feat(exp): extend evolution demo to all MOEA/D variants

Author: MichalRedm

scripts/experiment_evolution.py

@@ -19,6 +19,8 @@
 from src.portfolio import (
     calculate_expected_returns_and_cov,
     MOEADOptimizer,
+    MOEADDRAOptimizer,
+    MOEADAWAOptimizer,
 )
 from src.visualization import plot_population_evolution
 
@@ -38,14 +40,13 @@ def run_evolution_demo():
     seed_everything(42)
 
     # Setup folders
-    run_id = datetime.now().strftime("%Y%m%d_%H%M%S_moead_evolution")
+    run_id = datetime.now().strftime("%Y%m%d_%H%M%S_moead_evolution_all")
     run_folder = os.path.join(OUTPUT_FOLDER, run_id)
     os.makedirs(run_folder, exist_ok=True)
 
-    print(f"Starting MOEA/D Evolution Demo. Result will be saved to {run_folder}")
+    print(f"Starting Multi-Variant Evolution Demo. Results will be saved to {run_folder}")
 
     # 1. Load data and calculate expected returns/cov
-    # (Simplified data hash for speed in this demo)
     raw_data_dict = load_data(DATA_FOLDER, ASSET_NAMES, verbose=False)
     predictor = ExactGPPredictor(
         training_iterations=TRAINING_ITERATIONS,
@@ -62,35 +63,46 @@ def run_evolution_demo():
 
     data_kwargs = {"expected_returns": expected_returns, "cov_matrix": cov_matrix}
 
-    # 2. Run MOEA/D with history recording
-    print("Running MOEA/D with history recording (interval=10)...")
-    moead = MOEADOptimizer()
+    # 2. Variants to test
+    variants = {
+        "MOEA/D": MOEADOptimizer,
+        "MOEA/D-DRA": MOEADDRAOptimizer,
+        "MOEA/D-AWA": MOEADAWAOptimizer,
+    }
+
     history_interval = 10
     generations = 200
 
-    # generate_pareto_front returns: metrics, population, weight_vectors, history (if record_history=True)
-    results = moead.generate_pareto_front(
-        num_points=100,
-        generations=generations,
-        verbose=True,
-        record_history=True,
-        history_interval=history_interval,
-        **data_kwargs,
-    )
+    for name, OptimizerClass in variants.items():
+        print(f"\nProcessing {name}...")
+        optimizer = OptimizerClass()
 
-    # unpack results
-    moead_metrics, _, _, history = results
+        # generate_pareto_front returns: metrics, population, weight_vectors, history (if record_history=True)
+        results = optimizer.generate_pareto_front(
+            num_points=100,
+            generations=generations,
+            verbose=True,
+            record_history=True,
+            history_interval=history_interval,
+            **data_kwargs,
+        )
 
-    # 3. Plot Population Evolution
-    print("Generating Population Evolution Plot...")
-    obj_names = ["Expected Return", "Expected Risk"]
-    save_path = os.path.join(run_folder, "population_evolution.png")
+        # unpack results
+        _metrics, _, _, history = results
 
-    plot_population_evolution(
-        history, obj_names, history_interval=history_interval, save_path=save_path
-    )
+        # 3. Plot Population Evolution
+        print(f"Generating Population Evolution Plot for {name}...")
+        obj_names = ["Expected Return", "Expected Risk"]
+        safe_name = name.replace("/", "").replace("-", "_").lower()
+        save_path = os.path.join(run_folder, f"evolution_{safe_name}.png")
+
+        plot_population_evolution(
+            history, obj_names, history_interval=history_interval, save_path=save_path
+        )
+
+        print(f"Done! Plot saved to {save_path}")
 
-    print(f"Success! Plot saved to {save_path}")
+    print(f"\nAll experiments finished. Results in {run_folder}")
 
 
 if __name__ == "__main__":