Scoring functions commonly used for evaluation of NLP
systems. Most functions in this module work on lists, but some take
a precomputed table of
Counts. This will give a speedup if you
want to compute multiple scores on the same data. For example to
compute the Mutual Information, Variation of Information and the
Adujusted Rand Index on the same pair of clusterings:
let cs = counts $ zip "abcabc" "abaaba"
mapM_ (print . ($ cs)) [mi, ari, vi]
- accuracy :: (Eq a, Fractional n) => [a] -> [a] -> n
- recipRank :: (Eq a, Fractional n) => a -> [a] -> n
- avgPrecision :: (Fractional n, Ord a) => Set a -> [a] -> n
- ari :: (Ord a, Ord b) => Counts a b -> Double
- mi :: (Ord a, Ord b) => Counts a b -> Double
- vi :: (Ord a, Ord b) => Counts a b -> Double
- type Count = Double
- data Counts a b
- sum :: Num a => [a] -> a
- mean :: (Fractional n, Real a) => [a] -> n
- jaccard :: (Fractional n, Ord a) => Set a -> Set a -> n
- entropy :: [Count] -> Double
Scores for classification and ranking
Accuracy: the proportion of elements in the first list equal to elements at corresponding positions in second list. Lists should be of equal lengths.
Reciprocal rank: the reciprocal of the rank at which the first arguments occurs in the list given as the second argument.
Average precision. http://en.wikipedia.org/wiki/Information_retrieval#Average_precision
Scores for clustering
Adjusted Rand Index: http://en.wikipedia.org/wiki/Rand_index
Mutual information: MI(X,Y) = H(X) - H(X|Y) = H(Y) - H(Y|X). Also known as information gain.
Variation of information: VI(X,Y) = H(X) + H(Y) - 2 MI(X,Y)
Auxiliary types and functions
Jaccard coefficient J(A,B) = |AB| / |A union B|