1192 lines
46 KiB
Python
1192 lines
46 KiB
Python
from __future__ import annotations
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import logging
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from collections.abc import Sequence
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from datetime import datetime, timedelta, timezone
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from zoneinfo import ZoneInfo
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from app.actuators.models import (
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ActuatorRecord,
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BehaviorMode,
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BehaviorPattern,
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BehaviorPrediction,
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BehaviorState,
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BehaviorStatus,
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DecisionFactor,
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ExecutionEvent,
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RelatedAutomation,
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SafetyProfile,
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SafetyStage,
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)
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from app.actuators.store import ActuatorStore
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from app.config import Settings
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from app.ha.exceptions import HaClientError
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from app.ha.history import LogbookEntry, StateHistoryPoint, StateHistorySeries
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from app.ha.models import HaEntitySummary
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from app.ha.reader import HaReader
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_MAX_PATTERNS = 500
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_MAX_EXECUTION_EVENTS = 100
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_ACTION_LOGBOOK_TOLERANCE = timedelta(seconds=10)
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_CONTEXT_TRIGGER_TOLERANCE = timedelta(seconds=3)
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_OWN_ACTION_TOLERANCE = timedelta(seconds=20)
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_SAFE_ACTIVE_DOMAINS = frozenset({"cover", "fan", "humidifier", "light", "switch"})
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_AUTOMATION_CONTEXT_DOMAINS = frozenset({"automation", "script"})
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logger = logging.getLogger(__name__)
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class BehaviorEngine:
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def __init__(
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self,
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*,
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ha_reader: HaReader,
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store: ActuatorStore,
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settings: Settings,
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) -> None:
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self._ha_reader = ha_reader
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self._store = store
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self._settings = settings
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def train_all(self) -> list[ActuatorRecord]:
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results: list[ActuatorRecord] = []
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for record in self._store.list():
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try:
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results.append(self.train(record.actuator_entity_id))
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except Exception:
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logger.exception("Behavior training failed for %s", record.actuator_entity_id)
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results.append(record)
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return results
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def train(self, actuator_entity_id: str) -> ActuatorRecord:
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record = self._store.get(actuator_entity_id)
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now = datetime.now(timezone.utc)
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raw_context_ids = list(
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dict.fromkeys(
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[
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record.assignment.selected_numeric_entity_id,
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*record.assignment.selected_context_entity_ids,
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]
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)
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)
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context_ids = [
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entity_id for entity_id in raw_context_ids if isinstance(entity_id, str)
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]
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if not context_ids:
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return self._save_behavior(
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record,
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record.behavior.model_copy(
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update={
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"status": BehaviorStatus.COLLECTING,
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"activation_ready": False,
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"activation_reason": (
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"Freigabe gesperrt: Noch kein geeigneter Kontext erkannt."
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),
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"last_trained_at": now,
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"reason": "Noch kein geeigneter Kontext für Verhaltenslernen vorhanden.",
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}
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),
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)
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start = now - timedelta(days=self._settings.history_days)
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history_ids = [actuator_entity_id, *context_ids]
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try:
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history = {
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series.entity_id: series
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for series in self._ha_reader.read_state_history(history_ids, start, now)
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}
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except (HaClientError, ValueError) as exc:
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logger.warning("Behavior history unavailable for %s: %s", actuator_entity_id, exc)
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return self._save_behavior(
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record,
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record.behavior.model_copy(
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update={
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"status": BehaviorStatus.BLOCKED,
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"last_trained_at": now,
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"reason": f"Home-Assistant-Historie konnte nicht gelesen werden: {exc}",
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}
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),
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)
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actuator_history = history.get(actuator_entity_id)
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if actuator_history is None or len(actuator_history.points) < 2:
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return self._save_behavior(
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record,
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record.behavior.model_copy(
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update={
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"status": BehaviorStatus.COLLECTING,
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"sample_count": 0,
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"high_confidence_sample_count": 0,
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"activation_ready": False,
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"activation_reason": (
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"Freigabe gesperrt: Noch keine historischen "
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"Aktorhandlungen gefunden."
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),
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"patterns": [],
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"last_trained_at": now,
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"reason": "Noch keine historischen Aktorhandlungen gefunden.",
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}
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),
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)
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try:
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logbook = list(self._ha_reader.read_logbook(actuator_entity_id, start, now))
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except (HaClientError, ValueError) as exc:
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logger.warning("Logbook unavailable for %s: %s", actuator_entity_id, exc)
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logbook = []
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patterns = self._build_patterns(
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actuator_history=actuator_history,
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context_history=history,
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context_ids=context_ids,
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logbook=logbook,
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own_executions=record.behavior.execution_events,
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)
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trusted_actions = sum(
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1 for pattern in patterns if pattern.source in {"user", "automation"}
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)
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status = (
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BehaviorStatus.TRAINED
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if len(patterns) >= self._settings.min_behavior_actions
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else BehaviorStatus.COLLECTING
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)
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reason = (
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f"{len(patterns)} Handlungen mit automatisch erfasstem Kontext gelernt."
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if status is BehaviorStatus.TRAINED
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else (
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f"{len(patterns)} von mindestens {self._settings.min_behavior_actions} "
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"benötigten Handlungen gelernt."
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)
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)
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activation_ready = (
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status is BehaviorStatus.TRAINED
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and trusted_actions >= self._settings.min_behavior_actions
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)
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activation_reason = (
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"Freigabe bereit: Genügend eindeutig zugeordnete Handlungen gelernt."
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if activation_ready
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else (
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"Freigabe gesperrt: "
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f"{max(0, self._settings.min_behavior_actions - trusted_actions)} "
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"eindeutig zugeordnete Handlungen fehlen."
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)
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)
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behavior = record.behavior.model_copy(
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update={
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"status": status,
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"sample_count": len(patterns),
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"high_confidence_sample_count": trusted_actions,
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"activation_ready": activation_ready,
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"activation_reason": activation_reason,
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"patterns": patterns[-_MAX_PATTERNS:],
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"last_trained_at": now,
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"reason": reason,
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"sample_trend": [*record.behavior.sample_trend, len(patterns)][-30:],
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"knowledge": _knowledge_lines(record, len(patterns), trusted_actions),
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"assumptions": _assumption_lines(record),
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"uncertainties": _uncertainty_lines(record, len(patterns), trusted_actions),
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}
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)
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return self._save_behavior(record, behavior)
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def evaluate_all(self) -> list[ActuatorRecord]:
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results: list[ActuatorRecord] = []
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for record in self._store.list():
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try:
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results.append(self.evaluate(record.actuator_entity_id))
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except Exception:
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logger.exception("Behavior evaluation failed for %s", record.actuator_entity_id)
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results.append(record)
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return results
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def evaluate(
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self,
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actuator_entity_id: str,
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*,
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context_state_overrides: dict[str, str | None] | None = None,
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context_changed_at_overrides: dict[str, datetime | None] | None = None,
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current_entities: Sequence[HaEntitySummary] | None = None,
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) -> ActuatorRecord:
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record = self._store.get(actuator_entity_id)
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now = datetime.now(timezone.utc)
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if current_entities is None:
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try:
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current_entities = self._ha_reader.read_entities()
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except HaClientError as exc:
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logger.warning("Current HA state unavailable for %s: %s", actuator_entity_id, exc)
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return self._save_behavior(
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record,
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record.behavior.model_copy(
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update={
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"last_evaluated_at": now,
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"prediction": None,
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"reason": f"Aktueller Home-Assistant-Zustand ist nicht verfügbar: {exc}",
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}
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),
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)
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entities = {entity.entity_id: entity for entity in current_entities}
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actuator = entities.get(actuator_entity_id)
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if actuator is None:
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return self._save_behavior(
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record,
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record.behavior.model_copy(
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update={
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"last_evaluated_at": now,
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"prediction": None,
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"reason": "Aktor ist aktuell nicht in Home Assistant verfügbar.",
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}
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),
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)
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current_context = {
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entity_id: entities[entity_id].state
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for entity_id in (
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[
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record.assignment.selected_numeric_entity_id,
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*record.assignment.selected_context_entity_ids,
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]
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)
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if entity_id and entity_id in entities and entities[entity_id].state is not None
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}
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current_context_changed_at = {
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entity_id: entities[entity_id].last_changed
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for entity_id in current_context
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}
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selected_context_ids = {
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entity_id
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for entity_id in (
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[
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record.assignment.selected_numeric_entity_id,
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*record.assignment.selected_context_entity_ids,
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]
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)
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if entity_id
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}
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for entity_id, state in (context_state_overrides or {}).items():
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if entity_id in selected_context_ids and state is not None:
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current_context[entity_id] = state
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for entity_id, changed_at in (context_changed_at_overrides or {}).items():
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if entity_id in current_context:
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current_context_changed_at[entity_id] = changed_at or now
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prediction = predict_behavior(
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record.behavior.patterns,
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current_context=current_context,
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current_context_changed_at=current_context_changed_at,
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now=now,
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min_support=self._settings.min_behavior_actions,
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window_minutes=self._settings.prediction_window_minutes,
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causal_window_seconds=self._settings.prediction_interval_seconds * 2,
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timezone_name=self._settings.timezone,
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)
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if prediction is not None:
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safety_allowed, safety_blockers = self._assess_safety(
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record,
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actuator.state,
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prediction,
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now,
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)
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prediction = prediction.model_copy(
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update={
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"execution_reason": (
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"Ausführung ist freigegeben."
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if safety_allowed
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else "Nicht ausgeführt: " + " ".join(safety_blockers)
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)
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}
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)
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else:
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safety_allowed = False
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safety_blockers = ["Keine fällige Vorhersage."]
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decision_factors = _decision_factors_for(record, current_context, prediction)
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behavior = record.behavior.model_copy(
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update={
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"last_evaluated_at": now,
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"prediction": prediction,
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"reason": (
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prediction.reason
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if prediction is not None
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else "Aktuell ist kein gelerntes Handlungsmuster fällig."
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),
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"decision_factors": decision_factors,
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"knowledge": _knowledge_lines(record, record.behavior.sample_count, record.behavior.high_confidence_sample_count),
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"assumptions": _assumption_lines(record),
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"uncertainties": _uncertainty_lines(record, record.behavior.sample_count, record.behavior.high_confidence_sample_count),
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"safety_blockers": safety_blockers if prediction is not None else [],
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"confidence_trend": (
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[*record.behavior.confidence_trend, round(prediction.confidence, 4)][-30:]
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if prediction is not None
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else record.behavior.confidence_trend
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),
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}
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)
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if (
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prediction is not None
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and safety_allowed
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):
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domain = actuator_entity_id.split(".", 1)[0]
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service = service_for_state(domain, prediction.target_state)
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if service is not None:
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try:
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self._ha_reader.call_service(
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domain,
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service,
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{"entity_id": actuator_entity_id},
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)
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except (HaClientError, ValueError) as exc:
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logger.error(
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"Predicted action failed for %s: %s",
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actuator_entity_id,
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exc,
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)
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behavior = behavior.model_copy(
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update={
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"reason": f"Vorhersage wurde aus Sicherheitsgründen nicht ausgeführt: {exc}"
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}
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)
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return self._save_behavior(record, behavior)
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event = ExecutionEvent(
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target_state=prediction.target_state,
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executed_at=now,
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)
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behavior = behavior.model_copy(
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update={
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"prediction": prediction.model_copy(
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update={
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"executed": True,
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"execution_reason": (
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f"Ausgeführt mit {prediction.confidence:.0%} Sicherheit."
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),
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}
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),
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"last_executed_at": now,
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"execution_events": [
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*behavior.execution_events,
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event,
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][-_MAX_EXECUTION_EVENTS:],
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"reason": (
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f"Vorhersage mit {prediction.confidence:.0%} Sicherheit ausgeführt."
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),
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}
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)
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else:
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behavior = behavior.model_copy(
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update={
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"reason": (
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f"Der vorhergesagte Zustand {prediction.target_state!r} "
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"ist für autonomes Schalten nicht freigegeben."
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)
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}
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)
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return self._save_behavior(record, behavior)
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def record_feedback(
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self,
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actuator_entity_id: str,
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*,
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correct: bool,
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expected_state: str | None = None,
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) -> ActuatorRecord:
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record = self._store.get(actuator_entity_id)
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now = datetime.now(timezone.utc)
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entities = {entity.entity_id: entity for entity in self._ha_reader.read_entities()}
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actuator = entities.get(actuator_entity_id)
|
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if actuator is None:
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raise KeyError("Aktor ist aktuell nicht in Home Assistant verfügbar.")
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context_ids = [
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entity_id
|
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for entity_id in [
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record.assignment.selected_numeric_entity_id,
|
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*record.assignment.selected_context_entity_ids,
|
|
]
|
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if entity_id
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|
]
|
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current_context = {
|
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entity_id: entities[entity_id].state
|
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for entity_id in context_ids
|
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if entity_id in entities and entities[entity_id].state is not None
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}
|
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prediction = record.behavior.prediction
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patterns = list(record.behavior.patterns)
|
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reason = "Nutzerfeedback gespeichert."
|
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if correct and prediction is not None:
|
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local = now.astimezone(ZoneInfo(self._settings.timezone))
|
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patterns.append(
|
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BehaviorPattern(
|
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target_state=prediction.target_state,
|
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minute_of_day=local.hour * 60 + local.minute,
|
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weekday=local.weekday(),
|
|
context_states={
|
|
entity_id: state
|
|
for entity_id, state in current_context.items()
|
|
if state is not None
|
|
},
|
|
source="user_feedback",
|
|
weight=1.0,
|
|
observed_at=now,
|
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)
|
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)
|
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reason = "Vorhersage wurde vom Nutzer als korrekt bestätigt."
|
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correct_count = record.behavior.correct_feedback_count + 1
|
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incorrect_count = record.behavior.incorrect_feedback_count
|
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else:
|
|
target = prediction.target_state if prediction is not None else None
|
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if target:
|
|
patterns = [
|
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pattern.model_copy(update={"weight": 0.1})
|
|
if pattern.target_state == target
|
|
and _pattern_context_matches(pattern, current_context)
|
|
else pattern
|
|
for pattern in patterns
|
|
]
|
|
if expected_state:
|
|
local = now.astimezone(ZoneInfo(self._settings.timezone))
|
|
patterns.append(
|
|
BehaviorPattern(
|
|
target_state=expected_state,
|
|
minute_of_day=local.hour * 60 + local.minute,
|
|
weekday=local.weekday(),
|
|
context_states={
|
|
entity_id: state
|
|
for entity_id, state in current_context.items()
|
|
if state is not None
|
|
},
|
|
source="user_correction",
|
|
weight=1.0,
|
|
observed_at=now,
|
|
)
|
|
)
|
|
reason = "Vorhersage wurde vom Nutzer als falsch markiert."
|
|
correct_count = record.behavior.correct_feedback_count
|
|
incorrect_count = record.behavior.incorrect_feedback_count + 1
|
|
behavior = record.behavior.model_copy(
|
|
update={
|
|
"patterns": patterns[-_MAX_PATTERNS:],
|
|
"prediction": (
|
|
prediction.model_copy(update={"execution_reason": reason})
|
|
if prediction is not None
|
|
else None
|
|
),
|
|
"reason": reason,
|
|
"last_trained_at": now,
|
|
"correct_feedback_count": correct_count,
|
|
"incorrect_feedback_count": incorrect_count,
|
|
}
|
|
)
|
|
return self._save_behavior(record, behavior)
|
|
|
|
def set_safety_profile(
|
|
self,
|
|
actuator_entity_id: str,
|
|
*,
|
|
profile: SafetyProfile,
|
|
) -> ActuatorRecord:
|
|
record = self._store.get(actuator_entity_id)
|
|
behavior = record.behavior.model_copy(
|
|
update={
|
|
"safety": profile.model_copy(update={"updated_at": datetime.now(timezone.utc)}),
|
|
"reason": "Sicherheitsprofil wurde manuell aktualisiert.",
|
|
}
|
|
)
|
|
return self._save_behavior(record, behavior)
|
|
|
|
def refresh_related_automations(self, actuator_entity_id: str) -> ActuatorRecord:
|
|
record = self._store.get(actuator_entity_id)
|
|
related = [
|
|
RelatedAutomation(
|
|
entity_id=item.entity_id,
|
|
config_id=item.config_id,
|
|
friendly_name=item.friendly_name,
|
|
enabled=item.enabled,
|
|
)
|
|
for item in self._ha_reader.find_automations_for_entity(
|
|
actuator_entity_id
|
|
)
|
|
]
|
|
behavior = record.behavior.model_copy(
|
|
update={"related_automations": related}
|
|
)
|
|
return self._save_behavior(record, behavior)
|
|
|
|
def set_automation_enabled(
|
|
self,
|
|
actuator_entity_id: str,
|
|
automation_entity_id: str,
|
|
*,
|
|
enabled: bool,
|
|
) -> ActuatorRecord:
|
|
record = self.refresh_related_automations(actuator_entity_id)
|
|
if automation_entity_id not in {
|
|
item.entity_id for item in record.behavior.related_automations
|
|
}:
|
|
raise ValueError(
|
|
"Die Automation ist diesem Aktor nicht eindeutig zugeordnet."
|
|
)
|
|
self._ha_reader.call_service(
|
|
"automation",
|
|
"turn_on" if enabled else "turn_off",
|
|
{"entity_id": automation_entity_id},
|
|
)
|
|
related = [
|
|
item.model_copy(update={"enabled": enabled})
|
|
if item.entity_id == automation_entity_id
|
|
else item
|
|
for item in record.behavior.related_automations
|
|
]
|
|
paused = [
|
|
entity_id
|
|
for entity_id in record.behavior.paused_automation_entity_ids
|
|
if entity_id != automation_entity_id
|
|
]
|
|
behavior = record.behavior.model_copy(
|
|
update={
|
|
"related_automations": related,
|
|
"paused_automation_entity_ids": paused,
|
|
}
|
|
)
|
|
return self._save_behavior(record, behavior)
|
|
|
|
def set_active(
|
|
self,
|
|
actuator_entity_id: str,
|
|
*,
|
|
active: bool,
|
|
pause_matching_automations: bool = False,
|
|
restore_paused_automations: bool = False,
|
|
) -> ActuatorRecord:
|
|
record = self.refresh_related_automations(actuator_entity_id)
|
|
now = datetime.now(timezone.utc)
|
|
if active:
|
|
domain = actuator_entity_id.split(".", 1)[0]
|
|
if domain not in _SAFE_ACTIVE_DOMAINS:
|
|
raise ValueError(
|
|
f"Automatisches Schalten ist für die Domain {domain} nicht freigegeben."
|
|
)
|
|
if record.behavior.status is not BehaviorStatus.TRAINED:
|
|
raise ValueError("Das Verhaltensmodell hat noch nicht genügend Handlungen gelernt.")
|
|
if not record.behavior.activation_ready:
|
|
raise ValueError(record.behavior.activation_reason)
|
|
mode = BehaviorMode.ACTIVE
|
|
approved_at = now
|
|
behavior = record.behavior.model_copy(
|
|
update={
|
|
"mode": mode,
|
|
"approved_at": approved_at,
|
|
"safety": record.behavior.safety.model_copy(
|
|
update={"stage": SafetyStage.ACTIVE, "updated_at": now}
|
|
),
|
|
"reason": (
|
|
"Autonomes Lernen und Schalten wurde ausdrücklich freigegeben."
|
|
),
|
|
}
|
|
)
|
|
record = self._save_behavior(record, behavior)
|
|
if pause_matching_automations:
|
|
paused: list[str] = []
|
|
try:
|
|
for automation in record.behavior.related_automations:
|
|
if not automation.enabled:
|
|
continue
|
|
self._ha_reader.call_service(
|
|
"automation",
|
|
"turn_off",
|
|
{"entity_id": automation.entity_id},
|
|
)
|
|
paused.append(automation.entity_id)
|
|
except (HaClientError, ValueError):
|
|
for entity_id in paused:
|
|
try:
|
|
self._ha_reader.call_service(
|
|
"automation",
|
|
"turn_on",
|
|
{"entity_id": entity_id},
|
|
)
|
|
except (HaClientError, ValueError):
|
|
logger.exception(
|
|
"Failed to restore automation %s after handoff error",
|
|
entity_id,
|
|
)
|
|
rollback = record.behavior.model_copy(
|
|
update={
|
|
"mode": BehaviorMode.SHADOW,
|
|
"approved_at": None,
|
|
"reason": (
|
|
"Übernahme fehlgeschlagen; SillyHome bleibt im "
|
|
"Shadow-Modus."
|
|
),
|
|
}
|
|
)
|
|
self._save_behavior(record, rollback)
|
|
raise
|
|
related = [
|
|
automation.model_copy(update={"enabled": False})
|
|
if automation.entity_id in paused
|
|
else automation
|
|
for automation in record.behavior.related_automations
|
|
]
|
|
behavior = record.behavior.model_copy(
|
|
update={
|
|
"related_automations": related,
|
|
"paused_automation_entity_ids": paused,
|
|
"reason": (
|
|
"SillyHome steuert aktiv; passende HA-Automationen "
|
|
"wurden pausiert."
|
|
),
|
|
}
|
|
)
|
|
return self._save_behavior(record, behavior)
|
|
return record
|
|
else:
|
|
if restore_paused_automations:
|
|
for entity_id in record.behavior.paused_automation_entity_ids:
|
|
self._ha_reader.call_service(
|
|
"automation",
|
|
"turn_on",
|
|
{"entity_id": entity_id},
|
|
)
|
|
mode = BehaviorMode.SHADOW
|
|
approved_at = None
|
|
reason = (
|
|
"Shadow-Modus aktiv; pausierte HA-Automationen wurden fortgesetzt."
|
|
if restore_paused_automations
|
|
else "Shadow-Modus aktiv; Vorhersagen werden nicht ausgeführt."
|
|
)
|
|
behavior = record.behavior.model_copy(
|
|
update={
|
|
"mode": mode,
|
|
"approved_at": approved_at,
|
|
"safety": record.behavior.safety.model_copy(
|
|
update={"stage": SafetyStage.SHADOW, "updated_at": now}
|
|
),
|
|
"related_automations": [
|
|
automation.model_copy(update={"enabled": True})
|
|
if (
|
|
restore_paused_automations
|
|
and automation.entity_id
|
|
in record.behavior.paused_automation_entity_ids
|
|
)
|
|
else automation
|
|
for automation in record.behavior.related_automations
|
|
],
|
|
"paused_automation_entity_ids": (
|
|
[]
|
|
if restore_paused_automations
|
|
else record.behavior.paused_automation_entity_ids
|
|
),
|
|
"reason": reason,
|
|
}
|
|
)
|
|
return self._save_behavior(record, behavior)
|
|
|
|
def _prediction_execution_reason(
|
|
self,
|
|
record: ActuatorRecord,
|
|
current_state: str | None,
|
|
prediction: BehaviorPrediction,
|
|
now: datetime,
|
|
) -> str:
|
|
if record.behavior.mode is not BehaviorMode.ACTIVE:
|
|
return "Nicht ausgeführt: SillyHome ist im Shadow-Modus."
|
|
if prediction.confidence < self._settings.prediction_confidence:
|
|
return (
|
|
"Nicht ausgeführt: Sicherheit liegt unter der "
|
|
f"Schaltschwelle von {self._settings.prediction_confidence:.0%}."
|
|
)
|
|
if current_state == prediction.target_state:
|
|
return "Nicht ausgeführt: Zielzustand ist bereits erreicht."
|
|
if not self._cooldown_elapsed(
|
|
record.behavior,
|
|
now,
|
|
prediction.target_state,
|
|
):
|
|
return "Nicht ausgeführt: Sicherheits-Cooldown ist noch aktiv."
|
|
return "Ausführung ist freigegeben."
|
|
|
|
def _assess_safety(
|
|
self,
|
|
record: ActuatorRecord,
|
|
current_state: str | None,
|
|
prediction: BehaviorPrediction,
|
|
now: datetime,
|
|
) -> tuple[bool, list[str]]:
|
|
profile = record.behavior.safety
|
|
blockers: list[str] = []
|
|
domain = record.actuator_entity_id.split(".", 1)[0]
|
|
if not record.enabled:
|
|
blockers.append("Aktor ist in SillyHome deaktiviert.")
|
|
if domain not in _SAFE_ACTIVE_DOMAINS:
|
|
blockers.append(f"Domain {domain} ist nicht für autonomes Schalten freigegeben.")
|
|
if profile.manual_block:
|
|
blockers.append("Manuelle Sicherheitssperre ist aktiv.")
|
|
stage = profile.stage
|
|
if (
|
|
record.behavior.mode is BehaviorMode.ACTIVE
|
|
and profile.updated_at is None
|
|
and stage is SafetyStage.SHADOW
|
|
):
|
|
stage = SafetyStage.ACTIVE
|
|
if stage not in {SafetyStage.ACTIVE, SafetyStage.PARTIAL}:
|
|
blockers.append(f"Safety-Stufe {stage.value} erlaubt noch kein Schalten.")
|
|
if record.behavior.mode is not BehaviorMode.ACTIVE:
|
|
blockers.append("SillyHome ist im Shadow-Modus.")
|
|
if not record.behavior.activation_ready:
|
|
blockers.append(record.behavior.activation_reason)
|
|
threshold = _confidence_threshold_for(profile, prediction.target_state)
|
|
if prediction.confidence < threshold:
|
|
blockers.append(
|
|
f"Sicherheit {prediction.confidence:.0%} liegt unter der Schwelle {threshold:.0%}."
|
|
)
|
|
if current_state == prediction.target_state:
|
|
blockers.append("Zielzustand ist bereits erreicht.")
|
|
if not self._cooldown_elapsed(
|
|
record.behavior,
|
|
now,
|
|
prediction.target_state,
|
|
cooldown_seconds=profile.cooldown_seconds,
|
|
):
|
|
blockers.append("Sicherheits-Cooldown ist noch aktiv.")
|
|
return not blockers, blockers
|
|
|
|
def _build_patterns(
|
|
self,
|
|
*,
|
|
actuator_history: StateHistorySeries,
|
|
context_history: dict[str, StateHistorySeries],
|
|
context_ids: list[str],
|
|
logbook: list[LogbookEntry],
|
|
own_executions: list[ExecutionEvent],
|
|
) -> list[BehaviorPattern]:
|
|
patterns: list[BehaviorPattern] = []
|
|
previous_state = actuator_history.points[0].state
|
|
for point in actuator_history.points[1:]:
|
|
if point.state == previous_state:
|
|
continue
|
|
previous_state = point.state
|
|
if _matches_own_execution(point, own_executions):
|
|
continue
|
|
source, weight = _action_source(point, logbook)
|
|
trigger = _recent_context_transition(
|
|
context_history,
|
|
context_ids,
|
|
point.timestamp,
|
|
)
|
|
contexts = {
|
|
entity_id: state
|
|
for entity_id in context_ids
|
|
if (state := _state_at(context_history.get(entity_id), point.timestamp)) is not None
|
|
}
|
|
local = point.timestamp.astimezone(ZoneInfo(self._settings.timezone))
|
|
patterns.append(
|
|
BehaviorPattern(
|
|
target_state=point.state,
|
|
minute_of_day=local.hour * 60 + local.minute,
|
|
weekday=local.weekday(),
|
|
context_states=contexts,
|
|
trigger_entity_id=trigger[0] if trigger else None,
|
|
trigger_from_state=trigger[1] if trigger else None,
|
|
trigger_to_state=trigger[2] if trigger else None,
|
|
source=source,
|
|
weight=weight,
|
|
observed_at=point.timestamp,
|
|
)
|
|
)
|
|
return patterns
|
|
|
|
def _cooldown_elapsed(
|
|
self,
|
|
behavior: BehaviorState,
|
|
now: datetime,
|
|
target_state: str,
|
|
*,
|
|
cooldown_seconds: int | None = None,
|
|
) -> bool:
|
|
if behavior.last_executed_at is None:
|
|
return True
|
|
last_event = behavior.execution_events[-1] if behavior.execution_events else None
|
|
if last_event is not None and last_event.target_state != target_state:
|
|
return True
|
|
return (now - behavior.last_executed_at) >= timedelta(
|
|
seconds=cooldown_seconds
|
|
if cooldown_seconds is not None
|
|
else self._settings.execution_cooldown_seconds
|
|
)
|
|
|
|
def _save_behavior(
|
|
self,
|
|
record: ActuatorRecord,
|
|
behavior: BehaviorState,
|
|
) -> ActuatorRecord:
|
|
updated = record.model_copy(
|
|
update={
|
|
"behavior": behavior,
|
|
"updated_at": datetime.now(timezone.utc),
|
|
}
|
|
)
|
|
return self._store.upsert(updated)
|
|
|
|
def handle_state_change(
|
|
self,
|
|
entity_id: str,
|
|
new_state: dict[str, object] | None,
|
|
*,
|
|
current_entities: Sequence[HaEntitySummary] | None = None,
|
|
) -> None:
|
|
"""Wird bei jedem HA-State-Change aufgerufen und löst sofortige Vorhersage aus.
|
|
|
|
- Wenn entity_id ein Aktor ist: evaluate() direkt.
|
|
- Wenn entity_id ein Kontext-Entity ist: alle betroffenen Aktoren evaluieren.
|
|
- Wenn current_entities gesetzt ist, kommt die Auswertung direkt aus dem
|
|
WebSocket-State-Cache statt aus einer frischen REST-Abfrage.
|
|
"""
|
|
# Aktor direkt evaluieren
|
|
for record in self._store.list():
|
|
if record.actuator_entity_id == entity_id:
|
|
try:
|
|
self.evaluate(record.actuator_entity_id, current_entities=current_entities)
|
|
except Exception:
|
|
logger.exception("Event-basierte Vorhersage fehlgeschlagen für %s", record.actuator_entity_id)
|
|
return
|
|
event_state = _event_state(new_state)
|
|
event_changed_at = _event_changed_at(new_state) or datetime.now(timezone.utc)
|
|
# Kontext-Entity: alle Aktoren finden, die diesen Kontext nutzen
|
|
affected_actuators = [
|
|
record.actuator_entity_id
|
|
for record in self._store.list()
|
|
if (
|
|
record.assignment.selected_numeric_entity_id == entity_id
|
|
or entity_id in record.assignment.selected_context_entity_ids
|
|
)
|
|
]
|
|
for actuator_entity_id in affected_actuators:
|
|
try:
|
|
self.evaluate(
|
|
actuator_entity_id,
|
|
context_state_overrides={entity_id: event_state},
|
|
context_changed_at_overrides={entity_id: event_changed_at},
|
|
current_entities=current_entities,
|
|
)
|
|
except Exception:
|
|
logger.exception("Event-basierte Vorhersage fehlgeschlagen für %s", actuator_entity_id)
|
|
|
|
|
|
def _event_state(new_state: dict[str, object] | None) -> str | None:
|
|
if not isinstance(new_state, dict):
|
|
return None
|
|
state = new_state.get("state")
|
|
return state if isinstance(state, str) else None
|
|
|
|
|
|
def _event_changed_at(new_state: dict[str, object] | None) -> datetime | None:
|
|
if not isinstance(new_state, dict):
|
|
return None
|
|
value = new_state.get("last_changed") or new_state.get("last_updated")
|
|
if not isinstance(value, str):
|
|
return None
|
|
try:
|
|
parsed = datetime.fromisoformat(value.replace("Z", "+00:00"))
|
|
except ValueError:
|
|
return None
|
|
if parsed.tzinfo is None:
|
|
return parsed.replace(tzinfo=timezone.utc)
|
|
return parsed
|
|
|
|
|
|
def _confidence_threshold_for(profile: SafetyProfile, target_state: str) -> float:
|
|
if target_state == "on" and profile.min_confidence_on is not None:
|
|
return profile.min_confidence_on
|
|
if target_state in {"off", "closed"} and profile.min_confidence_off is not None:
|
|
return profile.min_confidence_off
|
|
return profile.min_confidence
|
|
|
|
|
|
def _decision_factors_for(
|
|
record: ActuatorRecord,
|
|
current_context: dict[str, str | None],
|
|
prediction: BehaviorPrediction | None,
|
|
) -> list[DecisionFactor]:
|
|
factors: list[DecisionFactor] = []
|
|
candidates = {
|
|
candidate.entity_id: candidate
|
|
for candidate in [*record.numeric_candidates, *record.context_candidates]
|
|
}
|
|
for entity_id, state in current_context.items():
|
|
candidate = candidates.get(entity_id)
|
|
weight = candidate.effective_weight if candidate is not None else 1.0
|
|
relevance = candidate.confidence if candidate is not None else 0.5
|
|
contribution = round(min(1.0, weight * relevance), 4)
|
|
factors.append(
|
|
DecisionFactor(
|
|
entity_id=entity_id,
|
|
label=(
|
|
candidate.friendly_name
|
|
if candidate is not None and candidate.friendly_name
|
|
else entity_id
|
|
),
|
|
factor_type="context",
|
|
state=state,
|
|
weight=round(weight, 4),
|
|
contribution=contribution,
|
|
evidence=(
|
|
candidate.evidence[:4]
|
|
if candidate is not None
|
|
else ["Aktuell ausgewähltes Kontextsignal."]
|
|
),
|
|
)
|
|
)
|
|
if prediction is not None:
|
|
factors.append(
|
|
DecisionFactor(
|
|
label=f"Vorhersage {prediction.target_state}",
|
|
factor_type="prediction",
|
|
state=prediction.target_state,
|
|
weight=1.0,
|
|
contribution=prediction.confidence,
|
|
evidence=[prediction.reason],
|
|
)
|
|
)
|
|
return sorted(factors, key=lambda item: (-item.contribution, item.label))[:12]
|
|
|
|
|
|
def _knowledge_lines(
|
|
record: ActuatorRecord,
|
|
sample_count: int,
|
|
trusted_actions: int,
|
|
) -> list[str]:
|
|
lines = [
|
|
f"{sample_count} historische Aktorhandlungen sind ausgewertet.",
|
|
f"{trusted_actions} Handlungen stammen eindeutig von Nutzer oder HA-Automationen.",
|
|
]
|
|
if record.assignment.selected_numeric_entity_id:
|
|
lines.append(f"Hauptsensor: {record.assignment.selected_numeric_entity_id}.")
|
|
if record.assignment.selected_context_entity_ids:
|
|
lines.append(
|
|
f"{len(record.assignment.selected_context_entity_ids)} Kontextsignale sind verbunden."
|
|
)
|
|
return lines
|
|
|
|
|
|
def _assumption_lines(record: ActuatorRecord) -> list[str]:
|
|
lines = [
|
|
"Ähnliche Zeitfenster und ähnliche Kontextzustände deuten auf ähnliche Nutzerabsicht hin."
|
|
]
|
|
if record.manual_override is not None:
|
|
lines.append("Manuelle Sensor-/Kontextkorrekturen werden höher gewichtet.")
|
|
if record.behavior.related_automations:
|
|
lines.append("Passende HA-Automationen gelten als starker Hinweis auf vorhandene Logik.")
|
|
return lines
|
|
|
|
|
|
def _uncertainty_lines(
|
|
record: ActuatorRecord,
|
|
sample_count: int,
|
|
trusted_actions: int,
|
|
) -> list[str]:
|
|
lines: list[str] = []
|
|
if sample_count < trusted_actions + 3:
|
|
lines.append("Noch wenig Varianz in den gelernten Handlungen.")
|
|
if trusted_actions < sample_count:
|
|
lines.append("Ein Teil der Handlungen ist nicht eindeutig Nutzer oder Automation zugeordnet.")
|
|
if record.assignment.review_required:
|
|
lines.append("Die automatische Kontextzuordnung verlangt noch Prüfung.")
|
|
if record.behavior.incorrect_feedback_count:
|
|
lines.append(
|
|
f"{record.behavior.incorrect_feedback_count} negative Feedbacks senken Vertrauen."
|
|
)
|
|
return lines or ["Keine kritische Unsicherheit aus den lokalen Daten erkannt."]
|
|
|
|
|
|
def predict_behavior(
|
|
patterns: list[BehaviorPattern],
|
|
*,
|
|
current_context: dict[str, str | None],
|
|
now: datetime,
|
|
min_support: int,
|
|
window_minutes: int,
|
|
current_context_changed_at: dict[str, datetime | None] | None = None,
|
|
causal_window_seconds: int = 120,
|
|
timezone_name: str = "Europe/Berlin",
|
|
) -> BehaviorPrediction | None:
|
|
if not patterns:
|
|
return None
|
|
local = now.astimezone(ZoneInfo(timezone_name))
|
|
minute_of_day = local.hour * 60 + local.minute
|
|
changed_at = current_context_changed_at or {}
|
|
by_state: dict[str, list[float]] = {}
|
|
causal_support_by_state: dict[str, int] = {}
|
|
for pattern in patterns:
|
|
if pattern.trigger_entity_id and pattern.trigger_to_state:
|
|
trigger_changed_at = changed_at.get(pattern.trigger_entity_id)
|
|
trigger_age = (
|
|
(now - trigger_changed_at).total_seconds()
|
|
if trigger_changed_at is not None
|
|
else None
|
|
)
|
|
if not (
|
|
current_context.get(pattern.trigger_entity_id)
|
|
== pattern.trigger_to_state
|
|
and trigger_age is not None
|
|
and 0 <= trigger_age <= causal_window_seconds
|
|
):
|
|
continue
|
|
comparable = [
|
|
(entity_id, expected)
|
|
for entity_id, expected in pattern.context_states.items()
|
|
if entity_id in current_context
|
|
]
|
|
context_score = (
|
|
sum(
|
|
current_context[entity_id] == expected
|
|
for entity_id, expected in comparable
|
|
)
|
|
/ len(comparable)
|
|
if comparable
|
|
else 0.5
|
|
)
|
|
score = pattern.weight * (0.85 + 0.15 * context_score)
|
|
by_state.setdefault(pattern.target_state, []).append(score)
|
|
causal_support_by_state[pattern.target_state] = (
|
|
causal_support_by_state.get(pattern.target_state, 0) + 1
|
|
)
|
|
continue
|
|
distance = _circular_minute_distance(minute_of_day, pattern.minute_of_day)
|
|
if distance > window_minutes:
|
|
continue
|
|
time_score = 1.0 - (distance / max(window_minutes, 1))
|
|
weekday_score = (
|
|
1.0
|
|
if local.weekday() == pattern.weekday
|
|
else 0.5
|
|
if (local.weekday() >= 5) == (pattern.weekday >= 5)
|
|
else 0.0
|
|
)
|
|
comparable = [
|
|
(entity_id, expected)
|
|
for entity_id, expected in pattern.context_states.items()
|
|
if entity_id in current_context
|
|
]
|
|
context_score = (
|
|
sum(current_context[entity_id] == expected for entity_id, expected in comparable)
|
|
/ len(comparable)
|
|
if comparable
|
|
else 0.5
|
|
)
|
|
score = pattern.weight * (
|
|
0.45 * time_score + 0.45 * context_score + 0.10 * weekday_score
|
|
)
|
|
by_state.setdefault(pattern.target_state, []).append(score)
|
|
if not by_state:
|
|
return None
|
|
target_state, scores = max(
|
|
by_state.items(),
|
|
key=lambda item: (sum(item[1]), len(item[1]), item[0]),
|
|
)
|
|
support = len(scores)
|
|
causal_support = causal_support_by_state.get(target_state, 0)
|
|
confidence = min(1.0, (sum(scores) / support) * min(1.0, support / min_support))
|
|
if confidence <= 0:
|
|
return None
|
|
return BehaviorPrediction(
|
|
target_state=target_state,
|
|
confidence=round(confidence, 4),
|
|
generated_at=now,
|
|
matching_patterns=support,
|
|
reason=(
|
|
(
|
|
f"{causal_support} historische Handlungen folgten demselben "
|
|
"frischen Sensorwechsel."
|
|
)
|
|
if causal_support
|
|
else f"{support} ähnliche Handlungsmuster passen zu Zeit und aktuellem Kontext."
|
|
),
|
|
)
|
|
|
|
|
|
def service_for_state(domain: str, target_state: str) -> str | None:
|
|
if domain in {"fan", "humidifier", "light", "media_player", "remote", "switch"}:
|
|
return {"on": "turn_on", "off": "turn_off"}.get(target_state)
|
|
if domain == "scene":
|
|
return "turn_on" if target_state == "on" else None
|
|
if domain == "cover":
|
|
return {"open": "open_cover", "closed": "close_cover"}.get(target_state)
|
|
return None
|
|
|
|
|
|
def _state_at(series: StateHistorySeries | None, timestamp: datetime) -> str | None:
|
|
if series is None:
|
|
return None
|
|
state: str | None = None
|
|
for point in series.points:
|
|
if point.timestamp > timestamp:
|
|
break
|
|
state = point.state
|
|
return state
|
|
|
|
|
|
def _action_source(
|
|
point: StateHistoryPoint,
|
|
logbook: list[LogbookEntry],
|
|
) -> tuple[str, float]:
|
|
nearest = min(
|
|
logbook,
|
|
key=lambda item: abs(item.timestamp - point.timestamp),
|
|
default=None,
|
|
)
|
|
if nearest is None or abs(nearest.timestamp - point.timestamp) > _ACTION_LOGBOOK_TOLERANCE:
|
|
return "physical_or_unknown", 0.7
|
|
if nearest.context_user_id:
|
|
return "user", 1.0
|
|
if nearest.context_domain in _AUTOMATION_CONTEXT_DOMAINS:
|
|
return "automation", 1.0
|
|
return "physical_or_unknown", 0.7
|
|
|
|
|
|
def _matches_own_execution(
|
|
point: StateHistoryPoint,
|
|
own_executions: list[ExecutionEvent],
|
|
) -> bool:
|
|
return any(
|
|
event.target_state == point.state
|
|
and abs(event.executed_at - point.timestamp) <= _OWN_ACTION_TOLERANCE
|
|
for event in own_executions
|
|
)
|
|
|
|
|
|
def _pattern_context_matches(
|
|
pattern: BehaviorPattern,
|
|
current_context: dict[str, str | None],
|
|
) -> bool:
|
|
comparable = [
|
|
(entity_id, expected)
|
|
for entity_id, expected in pattern.context_states.items()
|
|
if entity_id in current_context
|
|
]
|
|
if not comparable:
|
|
return False
|
|
return all(current_context[entity_id] == expected for entity_id, expected in comparable)
|
|
|
|
|
|
def _recent_context_transition(
|
|
history: dict[str, StateHistorySeries],
|
|
context_ids: list[str],
|
|
timestamp: datetime,
|
|
) -> tuple[str, str, str] | None:
|
|
nearest: tuple[timedelta, str, str, str] | None = None
|
|
for entity_id in context_ids:
|
|
series = history.get(entity_id)
|
|
if series is None:
|
|
continue
|
|
previous_state: str | None = None
|
|
for point in series.points:
|
|
if point.timestamp > timestamp:
|
|
break
|
|
if previous_state is not None and point.state != previous_state:
|
|
age = timestamp - point.timestamp
|
|
if age <= _CONTEXT_TRIGGER_TOLERANCE and (
|
|
nearest is None or age < nearest[0]
|
|
):
|
|
nearest = (age, entity_id, previous_state, point.state)
|
|
previous_state = point.state
|
|
if nearest is None:
|
|
return None
|
|
return nearest[1], nearest[2], nearest[3]
|
|
|
|
|
|
def _circular_minute_distance(left: int, right: int) -> int:
|
|
direct = abs(left - right)
|
|
return min(direct, 1440 - direct)
|