def run_training(): df = read_data_as_df(DATA_PATH) new_df = get_feature_df(df) tfidf_df = get_tfidf(new_df) X, y = preprocess_data(tfidf_df) X_test, y_test = X.loc[X.index == 'TEST'], y.loc[y.index == 'TEST'].values X_train, y_train = X.loc[(X.index == 'TRAIN') | ( X.index == 'VALIDATION')], y.loc[(y.index == 'TRAIN') | (y.index == 'VALIDATION')].values LOG.info(f"Training set: {X_train.shape}, Testing set: {X_test.shape}") LOG.info( f"Training set positive examples: {y_train.sum()}, Testing set positive examples: {y_test.sum()}" ) clf_d = get_trained_models(["RF", "SGD", "LR", "SVM"], X_train, y_train) evaluate_models(clf_d, X_train, X_test, y_train, y_test)
import pytest from utils import get_trained_models def _assert_eq(left, right): assert left == right, f"{left} != {right}" FOLDER = "rl-trained-agents/" N_STEPS = 100 LOG_FOLDER = "logs/tests/" trained_models = get_trained_models(FOLDER) @pytest.mark.parametrize("trained_model", trained_models.keys()) def test_enjoy(trained_model): algo, env_id = trained_models[trained_model] args = ["-n", str(N_STEPS), "-f", FOLDER, "--algo", algo, "--env", env_id, "--no-render"] # Skip mujoco envs if "Fetch" in trained_model: return if "-MiniGrid-" in trained_model: args = args + ["--gym-packages", "gym_minigrid"] return_code = subprocess.call(["python", "enjoy.py"] + args)
type=int) parser.add_argument('--verbose', help='Verbose mode (0: no output, 1: INFO)', default=1, type=int) parser.add_argument('--seed', help='Random generator seed', type=int, default=0) parser.add_argument('--test-mode', action='store_true', default=False, help='Do only one experiments (useful for testing)') args = parser.parse_args() trained_models = get_trained_models(args.log_dir) n_experiments = len(trained_models) results = { 'algo': [], 'env_id': [], 'mean_reward': [], 'std_reward': [], 'n_timesteps': [], 'n_episodes': [] } for idx, trained_model in enumerate(trained_models.keys()): algo, env_id = trained_models[trained_model] n_envs = args.n_envs n_timesteps = args.n_timesteps if algo in ['dqn', 'ddpg', 'sac']: