comparison stacking_ensembles.py @ 30:60d80322e1e9 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7

30:60d80322e1e9

import argparse
import json
import pandas as pd
import pickle
import xgboost
import warnings
from sklearn import (cluster, compose, decomposition, ensemble,
                     feature_extraction, feature_selection,
                     gaussian_process, kernel_approximation, metrics,
                     model_selection, naive_bayes, neighbors,
                     pipeline, preprocessing, svm, linear_model,
                     tree, discriminant_analysis)
from sklearn.model_selection._split import check_cv
from feature_selectors import (DyRFE, DyRFECV,
                               MyPipeline, MyimbPipeline)
from iraps_classifier import (IRAPSCore, IRAPSClassifier,
                              BinarizeTargetClassifier,
                              BinarizeTargetRegressor)
from preprocessors import Z_RandomOverSampler
from utils import load_model, get_cv, get_estimator, get_search_params

from mlxtend.regressor import StackingCVRegressor, StackingRegressor
from mlxtend.classifier import StackingCVClassifier, StackingClassifier


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)

    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)
        base_estimators.append(model)

    if meta_path:
        with open(meta_path, 'rb') as f:
            meta_estimator = load_model(f)
    else:
        estimator_json = params['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

    if params['algo_selection']['estimator_type'] == 'StackingCVClassifier':
        ensemble_estimator = StackingCVClassifier(
            classifiers=base_estimators,
            meta_classifier=meta_estimator,
            **options)

    elif params['algo_selection']['estimator_type'] == 'StackingClassifier':
        ensemble_estimator = StackingClassifier(
            classifiers=base_estimators,
            meta_classifier=meta_estimator,
            **options)

    elif params['algo_selection']['estimator_type'] == 'StackingCVRegressor':
        ensemble_estimator = StackingCVRegressor(
            regressors=base_estimators,
            meta_regressor=meta_estimator,
            **options)

    else:
        ensemble_estimator = StackingRegressor(
            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)