auto-sync 2026-07-04T01:14:16Z workspace (part 4)

workspace/tests/test_dominance_selector.py

@@ -1,10 +1,13 @@
 from __future__ import annotations
 
+from types import SimpleNamespace
+
 import numpy as np
 import pytest
 
 from scripts.eval_dominance_selector import _choose_tau, _conformal_quantile, _evaluate_case
 from scripts.eval_learned_dominance_selector import (
+    _chart_compat_feature,
     _candidate_feature,
     _choose_thresholds,
     _evaluate_dataset,
@@ -330,6 +333,47 @@ def test_learned_dominance_tangent_features_and_success_weighted_target() -> Non
     assert _target_value("success_weighted_margin", utility_margin=0.2, candidate_success=1.0) == 1.2
 
 
+def test_chart_compat_feature_is_deployment_visible_and_fixed_width() -> None:
+    base = np.arange(14, dtype=float).reshape(2, 7) / 10.0
+    context = np.linspace(-0.5, 0.5, 18)
+    obs = np.linspace(0.0, 1.0, 32)
+    obj = np.linspace(1.0, 2.0, 64)
+    target = SimpleNamespace(
+        base_action=base,
+        feature=np.concatenate([base.reshape(-1), context, obs, obj]),
+    )
+    source = SimpleNamespace(
+        base_action=base + 0.1,
+        feature=np.concatenate([base.reshape(-1) + 0.1, context, obs + 0.2, obj + 0.3]),
+    )
+
+    compat = _chart_compat_feature(
+        target,
+        source,
+        chart_feature_mode="base_context_obs_obj",
+    )
+    feature = _candidate_feature(
+        score=1.0,
+        base_score=0.25,
+        score_mean=0.5,
+        score_std=0.5,
+        candidate_index=1,
+        candidate_type="ctt_transport_rank3",
+        tangent=np.zeros(21, dtype=float),
+        num_candidates=8,
+        feature_set="chart_compat",
+        chart_compat=compat,
+    )
+
+    assert compat.shape == (22,)
+    assert len(feature) == len(_feature_names("chart_compat")) == 32
+    assert compat[0] == 1.0
+    assert compat[12] == 1.0
+    assert compat[17] == 1.0
+    assert np.all(np.isfinite(compat))
+    assert np.all(_chart_compat_feature(target, None, chart_feature_mode="base_context_obs_obj") == 0.0)
+
+
 def test_nonlinear_dominance_split_is_row_disjoint() -> None:
     samples = []
     by_row = {}