Anto01
eabcc4c3ca
BREAKING CHANGE: Module paths have been reorganized for better maintainability. Backwards compatibility aliases with deprecation warnings are provided. New Structure: - core/ - Optimization runners (runner, intelligent_optimizer, etc.) - processors/ - Data processing - surrogates/ - Neural network surrogates - nx/ - NX/Nastran integration (solver, updater, session_manager) - study/ - Study management (creator, wizard, state, reset) - reporting/ - Reports and analysis (visualizer, report_generator) - config/ - Configuration management (manager, builder) - utils/ - Utilities (logger, auto_doc, etc.) - future/ - Research/experimental code Migration: - ~200 import changes across 125 files - All __init__.py files use lazy loading to avoid circular imports - Backwards compatibility layer supports old import paths with warnings - All existing functionality preserved To migrate existing code: OLD: from optimization_engine.nx_solver import NXSolver NEW: from optimization_engine.nx.solver import NXSolver OLD: from optimization_engine.runner import OptimizationRunner NEW: from optimization_engine.core.runner import OptimizationRunner 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
"""Test parametric surrogate integration."""
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import time
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from optimization_engine.processors.surrogates.neural_surrogate import create_parametric_surrogate_for_study
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print("Testing Parametric Neural Surrogate")
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print("=" * 60)
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# Create surrogate with auto-detection
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surrogate = create_parametric_surrogate_for_study()
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if surrogate is None:
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print("ERROR: Failed to create surrogate")
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exit(1)
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print(f"Surrogate created successfully!")
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print(f" Device: {surrogate.device}")
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print(f" Nodes: {surrogate.num_nodes}")
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print(f" Model val_loss: {surrogate.best_val_loss:.4f}")
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print(f" Design vars: {surrogate.design_var_names}")
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# Test prediction with example params
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test_params = {
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"beam_half_core_thickness": 7.0,
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"beam_face_thickness": 2.5,
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"holes_diameter": 35.0,
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"hole_count": 10.0
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}
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print(f"\nTest prediction with params: {test_params}")
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results = surrogate.predict(test_params)
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print(f"\nResults:")
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print(f" Mass: {results['mass']:.2f} g")
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print(f" Frequency: {results['frequency']:.2f} Hz")
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print(f" Max displacement: {results['max_displacement']:.6f} mm")
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print(f" Max stress: {results['max_stress']:.2f} MPa")
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print(f" Inference time: {results['inference_time_ms']:.2f} ms")
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# Speed test
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n = 100
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start = time.time()
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for _ in range(n):
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surrogate.predict(test_params)
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elapsed = time.time() - start
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print(f"\nSpeed test: {n} predictions in {elapsed:.3f}s")
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print(f" Average: {elapsed/n*1000:.2f} ms per prediction")
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# Compare with training data range
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print(f"\nExpected range (from training data):")
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print(f" Mass: ~2808 - 5107 g")
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print(f" Frequency: ~15.8 - 21.9 Hz")
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print(f" Max displacement: ~0.02-0.03 mm")
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stats = surrogate.get_statistics()
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print(f"\nStatistics:")
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print(f" Total predictions: {stats['total_predictions']}")
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print(f" Average time: {stats['average_time_ms']:.2f} ms")
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print("\nParametric surrogate test PASSED!")
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