Feature/modular error

src/Entities.jl

@@ -324,7 +324,7 @@ Updates chromosome's compiled_function and related fields
             chromosome.fitness = chromosome.toolbox.fitness_reset[2]
             chromosome.compiled = true
         catch e
-            @error "something went wrong" exception = (e, catch_backtrace())
+            #@error "something went wrong" exception = (e, catch_backtrace())
             chromosome.fitness = chromosome.toolbox.fitness_reset[1]
         end
     end

src/Gep.jl

@@ -344,7 +344,7 @@ The evolution process stops when either:
 
         if epoch < epochs
             if length(fits_representation[1]) == 1
-                selectedMembers = tournament_selection(fits_representation[1:mating_size], mating_size, tourni_size)
+                selectedMembers = tournament_selection(fits_representation, mating_size, tourni_size)
             else
                 selectedMembers = nsga_selection(fits_representation)
             end

src/RegressionWrapper.jl

@@ -263,7 +263,7 @@ Dictionary containing default probabilities and parameters for genetic algorithm
 These values can be adjusted to fine-tune the genetic algorithm's behavior.
 """
 const GENE_COMMON_PROBS = Dict{String,AbstractFloat}(
-    "one_point_cross_over_prob" => 0.4,
+    "one_point_cross_over_prob" => 0.5,
     "two_point_cross_over_prob" => 0.3,
     "mutation_prob" => 1.0,
     "mutation_rate" => 0.1,
@@ -274,9 +274,9 @@ const GENE_COMMON_PROBS = Dict{String,AbstractFloat}(
     "fusion_prob" => 0.1,
     "fusion_rate" => 0.2,
     "inversion_prob" => 0.1,
-    "reverse_insertion" => 0.05,
-    "reverse_insertion_tail" => 0.05,
-    "mating_size" => 0.5)
+    "reverse_insertion" => 0.1,
+    "reverse_insertion_tail" => 0.1,
+    "mating_size" => 0.7)
 
 const SymbolDict = OrderedDict{Int8,Int8}
 const CallbackDict = Dict{Int8,Function}
@@ -753,8 +753,8 @@ function fit!(regressor::GepRegressor, epochs::Int, population_size::Int, loss_f
     optimization_epochs::Int=100,
     hof::Int=3,
     correction_epochs::Int=1,
-    correction_amount::Real=0.05,
-    opt_method_const::Symbol=:cg,
+    correction_amount::Real=0.3,
+    opt_method_const::Symbol=:nd,
     target_dimension::Union{Vector{Float16},Nothing}=nothing,
     cycles::Int=10, max_iterations::Int=150, n_starts::Int=5,
     break_condition::Union{Function,Nothing}=nothing
@@ -827,7 +827,7 @@ function fit!(regressor::GepTensorRegressor, epochs::Int, population_size::Int,
         regressor.toolbox_,
         evalStrat;
         hof=hof,
-        tourni_size=max(Int(ceil(population_size * 0.03)), 3)
+        tourni_size=max(Int(ceil(population_size * 0.003)), 3)
     )
 
     regressor.best_models_ = best

src/Sbp.jl

@@ -74,6 +74,7 @@ const FAILURE_RECURSION_SIZE = -21
 const STD_DIM_SIZE = 7
 const ZERO_DIM = zeros(Float16, STD_DIM_SIZE)
 const EMPTY_DIM = Float16[typemax(Float16) for _ in 1:STD_DIM_SIZE]
+const F16_LOWER_BOUND = eps(Float16)
 
 using OrderedCollections
 using Random
@@ -126,11 +127,11 @@ function mul_unit_backward(u1::Vector{Float16}, u2::Vector{Float16}, expected_di
     elseif has_inf16(u1)
         return expected_dim .- u2, u2
     else
-        if isapprox(u1, u2, atol=eps(Float16))
+        if isapprox(u1, u2, atol=F16_LOWER_BOUND)
             lr = expected_dim .- expected_dim .÷ 2
             rl = expected_dim .- lr
             return lr, rl
-        elseif isapprox(u1, expected_dim, atol=eps(Float16))
+        elseif isapprox(u1, expected_dim, atol=F16_LOWER_BOUND)
             return u1, expected_dim .- u1
         else
             return expected_dim .- u2, u2
@@ -159,11 +160,11 @@ function div_unit_backward(u1::Vector{Float16}, u2::Vector{Float16}, expected_di
     elseif has_inf16(u1)
         return expected_dim .+ u2, u2
     else
-        if isapprox(u1, u2, atol=eps(Float16))
+        if isapprox(u1, u2, atol=F16_LOWER_BOUND)
             lr = expected_dim .- expected_dim .÷ 2
             rl = .-(expected_dim .+ lr)
             return lr, rl
-        elseif isapprox(u1, expected_dim, atol=eps(Float16))
+        elseif isapprox(u1, expected_dim, atol=F16_LOWER_BOUND)
             return u1, .-(expected_dim .+ u1)
         else
             return expected_dim .+ u2, u2
@@ -718,6 +719,7 @@ mutable struct TempComputeTree
     tokenDto::TokenDto
     depend_on_total_number::Int
     exchange_len::Int
+    modified::Bool
 
     function TempComputeTree(symbol::Int8,
         depend_on::Vector{T}=Union{TempComputeTree,Int8}[],
@@ -728,7 +730,7 @@ mutable struct TempComputeTree
             vector_dimension,
             tokenDto,
             length(depend_on),
-            -1)
+            -1,true)
     end
 end
 
@@ -750,6 +752,7 @@ function flush!(tree::TempComputeTree)
 end
 
 function calculate_vector_dimension!(tree::TempComputeTree)
+    !tree.modified && return tree.vector_dimension
     tdto = tree.tokenDto
     tokenLib = tdto.tokenLib
     point_operations = tdto.point_operations
@@ -765,7 +768,7 @@ function calculate_vector_dimension!(tree::TempComputeTree)
         function_op = tokenLib.physical_operation_dict[][tree.symbol]
         tree.vector_dimension = function_op(dims...)
     end
-
+    tree.modified = false
     return tree.vector_dimension
 end
 
@@ -794,8 +797,9 @@ function enforce_changes!(tree::TempComputeTree, expected_dim::Vector{Float16},
         return false
     end
     tree.depend_on[index] = rand(exchange_symbol)
+    tree.modified = true
     calculate_vector_dimension!(tree)
-    return calculate_distance(tree.vector_dimension, expected_dim) < eps(Float16)
+    return calculate_distance(tree.vector_dimension, expected_dim) < F16_LOWER_BOUND
 
 end
 
@@ -867,9 +871,10 @@ function enforce_changes!(tree::TempComputeTree, expected_dim::Vector{Float16};
         if new_tree isa TempComputeTree
             tree.symbol = new_tree.symbol
             tree.depend_on = new_tree.depend_on
+            tree.modified = true
             calculate_vector_dimension!(tree)
         end
-        return calculate_distance(tree.vector_dimension, expected_dim) < eps(Float16)
+        return calculate_distance(tree.vector_dimension, expected_dim) < F16_LOWER_BOUND
 end
 
 
@@ -912,7 +917,7 @@ function find_closest(distance::Vector{Float16},
         for key in all_keys
             if min_len <= key[2] <= expression_len
                 sim = calculate_darityistance(distance, key[1])
-                sim < eps(Float16) && return rand(lib[key])
+                sim < F16_LOWER_BOUND && return rand(lib[key])
             end
         end
     end
@@ -1010,7 +1015,7 @@ function propagate_necessary_changes!(
         return false
     end
 
-    if !has_inf16(tree.vector_dimension) && isapprox(tree.vector_dimension, expected_dim, atol=eps(Float16)) 
+    if !has_inf16(tree.vector_dimension) && isapprox(tree.vector_dimension, expected_dim, atol=F16_LOWER_BOUND) 
         return true
     end
 
@@ -1024,7 +1029,7 @@ function propagate_necessary_changes!(
         ret_val = handle_unary_operation(tree, expected_dim, distance_to_change)
     end
     calculate_vector_dimension!(tree)
-    return ret_val && isapprox(tree.vector_dimension, expected_dim, atol=eps(Float16))
+    return ret_val && isapprox(tree.vector_dimension, expected_dim, atol=F16_LOWER_BOUND)
 end
 
 
@@ -1228,7 +1233,7 @@ See also: [`TempComputeTree`](@ref), [`SBPUtils.propagate_necessary_changes!`](@
     distance = calculate_distance(target_dimension, calculate_vector_dimension!(tree))
 
     temp_tree = tree
-    if distance < eps(Float16)
+    if distance < F16_LOWER_BOUND
         for (count, index) in enumerate(reverse(start_indices))
             if index != start_indices[1]
                 genes[count] = temp_tree.symbol
@@ -1240,7 +1245,7 @@ See also: [`TempComputeTree`](@ref), [`SBPUtils.propagate_necessary_changes!`](@
         end
     end
 
-    return distance, distance < eps(Float16)
+    return distance, distance < F16_LOWER_BOUND
 end
 
 function get_feature_dims_json(json_data::Dict{String,Any}, features::Vector{String}, case_name::String; dims_identifier::String="dims")

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 = 100
-population_size = 20
+epochs = 101
+population_size = 100
 
 #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] * rand()
+x_data = randn(Float64, 1000, number_features)
+y_data = @. x_data[:,1] * x_data[:,1] + x_data[:,1] * x_data[:,2] - 2 * x_data[:,2] * x_data[:,2]
 
 #define the 
 regressor = GepRegressor(number_features)