initial commit FESurrogateModelTutorial

2026-05-21 17:03:51 +09:00
parent 93665d9ee6
commit 43b86669fa
122 changed files with 7929 additions and 0 deletions
@@ -0,0 +1,47 @@
+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,)