custom_config.py 1.7 KB

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  1. from modules.config.attributes_config import *
  2. import os
  3. # store all variables from global config
  4. context_vars = vars()
  5. # folders
  6. output_data_folder = 'data'
  7. output_data_generated = os.path.join(output_data_folder, 'generated')
  8. output_datasets = os.path.join(output_data_folder, 'datasets')
  9. output_zones_learned = os.path.join(output_data_folder, 'learned_zones')
  10. output_models = os.path.join(output_data_folder, 'saved_models')
  11. output_results_folder = os.path.join(output_data_folder, 'results')
  12. output_logs_folder = os.path.join(output_data_folder, 'logs')
  13. output_backup_folder = os.path.join(output_data_folder, 'backups')
  14. results_information_folder = os.path.join(output_data_folder, 'results')
  15. ## min_max_custom_folder = 'custom_norm'
  16. ## correlation_indices_folder = 'corr_indices'
  17. # variables
  18. features_choices_labels = features_choices_labels + ['filters_statistics', 'statistics_extended']
  19. optimization_filters_result_filename = 'optimization_comparisons_filters.csv'
  20. optimization_attributes_result_filename = 'optimization_comparisons_attributes.csv'
  21. filter_reduction_choices = ['attributes', 'filters']
  22. models_names_list = ["svm_model","ensemble_model","ensemble_model_v2","deep_keras", "svm_gpu"]
  23. ## models_names_list = ["svm_model","ensemble_model","ensemble_model_v2","deep_keras"]
  24. ## normalization_choices = ['svd', 'svdn', 'svdne']
  25. # parameters
  26. ## keras_epochs = 500
  27. ## keras_batch = 32
  28. ## val_dataset_size = 0.2