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')

## min_max_custom_folder           = 'custom_norm'
## correlation_indices_folder      = 'corr_indices'
data_augmented_filename         = 'augmented_dataset.csv'

# variables
features_choices_labels         = ['lab', 'mscn', 'low_bits_2', 'low_bits_3', 'low_bits_4', 'low_bits_5', 'low_bits_6','low_bits_4_shifted_2', 
                                    'sub_blocks_stats', 'sub_blocks_area', 'sub_blocks_stats_reduced', 'sub_blocks_area_normed', 
                                    'mscn_var_4', 'mscn_var_16', 'mscn_var_64', 'mscn_var_16_max', 'mscn_var_64_max', 
                                    'ica_diff', 'svd_trunc_diff', 'ipca_diff', 'svd_reconstruct', 
                                    'highest_sv_std_filters', 'lowest_sv_std_filters', 'highest_wave_sv_std_filters', 'lowest_wave_sv_std_filters', 
                                    'highest_sv_std_filters_full', 'lowest_sv_std_filters_full', 'highest_sv_entropy_std_filters', 'lowest_sv_entropy_std_filters', 
                                    'convolutional_kernels_std_normed', 'convolutional_kernels_mean_normed', 'convolutional_kernels_std_max_blocks', 'convolutional_kernels_mean_max_blocks', 'convolutional_kernel_stats_svd',
                                    'svd_entropy', 'svd_entropy_20', 'svd_entropy_noise_20']

## models_names_list               = ["svm_model","ensemble_model","ensemble_model_v2","deep_keras"]
## normalization_choices           = ['svd', 'svdn', 'svdne']

# parameters
keras_epochs                    = 30
keras_batch                     = 32
## val_dataset_size                = 0.2