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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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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: