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- from modules.config.attributes_config import *
- import os
- # store all variables from global config
- context_vars = vars()
- # folders
- output_data_folder = 'data'
- output_data_generated = os.path.join(output_data_folder, 'generated')
- output_datasets = os.path.join(output_data_folder, 'datasets')
- output_zones_learned = os.path.join(output_data_folder, 'learned_zones')
- output_models = os.path.join(output_data_folder, 'saved_models')
- output_results_folder = os.path.join(output_data_folder, 'results')
- output_logs_folder = os.path.join(output_data_folder, 'logs')
- output_backup_folder = os.path.join(output_data_folder, 'backups')
- output_surrogates_folder = os.path.join(output_data_folder, 'surrogate')
- output_surrogates_model_folder = os.path.join(output_surrogates_folder, 'models')
- output_surrogates_data_folder = os.path.join(output_surrogates_folder, 'data')
- results_information_folder = os.path.join(output_data_folder, 'results')
- ## min_max_custom_folder = 'custom_norm'
- ## correlation_indices_folder = 'corr_indices'
- # variables
- features_choices_labels = features_choices_labels + ['filters_statistics', 'statistics_extended']
- optimization_filters_result_filename = 'optimization_comparisons_filters.csv'
- optimization_attributes_result_filename = 'optimization_comparisons_attributes.csv'
- filter_reduction_choices = ['attributes', 'filters']
- # models_names_list = ["svm_model","ensemble_model","ensemble_model_v2","deep_keras", "svm_gpu"]
- models_names_list = ["svm_model","ensemble_model","ensemble_model_v2"]
- ## models_names_list = ["svm_model","ensemble_model","ensemble_model_v2","deep_keras"]
- ## normalization_choices = ['svd', 'svdn', 'svdne']
- # parameters
- ## keras_epochs = 500
- ## keras_batch = 32
- ## val_dataset_size = 0.2
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