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@@ -161,8 +161,23 @@ class LearningManager:
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and (len(past_cycles) - last_apply_count) < MIN_SUGGESTION_COOLDOWN_CYCLES
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)
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# Locked keys (#343): the user has told the auto-tuner to stop proposing
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# these (e.g. thresholds that break an anti-crease-tuned device). Drop them
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# from the pending map so they never re-surface until unlocked.
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try:
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locked = set(self.profile_store.get_locked_suggestions())
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except Exception: # pylint: disable=broad-exception-caught
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locked = set()
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existing_suggestions = self.profile_store.get_suggestions()
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filtered_suggestions: dict[str, Any] = {}
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any_deleted = False
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for key, data in suggestions.items():
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if key in locked:
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if key in existing_suggestions:
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self.profile_store.delete_suggestion(key)
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any_deleted = True
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continue
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if isinstance(data, dict) and "value" in data:
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current_val = current_options.get(key)
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suggested_val = data["value"]
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@@ -174,6 +189,7 @@ class LearningManager:
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# Gate 1: exact equality → stale, delete so it doesn't linger.
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if abs_delta < 1e-9:
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self.profile_store.delete_suggestion(key)
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any_deleted = True
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continue
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# Gate 2: change too small to be meaningful → delete (noise).
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@@ -181,6 +197,7 @@ class LearningManager:
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if (rel_delta < MIN_SUGGESTION_REL_DELTA
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and abs_delta < _suggestion_min_abs_delta(key)):
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self.profile_store.delete_suggestion(key)
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any_deleted = True
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continue
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# Gate 3: cooldown active → skip update without deleting.
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@@ -195,6 +212,8 @@ class LearningManager:
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filtered_suggestions[key] = data
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if not filtered_suggestions:
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if any_deleted:
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self.hass.async_create_task(self.profile_store.async_save())
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return
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self.suggestion_engine.apply_suggestions(filtered_suggestions)
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@@ -295,8 +314,9 @@ class LearningManager:
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async def _async_run_batch_simulation(self, cycles: list[dict[str, Any]]) -> None:
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"""Run multi-cycle batch simulation asynchronously."""
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try:
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engine = self.suggestion_engine.for_job()
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new_suggestions = await self.hass.async_add_executor_job(
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self.suggestion_engine.run_batch_simulation, cycles
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engine.run_batch_simulation, cycles
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)
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if new_suggestions:
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self._apply_suggestions_and_notify(new_suggestions)
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@@ -313,8 +333,9 @@ class LearningManager:
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try:
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# Simulation runner derives optimal thresholds
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# Offload to executor since simulation can be heavy (CPU bound)
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engine = self.suggestion_engine.for_job()
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new_suggestions = await self.hass.async_add_executor_job(
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self.suggestion_engine.run_simulation, cycle_data
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engine.run_simulation, cycle_data
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)
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if new_suggestions:
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self._apply_suggestions_and_notify(new_suggestions)
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@@ -342,8 +363,10 @@ class LearningManager:
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# Throttle before dispatching so repeated readings within the window do
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# not schedule overlapping passes.
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self._last_suggestion_update = now
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# Bind the config snapshot here, on the loop, not inside the executor job.
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engine = self.suggestion_engine.for_job()
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self._dispatch_scan_and_apply(
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lambda: self.suggestion_engine.generate_operational_suggestions(p95, median),
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lambda: engine.generate_operational_suggestions(p95, median),
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"Operational",
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)
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@@ -354,7 +377,7 @@ class LearningManager:
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offloaded to an executor thread by ``_dispatch_scan_and_apply``.
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"""
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self._dispatch_scan_and_apply(
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self.suggestion_engine.generate_model_suggestions,
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self.suggestion_engine.for_job().generate_model_suggestions,
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"Model",
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)
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@@ -408,8 +431,9 @@ class LearningManager:
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async def _async_run_detection_suggestions(self) -> None:
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"""Run the detection-suggestion pass off the event loop."""
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try:
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engine = self.suggestion_engine.for_job()
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new_suggestions = await self.hass.async_add_executor_job(
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self.suggestion_engine.generate_detection_suggestions
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engine.generate_detection_suggestions
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)
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if new_suggestions:
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self._apply_suggestions_and_notify(new_suggestions)
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@@ -432,12 +456,13 @@ class LearningManager:
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if model.count >= 20 and model.p95 is not None and model.median is not None:
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p95, median = model.p95, model.median
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op = await self.hass.async_add_executor_job(
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self.suggestion_engine.generate_operational_suggestions, p95, median
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self.suggestion_engine.for_job().generate_operational_suggestions,
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p95, median
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)
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if op:
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self._apply_suggestions_and_notify(op)
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model_sug = await self.hass.async_add_executor_job(
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self.suggestion_engine.generate_model_suggestions
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self.suggestion_engine.for_job().generate_model_suggestions
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)
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if model_sug:
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self._apply_suggestions_and_notify(model_sug)
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@@ -445,7 +470,7 @@ class LearningManager:
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# Snapshot the live cycles list before handing it to the executor.
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cycles = list(self.profile_store.get_past_cycles())
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batch = await self.hass.async_add_executor_job(
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self.suggestion_engine.run_batch_simulation, cycles
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self.suggestion_engine.for_job().run_batch_simulation, cycles
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)
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if batch:
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self._apply_suggestions_and_notify(batch)
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@@ -844,10 +869,49 @@ class LearningManager:
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affected envelopes), so this only records the feedback response and removes
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the pending entry - it deliberately does NOT re-label or rebuild again.
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Returns True when a pending entry existed and was resolved.
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Returns True when a pending entry existed and was resolved, OR when no
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pending feedback was found but the cycle was sitting in the review queue
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(uncertain quality / force_stopped / interrupted) and was marked reviewed.
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Returns False when there was nothing to resolve or update.
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"""
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pending = self.profile_store.get_pending_feedback().get(cycle_id)
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if not pending:
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# No detection feedback to resolve. A manual (re)label is still the user
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# engaging with the cycle, so if it is sitting in the review queue only
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# for an uncertain quality label or a force_stopped/interrupted status
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# (no pending feedback, so it has no resolve buttons), stamp it reviewed
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# to clear the red dot (#331 residual). The quality/label fields are left
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# untouched, so no training signal is lost; normal cycles are not touched.
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try:
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cycle = next(
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(c for c in self.profile_store.get_past_cycles() if c.get("id") == cycle_id),
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None,
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)
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except Exception: # pylint: disable=broad-exception-caught
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cycle = None
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if cycle is not None:
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rv = cycle.get("ml_review") if isinstance(cycle.get("ml_review"), dict) else {}
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already_reviewed = bool(rv.get("reviewed_at"))
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needs_review = (
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rv.get("label") in ("uncertain", "review")
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or rv.get("quality") in ("uncertain", "review")
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or cycle.get("status") in ("force_stopped", "interrupted")
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or (cycle.get("ml_health") or {}).get("label") in ("uncertain", "review")
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)
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if needs_review and not already_reviewed:
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try:
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await self.profile_store.set_cycle_review(cycle_id)
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except Exception as err: # pylint: disable=broad-exception-caught
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self._logger.debug(
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"set_cycle_review failed for cycle %s: %s", cycle_id, err
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)
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return False
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async_dispatcher_send(self.hass, f"ha_washdata_update_{self.entry_id}")
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self._logger.info(
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"Marked cycle %s reviewed from manual label (no pending feedback; "
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"cleared needs-review red dot)", cycle_id
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)
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return True
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return False
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detected = pending.get("detected_profile")
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