def fit_and_predict(config, experiment_name, test_set, model_name): """Runs: - train - model_selection_df - predict - analyze """ run_name = "fit_and_predict:{}:{}:{}".format( experiment_name, test_set, model_name) config = C.Config.load(config) print(utils.colored("Running {} end-to-end.".format(run_name))) timer = utils.TimerHolder() timer.start(run_name) logger.debug("Running model={} with experiment_name={} at {}" .format(model_name, experiment_name, timer.get_start(run_name))) driver = hcnn.driver.Driver(config, model_name=model_name, experiment_name=experiment_name, load_features=True) result = driver.fit_and_predict_one(test_set) print("{} - {} complted in duration {}".format( run_name, utils.result_colored(result), timer.end(run_name))) return result
def extract_features(master_config): """Extract CQTs from all files collected in collect.""" config = C.Config.load(master_config) print(utils.colored("Extracting CQTs from note audio.")) driver = hcnn.driver.Driver(config, load_features=False) result = driver.extract_features() print("Extraction {}".format(utils.result_colored(result))) return result
def integration_test(config): """AKA "model" test. This is equivalent to running python manage.py -c data/integrationtest_config.yaml run_all_experiments """ # Load integrationtest config experiment_name = "integrationtest" print(utils.colored("Extracting features from tinydata set.")) print(utils.colored( "Running integration test on tinydata set : {}." .format(config))) # Begin by cleaning the feature data result = clean(config, force=True) if result: result = run_all_experiments(config, experiment_root=experiment_name) print("IntegrationTest Result: {}".format(utils.result_colored(result))) return result