Sweeps: Conditional parameters

It seems to me that there is currently no way to define a sweep where some parameters depends on another one, is it correct?

What I have in mind is something like this example from hyperopt (source):

from hyperopt import hp
space = hp.choice('classifier_type', [
    {
        'type': 'naive_bayes',
    },
    {
        'type': 'svm',
        'C': hp.lognormal('svm_C', 0, 1),
        'kernel': hp.choice('svm_kernel', [
            {'ktype': 'linear'},
            {'ktype': 'RBF', 'width': hp.lognormal('svm_rbf_width', 0, 1)},
        ]),
    },
    {
        'type': 'dtree',
        'criterion': hp.choice('dtree_criterion', ['gini', 'entropy']),
        'max_depth': hp.choice('dtree_max_depth',
            [None, hp.qlognormal('dtree_max_depth_int', 3, 1, 1)]),
        'min_samples_split': hp.qlognormal('dtree_min_samples_split', 2, 1, 1),
    },
])

At the moment this is not support by neither the “standard” sweeps nor by the beta Ray Tune sweeps, right?

[emanuelevivoli]

hi, any news?

[exalate-issue-sync[bot]]

Leslie commented: We have this project listed as something we are going to do in the future