Mercurial > repos > bgruening > sklearn_build_pipeline
diff stacking_ensembles.py @ 19:866dec0ace48 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ca87db9c038a6fcf96aa39da50f384865fd932ff"
author | bgruening |
---|---|
date | Tue, 20 Apr 2021 16:57:34 +0000 |
parents | 449bd57f70f4 |
children | 15815a470e6b |
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--- a/stacking_ensembles.py Tue Apr 13 21:00:31 2021 +0000 +++ b/stacking_ensembles.py Tue Apr 20 16:57:34 2021 +0000 @@ -8,8 +8,8 @@ import mlxtend.classifier import mlxtend.regressor import pandas as pd -from galaxy_ml.utils import get_cv, get_estimator, get_search_params, load_model - +from galaxy_ml.utils import (get_cv, get_estimator, get_search_params, + load_model) warnings.filterwarnings("ignore") @@ -62,7 +62,9 @@ with open(meta_path, "rb") as f: meta_estimator = load_model(f) else: - estimator_json = params["algo_selection"]["meta_estimator"]["estimator_selector"] + estimator_json = params["algo_selection"]["meta_estimator"][ + "estimator_selector" + ] meta_estimator = get_estimator(estimator_json) options = params["algo_selection"]["options"] @@ -89,10 +91,14 @@ ensemble_estimator = klass(base_estimators, **options) elif mod == mlxtend.classifier: - ensemble_estimator = klass(classifiers=base_estimators, meta_classifier=meta_estimator, **options) + ensemble_estimator = klass( + classifiers=base_estimators, meta_classifier=meta_estimator, **options + ) else: - ensemble_estimator = klass(regressors=base_estimators, meta_regressor=meta_estimator, **options) + ensemble_estimator = klass( + regressors=base_estimators, meta_regressor=meta_estimator, **options + ) print(ensemble_estimator) for base_est in base_estimators: