김경종
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
import warnings
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from sklearn.exceptions import ConvergenceWarning
|
|
|
|
from femsurrogate.surrogates.registry import MODEL_NAMES, make_model
|
|
|
|
|
|
def _toy_regression_data():
|
|
X = pd.DataFrame(
|
|
{
|
|
"L_m": np.linspace(1.0, 3.0, 12),
|
|
"b_m": np.linspace(0.02, 0.08, 12),
|
|
"h_m": np.linspace(0.04, 0.16, 12),
|
|
"E_pa": np.linspace(100e9, 220e9, 12),
|
|
"P_n": np.linspace(100.0, 2000.0, 12),
|
|
}
|
|
)
|
|
y = -X["P_n"] * X["L_m"] ** 3 / (3.0 * X["E_pa"] * X["b_m"] * X["h_m"] ** 3)
|
|
return X, y
|
|
|
|
|
|
def test_registry_exposes_expected_surrogate_names():
|
|
assert MODEL_NAMES == ("rsm", "gpr", "random_forest", "gradient_boosting", "mlp")
|
|
|
|
|
|
def test_make_model_builds_estimators_that_fit_and_predict():
|
|
X, y = _toy_regression_data()
|
|
fast_overrides = {
|
|
"random_forest": {"n_estimators": 5, "n_jobs": 1},
|
|
"gradient_boosting": {"n_estimators": 5},
|
|
"mlp": {"hidden_layer_sizes": (4,), "max_iter": 25, "early_stopping": False},
|
|
}
|
|
|
|
for model_name in MODEL_NAMES:
|
|
model = make_model(
|
|
model_name,
|
|
random_state=20260521,
|
|
**fast_overrides.get(model_name, {}),
|
|
)
|
|
with warnings.catch_warnings():
|
|
warnings.filterwarnings("ignore", category=ConvergenceWarning)
|
|
model.fit(X, y)
|
|
predictions = model.predict(X.iloc[:3])
|
|
|
|
assert predictions.shape == (3,)
|