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tingtingwuwu / MPNN-MLP-for-Viscosity-prediction-of-DESs

Multiscale exploration of informative latent features for accurate deep eutectic solvents viscosity prediction
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hyperparameter tuning #2

Open qqyqqyqqy opened 1 month ago

qqyqqyqqy commented 1 month ago

Although Optuna is used for hyperparameter tuning in the project, the tuning results may vary across different environments. How can the reproducibility of hyperparameter tuning be ensured?

tingtingwuwu commented 1 month ago

To ensure consistency in hyperparameter tuning results, the following measures can be implemented:

Set Random Seeds: Establish fixed random seeds in all relevant code components (e.g., torch.manual_seed() and numpy.random.seed()) to guarantee the reproducibility of the tuning process.

Log All Experimental Configurations: Automatically save configuration files and parameter combinations in a log file during each hyperparameter tuning run, ensuring that the results can be fully replicated.

Use a Fixed Validation Set: Ensure that the validation set remains unchanged during hyperparameter tuning to avoid discrepancies in tuning outcomes caused by variations in data distribution.

Dockerized Environment: Recommend that users run tuning scripts within a consistent Docker environment to maintain uniformity in dependencies and environment configurations.