Mercurial > repos > bgruening > sklearn_train_test_eval
view stacking_ensembles.py @ 2:e23cfe4be9d4 draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"
author | bgruening |
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date | Wed, 02 Oct 2019 03:46:45 -0400 |
parents | 68aaa903052a |
children | 20bb2a45f922 |
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import argparse import ast import json import mlxtend.regressor 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) 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: options['weights'] = ast.literal_eval(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)