"regression_" + name, case_name
)
n_samples_list = regression_history_all[0][0].keys()
error_samples = sorted(n_samples_list)
error_avgs = np.zeros(len(n_samples_list))
error_min = np.zeros(len(n_samples_list))
error_max = np.zeros(len(n_samples_list))
error_repetitions = np.zeros(len(n_samples_list))
for i, n_samples in enumerate(error_samples):
error_list = []
for regression_history, features_range in regression_history_all:
error = calculate_error(
X_true,
y_true,
regression_history[n_samples],
features_range,
error_measure=mean_relative_error,
)
error_list.append(error)
error_avgs[i] = np.average(error_list)
error_min[i], error_max[i] = sp.stats.t.interval(
0.95,
len(error_list) - 1,
loc=np.mean(error_list),
scale=sp.stats.sem(error_list),
)
error_repetitions[i] = len(error_list)
plt.plot(error_samples, error_avgs * 100, label="\\small " + label)
plt.fill_between(error_samples, error_min * 100, error_max * 100, alpha=0.5)
from active_learning_cfd.error_measures import calculate_error
reference_filename = "reference_solution.csv"
reference_solution = np.genfromtxt(reference_filename, delimiter=",")
X_true = reference_solution[:, 0:-1]
y_true = reference_solution[:, -1]
strategy_list = (
"gp_rbf_std",
"gp_52_std",
"lin_greedyio",
"rfr_greedyio",
"svr_greedyio",
"nn_greedyio",
)
plt.figure()
for name in strategy_list:
regression_history = load_regression_history("regression_" + name,
"algebraic")
error_list = np.array(
[[n_samples, calculate_error(X_true, y_true, regressor)]
for n_samples, regressor in regression_history])
plt.plot(error_list[:, 0], error_list[:, 1], label=name)
plt.legend()
plt.show()
