Mercurial > repos > bgruening > sklearn_pca
diff stacking_ensembles.py @ 1:132805688fa3 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04"
author | bgruening |
---|---|
date | Tue, 13 Apr 2021 18:49:35 +0000 |
parents | 2d7016b3ae92 |
children | e36ab18cbaca |
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--- a/stacking_ensembles.py Fri Oct 02 08:45:21 2020 +0000 +++ b/stacking_ensembles.py Tue Apr 13 18:49:35 2021 +0000 @@ -5,22 +5,17 @@ import mlxtend.classifier import pandas as pd import pickle -import sklearn import sys import warnings -from sklearn import ensemble - -from galaxy_ml.utils import (load_model, get_cv, get_estimator, - get_search_params) +from galaxy_ml.utils import load_model, get_cv, get_estimator, get_search_params -warnings.filterwarnings('ignore') +warnings.filterwarnings("ignore") -N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) +N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1)) -def main(inputs_path, output_obj, base_paths=None, meta_path=None, - outfile_params=None): +def main(inputs_path, output_obj, base_paths=None, meta_path=None, outfile_params=None): """ Parameter --------- @@ -39,87 +34,79 @@ outfile_params : str File path for params output """ - with open(inputs_path, 'r') as param_handler: + with open(inputs_path, "r") as param_handler: params = json.load(param_handler) - estimator_type = params['algo_selection']['estimator_type'] + estimator_type = params["algo_selection"]["estimator_type"] # get base estimators base_estimators = [] - for idx, base_file in enumerate(base_paths.split(',')): - if base_file and base_file != 'None': - with open(base_file, 'rb') as handler: + for idx, base_file in enumerate(base_paths.split(",")): + if base_file and base_file != "None": + with open(base_file, "rb") as handler: model = load_model(handler) else: - estimator_json = (params['base_est_builder'][idx] - ['estimator_selector']) + estimator_json = params["base_est_builder"][idx]["estimator_selector"] model = get_estimator(estimator_json) - if estimator_type.startswith('sklearn'): + if estimator_type.startswith("sklearn"): named = model.__class__.__name__.lower() - named = 'base_%d_%s' % (idx, named) + named = "base_%d_%s" % (idx, named) base_estimators.append((named, model)) else: base_estimators.append(model) # get meta estimator, if applicable - if estimator_type.startswith('mlxtend'): + if estimator_type.startswith("mlxtend"): if meta_path: - with open(meta_path, 'rb') as f: + 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'] + options = params["algo_selection"]["options"] - cv_selector = options.pop('cv_selector', None) + cv_selector = options.pop("cv_selector", None) if cv_selector: - splitter, groups = get_cv(cv_selector) - options['cv'] = splitter + splitter, _groups = get_cv(cv_selector) + options["cv"] = splitter # set n_jobs - options['n_jobs'] = N_JOBS + options["n_jobs"] = N_JOBS - weights = options.pop('weights', None) + weights = options.pop("weights", None) if weights: weights = ast.literal_eval(weights) if weights: - options['weights'] = weights + options["weights"] = weights - mod_and_name = estimator_type.split('_') + mod_and_name = estimator_type.split("_") mod = sys.modules[mod_and_name[0]] klass = getattr(mod, mod_and_name[1]) - if estimator_type.startswith('sklearn'): - options['n_jobs'] = N_JOBS + if estimator_type.startswith("sklearn"): + options["n_jobs"] = N_JOBS 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: print(base_est) - with open(output_obj, 'wb') as out_handler: + with open(output_obj, "wb") as out_handler: pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL) - if params['get_params'] and outfile_params: + if params["get_params"] and outfile_params: results = get_search_params(ensemble_estimator) - df = pd.DataFrame(results, columns=['', 'Parameter', 'Value']) - df.to_csv(outfile_params, sep='\t', index=False) + df = pd.DataFrame(results, columns=["", "Parameter", "Value"]) + df.to_csv(outfile_params, sep="\t", index=False) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-b", "--bases", dest="bases") aparser.add_argument("-m", "--meta", dest="meta") @@ -128,5 +115,10 @@ aparser.add_argument("-p", "--outfile_params", dest="outfile_params") args = aparser.parse_args() - main(args.inputs, args.outfile, base_paths=args.bases, - meta_path=args.meta, outfile_params=args.outfile_params) + main( + args.inputs, + args.outfile, + base_paths=args.bases, + meta_path=args.meta, + outfile_params=args.outfile_params, + )