Levi Burner
82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
# Copyright 2026 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""I/O utilities for saving system identification results."""
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from collections.abc import Sequence
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import os
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import pathlib
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import pickle
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from absl import logging
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from mujoco.sysid._src import parameter
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from mujoco.sysid._src.optimize import calculate_intervals
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from mujoco.sysid._src.trajectory import ModelSequences
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import scipy.optimize as scipy_optimize
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def save_results(
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experiment_results_folder: str | os.PathLike[str],
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models_sequences: Sequence[ModelSequences],
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initial_params: parameter.ParameterDict,
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opt_params: parameter.ParameterDict,
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opt_result: scipy_optimize.OptimizeResult,
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residual_fn,
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):
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"""Save optimization results and confidence intervals to disk.
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Args:
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experiment_results_folder: Directory where results are written.
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models_sequences: Model/sequence groups; identified XMLs are saved here.
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initial_params: Parameters before optimization.
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opt_params: Parameters after optimization.
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opt_result: Scipy OptimizeResult from the optimizer.
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residual_fn: Residual function used to compute confidence intervals.
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"""
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experiment_results_folder = pathlib.Path(experiment_results_folder)
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if not experiment_results_folder.exists():
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experiment_results_folder.mkdir(parents=True, exist_ok=True)
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logging.info(
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"Experiment results will be saved to %s", experiment_results_folder
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)
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initial_params.save_to_disk(experiment_results_folder / "params_x_0.yaml")
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opt_params.save_to_disk(experiment_results_folder / "params_x_hat.yaml")
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with open(
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os.path.join(experiment_results_folder, "results.pkl"), "wb"
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) as handle:
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pickle.dump(opt_result, handle, protocol=pickle.HIGHEST_PROTOCOL)
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# TODO(b/0): these intervals should be part of the params object.
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residuals_star, _, _ = residual_fn(
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opt_result.x, opt_params, return_pred_all=True
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)
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covariance, intervals = calculate_intervals(residuals_star, opt_result.jac)
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with open(
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os.path.join(experiment_results_folder, "confidence.pkl"), "wb"
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) as handle:
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pickle.dump(
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{"cov": covariance, "intervals": intervals},
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handle,
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protocol=pickle.HIGHEST_PROTOCOL,
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)
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# Dump identified models to disk.
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for model_sequences in models_sequences:
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model_sequences.spec.to_file(
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(experiment_results_folder / f"{model_sequences.name}.xml").as_posix()
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)
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