I know how to specify Feature Selection methods and the list of the Algorithms used in Auto-Sklearn 2.0
mdl = autosklearn.classification.AutoSklearnClassifier( include = { 'classifier': ["random_forest", "gaussian_nb", "libsvm_svc", "adaboost"], 'feature_preprocessor': ["no_preprocessing"] }, exclude=None)
I know that Auto-Sklearn use Bayesian Optimisation SMAC
but I would like to specify the HyperParameters in AutoSklearn
For example, I want to specify random_forest with Estimator = 1000
only or MLP with HiddenLayerSize = 100
only.
How to do that?
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Answer
You need to edit the config as specified in the docs.
In your case it would be something like:
cs = mdl.get_configuration_space(X, y) config = cs.sample_configuration() config._values['classifier:random_forest:n_estimators'] = 1000 pipeline, run_info, run_value = mdl.fit_pipeline(X=X_train, y=y_train, config=config, X_test=X_test, y_test=y_test)