Add learned dominance selector diagnostic

workspace/tests/test_dominance_selector.py

@@ -1,6 +1,9 @@
 from __future__ import annotations
 
+import numpy as np
+
 from scripts.eval_dominance_selector import _choose_tau, _conformal_quantile, _evaluate_case
+from scripts.eval_learned_dominance_selector import _evaluate_dataset, _fit_select_ridge
 
 
 def test_conformal_quantile_and_fallback_selection() -> None:
@@ -64,3 +67,45 @@ def test_choose_tau_prefers_success_then_utility_then_coverage() -> None:
     tau = _choose_tau(cases, residual_quantile=0.0)
     evaluated = [_evaluate_case(case, residual_quantile=0.0, tau=tau) for case in cases]
     assert sum(row["selected_success"] for row in evaluated) == 2.0
+
+
+def test_learned_dominance_ridge_prefers_positive_candidate() -> None:
+    def sample(row: int, candidate: int, score: float, margin: float, success: float) -> dict:
+        feature = np.zeros(10, dtype=float)
+        feature[0] = 1.0
+        feature[1] = score
+        feature[2] = margin
+        feature[4] = float(candidate)
+        return {
+            "row_index": row,
+            "candidate_index": candidate,
+            "feature": feature,
+            "target_margin": success - 0.2,
+            "candidate_utility": success,
+            "candidate_success": success,
+            "base_utility": 0.2,
+            "base_success": 0.0,
+            "proposal_oracle_utility": success,
+            "proposal_oracle_success": success,
+            "hidden_chart_oracle_utility": 1.0,
+            "hidden_chart_oracle_success": 1.0,
+            "outcome_ptr": success,
+            "chart_id": f"c{row}",
+            "task_id": "pick",
+            "seed": "0",
+            "train_seed": "0",
+        }
+
+    dataset = {
+        "samples": [
+            sample(0, 0, 2.0, 2.0, 1.0),
+            sample(0, 1, -1.0, -1.0, 0.0),
+            sample(1, 0, 2.0, 2.0, 1.0),
+            sample(1, 1, -1.0, -1.0, 0.0),
+        ],
+        "by_row": {0: [0, 1], 1: [2, 3]},
+        "num_rows": 2,
+    }
+    fit = _fit_select_ridge(dataset, lambdas=[0.1])
+    rows = _evaluate_dataset(dataset, fit["weights"], fit["mean"], fit["std"], tau=fit["tau"])
+    assert [row["selected_success"] for row in rows] == [1.0, 1.0]