'boosting_type': 'gbdt', 'metric': METRIC, 'num_threads': N_THREADS, 'verbose': VERBOSE, 'seed': SEED, 'n_estimators': N_ESTIMATORS, 'early_stopping_rounds': EARLY_STOPPING_ROUNDS } logger = utility.get_logger(LOGGER_NAME, MODEL_NUMBER, run_id, LOG_DIR) utility.set_seed(SEED) logger.info(f'Running for Model Number {MODEL_NUMBER}') utility.update_tracking(run_id, "model_number", MODEL_NUMBER, drop_incomplete_rows=True) utility.update_tracking(run_id, "model_type", MODEL_TYPE) utility.update_tracking(run_id, "is_test", IS_TEST) utility.update_tracking(run_id, "n_estimators", N_ESTIMATORS) utility.update_tracking(run_id, "early_stopping_rounds", EARLY_STOPPING_ROUNDS) utility.update_tracking(run_id, "random_state", SEED) utility.update_tracking(run_id, "n_threads", N_THREADS) #utility.update_tracking(run_id, "learning_rate", LEARNING_RATE) utility.update_tracking(run_id, "n_fold", N_FOLDS) ############################################ # Preparaing Data ############################################ #Read the data file
SHUFFLE = True # Parameters related to model MODEL_TYPE = "LinearDiscriminantAnalysis" N_THREADS = -1 SOLVER = 'eigen' # Name of the target TARGET = 'target' logger = utility.get_logger(LOGGER_NAME, MODEL_NUMBER, run_id, LOG_DIR) utility.set_seed(SEED) logger.info(f'Running for Model Number {MODEL_NUMBER}') utility.update_tracking(run_id, "model_number", MODEL_NUMBER, drop_incomplete_rows=True) utility.update_tracking(run_id, "model_type", MODEL_TYPE) utility.update_tracking(run_id, "is_test", IS_TEST) utility.update_tracking(run_id, "random_state", SEED) utility.update_tracking(run_id, "n_threads", N_THREADS) utility.update_tracking(run_id, "n_fold", N_FOLDS) utility.update_tracking(run_id, "solver", SOLVER) ############################################ # Preparaing Data ############################################ #Read the data file train, test, submission = utility.read_files(logger=logger, dir_path=DATA_DIR, index_col='id') combined_df = pd.concat([train.drop('target', axis=1), test])