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"}
    assert next(metric.value for metric in report.metrics if metric.name == "coverage") == 1.0


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


def test_coverage_is_bounded_and_requires_exact_sensor_match() -> None:
    evaluator = evaluator_factory()
    report = evaluator.evaluate(
        "artifact_v1",
        [
            "artifact_v1:sensor.kitchen:{'note': 'sensor.bedroom'}",
            "artifact_v1:sensor.kitchen_extra:{}",
            "malformed",
        ],
    )

    metrics = {metric.name: metric.value for metric in report.metrics}
    assert metrics == {"coverage": pytest.approx(1 / 3), "unknown_rate": pytest.approx(2 / 3)}