482 lines
18 KiB
Python
482 lines
18 KiB
Python
from __future__ import annotations
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import logging
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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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ExecutionEvent,
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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.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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_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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"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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"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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high_confidence = sum(1 for pattern in patterns if pattern.source == "user")
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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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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": high_confidence,
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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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}
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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(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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try:
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entities = {entity.entity_id: entity for entity in 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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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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prediction = predict_behavior(
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record.behavior.patterns,
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current_context=current_context,
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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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timezone_name=self._settings.timezone,
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)
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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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}
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)
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if (
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prediction is not None
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and behavior.mode is BehaviorMode.ACTIVE
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and prediction.confidence >= self._settings.prediction_confidence
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and actuator.state != prediction.target_state
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and self._cooldown_elapsed(behavior, now)
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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(update={"executed": True}),
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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 set_active(self, actuator_entity_id: str, *, active: bool) -> 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 active:
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domain = actuator_entity_id.split(".", 1)[0]
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if domain not in _SAFE_ACTIVE_DOMAINS:
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raise ValueError(
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f"Automatisches Schalten ist für die Domain {domain} nicht freigegeben."
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)
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if record.behavior.status is not BehaviorStatus.TRAINED:
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raise ValueError("Das Verhaltensmodell hat noch nicht genügend Handlungen gelernt.")
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if (
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record.behavior.high_confidence_sample_count
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< self._settings.min_behavior_actions
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):
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raise ValueError(
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"Für die Freigabe fehlen noch eindeutig dir zugeordnete Handlungen. "
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"Bediene den Aktor einige Male über Home Assistant."
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)
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mode = BehaviorMode.ACTIVE
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approved_at = now
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reason = "Autonomes Lernen und Schalten wurde ausdrücklich freigegeben."
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else:
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mode = BehaviorMode.SHADOW
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approved_at = None
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reason = "Shadow-Modus aktiv; Vorhersagen werden nicht ausgeführt."
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behavior = record.behavior.model_copy(
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update={
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"mode": mode,
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"approved_at": approved_at,
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"reason": reason,
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}
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)
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return self._save_behavior(record, behavior)
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def _build_patterns(
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self,
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*,
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actuator_history: StateHistorySeries,
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context_history: dict[str, StateHistorySeries],
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context_ids: list[str],
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logbook: list[LogbookEntry],
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own_executions: list[ExecutionEvent],
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) -> list[BehaviorPattern]:
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patterns: list[BehaviorPattern] = []
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previous_state = actuator_history.points[0].state
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for point in actuator_history.points[1:]:
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if point.state == previous_state:
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continue
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previous_state = point.state
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if _matches_own_execution(point, own_executions):
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continue
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source, weight = _action_source(point, logbook)
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if source == "automation":
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continue
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contexts = {
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entity_id: state
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for entity_id in context_ids
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if (state := _state_at(context_history.get(entity_id), point.timestamp)) is not None
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}
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local = point.timestamp.astimezone(ZoneInfo(self._settings.timezone))
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patterns.append(
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BehaviorPattern(
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target_state=point.state,
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minute_of_day=local.hour * 60 + local.minute,
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weekday=local.weekday(),
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context_states=contexts,
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source=source,
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weight=weight,
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observed_at=point.timestamp,
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)
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)
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return patterns
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def _cooldown_elapsed(self, behavior: BehaviorState, now: datetime) -> bool:
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return behavior.last_executed_at is None or (
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now - behavior.last_executed_at
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) >= timedelta(seconds=self._settings.execution_cooldown_seconds)
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def _save_behavior(
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self,
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record: ActuatorRecord,
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behavior: BehaviorState,
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) -> ActuatorRecord:
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updated = record.model_copy(
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update={
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"behavior": behavior,
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"updated_at": datetime.now(timezone.utc),
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}
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)
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return self._store.upsert(updated)
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def predict_behavior(
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patterns: list[BehaviorPattern],
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*,
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current_context: dict[str, str | None],
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now: datetime,
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min_support: int,
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window_minutes: int,
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timezone_name: str = "Europe/Berlin",
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) -> BehaviorPrediction | None:
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if not patterns:
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return None
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local = now.astimezone(ZoneInfo(timezone_name))
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minute_of_day = local.hour * 60 + local.minute
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by_state: dict[str, list[float]] = {}
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for pattern in patterns:
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distance = _circular_minute_distance(minute_of_day, pattern.minute_of_day)
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if distance > window_minutes:
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continue
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time_score = 1.0 - (distance / max(window_minutes, 1))
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weekday_score = (
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1.0
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if local.weekday() == pattern.weekday
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else 0.5
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if (local.weekday() >= 5) == (pattern.weekday >= 5)
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else 0.0
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)
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comparable = [
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(entity_id, expected)
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for entity_id, expected in pattern.context_states.items()
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if entity_id in current_context
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]
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context_score = (
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sum(current_context[entity_id] == expected for entity_id, expected in comparable)
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/ len(comparable)
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if comparable
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else 0.5
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)
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score = pattern.weight * (
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0.45 * time_score + 0.45 * context_score + 0.10 * weekday_score
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)
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by_state.setdefault(pattern.target_state, []).append(score)
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if not by_state:
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return None
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target_state, scores = max(
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by_state.items(),
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key=lambda item: (sum(item[1]), len(item[1]), item[0]),
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)
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support = len(scores)
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confidence = min(1.0, (sum(scores) / support) * min(1.0, support / min_support))
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if confidence <= 0:
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return None
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return BehaviorPrediction(
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target_state=target_state,
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confidence=round(confidence, 4),
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generated_at=now,
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matching_patterns=support,
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reason=(
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f"{support} ähnliche Handlungsmuster passen zu Zeit und aktuellem Kontext."
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),
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)
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def service_for_state(domain: str, target_state: str) -> str | None:
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if domain in {"fan", "humidifier", "light", "switch"}:
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return {"on": "turn_on", "off": "turn_off"}.get(target_state)
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if domain == "cover":
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return {"open": "open_cover", "closed": "close_cover"}.get(target_state)
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return None
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def _state_at(series: StateHistorySeries | None, timestamp: datetime) -> str | None:
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if series is None:
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return None
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state: str | None = None
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for point in series.points:
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if point.timestamp > timestamp:
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break
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state = point.state
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return state
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def _action_source(
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point: StateHistoryPoint,
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logbook: list[LogbookEntry],
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) -> tuple[str, float]:
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nearest = min(
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logbook,
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key=lambda item: abs(item.timestamp - point.timestamp),
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default=None,
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)
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if nearest is None or abs(nearest.timestamp - point.timestamp) > _ACTION_LOGBOOK_TOLERANCE:
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return "physical_or_unknown", 0.7
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if nearest.context_user_id:
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return "user", 1.0
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if nearest.context_domain in _AUTOMATION_CONTEXT_DOMAINS:
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return "automation", 0.1
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return "physical_or_unknown", 0.7
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def _matches_own_execution(
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point: StateHistoryPoint,
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own_executions: list[ExecutionEvent],
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) -> bool:
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return any(
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event.target_state == point.state
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and abs(event.executed_at - point.timestamp) <= _OWN_ACTION_TOLERANCE
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for event in own_executions
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)
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def _circular_minute_distance(left: int, right: int) -> int:
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direct = abs(left - right)
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return min(direct, 1440 - direct)
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