from __future__ import annotations

from pathlib import Path

import pytest

from app.ml.feature_store import FeatureVector
from app.ml.registry.model_registry import ModelRegistry
from app.ml.retraining import RetrainingService, retrain_model


def _vector(sensor_id: str) -> FeatureVector:
    return FeatureVector(sensor_id=sensor_id, values={"temperature": 21.0})


def test_retraining_registers_new_artifact(tmp_path: Path) -> None:
    registry = ModelRegistry(tmp_path)

    result = retrain_model(registry, "home-model", [_vector("sensor.kitchen")])

    assert result.replaced is False
    assert registry.load_artifact("home-model") == result.artifact


def test_retraining_replaces_existing_artifact(tmp_path: Path) -> None:
    registry = ModelRegistry(tmp_path)
    service = RetrainingService(registry)
    service.retrain("home-model", [_vector("sensor.kitchen")])

    result = service.retrain("home-model", [_vector("sensor.bedroom")])

    assert result.replaced is True
    assert result.artifact.supported_sensors == ("sensor.bedroom",)
    assert ModelRegistry(tmp_path).load_artifact("home-model") == result.artifact


def test_retraining_rejects_empty_training_data(tmp_path: Path) -> None:
    with pytest.raises(ValueError, match="keine Trainingsdaten"):
        retrain_model(ModelRegistry(tmp_path), "home-model", [])