add constructor for binary classifiers

src/classifier.jl

@@ -14,14 +14,14 @@ function MLJFlux.shape(model::NeuralNetworkClassifier, X, y)
 end
 
 # builds the end-to-end Flux chain needed, given the `model` and `shape`:
-MLJFlux.build(model::NeuralNetworkClassifier, rng, shape) =
+MLJFlux.build(model::Union{NeuralNetworkClassifier, NeuralNetworkBinaryClassifier}, rng, shape) =
     Flux.Chain(build(model.builder, rng, shape...),
         model.finaliser)
 
 # returns the model `fitresult` (see "Adding Models for General Use"
 # section of the MLJ manual) which must always have the form `(chain,
 # metadata)`, where `metadata` is anything extra needed by `predict`:
-MLJFlux.fitresult(model::NeuralNetworkClassifier, chain, y) =
+MLJFlux.fitresult(model::Union{NeuralNetworkClassifier, NeuralNetworkBinaryClassifier}, chain, y) =
     (chain, MLJModelInterface.classes(y[1]))
 
 function MLJModelInterface.predict(model::NeuralNetworkClassifier,
@@ -37,3 +37,25 @@ MLJModelInterface.metadata_model(NeuralNetworkClassifier,
     input=Union{AbstractMatrix{Continuous},Table(Continuous)},
     target=AbstractVector{<:Finite},
     path="MLJFlux.NeuralNetworkClassifier")
+
+#### Binary Classifier
+
+function MLJFlux.shape(model::NeuralNetworkBinaryClassifier, X, y)
+    X = X isa Matrix ? Tables.table(X) : X
+    n_input = Tables.schema(X).names |> length
+    return (n_input, 1) # n_output is always 1 for a binary classifier
+end
+
+function MLJModelInterface.predict(model::NeuralNetworkBinaryClassifier,
+    fitresult,
+    Xnew)
+    chain, levels = fitresult
+    X = reformat(Xnew)
+    probs = vec(chain(X))
+    return MLJModelInterface.UnivariateFinite(levels, probs; augment = true)
+end
+
+MLJModelInterface.metadata_model(NeuralNetworkBinaryClassifier,
+    input=Union{AbstractMatrix{Continuous},Table(Continuous)},
+    target=AbstractVector{<:Finite{2}},
+    path="MLJFlux.NeuralNetworkBinaryClassifier")

src/core.jl

@@ -234,3 +234,9 @@ function collate(model, X, y)
     ymatrix = reformat(y)
     return [_get(Xmatrix, b) for b in row_batches], [_get(ymatrix, b) for b in row_batches]
 end
+function collate(model::NeuralNetworkBinaryClassifier, X, y)
+    row_batches = Base.Iterators.partition(1:nrows(y), model.batch_size)
+    Xmatrix = reformat(X)
+    yvec = (y .== classes(y)[2])' # convert to boolean
+    return [_get(Xmatrix, b) for b in row_batches], [_get(yvec, b) for b in row_batches]
+end

src/types.jl

@@ -3,10 +3,15 @@ abstract type MLJFluxDeterministic <: MLJModelInterface.Deterministic end
 
 const MLJFluxModel = Union{MLJFluxProbabilistic,MLJFluxDeterministic}
 
-for Model in [:NeuralNetworkClassifier, :ImageClassifier]
+for Model in [:NeuralNetworkClassifier, :NeuralNetworkBinaryClassifier, :ImageClassifier]
 
+  # default settings that are not equal across models
   default_builder_ex =
     Model == :ImageClassifier ? :(image_builder(VGGHack)) : Short()
+  default_finaliser = 
+    Model == :NeuralNetworkBinaryClassifier ? Flux.σ : Flux.softmax
+  default_loss = 
+    Model == :NeuralNetworkBinaryClassifier ? Flux.binarycrossentropy : Flux.crossentropy
 
   ex = quote
     mutable struct $Model{B,F,O,L} <: MLJFluxProbabilistic
@@ -23,7 +28,7 @@ for Model in [:NeuralNetworkClassifier, :ImageClassifier]
       acceleration::AbstractResource  # eg, `CPU1()` or `CUDALibs()`
     end
 
-    function $Model(; builder::B=$default_builder_ex, finaliser::F=Flux.softmax, optimiser::O=Flux.Optimise.Adam(), loss::L=Flux.crossentropy, epochs=10, batch_size=1, lambda=0, alpha=0, rng=Random.GLOBAL_RNG, optimiser_changes_trigger_retraining=false, acceleration=CPU1()
+    function $Model(; builder::B=$default_builder_ex, finaliser::F=$default_finaliser, optimiser::O=Flux.Optimise.Adam(), loss::L=$default_loss, epochs=10, batch_size=1, lambda=0, alpha=0, rng=Random.GLOBAL_RNG, optimiser_changes_trigger_retraining=false, acceleration=CPU1()
     ) where {B,F,O,L}
 
       model = $Model{B,F,O,L}(builder, finaliser, optimiser, loss, epochs, batch_size, lambda, alpha, rng, optimiser_changes_trigger_retraining, acceleration