comparison stacking_ensembles.py @ 26:9bb505eafac9 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 60f0fbc0eafd7c11bc60fb6c77f2937782efd8a9-dirty

25:3e2921875c58

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))

        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)

26:9bb505eafac9

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))

        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)