feat(results): accuracy, recall and precision functions used to calculate measures

fix: read data from another repository

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "examples/doc-classifier-data"]
+	path = examples/doc-classifier-data
+	url = git@github.com:mdibaiee/doc-classifier-data

README.md

@@ -26,6 +26,8 @@ module Main where
         network = session inputs rnetwork labels learning_rate (iterations, epochs)
 
         -- run inputs through the trained network
+        -- note: here we are using the examples in the training dataset to test the network,
+        --       this is here just to demonstrate the way the library works, you should not do this
         results = map (`forward` network) inputs
 
         -- compute the new cost

examples/doc-classifier-data

@@ -0,0 +1 @@
+Subproject commit 5b069a54a6a68efee0ef4bb15c1aa56414f12c28

examples/naivebayes-doc-classifier.hs

@@ -6,24 +6,24 @@ module Main
     import Data.List
     import Data.Maybe
     import Debug.Trace
+    import Data.List.Split
 
     main = do
-      dataset <- readFile "examples/naivebayes-doc-classifier/data-reuters"
+      dataset <- readFile "examples/doc-classifier-data/data-reuters"
-      test <- readFile "examples/naivebayes-doc-classifier/data-reuters-test"
+      test <- readFile "examples/doc-classifier-data/data-reuters-test"
 
-      classes <- map (filter (/= ' ')) . lines <$> readFile "examples/naivebayes-doc-classifier/data-classes"
+      classes <- map (filter (/= ' ')) . lines <$> readFile "examples/doc-classifier-data/data-classes"
 
       let intClasses = [0..length classes - 1]
           documents = createDocuments classes dataset
           testDocuments = createDocuments classes test
           nb = initialize documents
 
-      let testResults (Document text c) =
+          results = map (\(Document text c) -> (c, determine text nb intClasses documents)) testDocuments
-            let r = determine text nb intClasses documents
-            in trace (classes !! c ++ " ~ " ++ classes !! r) c == r
 
-      let results = map testResults testDocuments
+      let showResults (c, r) = putStrLn (classes !! c ++ " ~ " ++ classes !! r)
+      mapM_ showResults results
 
-      putStr "Accuracy: "
+      putStrLn $ "Recall: " ++ show (recall results) ++ "%"
-      putStr . show . round $ (genericLength (filter (==True) results) / genericLength results) * 100
+      putStrLn $ "Precision: " ++ show (precision results) ++ "%"
-      putStrLn "%"
+      putStrLn $ "Accuracy: " ++ show (accuracy results) ++ "%"

sibe.cabal

@@ -50,6 +50,7 @@ executable example-naivebayes-doc-classifier
                      , sibe
                      , hmatrix
                      , containers
+                     , split
   default-language:    Haskell2010
 
 test-suite sibe-test

src/Sibe/NaiveBayes.hs

@@ -4,7 +4,11 @@ module Sibe.NaiveBayes
    createDocuments,
    initialize,
    calculate,
-   determine
+   determine,
+   ordNub,
+   accuracy,
+   precision,
+   recall,
   )
   where
     import Data.List
@@ -63,3 +67,30 @@ module Sibe.NaiveBayes
         go _ [] = []
         go s (x:xs) = if x `Set.member` s then go s xs
                                           else x : go (Set.insert x s) xs
+
+    accuracy :: [(Int, Int)] -> Int
+    accuracy results =
+      let correct = filter (uncurry (==)) results
+      in round $ genericLength correct / genericLength results * 100
+
+    recall :: [(Int, Int)] -> Int
+    recall results =
+      let classes = ordNub (map fst results)
+          s = sum (map rec classes) / genericLength results
+      in round $ s * 100
+      where
+        rec a =
+          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)] -> Int
+    precision results =
+      let classes = ordNub (map fst results)
+          s = sum (map prec classes) / genericLength results
+      in round $ s * 100
+      where
+        prec a =
+          let t = genericLength $ filter (\(c, r) -> c == r && c == a) results
+              y = genericLength $ filter (\(c, r) -> r == a) results
+          in t / y