Merge branch 'main' into fix_lime_example

CHANGELOG.rst

@@ -7,6 +7,13 @@
 Changelog
 =========
 
+0.6.1 (2024-07-xx)
+------------------
+
+**Other changes**
+
+* Changed the index columns order in ``MetaLearnerGridSearch.results_``.
+
 0.6.0 (2024-07-08)
 ------------------
 

metalearners/grid_search.py

@@ -83,12 +83,12 @@ def _format_results(results: Sequence[_GSResult]) -> pd.DataFrame:
     for result in results:
         row: dict[str, str | int | float] = {}
         row["metalearner"] = result.metalearner.__class__.__name__
-        nuisance_models = (
+        nuisance_models = sorted(
             set(result.metalearner.nuisance_model_specifications().keys())
             - result.metalearner._prefitted_nuisance_models
         )
-        treatment_models = set(
-            result.metalearner.treatment_model_specifications().keys()
+        treatment_models = sorted(
+            set(result.metalearner.treatment_model_specifications().keys())
         )
         for model_kind in nuisance_models:
             row[model_kind] = result.metalearner.nuisance_model_factory[
@@ -115,13 +115,16 @@ def _format_results(results: Sequence[_GSResult]) -> pd.DataFrame:
                 row[f"test_{name}"] = value
         rows.append(row)
     df = pd.DataFrame(rows)
-    index_columns = [
-        c
-        for c in df.columns
-        if not c.endswith("_time")
-        and not c.startswith("train_")
-        and not c.startswith("test_")
-    ]
+    sorted_cols = sorted(df.columns)
+    index_columns = ["metalearner"]
+    for model_kind in nuisance_models:
+        for c in sorted_cols:
+            if c.startswith(model_kind):
+                index_columns.append(c)
+    for model_kind in treatment_models:
+        for c in sorted_cols:
+            if c.startswith(model_kind):
+                index_columns.append(c)
     df = df.set_index(index_columns)
     return df
 

tests/test_grid_search.py

@@ -25,23 +25,27 @@
             {"base_model": [LinearRegression, LGBMRegressor]},
             {"base_model": {"LGBMRegressor": {"n_estimators": [1, 2]}}},
             3,
-            3,
+            ["metalearner", "base_model", "base_model_n_estimators"],
         ),
         (
             SLearner,
             True,
             {"base_model": [LogisticRegression, LGBMClassifier]},
             {"base_model": {"LGBMClassifier": {"n_estimators": [1, 2]}}},
             3,
-            3,
+            ["metalearner", "base_model", "base_model_n_estimators"],
         ),
         (
             TLearner,
             False,
             {"variant_outcome_model": [LinearRegression, LGBMRegressor]},
             {"variant_outcome_model": {"LGBMRegressor": {"n_estimators": [1, 2, 3]}}},
             4,
-            3,
+            [
+                "metalearner",
+                "variant_outcome_model",
+                "variant_outcome_model_n_estimators",
+            ],
         ),
         (
             XLearner,
@@ -58,7 +62,16 @@
                 "treatment_effect_model": {"LGBMRegressor": {"n_estimators": [1]}},
             },
             6,
-            8,
+            [
+                "metalearner",
+                "propensity_model",
+                "propensity_model_n_estimators",
+                "variant_outcome_model",
+                "control_effect_model",
+                "control_effect_model_n_estimators",
+                "treatment_effect_model",
+                "treatment_effect_model_n_estimators",
+            ],
         ),
         (
             RLearner,
@@ -75,7 +88,15 @@
                 },
             },
             9,
-            7,
+            [
+                "metalearner",
+                "outcome_model",
+                "propensity_model",
+                "propensity_model_n_estimators",
+                "treatment_model",
+                "treatment_model_learning_rate",
+                "treatment_model_n_estimators",
+            ],
         ),
         (
             DRLearner,
@@ -89,7 +110,13 @@
                 "propensity_model": {"LGBMClassifier": {"n_estimators": [1, 2, 3, 4]}},
             },
             4,
-            5,
+            [
+                "metalearner",
+                "propensity_model",
+                "propensity_model_n_estimators",
+                "variant_outcome_model",
+                "treatment_model",
+            ],
         ),
     ],
 )
@@ -125,7 +152,7 @@ def test_metalearnergridsearch_smoke(
     gs.fit(X, y, w, X_test, y_test, w_test)
     assert gs.results_ is not None
     assert gs.results_.shape[0] == expected_n_configs
-    assert len(gs.results_.index.names) == expected_index_cols
+    assert gs.results_.index.names == expected_index_cols
 
     train_scores_cols = set(
         c[6:] for c in list(gs.results_.columns) if c.startswith("train_")