#models_processor.train_validate_test(X_train=dataset.get_X_train(), y_train=dataset.get_y_train(), X_test=dataset.get_X_test(), y_test=dataset.get_y_test())
#models_processor.train_validate_test_models()
models_processor.train_validate_test_model(model_name = 'KNN')
models_processor.get_models().get_models().keys()
X, Y, train_accuracy, train_F1, train_precision, train_recall, train_tn, train_fp, train_fn, train_tp, valid_accuracy, valid_F1, valid_precision, valid_recall, valid_tn, valid_fp, valid_fn, valid_tp, test_accuracy, test_F1, test_precision, test_recall, test_tn, test_fp, test_fn, test_tp, grid_search_df = models_processor.exhaustive_grid_search(interval = 15
, min_no_of_steps = 5
, max_no_of_steps = 30
, no_of_steps_interval = 5
, min_window_size = 5
, max_window_size = 20
, window_size_interval = 5
, interpolation_method = 'linear'
, train_start = '2009-01-01'
, train_end = '2009-12-31'
, valid_start = '2010-01-01'
, valid_end = '2010-12-31'
, test_start = '2011-01-01'
, test_end = '2011-12-31'
, dimensions = 3
, model_name = 'RBF SVM')
models_processor.delete_table(sql_string = 'delete [DBHKUDissertation].[dbo].[GridSearchResult]')
models_processor.to_sql(table_name = 'GridSearchResult', if_exists = 'replace')
#models_processor.plot_grid_search(is_saved = 0, save_path = r"C:\Users\Kelvin\CloudStation\MSC COMPUTER SCIENCE\Dissertation\CODE\Dissertation\Dissertation\GridSearchImage")
models_processor.plot_grid_search(is_saved = 0, save_path = r"C:\Users\Kelvi\CloudStation\MSC COMPUTER SCIENCE\Dissertation\CODE\Dissertation\Dissertation\GridSearchImage")
