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Improve SillyHome discovery and feedback learning

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Otto
2026-06-16 11:38:32 +02:00
parent c5f42a39a9
commit e2826e92ec
12 changed files with 513 additions and 8 deletions
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35
tests/api/test_actuators.py
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@@ -9,6 +9,7 @@ from app.actuators.lifecycle import ActuatorReconciliationService
from app.actuators.store import ActuatorStore
from app.behavior.engine import BehaviorEngine
from app.config import Settings
from app.api.v1.actuators import _deduplicate_actuator_ids
from app.ha.discovery import DiscoveredEntity
from app.ha.discovery import discover_entities
from app.ha.history import (
@@ -227,3 +228,37 @@ def test_context_options_returns_learnable_entities(tmp_path: Path) -> None:
assert "sensor.abstellkammer_illuminance" in entity_ids
assert "binary_sensor.abstellkammer_motion" in entity_ids
assert "sensor.pfsense_interface_vpn_inbytes" not in entity_ids
def test_actuator_discovery_prefers_light_over_duplicate_switch() -> None:
entities = {
"light.schreibtisch": HaEntitySummary(
entity_id="light.schreibtisch",
domain="light",
friendly_name="Schreibtisch Licht",
device_id="device-1",
),
"switch.schreibtisch": HaEntitySummary(
entity_id="switch.schreibtisch",
domain="switch",
friendly_name="Schreibtisch Schalter",
device_id="device-1",
),
"cover.rollladen": HaEntitySummary(
entity_id="cover.rollladen",
domain="cover",
friendly_name="Rollladen",
device_id="device-2",
),
}
result = _deduplicate_actuator_ids(
[
("switch.schreibtisch", "switch_socket"),
("light.schreibtisch", "light"),
("cover.rollladen", "cover_shutter"),
],
entities,
)
assert result == ["cover.rollladen", "light.schreibtisch"]

2
tests/api/test_entities.py
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@@ -27,6 +27,7 @@ class FakeHaReader(HaReader):
entity_id="sensor.temperature",
domain="sensor",
device_class="temperature",
category="temperature",
role=EntityRole.MEASUREMENT,
learnable=True,
reason="Numerischer Messsensor für Zeitreihen und Training.",
@@ -116,6 +117,7 @@ def test_discovery_filters_entities() -> None:
"device_class": "temperature",
"state_class": None,
"unit_of_measurement": None,
"category": "temperature",
"role": "measurement",
"learnable": True,
"reason": "Numerischer Messsensor für Zeitreihen und Training.",

120
tests/behavior/test_engine.py
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@@ -8,6 +8,7 @@ import pytest
from app.actuators.models import (
BehaviorMode,
BehaviorPattern,
BehaviorPrediction,
BehaviorState,
BehaviorStatus,
ExecutionEvent,
@@ -212,6 +213,125 @@ def test_engine_counts_known_automation_actions_like_manual_actions(
assert {pattern.weight for pattern in trained.behavior.patterns} == {1.0}
def test_feedback_marks_prediction_correct_as_learning_pattern(
tmp_path: Path,
) -> None:
now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
settings = _settings(tmp_path)
store = ActuatorStore(settings.actuator_store)
record = store.configure("light.office")
record = record.model_copy(
update={
"assignment": record.assignment.model_copy(
update={
"selected_context_entity_ids": [
"binary_sensor.office_presence"
],
}
),
"behavior": record.behavior.model_copy(
update={
"prediction": BehaviorPrediction(
target_state="on",
confidence=0.9,
generated_at=now,
reason="test",
)
}
),
}
)
store.upsert(record)
reader = FakeBehaviorReader(
entities=[
HaEntitySummary(entity_id="light.office", domain="light", state="off"),
HaEntitySummary(
entity_id="binary_sensor.office_presence",
domain="binary_sensor",
state="on",
),
],
history=[],
logbook=[],
)
engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
result = engine.record_feedback("light.office", correct=True)
assert result.behavior.patterns[-1].target_state == "on"
assert result.behavior.patterns[-1].context_states == {
"binary_sensor.office_presence": "on"
}
assert result.behavior.patterns[-1].source == "user_feedback"
assert result.behavior.reason == "Vorhersage wurde vom Nutzer als korrekt bestätigt."
def test_feedback_marks_prediction_wrong_and_adds_correction(
tmp_path: Path,
) -> None:
now = datetime.now(timezone.utc).replace(second=0, microsecond=0)
settings = _settings(tmp_path)
store = ActuatorStore(settings.actuator_store)
record = store.configure("light.office")
record = record.model_copy(
update={
"assignment": record.assignment.model_copy(
update={
"selected_context_entity_ids": [
"binary_sensor.office_presence"
],
}
),
"behavior": record.behavior.model_copy(
update={
"patterns": [
BehaviorPattern(
target_state="on",
minute_of_day=60,
weekday=0,
context_states={"binary_sensor.office_presence": "on"},
source="automation",
weight=1.0,
observed_at=now - timedelta(days=1),
)
],
"prediction": BehaviorPrediction(
target_state="on",
confidence=0.9,
generated_at=now,
reason="test",
),
}
),
}
)
store.upsert(record)
reader = FakeBehaviorReader(
entities=[
HaEntitySummary(entity_id="light.office", domain="light", state="off"),
HaEntitySummary(
entity_id="binary_sensor.office_presence",
domain="binary_sensor",
state="on",
),
],
history=[],
logbook=[],
)
engine = BehaviorEngine(ha_reader=reader, store=store, settings=settings)
result = engine.record_feedback(
"light.office",
correct=False,
expected_state="off",
)
assert result.behavior.patterns[0].weight == 0.1
assert result.behavior.patterns[-1].target_state == "off"
assert result.behavior.patterns[-1].source == "user_correction"
assert result.behavior.reason == "Vorhersage wurde vom Nutzer als falsch markiert."
def test_engine_learns_causal_automation_with_activation_credit(
tmp_path: Path,
) -> None:

53
tests/ha/test_discovery.py
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@@ -74,3 +74,56 @@ def test_discovery_filters_domain_and_learnable() -> None:
result = discover_entities(entities, domains={" SENSOR "}, learnable=True)
assert [item.entity_id for item in result] == ["sensor.temperature"]
@pytest.mark.parametrize(
("entity", "category"),
[
(
HaEntitySummary(entity_id="climate.bad", domain="climate"),
"heating",
),
(
HaEntitySummary(entity_id="lock.front_door", domain="lock"),
"lock",
),
(
HaEntitySummary(entity_id="input_boolean.sleep_mode", domain="input_boolean"),
"helper",
),
(
HaEntitySummary(
entity_id="sensor.brightness",
domain="sensor",
device_class="illuminance",
),
"brightness",
),
(
HaEntitySummary(
entity_id="binary_sensor.motion",
domain="binary_sensor",
device_class="motion",
),
"presence_motion",
),
],
)
def test_classify_entity_categories(entity: HaEntitySummary, category: str) -> None:
assert classify_entity(entity).category == category
@pytest.mark.parametrize(
"entity",
[
HaEntitySummary(entity_id="automation.lights", domain="automation"),
HaEntitySummary(entity_id="update.core", domain="update"),
],
)
def test_classify_excludes_non_actuator_management_entities(
entity: HaEntitySummary,
) -> None:
result = classify_entity(entity)
assert result.role is EntityRole.UNSUPPORTED
assert result.learnable is False