Mercurial > repos > bgruening > sklearn_discriminant_classifier
view stacking_ensembles.py @ 39:78a60f884ac9 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 8850f42c2c3763e614f7454c9c006f3d2ff572c0
author | bgruening |
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date | Fri, 27 May 2022 11:47:11 +0000 |
parents | e76f6dfea5c9 |
children | d769d83ec796 |
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import argparse import ast import json import pickle import sys import warnings import mlxtend.classifier import mlxtend.regressor import pandas as pd from galaxy_ml.utils import (get_cv, get_estimator, get_search_params, load_model) warnings.filterwarnings("ignore") 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): """ Parameter --------- inputs_path : str File path for Galaxy parameters output_obj : str File path for ensemble estimator ouput base_paths : str File path or paths concatenated by comma. meta_path : str File path outfile_params : str File path for params output """ with open(inputs_path, "r") as param_handler: params = json.load(param_handler) 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: model = load_model(handler) else: estimator_json = params["base_est_builder"][idx]["estimator_selector"] model = get_estimator(estimator_json) if estimator_type.startswith("sklearn"): named = model.__class__.__name__.lower() 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 meta_path: with open(meta_path, "rb") as f: meta_estimator = load_model(f) else: estimator_json = params["algo_selection"]["meta_estimator"][ "estimator_selector" ] meta_estimator = get_estimator(estimator_json) options = params["algo_selection"]["options"] cv_selector = options.pop("cv_selector", None) if cv_selector: splitter, _groups = get_cv(cv_selector) options["cv"] = splitter # set n_jobs options["n_jobs"] = N_JOBS weights = options.pop("weights", None) if weights: weights = ast.literal_eval(weights) if weights: options["weights"] = weights 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 ensemble_estimator = klass(base_estimators, **options) elif mod == mlxtend.classifier: ensemble_estimator = klass( classifiers=base_estimators, meta_classifier=meta_estimator, **options ) else: 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: pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL) 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) if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-b", "--bases", dest="bases") aparser.add_argument("-m", "--meta", dest="meta") aparser.add_argument("-i", "--inputs", dest="inputs") aparser.add_argument("-o", "--outfile", dest="outfile") 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, )