#!/usr/bin/env python
# Copyright 2016-2023 Biomedical Imaging Group Rotterdam, Departments of
# Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import configparser
import os
import WORC.addexceptions as ae
[docs]def load_config(config_file_path):
"""Load the config ini, parse settings to WORC.
Args:
config_file_path (String): path of the .ini config file
Returns:
settings_dict (dict): dict with the loaded settings
"""
if not os.path.exists(config_file_path):
e = f'File {config_file_path} does not exist!'
raise ae.WORCKeyError(e)
settings = configparser.ConfigParser()
settings.read(config_file_path)
settings_dict = {'General': dict(), 'CrossValidation': dict(),
'Labels': dict(), 'HyperOptimization': dict(),
'Classification': dict(), 'SelectFeatGroup': dict(),
'Featsel': dict(), 'FeatureScaling': dict(),
'Resampling': dict(), 'Imputation': dict(),
'Ensemble': dict(), 'Bootstrap': dict(),
'FeatPreProcess': dict(), 'Evaluation': dict(),
'OneHotEncoding': dict(), 'SMAC': dict()}
settings_dict['General']['cross_validation'] =\
settings['General'].getboolean('cross_validation')
settings_dict['General']['Joblib_ncores'] =\
settings['General'].getint('Joblib_ncores')
settings_dict['General']['Joblib_backend'] =\
str(settings['General']['Joblib_backend'])
settings_dict['General']['tempsave'] =\
settings['General'].getboolean('tempsave')
settings_dict['General']['DoTestNRSNEns'] =\
settings['General'].getboolean('DoTestNRSNEns')
# Feature Scaling
settings_dict['FeatureScaling']['scale_features'] =\
settings['FeatureScaling'].getboolean('scale_features')
settings_dict['FeatureScaling']['scaling_method'] =\
[str(item).strip() for item in
settings['FeatureScaling']['scaling_method'].split(',')]
settings_dict['FeatureScaling']['skip_features'] =\
[str(item).strip() for item in
settings['FeatureScaling']['skip_features'].split(',')]
# Feature selection
settings_dict['Featsel']['Variance'] =\
settings['Featsel'].getfloat('Variance')
settings_dict['Featsel']['SelectFromModel'] =\
settings['Featsel'].getfloat('SelectFromModel')
settings_dict['Featsel']['SelectFromModel_lasso_alpha'] =\
[float(str(item).strip()) for item in
settings['Featsel']['SelectFromModel_lasso_alpha'].split(',')]
settings_dict['Featsel']['SelectFromModel_estimator'] =\
[str(item).strip() for item in
settings['Featsel']['SelectFromModel_estimator'].split(',')]
settings_dict['Featsel']['SelectFromModel_n_trees'] =\
[int(str(item).strip()) for item in
settings['Featsel']['SelectFromModel_n_trees'].split(',')]
settings_dict['Featsel']['RFE'] =\
settings['Featsel'].getfloat('RFE')
settings_dict['Featsel']['RFE_lasso_alpha'] =\
[float(str(item).strip()) for item in
settings['Featsel']['RFE_lasso_alpha'].split(',')]
settings_dict['Featsel']['RFE_estimator'] =\
[str(item).strip() for item in
settings['Featsel']['RFE_estimator'].split(',')]
settings_dict['Featsel']['RFE_n_trees'] =\
[int(str(item).strip()) for item in
settings['Featsel']['RFE_n_trees'].split(',')]
settings_dict['Featsel']['RFE_n_features_to_select'] =\
[float(str(item).strip()) for item in
settings['Featsel']['RFE_n_features_to_select'].split(',')]
settings_dict['Featsel']['RFE_step'] =\
[int(str(item).strip()) for item in
settings['Featsel']['RFE_step'].split(',')]
settings_dict['Featsel']['GroupwiseSearch'] =\
[str(item).strip() for item in
settings['Featsel']['GroupwiseSearch'].split(',')]
settings_dict['Featsel']['UsePCA'] =\
settings['Featsel'].getfloat('UsePCA')
settings_dict['Featsel']['PCAType'] =\
[str(item).strip() for item in
settings['Featsel']['PCAType'].split(',')]
settings_dict['Featsel']['StatisticalTestUse'] =\
settings['Featsel'].getfloat('StatisticalTestUse')
settings_dict['Featsel']['StatisticalTestMetric'] =\
[str(item).strip() for item in
settings['Featsel']['StatisticalTestMetric'].split(',')]
settings_dict['Featsel']['StatisticalTestThreshold'] =\
[float(str(item).strip()) for item in
settings['Featsel']['StatisticalTestThreshold'].split(',')]
settings_dict['Featsel']['ReliefUse'] =\
settings['Featsel'].getfloat('ReliefUse')
settings_dict['Featsel']['ReliefNN'] =\
[int(str(item).strip()) for item in
settings['Featsel']['ReliefNN'].split(',')]
settings_dict['Featsel']['ReliefSampleSize'] =\
[float(str(item).strip()) for item in
settings['Featsel']['ReliefSampleSize'].split(',')]
settings_dict['Featsel']['ReliefDistanceP'] =\
[int(str(item).strip()) for item in
settings['Featsel']['ReliefDistanceP'].split(',')]
settings_dict['Featsel']['ReliefNumFeatures'] =\
[int(str(item).strip()) for item in
settings['Featsel']['ReliefNumFeatures'].split(',')]
# Feature preprocessing before the whole HyperOptimization
settings_dict['FeatPreProcess']['Use'] =\
[str(settings['FeatPreProcess']['Use'])]
settings_dict['FeatPreProcess']['Combine'] =\
settings['FeatPreProcess'].getboolean('Combine')
settings_dict['FeatPreProcess']['Combine_method'] =\
str(settings['FeatPreProcess']['Combine_method'])
# Imputation
settings_dict['Imputation']['use'] =\
[str(item).strip() for item in
settings['Imputation']['use'].split(',')]
settings_dict['Imputation']['strategy'] =\
[str(item).strip() for item in
settings['Imputation']['strategy'].split(',')]
settings_dict['Imputation']['n_neighbors'] =\
[int(str(item).strip()) for item in
settings['Imputation']['n_neighbors'].split(',')]
settings_dict['Imputation']['skipallNaN'] =\
[str(settings['Imputation']['skipallNaN'])]
# OneHotEncoding
settings_dict['OneHotEncoding']['Use'] =\
[str(item).strip() for item in
settings['Imputation']['use'].split(',')]
settings_dict['OneHotEncoding']['feature_labels_tofit'] =\
[str(item).strip() for item in
settings['OneHotEncoding']['feature_labels_tofit'].split(',')]
# General
settings_dict['General']['FeatureCalculators'] =\
[str(item).strip() for item in
settings['General']['FeatureCalculators'].split(',')]
# Feature selection options
for key in settings['SelectFeatGroup'].keys():
settings_dict['SelectFeatGroup'][key] =\
[str(item).strip() for item in
settings['SelectFeatGroup'][key].split(',')]
# Settings for sample processing, i.e. oversampling, undersampling etc
settings_dict['Resampling']['Use'] =\
settings['Resampling'].getfloat('Use')
settings_dict['Resampling']['Method'] =\
[str(item).strip() for item in
settings['Resampling']['Method'].split(',')]
settings_dict['Resampling']['sampling_strategy'] =\
[str(item).strip() for item in
settings['Resampling']['sampling_strategy'].split(',')]
settings_dict['Resampling']['n_neighbors'] =\
[int(str(item).strip()) for item in
settings['Resampling']['n_neighbors'].split(',')]
settings_dict['Resampling']['k_neighbors'] =\
[int(str(item).strip()) for item in
settings['Resampling']['k_neighbors'].split(',')]
settings_dict['Resampling']['threshold_cleaning'] =\
[float(str(item).strip()) for item in
settings['Resampling']['threshold_cleaning'].split(',')]
# Classification options
settings_dict['Classification']['fastr'] =\
settings['Classification'].getboolean('fastr')
settings_dict['Classification']['fastr_plugin'] =\
str(settings['Classification']['fastr_plugin'])
settings_dict['Classification']['classifiers'] =\
[str(item).strip() for item in
settings['Classification']['classifiers'].split(',')]
settings_dict['Classification']['max_iter'] =\
[int(str(item).strip()) for item in
settings['Classification']['max_iter'].split(',')]
# Specific SVM options
settings_dict['Classification']['SVMKernel'] =\
[str(item).strip() for item in
settings['Classification']['SVMKernel'].split(',')]
settings_dict['Classification']['SVMC'] =\
[int(str(item).strip()) for item in
settings['Classification']['SVMC'].split(',')]
settings_dict['Classification']['SVMdegree'] =\
[int(str(item).strip()) for item in
settings['Classification']['SVMdegree'].split(',')]
settings_dict['Classification']['SVMcoef0'] =\
[int(str(item).strip()) for item in
settings['Classification']['SVMcoef0'].split(',')]
settings_dict['Classification']['SVMgamma'] =\
[int(str(item).strip()) for item in
settings['Classification']['SVMgamma'].split(',')]
# Specific RF options
settings_dict['Classification']['RFn_estimators'] =\
[int(str(item).strip()) for item in
settings['Classification']['RFn_estimators'].split(',')]
settings_dict['Classification']['RFmin_samples_split'] =\
[int(str(item).strip()) for item in
settings['Classification']['RFmin_samples_split'].split(',')]
settings_dict['Classification']['RFmax_depth'] =\
[int(str(item).strip()) for item in
settings['Classification']['RFmax_depth'].split(',')]
# Specific LR options
settings_dict['Classification']['LRpenalty'] =\
[str(item).strip() for item in
settings['Classification']['LRpenalty'].split(',')]
settings_dict['Classification']['LRC'] =\
[float(str(item).strip()) for item in
settings['Classification']['LRC'].split(',')]
settings_dict['Classification']['LR_solver'] =\
[str(item).strip() for item in
settings['Classification']['LR_solver'].split(',')]
settings_dict['Classification']['LR_l1_ratio'] =\
[float(str(item).strip()) for item in
settings['Classification']['LR_l1_ratio'].split(',')]
# Specific LDA/QDA options
settings_dict['Classification']['LDA_solver'] =\
[str(item).strip() for item in
settings['Classification']['LDA_solver'].split(',')]
settings_dict['Classification']['LDA_shrinkage'] =\
[int(str(item).strip()) for item in
settings['Classification']['LDA_shrinkage'].split(',')]
settings_dict['Classification']['QDA_reg_param'] =\
[int(str(item).strip()) for item in
settings['Classification']['QDA_reg_param'].split(',')]
# ElasticNet options
settings_dict['Classification']['ElasticNet_alpha'] =\
[int(str(item).strip()) for item in
settings['Classification']['ElasticNet_alpha'].split(',')]
settings_dict['Classification']['ElasticNet_l1_ratio'] =\
[float(str(item).strip()) for item in
settings['Classification']['ElasticNet_l1_ratio'].split(',')]
# SGD (R) options
settings_dict['Classification']['SGD_alpha'] =\
[int(str(item).strip()) for item in
settings['Classification']['SGD_alpha'].split(',')]
settings_dict['Classification']['SGD_l1_ratio'] =\
[float(str(item).strip()) for item in
settings['Classification']['SGD_l1_ratio'].split(',')]
settings_dict['Classification']['SGD_loss'] =\
[str(item).strip() for item in
settings['Classification']['SGD_loss'].split(',')]
settings_dict['Classification']['SGD_penalty'] =\
[str(item).strip() for item in
settings['Classification']['SGD_penalty'].split(',')]
# Naive Bayes options
settings_dict['Classification']['CNB_alpha'] =\
[int(str(item).strip()) for item in
settings['Classification']['CNB_alpha'].split(',')]
# AdaBoost
settings_dict['Classification']['AdaBoost_n_estimators'] =\
[int(str(item).strip()) for item in
settings['Classification']['AdaBoost_n_estimators'].split(',')]
settings_dict['Classification']['AdaBoost_learning_rate'] =\
[float(str(item).strip()) for item in
settings['Classification']['AdaBoost_learning_rate'].split(',')]
# XGD Boost
settings_dict['Classification']['XGB_boosting_rounds'] =\
[int(str(item).strip()) for item in
settings['Classification']['XGB_boosting_rounds'].split(',')]
settings_dict['Classification']['XGB_max_depth'] =\
[int(str(item).strip()) for item in
settings['Classification']['XGB_max_depth'].split(',')]
settings_dict['Classification']['XGB_learning_rate'] =\
[float(str(item).strip()) for item in
settings['Classification']['XGB_learning_rate'].split(',')]
settings_dict['Classification']['XGB_gamma'] =\
[float(str(item).strip()) for item in
settings['Classification']['XGB_gamma'].split(',')]
settings_dict['Classification']['XGB_min_child_weight'] =\
[int(str(item).strip()) for item in
settings['Classification']['XGB_min_child_weight'].split(',')]
settings_dict['Classification']['XGB_colsample_bytree'] =\
[float(str(item).strip()) for item in
settings['Classification']['XGB_colsample_bytree'].split(',')]
# Light GBM
settings_dict['Classification']['LightGBM_num_leaves'] =\
[int(str(item).strip()) for item in
settings['Classification']['LightGBM_num_leaves'].split(',')]
settings_dict['Classification']['LightGBM_max_depth'] =\
[int(str(item).strip()) for item in
settings['Classification']['LightGBM_max_depth'].split(',')]
settings_dict['Classification']['LightGBM_min_child_samples'] =\
[int(str(item).strip()) for item in
settings['Classification']['LightGBM_min_child_samples'].split(',')]
settings_dict['Classification']['LightGBM_reg_alpha'] =\
[float(str(item).strip()) for item in
settings['Classification']['LightGBM_reg_alpha'].split(',')]
settings_dict['Classification']['LightGBM_reg_lambda'] =\
[float(str(item).strip()) for item in
settings['Classification']['LightGBM_reg_lambda'].split(',')]
settings_dict['Classification']['LightGBM_min_child_weight'] =\
[int(str(item).strip()) for item in
settings['Classification']['LightGBM_min_child_weight'].split(',')]
# Cross validation settings
settings_dict['CrossValidation']['Type'] =\
str(settings['CrossValidation']['Type'])
settings_dict['CrossValidation']['N_iterations'] =\
settings['CrossValidation'].getint('N_iterations')
settings_dict['CrossValidation']['test_size'] =\
settings['CrossValidation'].getfloat('test_size')
settings_dict['CrossValidation']['fixed_seed'] =\
settings['CrossValidation'].getboolean('fixed_seed')
# Label settings
settings_dict['Labels']['label_names'] =\
[str(item).strip() for item in
settings['Labels']['label_names'].split(',')]
settings_dict['Labels']['modus'] =\
str(settings['Labels']['modus'])
# Settings for hyper optimization
settings_dict['HyperOptimization']['scoring_method'] =\
str(settings['HyperOptimization']['scoring_method'])
settings_dict['HyperOptimization']['test_size'] =\
settings['HyperOptimization'].getfloat('test_size')
settings_dict['HyperOptimization']['N_iter'] =\
settings['HyperOptimization'].getint('N_iterations')
settings_dict['HyperOptimization']['n_splits'] =\
settings['HyperOptimization'].getint('n_splits')
settings_dict['HyperOptimization']['n_jobspercore'] =\
int(settings['HyperOptimization']['n_jobspercore'])
settings_dict['HyperOptimization']['maxlen'] = \
settings['HyperOptimization'].getint('maxlen')
settings_dict['HyperOptimization']['ranking_score'] = \
str(settings['HyperOptimization']['ranking_score'])
settings_dict['HyperOptimization']['refit_training_workflows'] =\
settings['HyperOptimization'].getboolean('refit_training_workflows')
settings_dict['HyperOptimization']['refit_validation_workflows'] =\
settings['HyperOptimization'].getboolean('refit_validation_workflows')
settings_dict['HyperOptimization']['memory'] = \
str(settings['HyperOptimization']['memory'])
settings_dict['HyperOptimization']['fix_random_seed'] = \
settings['HyperOptimization'].getboolean('fix_random_seed')
# Settings for SMAC
settings_dict['SMAC']['use'] =\
settings['SMAC'].getboolean('use')
settings_dict['SMAC']['n_smac_cores'] =\
int(settings['SMAC']['n_smac_cores'])
settings_dict['SMAC']['budget_type'] =\
str(settings['SMAC']['budget_type'])
settings_dict['SMAC']['budget'] =\
int(settings['SMAC']['budget'])
settings_dict['SMAC']['init_method'] =\
str(settings['SMAC']['init_method'])
settings_dict['SMAC']['init_budget'] =\
int(settings['SMAC']['init_budget'])
# Settings for ensembling
settings_dict['Ensemble']['Method'] =\
str(settings['Ensemble']['Method'])
settings_dict['Ensemble']['Size'] =\
int(settings['Ensemble']['Size'])
#settings['Ensemble'].getint('Use')
settings_dict['Ensemble']['Metric'] =\
settings['Ensemble']['Metric']
# Settings for bootstrapping
settings_dict['Bootstrap']['Use'] =\
settings['Bootstrap'].getboolean('Use')
settings_dict['Bootstrap']['N_iterations'] =\
settings['Bootstrap'].getint('N_iterations')
# Settings for evaluation
settings_dict['Evaluation']['OverfitScaler'] =\
settings['Evaluation'].getboolean('OverfitScaler')
return settings_dict