comparison stacking_ensembles.py @ 37:e76f6dfea5c9 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"

36:420a4bf99244

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))

    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 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)

37:e76f6dfea5c9

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))

    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 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)