Update

src/predictors/Sigmoid.jl

@@ -58,17 +58,9 @@ ReducedSpace(Sigmoid())
 """
 struct Sigmoid <: AbstractPredictor end
 
-_eval(::Sigmoid, x::Real) = 1 / (1 + exp(-x))
-
 function add_predictor(model::JuMP.AbstractModel, predictor::Sigmoid, x::Vector)
     y = JuMP.@variable(model, [1:length(x)], base_name = "moai_Sigmoid")
-    cons = Any[]
-    for i in 1:length(x)
-        x_l, x_u = _get_variable_bounds(x[i])
-        y_l = x_l === nothing ? 0 : _eval(predictor, x_l)
-        y_u = x_u === nothing ? 1 : _eval(predictor, x_u)
-        _set_bounds_if_finite(cons, y[i], y_l, y_u)
-    end
+    cons = _set_direct_bounds(x -> 1 / (1 + exp(-x)), 0, 1, x, y)
     append!(cons, JuMP.@constraint(model, y .== 1 ./ (1 .+ exp.(-x))))
     return y, Formulation(predictor, y, cons)
 end

src/predictors/SoftPlus.jl

@@ -61,22 +61,16 @@ struct SoftPlus <: AbstractPredictor
     SoftPlus(; beta::Float64 = 1.0) = new(beta)
 end
 
-_eval(f::SoftPlus, x::Real) = log(1 + exp(f.beta * x)) / f.beta
+_softplus(f::SoftPlus, x::Real) = log(1 + exp(f.beta * x)) / f.beta
 
 function add_predictor(
     model::JuMP.AbstractModel,
     predictor::SoftPlus,
     x::Vector,
 )
-    y = JuMP.@variable(model, [1:length(x)], base_name = "moai_SoftPlus")
-    cons = Any[]
-    for i in 1:length(x)
-        x_l, x_u = _get_variable_bounds(x[i])
-        y_l = x_l === nothing ? 0 : _eval(predictor, x_l)
-        y_u = x_u === nothing ? nothing : _eval(predictor, x_u)
-        _set_bounds_if_finite(cons, y[i], y_l, y_u)
-    end
     beta = predictor.beta
+    y = JuMP.@variable(model, [1:length(x)], base_name = "moai_SoftPlus")
+    cons = _set_variable_bounds(x -> _softplus(beta), 0, nothing, x, y)
     append!(
         cons,
         JuMP.@constraint(model, y .== log.(1 .+ exp.(beta .* x)) ./ beta),

src/predictors/Tanh.jl

@@ -62,13 +62,7 @@ _eval(::Tanh, x::Real) = tanh(x)
 
 function add_predictor(model::JuMP.AbstractModel, predictor::Tanh, x::Vector)
     y = JuMP.@variable(model, [1:length(x)], base_name = "moai_Tanh")
-    cons = Any[]
-    for i in 1:length(x)
-        x_l, x_u = _get_variable_bounds(x[i])
-        y_l = x_l === nothing ? -1 : _eval(predictor, x_l)
-        y_u = x_u === nothing ? 1 : _eval(predictor, x_u)
-        _set_bounds_if_finite(cons, y[i], y_l, y_u)
-    end
+    cons = _set_variable_bounds(tanh, -1, 1, x, y)
     append!(cons, JuMP.@constraint(model, y .== tanh.(x)))
     return y, Formulation(predictor, y, cons)
 end

src/utilities.jl

@@ -43,3 +43,15 @@ _get_variable_bounds(::Any) = -Inf, Inf
 
 # Default fallback: skip setting variable bound
 _set_bounds_if_finite(::Vector, ::Any, ::Any, ::Any) = nothing
+
+
+function _set_direct_bounds(f::F, l, u, x::Vector, y::Vector) where {F}
+    cons = Any[]
+    for (xi, yi) in zip(x, y)
+        x_l, x_u = _get_variable_bounds(xi)
+        y_l = x_l === nothing ? l : f(x_l)
+        y_u = x_u === nothing ? u : f(x_u)
+        _set_bounds_if_finite(cons, yi, y_l, y_u)
+    end
+    return cons
+end