Mercurial > repos > bgruening > sklearn_numeric_clustering

comparison stacking_ensembles.py @ 36:73e7f1c76ece draft

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"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"
author bgruening
date Sat, 01 May 2021 00:48:46 +0000
parents e7f047a9dca9
children 06d772036a62
comparison
equal deleted inserted replaced
35:e7f047a9dca9 36:73e7f1c76ece
6 import warnings 6 import warnings
7 7
8 import mlxtend.classifier 8 import mlxtend.classifier
9 import mlxtend.regressor 9 import mlxtend.regressor
10 import pandas as pd 10 import pandas as pd
11 from galaxy_ml.utils import get_cv, get_estimator, get_search_params, load_model 11 from galaxy_ml.utils import (get_cv, get_estimator, get_search_params,
12 12 load_model)
13 13
14 warnings.filterwarnings("ignore") 14 warnings.filterwarnings("ignore")
15 15
16 N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1)) 16 N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))
17 17
60 if estimator_type.startswith("mlxtend"): 60 if estimator_type.startswith("mlxtend"):
61 if meta_path: 61 if meta_path:
62 with open(meta_path, "rb") as f: 62 with open(meta_path, "rb") as f:
63 meta_estimator = load_model(f) 63 meta_estimator = load_model(f)
64 else: 64 else:
65 estimator_json = params["algo_selection"]["meta_estimator"]["estimator_selector"] 65 estimator_json = params["algo_selection"]["meta_estimator"][
66 "estimator_selector"
67 ]
66 meta_estimator = get_estimator(estimator_json) 68 meta_estimator = get_estimator(estimator_json)
67 69
68 options = params["algo_selection"]["options"] 70 options = params["algo_selection"]["options"]
69 71
70 cv_selector = options.pop("cv_selector", None) 72 cv_selector = options.pop("cv_selector", None)
87 if estimator_type.startswith("sklearn"): 89 if estimator_type.startswith("sklearn"):
88 options["n_jobs"] = N_JOBS 90 options["n_jobs"] = N_JOBS
89 ensemble_estimator = klass(base_estimators, **options) 91 ensemble_estimator = klass(base_estimators, **options)
90 92
91 elif mod == mlxtend.classifier: 93 elif mod == mlxtend.classifier:
92 ensemble_estimator = klass(classifiers=base_estimators, meta_classifier=meta_estimator, **options) 94 ensemble_estimator = klass(
95 classifiers=base_estimators, meta_classifier=meta_estimator, **options
96 )
93 97
94 else: 98 else:
95 ensemble_estimator = klass(regressors=base_estimators, meta_regressor=meta_estimator, **options) 99 ensemble_estimator = klass(
100 regressors=base_estimators, meta_regressor=meta_estimator, **options
101 )
96 102
97 print(ensemble_estimator) 103 print(ensemble_estimator)
98 for base_est in base_estimators: 104 for base_est in base_estimators:
99 print(base_est) 105 print(base_est)
100 106