from __future__ import annotations

from app.ml.evaluation import Evaluator, EvalReport, Metric
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 test_end_to_end_training_then_evaluation() -> None:
    store = FeatureStore()
    store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
    pipeline = TrainingPipeline(store)
    artifact = pipeline.run("artifact_v1")

    evaluator = Evaluator(pipeline)
    predictions = [
        "artifact_v1:sensor.kitchen:{'temperature': 21.0}",
        "artifact_v1:sensor.bedroom:{'temperature': 18.5}",
    ]
    report = evaluator.evaluate(artifact.artifact_id, predictions)
    assert isinstance(report, EvalReport)
    assert report.sample_size == len(predictions)
    assert any(metric.name == "coverage" for metric in report.metrics)


def test_metric_helpers_are_serializable() -> None:
    metric = Metric(name="coverage", value=0.85, threshold=0.8)
    assert metric.name == "coverage"
    assert metric.value == 0.85
    assert metric.threshold == 0.8