Add test propensity model reuse

tests/test_grid_search.py

@@ -8,7 +8,7 @@
 
 from metalearners.drlearner import DRLearner
 from metalearners.grid_search import MetaLearnerGridSearch
-from metalearners.metalearner import VARIANT_OUTCOME_MODEL
+from metalearners.metalearner import PROPENSITY_MODEL, VARIANT_OUTCOME_MODEL
 from metalearners.rlearner import RLearner
 from metalearners.slearner import SLearner
 from metalearners.tlearner import TLearner
@@ -131,7 +131,7 @@ def test_metalearnergridsearch_smoke(
     assert train_scores_cols == test_scores_cols
 
 
-def test_metalearnergridsearch_reuse_smoke(rng):
+def test_metalearnergridsearch_reuse_nuisance_smoke(rng):
     n_variants = 3
     n_samples = 250
     n_test_samples = 100
@@ -188,3 +188,56 @@ def test_metalearnergridsearch_reuse_smoke(rng):
         }
     assert gs.results_ is not None
     assert gs.results_.shape[0] == 8
+
+
+def test_metalearnergridsearch_reuse_propensity_smoke(rng):
+    n_variants = 3
+    n_samples = 250
+    n_test_samples = 100
+
+    X = rng.standard_normal((n_samples, 3))
+    X_test = rng.standard_normal((n_test_samples, 3))
+    y = rng.standard_normal(n_samples)
+    y_test = rng.standard_normal(n_test_samples)
+    w = rng.integers(0, n_variants, n_samples)
+    w_test = rng.integers(0, n_variants, n_test_samples)
+
+    rl = RLearner(
+        False,
+        n_variants,
+        LGBMRegressor,
+        LGBMRegressor,
+        LGBMClassifier,
+        nuisance_model_params={"verbose": -1, "n_estimators": 1},
+        treatment_model_params={"verbose": -1, "n_estimators": 1},
+        propensity_model_params={"verbose": -1, "n_estimators": 1},
+        n_folds=2,
+    )
+    rl.fit(X, y, w)
+
+    gs = MetaLearnerGridSearch(
+        DRLearner,
+        {
+            "is_classification": False,
+            "n_variants": n_variants,
+            "n_folds": 5,  # To test with different n_folds than the pretrained
+            "fitted_propensity_model": rl._nuisance_models[PROPENSITY_MODEL][0],
+        },
+        {
+            "treatment_model": [LGBMRegressor],
+            "variant_outcome_model": [LinearRegression],
+        },
+        {
+            "treatment_model": {
+                "LGBMRegressor": {"n_estimators": [1, 2], "verbose": [-1]}
+            },
+        },
+        verbose=3,
+        random_state=1,
+    )
+    gs.fit(X, y, w, X_test, y_test, w_test)
+    assert gs.raw_results_ is not None
+    for raw_result in gs.raw_results_:
+        assert raw_result.metalearner._prefitted_nuisance_models == {PROPENSITY_MODEL}
+    assert gs.results_ is not None
+    assert gs.results_.shape[0] == 2