fix(bayesian): surface skipped optimization and clamp warm start to search space

tests/test_bayesian_optimizer_contracts.py

@@ -443,13 +443,18 @@ def test_run_bayesian_optimization_no_pareto_uses_objective_best_and_restores_af
         n_kl_prompts=1,
     )
 
+    # peak_position was 0.0 here before the warm start was clamped. 0.0 is
+    # outside the distribution `objective` declares for it (0.1..0.9), so Optuna
+    # rejected the enqueued trial with a warning and the informed warm start was
+    # silently discarded. This assertion previously encoded that defect as the
+    # expected behaviour, which is why it went unnoticed; 0.1 is the floor.
     assert study.enqueued == [{
         "attn_max_weight": 0.9,
-        "attn_peak_position": 0.0,
+        "attn_peak_position": 0.1,
         "attn_min_weight": 0.05,
         "attn_spread": 0.3,
         "mlp_max_weight": 0.6,
-        "mlp_peak_position": 0.0,
+        "mlp_peak_position": 0.1,
         "mlp_min_weight": 0.05,
         "mlp_spread": 0.3,
         "dir_idx": 0.0,
@@ -460,3 +465,96 @@ def test_run_bayesian_optimization_no_pareto_uses_objective_best_and_restores_af
     assert pipeline.moe_calls == []
     assert torch.allclose(layers[0].self_attn.o_proj.weight, original_attn)
     assert torch.allclose(layers[0].mlp.down_proj.weight, original_mlp)
+
+
+def test_missing_optuna_is_surfaced_on_the_pipeline_log_and_recorded(monkeypatch):
+    """A skipped optimization must not be indistinguishable from a completed one.
+
+    `logger.warning` reaches stderr only through logging's last-resort handler —
+    the `obliterate` command never configures logging — while the console panel
+    the operator watches is fed by `pipeline.log`. Before this contract the
+    pipeline log received nothing at all, so a run whose headline feature never
+    executed still read as a clean success.
+    """
+    real_import = builtins.__import__
+
+    def fake_import(name, *args, **kwargs):
+        if name == "optuna" or name.startswith("optuna."):
+            raise ImportError("no optuna in this test")
+        return real_import(name, *args, **kwargs)
+
+    monkeypatch.setattr(builtins, "__import__", fake_import)
+
+    pipeline = _Pipeline()
+    pipeline.handle = object()
+    pipeline._strong_layers = [0]
+
+    assert bo.run_bayesian_optimization(pipeline) == {}
+    assert any("SKIPPED" in message for message in pipeline.logs)
+    assert any("optuna" in message.lower() for message in pipeline.logs)
+    assert pipeline._bayesian_skipped == "optuna-not-installed"
+
+
+def test_skip_without_handle_or_layers_is_also_recorded(monkeypatch):
+    _install_fake_optuna(monkeypatch)
+
+    pipeline = _Pipeline()
+    pipeline.handle = None
+    pipeline._strong_layers = [0]
+
+    assert bo.run_bayesian_optimization(pipeline) == {}
+    assert pipeline._bayesian_skipped == "no-strong-layers"
+
+
+def test_clamp_to_space_pulls_out_of_range_warm_start_into_the_distribution():
+    """Reachable in normal use, not a synthetic edge case.
+
+    `attn_peak_position` is seeded from ``peak_layer / (n_layers - 1)``; a
+    36-layer model whose peak layer is 2 seeds 0.057, below the 0.1 floor.
+    Optuna rejects an enqueued value outside its distribution, so the warm-start
+    trial silently loses the informed starting point it exists to provide.
+    """
+    clamped = bo._clamp_to_space(
+        {
+            "attn_peak_position": 2 / 35,   # early peak layer on a 36-layer model
+            "mlp_peak_position": 34 / 35,   # late peak layer
+            "mlp_max_weight": 0.25,         # max_weight 0.5 x mlp_scale 0.5
+            "dir_idx": 3.0,                 # bounds are dynamic; must pass through
+        }
+    )
+
+    for name, value in clamped.items():
+        if name in bo.KERNEL_SPACE:
+            low, high = bo.KERNEL_SPACE[name]
+            assert low <= value <= high, f"{name}={value} outside {low}..{high}"
+
+    assert clamped["attn_peak_position"] == pytest.approx(0.1)
+    assert clamped["mlp_peak_position"] == pytest.approx(0.9)
+    assert clamped["mlp_max_weight"] == pytest.approx(0.3)
+    assert clamped["dir_idx"] == 3.0
+
+
+def test_objective_bounds_and_warm_start_share_one_declaration(monkeypatch):
+    """The two copies of the search space are why the warm start could drift."""
+    study = _FakeStudy(best_trials=[_FakeTrial()])
+    _install_fake_optuna(monkeypatch, study=study)
+
+    recorded: dict[str, tuple[float, float]] = {}
+
+    class _RecordingTrial(_FakeTrial):
+        def suggest_float(self, name, low, high):
+            recorded[name] = (low, high)
+            return self.params[name]
+
+    monkeypatch.setattr(
+        study,
+        "optimize",
+        lambda objective, n_trials, show_progress_bar: objective(_RecordingTrial()),
+    )
+    monkeypatch.setattr(bo, "_measure_refusal_rate", lambda *_a, **_k: 0.25)
+    monkeypatch.setattr(bo, "_measure_kl_divergence", lambda *_a, **_k: 0.1)
+
+    bo.run_bayesian_optimization(_optimization_pipeline([_Layer()]), n_trials=1)
+
+    for name, bounds in bo.KERNEL_SPACE.items():
+        assert recorded.get(name) == bounds, f"{name} suggested outside KERNEL_SPACE"