Reorganize codebase

src/LossFunctions.jl

@@ -76,23 +76,20 @@ export
     ZeroOneLoss,
 
     OrdinalMarginLoss,
-    OrdinalHingeLoss,
 
     weightedloss,
 
     AggMode
 
+# for maintainers
 include("devutils.jl")
-include("aggregatemode.jl")
 
-include("supervised/supervised.jl")
-include("supervised/sparse.jl")
-include("supervised/distance.jl")
-include("supervised/margin.jl")
-include("supervised/scaledloss.jl")
-include("supervised/weightedbinary.jl")
-include("supervised/other.jl")
-include("supervised/ordinal.jl")
-include("supervised/io.jl")
+# loss and aggregation
+include("aggmode.jl")
+include("supervised.jl")
+
+# IO functionality
+include("printing.jl")
+include("plotrecipes.jl")
 
 end # module

src/plotrecipes.jl

@@ -0,0 +1,11 @@
+_loss_xguide(loss::MarginLoss) = "y * h(x)"
+_loss_xguide(loss::DistanceLoss) = "h(x) - y"
+_loss_yguide(loss::SupervisedLoss) = "L("*_loss_xguide(loss)*")"
+
+@recipe function plot(loss::SupervisedLoss, range=-2:0.05:2; fun=value)
+    xguide --> _loss_xguide(loss)
+    yguide --> _loss_yguide(loss)
+    label  --> string(loss)
+    l(a) = fun(loss, a)
+    l, range
+end

src/supervised/io.jl → src/printing.jl

@@ -1,4 +1,3 @@
-
 Base.print(io::IO, loss::SupervisedLoss, args...) = print(io, typeof(loss).name.name, args...)
 Base.print(io::IO, loss::L1DistLoss, args...) = print(io, "L1DistLoss", args...)
 Base.print(io::IO, loss::L2DistLoss, args...) = print(io, "L2DistLoss", args...)
@@ -12,7 +11,6 @@ Base.print(io::IO, loss::DWDMarginLoss, args...) = print(io, typeof(loss).name.n
 Base.print(io::IO, loss::PeriodicLoss, args...) = print(io, typeof(loss).name.name, " with c = $(round(2π / loss.k, digits=1))", args...)
 Base.print(io::IO, loss::ScaledLoss{T,K}, args...) where {T,K} = print(io, "$(K) * ($(loss.loss))", args...)
 
-# Pretty print weighted loss
 _round(num) = round(num) == round(num, digits=1) ? round(Int, num) : round(num, digits=1)
 function _relation(num)
     if num <= 0
@@ -25,20 +23,4 @@ function _relation(num)
         "$(_round(num/(1-num))):1 weighted"
     end
 end
-
 Base.print(io::IO, loss::WeightedBinaryLoss{T,W}, args...) where {T,W} = print(io, "$(_relation(W)) $(loss.loss)", args...)
-
-# -------------------------------------------------------------
-# Plot Recipes
-
-_loss_xguide(loss::MarginLoss) = "y * h(x)"
-_loss_xguide(loss::DistanceLoss) = "h(x) - y"
-_loss_yguide(loss::SupervisedLoss) = "L("*_loss_xguide(loss)*")"
-
-@recipe function plot(loss::SupervisedLoss, range=-2:0.05:2; fun=value)
-    xguide --> _loss_xguide(loss)
-    yguide --> _loss_yguide(loss)
-    label  --> string(loss)
-    l(a) = fun(loss, a)
-    l, range
-end

src/supervised/supervised.jl → src/supervised.jl

@@ -1467,3 +1467,14 @@ true
 issymmetric(::SupervisedLoss) = false
 
 isminimizable(l::SupervisedLoss) = isconvex(l)
+
+# -----------------
+# IMPLEMENTATIONS
+# -----------------
+include("supervised/sparse.jl")
+include("supervised/distance.jl")
+include("supervised/margin.jl")
+include("supervised/scaled.jl")
+include("supervised/weightedbinary.jl")
+include("supervised/other.jl")
+include("supervised/ordinal.jl")

src/supervised/margin.jl

@@ -1,9 +1,3 @@
-# Note: agreement = output * target
-#       Agreement is high when output and target are the same sign and |output| is large.
-#       It is an indication that the output represents the correct class in a margin-based model.
-
-# ============================================================
-
 @doc doc"""
     ZeroOneLoss <: MarginLoss
 

src/supervised/ordinal.jl

@@ -64,6 +64,3 @@ for fun in (:isdifferentiable, :istwicedifferentiable)
         return true
     end
 end
-
-const OrdinalHingeLoss = OrdinalMarginLoss{HingeLoss}
-# const OrdinalSmoothedHingeLoss = OrdinalMarginLoss{<:SmoothedL1HingeLoss}

test/tst_loss.jl

@@ -256,7 +256,6 @@ end
     @test L2DistLoss === LPDistLoss{2}
     @test HingeLoss === L1HingeLoss
     @test EpsilonInsLoss === L1EpsilonInsLoss
-    @test OrdinalHingeLoss === OrdinalMarginLoss{HingeLoss}
 end
 
 @testset "Test typestable supervised loss for type stability" begin
@@ -430,7 +429,6 @@ const OrdinalSmoothedHingeLoss = OrdinalMarginLoss{<:SmoothedL1HingeLoss}
         val
     end
     y = rand(1:5, 10); t = randn(10) .+ 3
-    @test OrdinalHingeLoss(5) === OrdinalMarginLoss(HingeLoss(), 5)
     test_value(OrdinalMarginLoss(HingeLoss(), 5), _ordinalhingeloss, y, t)
 end