test: add verification script for MOEA/D variants

Author: MichalRedm

scripts/verify_variants.py

@@ -0,0 +1,60 @@
+import os
+import sys
+import numpy as np
+from datetime import datetime
+
+# Add project root to sys.path
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from src.portfolio import (
+    calculate_expected_returns_and_cov,
+    MOEADOptimizer,
+    MOEADDRAOptimizer,
+    MOEADAWAOptimizer,
+    PortfolioProblem,
+    ReturnObjective,
+    RiskObjective,
+)
+
+def run_test():
+    print("Starting MOEA/D Variants Verification Test...")
+    
+    # Mock data for testing
+    num_assets = 5
+    expected_returns = np.array([0.1, 0.15, 0.12, 0.08, 0.11])
+    cov_matrix = np.eye(num_assets) * 0.05
+    cov_matrix[0, 1] = cov_matrix[1, 0] = 0.01
+    
+    data_kwargs = {
+        "expected_returns": expected_returns,
+        "cov_matrix": cov_matrix
+    }
+    
+    problem = PortfolioProblem([ReturnObjective(), RiskObjective()])
+    
+    optimizers = {
+        "Standard MOEA/D": MOEADOptimizer(problem=problem),
+        "MOEA/D-DRA": MOEADDRAOptimizer(problem=problem),
+        "MOEA/D-AWA": MOEADAWAOptimizer(problem=problem)
+    }
+    
+    for name, opt in optimizers.items():
+        print(f"\nRunning {name}...")
+        try:
+            metrics, weights, w_vectors = opt.generate_pareto_front(
+                num_points=50, 
+                generations=100, 
+                verbose=False, 
+                **data_kwargs
+            )
+            print(f"  {name} completed successfully.")
+            print(f"  Points found: {len(weights)}")
+            print(f"  Return range: [{np.min(metrics['Return']):.4f}, {np.max(metrics['Return']):.4f}]")
+            print(f"  Risk range:   [{np.min(metrics['Risk']):.4f}, {np.max(metrics['Risk']):.4f}]")
+        except Exception as e:
+            print(f"  {name} FAILED with error: {e}")
+            import traceback
+            traceback.print_exc()
+
+if __name__ == "__main__":
+    run_test()