args = parser.parse_args()
function = PitzDaily(np=args.np, hpc=args.hpc)
features_ranges = np.array(
[[4, 6], [20.0,
30.0]]) # List of maximum and minimum values for each parameter
query_minimum_spacing = np.array(
[0.01, 0.01]) # List of minimum spacing for each parameter
n_initial = 9 # Number of initial samples
n_queries = 30 # Number of queries
plot_regression = True
regressor_constructor = regressor_list["gaussian_process_rbf"]
regressor = regressor_constructor()
query_strategy = probabilistic_std_sampling
active_learner_regressor(
function,
features_ranges,
regressor,
query_minimum_spacing,
n_queries,
query_strategy,
n_initial,
plot_regression=plot_regression,
save_path="figs",
save_name="pitzdaily",
)
("nn_greedyio", "neural_network", greedy_sampling_input_output, 1),
("gp_52_rdm", "gaussian_process_matern52", random_sampling, 1),
)
if args.s >= 0:
if args.s < len(strategy_list):
strategy_list = [strategy_list[args.s]]
else:
raise ValueError("Invalid strategy index.")
for name, regressor_name, query_strategy, repetitions in strategy_list:
for n in range(repetitions):
np.random.seed(n)
regressor_constructor = regressor_list[regressor_name]
regressor = regressor_constructor()
active_learner_regressor(
function,
features_ranges,
regressor,
query_minimum_spacing,
n_queries,
query_strategy,
n_initial,
plot_regression=plot_regression,
plot_brute_force=plot_brute_force,
save_path="figs_" + name,
regression_history_path="regression_" + name,
save_name="mixer" + "_n{0:03d}".format(n),
)
plot_regression = True
plot_brute_force = True
strategy_list = (
("gp_rbf_std", "gaussian_process_rbf", probabilistic_std_sampling),
("gp_52_std", "gaussian_process_matern52", probabilistic_std_sampling),
("lin_greedyio", "linear_regression", greedy_sampling_input_output),
("rfr_greedyio", "random_forest_regression", greedy_sampling_input_output),
("svr_greedyio", "svr", greedy_sampling_input_output),
("nn_greedyio", "neural_network", greedy_sampling_input_output),
)
for name, regressor_name, query_strategy in strategy_list:
regressor_constructor = regressor_list[regressor_name]
regressor = regressor_constructor()
active_learner_regressor(
function,
features_ranges,
regressor,
query_minimum_spacing,
n_queries,
query_strategy,
n_initial,
plot_regression=plot_regression,
plot_brute_force=plot_brute_force,
save_path="figs_" + name,
regression_history_path="regression_" + name,
save_name="algebraic",
)