Improved MLJ interface also for PerceptronClassifier and PegasosClass…

…ifier

This closes issue #31

src/Perceptron/Perceptron_MLJ.jl

@@ -73,11 +73,12 @@ PegasosClassifier(;
 
 function MMI.fit(model::PerceptronClassifier, verbosity, X, y)
  x = MMI.matrix(X)                     # convert table to matrix
+ allClasses = levels(y)
  initialθ  = length(model.initialθ) == 0 ? zeros(size(x,2)) : model.initialθ
  fitresult = perceptron(x, y; θ=initialθ, θ₀=model.initialθ₀, T=model.maxEpochs, nMsgs=0, shuffle=model.shuffle, forceOrigin=model.forceOrigin, returnMeanHyperplane=model.returnMeanHyperplane,rng=model.rng)
  cache=nothing
  report=nothing
- return fitresult, cache, report
+ return (fitresult,allClasses), cache, report
 end
 
 function MMI.fit(model::KernelPerceptronClassifier, verbosity, X, y)
@@ -92,34 +93,36 @@ end
 
 function MMI.fit(model::PegasosClassifier, verbosity, X, y)
  x = MMI.matrix(X)                     # convert table to matrix
+ allClasses = levels(y)
  initialθ  = length(model.initialθ) == 0 ? zeros(size(x,2)) : model.initialθ
  fitresult = pegasos(x, y; θ=initialθ,θ₀=model.initialθ₀, λ=model.λ,η=model.η, T=model.maxEpochs, nMsgs=0, shuffle=model.shuffle, forceOrigin=model.forceOrigin, returnMeanHyperplane=model.returnMeanHyperplane,rng=model.rng)
  cache=nothing
  report=nothing
- return fitresult, cache, report
+ return (fitresult,allClasses), cache, report
 end
 
 # ------------------------------------------------------------------------------
 # Predict functions....
 
 function MMI.predict(model::Union{PerceptronClassifier,PegasosClassifier}, fitresult, Xnew)
-    fittedModel      = fitresult
+    fittedModel      = fitresult[1]
     #classes          = CategoricalVector(fittedModel.classes)
     classes          = fittedModel.classes
-    nLevels          = length(classes)
+    allClasses       = fitresult[2] # as classes do not includes classes unsees at training time
+    nLevels          = length(allClasses)
     nRecords         = MMI.nrows(Xnew)
     modelPredictions = Perceptron.predict(MMI.matrix(Xnew), fittedModel.θ, fittedModel.θ₀, fittedModel.classes)
     predMatrix       = zeros(Float64,(nRecords,nLevels))
     # Transform the predictions from a vector of dictionaries to a matrix
     # where the rows are the PMF of each record
     for n in 1:nRecords
-        for (c,cl) in enumerate(classes)
+        for (c,cl) in enumerate(allClasses)
             predMatrix[n,c] = get(modelPredictions[n],cl,0.0)
         end
     end
     #predictions = [MMI.UnivariateFinite(classes, predMatrix[i,:])
     #               for i in 1:nRecords]
-    predictions = MMI.UnivariateFinite(fittedModel.classes,predMatrix,pool=missing)
+    predictions = MMI.UnivariateFinite(allClasses,predMatrix,pool=missing)
     return predictions
 end