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:
X1,X2,...,Xn,SPAM
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 maximumBy
again?
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))
FromRecord
instance for unboxed vectors to cassava. \$\endgroup\$