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custom_config.py
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custom_config.py
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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