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ChaCha for Online AutoML

FLAML includes ChaCha which is an automatic hyperparameter tuning solution for online machine learning. Online machine learning has the following properties: (1) data comes in sequential order; and (2) the performance of the machine learning model is evaluated online, i.e., at every iteration. ChaCha performs online AutoML respecting the aforementioned properties of online learning, and at the same time respecting the following constraints: (1) only a small constant number of 'live' models are allowed to perform online learning at the same time; and (2) no model persistence or offline training is allowed, which means that once we decide to replace a 'live' model with a new one, the replaced model can no longer be retrieved.

For more technical details about ChaCha, please check our paper.

@inproceedings{wu2021chacha,
    title={ChaCha for online AutoML},
    author={Qingyun Wu and Chi Wang and John Langford and Paul Mineiro and Marco Rossi},
    year={2021},
    booktitle={ICML},
}

AutoVW

flaml.AutoVW is a realization of ChaCha AutoML method with online learners from the open-source online machine learning library Vowpal Wabbit learner. It can be used to tune both conventional numerical and categorical hyperparameters, such as learning rate, and hyperparameters for featurization choices, such as the namespace (a namespace is a group of features) interactions in Vowpal Wabbit.

An example of online namespace interactions tuning in VW:

# require: pip install flaml[vw]
from flaml import AutoVW
'''create an AutoVW instance for tuning namespace interactions'''
autovw = AutoVW(max_live_model_num=5, search_space={'interactions': AutoVW.AUTOMATIC})

An example of online tuning of both namespace interactions and learning rate in VW:

# require: pip install flaml[vw]
from flaml import AutoVW
from flaml.tune import loguniform
''' create an AutoVW instance for tuning namespace interactions and learning rate'''
# set up the search space and init config
search_space_nilr = {'interactions': AutoVW.AUTOMATIC, 'learning_rate': loguniform(lower=2e-10, upper=1.0)}
init_config_nilr = {'interactions': set(), 'learning_rate': 0.5}
# create an AutoVW instance
autovw = AutoVW(max_live_model_num=5, search_space=search_space_nilr, init_config=init_config_nilr)

A user can use the resulting AutoVW instances autovw in a similar way to a vanilla Vowpal Wabbit instance, i.e., pyvw.vw, to perform online learning by iteratively calling its predict(data_example) and learn(data_example) functions at each data example.

For more examples, please check out AutoVW notebook.