[WIP] check_model.py: test model on simulation data too#422
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RemiLehe
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Apr 8, 2026
| if model_type not in ("NN", "ensemble_NN", "GP"): | ||
| raise ValueError(f"Unsupported model type: {model_type}") | ||
| # Populate inferred calibration in physics units for GUI | ||
| # (only meaningful inside the dashboard where state.simulation_calibration is set) |
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We now also set state.simulation_calibration in check_model.py, so this check is not meaningful anymore.
NNEnsemble does not expose input_transformers/output_transformers directly; those attributes live on each inner TorchModel. Access them via models[0] when the model type is ensemble_NN. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This reverts commit 9d9df82.
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Summary
Extended
check_model.pyto test accuracy on simulation data. After loading the model, simulation points are converted to experimental units using the inferred calibration (viacal_manager.convert_sim_to_exp), then evaluated against the model. Both experimental and simulation datasets must pass the relative RMSE tolerance for the test to succeed.Fixed
AttributeErrorwhen loadingensemble_NNmodels.NNEnsembledoes not exposeinput_transformers/output_transformersdirectly — those live on each innerTorchModel.ModelManagernow accesses them viamodels[0]for the ensemble case.