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Description:

optunafastfanova 0.0.4

optuna-fast-fanova
optuna-fast-fanova provides Cython-accelerated version of FanovaImportanceEvaluator.

n_trials
n_params
n_trees
fANOVA (Optuna)
fast-fanova

1000
32
64
71.431s
2.963s (-95.9%)

1000
8
64
92.307s
2.315s (-97.5%)

1000
2
64
52.295s
1.297s (-97.5%)

100
32
64
1.668s
0.306s (-81.6%)

100
8
64
1.652s
0.138s (-91.7%)

100
2
64
1.242s
0.095s (-92.4%)

The benchmark script was run on my laptop (Macbook M1 Pro) so the times should not be taken precisely.
Installation
Supported Python versions are 3.7 or later.
$ pip install optuna-fast-fanova

Please note that this library depends on the scikit-learn's C-API (Cython pxd files).
However, its ABI may contain breaking changes, even in patch releases.
If you install optuna-fast-fanova with scikit-learn v1.1.1 and then upgrade scikit-learn to v1.1.2, optuna-fast-fanova will not work.
Please reinstall optuna-fast-fanova if you update scikit-learn.
Usage
Usage is like this:
import optuna
from optuna_fast_fanova import FanovaImportanceEvaluator

def objective(trial):
x = trial.suggest_float("x", -10, 10)
y = trial.suggest_int("y", -10, 10)
return x ** 2 + y

if __name__ == "__main__":
study = optuna.create_study()
study.optimize(objective, n_trials=1000)

importance = optuna.importance.get_param_importances(
study, evaluator=FanovaImportanceEvaluator()
)
print(importance)

You can use optuna-fast-fanova in only two steps.

Add an import statement: from optuna_fast_fanova import FanovaImportanceEvaluator.
Pass a FanovaImportanceEvaluator() object to an evaluator argument of get_param_importances() function.

How to cite fANOVA
This is a derived work of https://github.com/automl/fanova.
For how to cite the original work, please refer to https://automl.github.io/fanova/cite.html.