Log score after each tuning trial #199
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flinder
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Summary: Pull Request resolved: facebookincubator#199 Log the completion score after each trial iteration, not just the initial trial. This provides better visibility into the tuning progress and helps users monitor how the hyperparameter search is progressing. Differential Revision: D91884586
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Summary: Pull Request resolved: facebookincubator#199 Log the completion score after each trial iteration, not just the initial trial. This provides better visibility into the tuning progress and helps users monitor how the hyperparameter search is progressing. Differential Revision: D91884586
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Summary: Add a `use_model_predictions` parameter to `tune_mcgrad_params` with `False` as the default. When False, returns the actual observed best trial parameters. When True, uses the Bayesian surrogate model's predicted best parameters. The default is False because with few tuning trials, the surrogate model may not be well-calibrated and could return suboptimal parameters that don't match the actual best observed trial. Users who want the model-predicted best can set this to True. Differential Revision: D91889101
Summary: Use log_loss from sklearn instead of normalized_entropy as the tuning objective. This matches MCGrad's early stopping metric, providing directly comparable outputs between tuning and model training. The log_loss metric is a standard choice for probabilistic predictions and is widely understood. Reviewed By: Lorenzo-Perini Differential Revision: D91884587
Summary: Suppress _utils.logger alongside methods.logger during tuning trials to prevent "Unshrink is not close to 1" warnings from appearing. These warnings are expected during hyperparameter exploration but can be noisy for users running tuning jobs. Reviewed By: Lorenzo-Perini Differential Revision: D91884585
Summary: Log the completion score after each trial iteration, not just the initial trial. This provides better visibility into the tuning progress and helps users monitor how the hyperparameter search is progressing. Reviewed By: Lorenzo-Perini Differential Revision: D91884586
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Summary:
Log the completion score after each trial iteration, not just the initial trial.
This provides better visibility into the tuning progress and helps users monitor
how the hyperparameter search is progressing.
Differential Revision: D91884586