Feature/modular error

src/Entities.jl

@@ -313,15 +313,6 @@ Compiles chromosome's genes into executable function - using the types from the
 # Effects
 Updates chromosome's compiled_function and related fields
 """
-
-"""
-    fitness(chromosome::Chromosome)
-
-Get chromosome's fitness value.
-
-# Returns
-Fitness value or tuple
-"""
 @inline function compile_expression!(chromosome::Chromosome; force_compile::Bool=false)
     if !chromosome.compiled || force_compile
         try

src/RegressionWrapper.jl

@@ -716,8 +716,8 @@ function fit!(regressor::GepRegressor, epochs::Int, population_size::Int, x_trai
                 opt_method=opt_method_const, max_iterations=max_iterations, n_restarts=n_starts)
             population[1].fitness = (result,)
             population[1].compiled_function = eqn
-        catch
-            @show "Ignored constant opt."
+        catch e
+            @show "Ignored constant opt." e
         end
     end
 

src/TensorOps.jl

@@ -2,7 +2,6 @@ module TensorRegUtils
 
 using Flux, LinearAlgebra, OrderedCollections, ChainRulesCore, Tensors, PrecompileTools
 
-
 # Abstract base type with parametric types for improved type stability
 abstract type AbstractOperationNode{T} end
 

src/Util.jl

@@ -396,8 +396,8 @@ end
         @inbounds begin
             history.train_loss[epoch] = train_loss
             history.val_loss[epoch] = val_loss
-            #history.train_mean[epoch] = tuple_agg(fit_vector,mean)
-            #history.train_std[epoch] = tuple_agg(fit_vector, std)
+            history.train_mean[epoch] = tuple_agg(fit_vector, mean)
+            history.train_std[epoch] = tuple_agg(fit_vector, std)
         end
     end
 end
@@ -660,12 +660,13 @@ See also: [`DynamicExpressions.Node`](@ref), [`Optim.optimize`](@ref), [`LineSea
 )
 
     nconst = count_constant_nodes(node)
+    baseline = loss(node)
 
     if nconst == 0
-        return node, 0.0
+        return node, baseline
     end
 
-    baseline = loss(node)
+
     best_node = deepcopy(node)
     best_loss = baseline
 
@@ -689,11 +690,19 @@ See also: [`DynamicExpressions.Node`](@ref), [`Optim.optimize`](@ref), [`LineSea
                 end
             end
         end
+        #needs to be revised!
+        x0, refs = get_scalar_constants(current_node)
+
+
+        function opt_step(x::AbstractVector)
+            set_scalar_constants!(current_node,x, refs)
+            loss(current_node)
+        end
 
-        result = Optim.optimize(loss, current_node, algorithm, optimizer_options)
+        result = Optim.optimize(opt_step, x0, algorithm, optimizer_options)
 
         if result.minimum < best_loss
-            best_node = result.minimizer
+            best_node = current_node
             best_loss = result.minimum
         end
     end

test/Main_min_bench.jl

@@ -8,14 +8,14 @@ using BenchmarkTools
 Random.seed!(1)
 
 #Define the iterations for the algorithm and the population size
-epochs = 10
+epochs = 100
 population_size = 20
 
 #Number of features which needs to be inserted
 number_features = 2
 
 x_data = randn(Float64, 10000, number_features)
-y_data = @. x_data[:,1] * x_data[:,1] + x_data[:,1] * x_data[:,2] - 2 * x_data[:,2] * x_data[:,2]
+y_data = @. x_data[:,1] * x_data[:,1] + x_data[:,1] * x_data[:,2] - 2 * x_data[:,2] * x_data[:,2] * rand()
 
 #define the 
 regressor = GepRegressor(number_features)