feat(w2v): draw text charts for words

examples/notmnist.hs

@@ -17,9 +17,6 @@ module Main where
   import System.Random.Shuffle
   import Data.Default.Class
 
-  import qualified Graphics.Rendering.Chart.Easy as Chart
-  import Graphics.Rendering.Chart.Backend.Cairo
-
   main = do
     -- random seed, you might comment this line to get real random results
     setStdGen (mkStdGen 100)

examples/word2vec.hs

@@ -42,28 +42,25 @@ module Main where
 
     let ds = ["the king loves the queen", "the queen loves the king",
               "the dwarf hates the king", "the queen hates the dwarf",
-              "the dwarf poisons the king", "the dwarf poisons the queen"]
+              "the dwarf poisons the king", "the dwarf poisons the queen",
+              "the man loves the woman", "the woman loves the man",
+              "the thief hates the man", "the woman hates the thief",
+              "the thief robs the man", "the thief robs the woman"]
 
-    let session = def { learningRate = 1e-1
+    let session = def { learningRate = 5e-1
                       , batchSize = 1
-                      , epochs = 200
+                      , epochs = 1000
                       , debug = True
                       } :: Session
         w2v = def { docs = ds
                   , dimensions = 25
                   , method = SkipGram
                   , window = 2
+                  , w2vDrawChart = True
+                  , w2vChartName = "w2v.png"
                   } :: Word2Vec
 
-
     (computed, vocvec) <- word2vec w2v session
-    
-    mapM_ (\(w, v) -> do
-                    putStr $ w ++ ": "
-                    let similarities = map (similarity v . snd) computed
-                    let sorted = sortBy (compare `on` similarity v . snd) computed
-                    print . take 2 . drop 1 . reverse $ map fst sorted
-          ) computed
 
     return ()
 

sibe.cabal

@@ -32,6 +32,7 @@ library
                      , data-default-class
                      , Chart
                      , Chart-cairo
+                     , lens
   default-language:    Haskell2010
 
 executable example-xor

src/Sibe.hs

@@ -26,7 +26,6 @@ module Sibe
      sigmoid',
      softmax,
      softmax',
-     sampledSoftmax,
      relu,
      relu',
      crossEntropy,
@@ -183,10 +182,10 @@ module Sibe
           sig x = 1 / max (1 + exp (-x)) 1e-10
 
       -- used for negative sampling
-      sampledSoftmax :: Int -> Vector Double -> Vector Double
-      sampledSoftmax n x = cmap (\a -> exp a / s) x
-        where
-          s = V.sum . exp $ V.take n x
+      {-sampledSoftmax :: Vector Double -> Vector Double-}
+      {-sampledSoftmax x = cmap (\a -> exp a / s) x-}
+        {-where-}
+          {-s = V.sum . exp $ x-}
 
       relu :: Vector Double -> Vector Double
       relu = cmap (max 0.1)

src/Sibe/Word2Vec.hs

@@ -16,19 +16,28 @@ module Sibe.Word2Vec
     import Control.Monad
     import System.Random
 
+    import Graphics.Rendering.Chart as Chart
+    import Graphics.Rendering.Chart.Backend.Cairo
+    import Control.Lens
+
     data W2VMethod = SkipGram | CBOW
     data Word2Vec = Word2Vec { docs :: [String]
                              , window :: Int
                              , dimensions :: Int
                              , method :: W2VMethod
+                             , w2vChartName :: String
+                             , w2vDrawChart :: Bool
                              }
     instance Default Word2Vec where
       def = Word2Vec { docs = []
                      , window = 2
+                     , w2vChartName = "w2v.png"
+                     , w2vDrawChart = False
                      }
 
     word2vec w2v session = do
       seed <- newStdGen
+
       let s = session { training = trainingData
                       , network = randomNetwork 0 (-1, 1) v [(dimensions w2v, (id, one))] (v, (softmax, crossEntropy'))
                       }
@@ -49,6 +58,26 @@ module Sibe.Word2Vec
       -- run words through the hidden layer alone to get the word vector
       let computedVocVec = map (\(w, v) -> (w, runLayer' v hidden)) vocvec
 
+      when (w2vDrawChart w2v) $ do
+        let mat = fromColumns . map snd $ computedVocVec
+            (u, s, v) = svd mat
+            cut = subMatrix (0, 0) (2, cols mat)
+            diagS = diagRect 0 (V.take 2 s) (rows mat) (cols mat)
+
+            twoDimensions = cut $ u <> diagS <> tr v
+            textData = zipWith (\s l -> (V.head l, V.last l, s)) (map fst computedVocVec) (toColumns twoDimensions)
+
+            chart = toRenderable layout
+              where
+                textP  = plot_annotation_values .~ textData
+                      $ def
+                layout = layout_title .~ "word vectors"
+                      $ layout_plots .~ [toPlot textP]
+                      $ def
+                    
+        renderableToFile def (w2vChartName w2v) chart
+        return ()
+
       return (computedVocVec, vocvec)
       where
         -- clean documents