Thesis-NoiseDetection-26-attributes/custom_config.py

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