adapt sbp for mitigating sens on noise

Author: Maximilian Reißmann

paper/ConstraintViaSBP.jl

@@ -120,16 +120,16 @@ function main()
                 start_time = time_ns()
 
                 regressor = GepRegressor(num_cols - 1;
-                    considered_dimensions=phy_dims,
+                    considered_dimensions=phy_dims, gene_count=3, head_len=6,
                     entered_non_terminals=[:+, :-, :*, :/, :sqrt, :sin, :cos, :exp, :log],
-                    max_permutations_lib=5000, rounds=7, number_of_objectives=1)
+                    max_permutations_lib=10000, rounds=5, number_of_objectives=1)
 
 
                 @inline function loss_new_(elem, validate::Bool)
                     try
                         if isnan(mean(elem.fitness)) && elem.dimension_homogene || validate
                             y_pred = elem.compiled_function(x_train', regressor.operators_)
-                            fit = sqrt(get_loss_function("mse")(y_train, y_pred))
+                            fit = sqrt(get_loss_function("rmse")(y_train, y_pred))
                             elem.fitness =  (fit+length(elem.expression_raw)*0.1*fit,)
                         end
                     catch e
@@ -139,8 +139,8 @@ function main()
 
 
                 #perform the regression by entering epochs, population_size, the feature cols, the target col and the loss function
-                fit!(regressor, epochs, population_size, loss_new_; target_dimension=target_dim,
-                break_condition=break_condition, correction_amount=0.5)
+		fit!(regressor, epochs, population_size, loss_new_; target_dimension=target_dim,
+                break_condition=break_condition, correction_amount=0.5, cycles=30)
 
                 end_time = (time_ns() - start_time) / 1e9
                 elem = regressor.best_models_[1]

src/Gep.jl

@@ -216,7 +216,7 @@ Applies correction operations to ensure dimensional homogeneity in chromosomes.
         Threads.@threads for i in 1:pop_amount
             if !(population[i].dimension_homogene) && population[i].compiled && isnan(mean(population[i].fitness))
                 distance, correction = correction_callback(population[i].genes, population[i].toolbox.gen_start_indices,
-                    population[i].expression_raw)
+                    population[i].expression_raw, epoch)
                 if correction
                     compile_expression!(population[i]; force_compile=true)
                     population[i].dimension_homogene = true

src/RegressionWrapper.jl

@@ -713,13 +713,13 @@ function fit!(regressor::GepRegressor, epochs::Int, population_size::Int, x_trai
 )
 
     correction_callback = if !isnothing(target_dimension)
-        (genes, start_indices, expression) -> correct_genes!(
+        (genes, start_indices, expression, generation) -> correct_genes!(
             genes,
             start_indices,
             expression,
             target_dimension,
             regressor.token_dto_;
-            cycles=cycles
+            cycles=generation>1 ? 10 : cycles
         )
     else
         nothing
@@ -792,13 +792,13 @@ function fit!(regressor::GepRegressor, epochs::Int, population_size::Int, loss_f
 )
 
     correction_callback = if !isnothing(target_dimension)
-        (genes, start_indices, expression) -> correct_genes!(
+        (genes, start_indices, expression, generation) -> correct_genes!(
             genes,
             start_indices,
             expression,
             target_dimension,
             regressor.token_dto_;
-            cycles=cycles
+            cycles=generation>1 ? 10 : cycles
         )
     else
         nothing

src/Sbp.jl

@@ -1164,11 +1164,11 @@ function calculate_vector_dimension!(tree::TempComputeTree)
     tree.vector_dimension = function_op(dims...)
 
     #needs to be revised
-    #if length(dims) == 2 && has_inf16(tree.vector_dimension)
-    #    tree.symbol = rand(point_operations)
-    #    function_op = tokenLib.physical_operation_dict[][tree.symbol]
-    #    tree.vector_dimension = function_op(dims...)
-    #end
+    if length(dims) == 2 && has_inf16(tree.vector_dimension)
+        tree.symbol = rand(point_operations)
+        function_op = tokenLib.physical_operation_dict[][tree.symbol]
+        tree.vector_dimension = function_op(dims...)
+    end
     tree.modified = false
     return tree.vector_dimension
 end
@@ -1197,6 +1197,10 @@ function enforce_changes!(tree::TempComputeTree, expected_dim::Vector{Float16},
     if isnothing(exchange_symbol) || isempty(exchange_symbol)
         return false
     end
+    
+    
+    
+    
     tree.depend_on[index] = rand(exchange_symbol)
     tree.modified = true
     calculate_vector_dimension!(tree)
@@ -1451,7 +1455,7 @@ function propagate_necessary_changes!(
         return true
     end
 
-    if check_crit_up!(tree.depend_on_total_number + 1, expected_dim, tree) && distance_to_change <= 0
+    if check_crit_up!(tree.depend_on_total_number + 1, expected_dim, tree) && distance_to_change <= 0 && rand() > 0.01
         return enforce_changes!(tree, expected_dim)
     end