from __future__ import annotations

import pytest

from app.ml.evaluation import Evaluator
from app.ml.feature_store import FeatureStore, FeatureVector
from app.ml.training import TrainingPipeline


def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
    return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)


def evaluator_factory() -> Evaluator:
    store = FeatureStore()
    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
    pipeline = TrainingPipeline(store)
    pipeline.run("artifact_v1")
    return Evaluator(pipeline)


def test_evaluate_returns_report_with_metrics() -> None:
    evaluator = evaluator_factory()
    report = evaluator.evaluate("artifact_v1", ["artifact_v1:sensor.kitchen:{'temperature': 21.0}", "artifact_v1:sensor.bedroom:{'temperature': 18.5}"])
    assert report.artifact_id == "artifact_v1"
    assert report.sample_size == 2
    assert {metric.name for metric in report.metrics} == {"coverage", "unknown_rate"}


def test_evaluate_without_training_raises_value_error() -> None:
    evaluator = Evaluator(TrainingPipeline(FeatureStore()))
    with pytest.raises(ValueError):
        evaluator.evaluate("artifact_v1", [])