there a way to write
getMostCommon, without having to
groupBy and then run a
import Text.CSV import Data.List data SpamType = Spam | NotSpam deriving (Show, Eq) type FeatureSet = [Float] toSpam :: Int -> SpamType toSpam 0 = NotSpam toSpam 1 = Spam toSpam a = NotSpam parseClassifiedRecord :: Record -> (FeatureSet, SpamType) parseClassifiedRecord x = (init converted, toSpam (truncate (last converted))) where converted = map (\val -> read val :: Float) x -- returns the euclidean distance between two feature sets difference :: FeatureSet -> FeatureSet -> Float difference first second = sqrt (sum (zipWith (\x y -> (x - y)^2) first second)) -- finds the SpamType of the k nearest 'nodes' in the training set findKNearest :: [(FeatureSet, SpamType)] -> FeatureSet -> Int -> [SpamType] findKNearest trainingSet toMatch k = take k (map snd (sortBy (\x y -> (compare (fst x) (fst y))) [(difference (fst x) toMatch, snd x) | x <- trainingSet])) -- returns item which occurs most often in the list getMostCommon :: (Eq a) => [a] -> a getMostCommon list = head (maximumBy (\x y -> (compare (length x) (length y))) (groupBy (\x y -> (x == y)) list)) -- given a feature set, returns an ordered (i.e. same order as input) -- list of whether or not feature is spam or not spam -- looks at the closest k neighbours classify :: [(FeatureSet, SpamType)] -> FeatureSet -> Int -> SpamType classify trainingSet toClassify k = getMostCommon (findKNearest trainingSet toClassify k) -- gives a value for the accuracy of expected vs actual for spam classification -- i.e. num classified correctly / num total accuracy :: [(SpamType, SpamType)] -> Float accuracy classifications = (fromIntegral $ length (filter (\x -> (fst x) == (snd x)) classifications)) / (fromIntegral $ length classifications) main = do packed <- parseCSVFromFile "spam-train.csv" packedtest <- parseCSVFromFile "spam-test.csv" let (Right trainingSet) = packed let (Right testSet) = packedtest let classifiedTrainingSet = map parseClassifiedRecord (tail (init trainingSet)) let unclassified = map parseClassifiedRecord (tail (init testSet)) let classified = map (\x -> (classify classifiedTrainingSet (fst x) 1, snd x)) unclassified putStrLn (show (accuracy classified))