I am confused about the
max_iters parameter in
Assume I have e.g. an sklearn.ElasticNet trainable. In that trainable, I define the number of iterations that I allow the respective optimizer to run:
ELN = ElasticNet(max_iter=1000)
Now when I want to tune this trainable via
ray.tune.sklearn.TuneGridSearchCV, I can define a
max_iters which, according to the documentation “Indicates the maximum number of epochs to run for each hyperparameter configuration sampled.”. E.g. assuming some pre-defined parameter space (
ray.tune.sklearn.TuneGridSearchCV(estimator=ELN, param_grid=param, max_iters=100)
Now my question, formulated as an example: Let’s say I set
max_iters=100 as in the example above. Does my optimizer run for 100 iterations and the scheduler checks every iteration wether to stop or not, or does it run for 1000 iterations and the scheduler checks every 10 iterations whether to stop a trial or not?
I want to tune an ElasticNet object, such that the training stops if the scoring does not improve for 8 iterations, and I am currently confused how to do that.