[FIXED] How to set a minimum number of epoch in Optuna SuccessiveHalvingPruner()?

Issue

I'm using Optuna 2.5 to optimize a couple of hyperparameters on a tf.keras CNN model. I want to use pruning so that the optimization skips the less promising corners of the hyperparameters space. I'm using something like this:

study0 = optuna.create_study(study_name=study_name,
                             storage=storage_name,
                             direction='minimize', 
                             sampler=TPESampler(n_startup_trials=25, multivariate=True, seed=123),
                             pruner=optuna.pruners.SuccessiveHalvingPruner(min_resource='auto',
                             reduction_factor=4, min_early_stopping_rate=0),
                             load_if_exists=True)

Sometimes the model stops after 2 epochs, some other times it stops after 12 epochs, 48 and so forth. What I want is to ensure that the model always trains at least 30 epochs before being pruned. I guess that the parameter min_early_stopping_rate might have some control on this but I've tried to change it from 0 to 30 and then the models never get pruned. Can someone explain me a bit better than the Optuna documentation, what these parameters in the SuccessiveHalvingPruner() really do (specially min_early_stopping_rate)? Thanks

Solution

min_resource's explanation on the documentation says

A trial is never pruned until it executes min_resource * reduction_factor ** min_early_stopping_rate steps.

So, I suppose that we need to replace the value of min_resource with a specific number depending on reduction_factor and min_early_stopping_rate.

Answered By - nzw0301