The pbmohpo's discuss from ja-thomas

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Preferential Bayesian Multi-Objective Hyperparameter Optimization

License: GNU Lesser General Public License v2.1

Python 62.66% Jupyter Notebook 37.34%

pbmohpo's Issues

YAHPO problem wrapper changes dict representation of a config after evaluating that config

Somewhat weird bug:

from pbmohpo.problems.yahpo import YAHPO

problem = YAHPO("iaml_xgboost", instance = "40981", objective_names = ["mmce", "nf"])
problem.get_config_space()

problem.fix_hps = {"booster": "gbtree", "trainsize" : 1}
cs = problem.get_config_space()
cs.seed(0)
x = cs.sample_configuration(1)

# looks as expected
x.get_dictionary()

{'alpha': 0.6945356257424766,
 'colsample_bylevel': 0.7180374727086953,
 'colsample_bytree': 0.6067357423109274,
 'eta': 0.01511933646764101,
 'gamma': 0.011288832727483048,
 'lambda': 3.3209228886981608,
 'max_depth': 7,
 'min_child_weight': 97.18110224911669,
 'nrounds': 1569,
 'subsample': 0.4450973669431999}

problem(x)

{'mmce': -0.49803417921066284, 'nf': -0.0}

x.get_dictionary()
# now also includes the fixed hyperparameters from `problem.fix_hps`  which is unintended behavior

{'alpha': 0.6945356257424766,
 'colsample_bylevel': 0.7180374727086953,
 'colsample_bytree': 0.6067357423109274,
 'eta': 0.01511933646764101,
 'gamma': 0.011288832727483048,
 'lambda': 3.3209228886981608,
 'max_depth': 7,
 'min_child_weight': 97.18110224911669,
 'nrounds': 1569,
 'subsample': 0.4450973669431999,
 'task_id': '40981',
 'booster': 'gbtree',
 'trainsize': 1}

to fix this we should work with a deepcopy within the YAHPO wrapper https://github.com/ja-thomas/pbmohpo/blob/d0e5ae3db39cad3b9f8068bae1fbe57ddc2ca6ea/pbmohpo/problems/yahpo.py#LL68C1-L68C1

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