Multi Processing?

src/nsga_exp.py

@@ -13,6 +13,7 @@ import shap
 from deap import base, creator, tools, algorithms
 from algorithms import lasso, random_forest, gradient_boosting, decision_tree_regressor, ridge_regressor, stacking_lasso
 
+from multiprocessing import Pool, cpu_count
 import argparse
 
 creator.create("FitnessMin", base.Fitness, weights=(-1.0, -1.0))  # Minimize both objectives
@@ -162,28 +163,33 @@ def run_nsga_experiment(output_base_directory="./results_nsga",
     start_time = time.time()
     pop = toolbox.population(n=population_size)
 
-    # Evaluate the initial population
-    fits = toolbox.map(toolbox.evaluate, pop)
+# Use a Pool to parallelize evaluations
+with Pool(processes=cpu_count()) as pool:
+    # Evaluate the initial population in parallel
+    fits = list(pool.map(toolbox.evaluate, pop))
     for fit, ind in zip(fits, pop):
         ind.fitness.values = fit[:2]
         ind.shap_values = fit[2]
 
-    # Use the number of parents and offspring in the evolution process
+    # Evolution loop
     for gen in range(n_generations):
         if time.time() - start_time > time_limit:
             print("Time limit exceeded, stopping evolution.")
             break
 
         offspring = algorithms.varAnd(pop, toolbox, cxpb=0.7, mutpb=0.2)
-        fits = toolbox.map(toolbox.evaluate, offspring)
 
+        # Evaluate offspring in parallel
+        fits = list(pool.map(toolbox.evaluate, offspring))
         for fit, ind in zip(fits, offspring):
-            ind.fitness.values = fit[:2]  # Ensure only the first two values are assigned to fitness
+            ind.fitness.values = fit[:2]
             ind.shap_values = fit[2]
 
-        # Select the next generation of parents from the combined pool of parents and offspring
+        # Select next generation
         pop = toolbox.select(pop + offspring, k=population_size)
 
+
+
     for ind in pop:
         for fold_idx, shap_values in enumerate(ind.shap_values):
             shap_output_path = os.path.join(output_directory, f"shap_values_{int(ind[0])}_fold_{fold_idx + 1}.npy")