56 lines
1.9 KiB
Python
56 lines
1.9 KiB
Python
from __future__ import annotations
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import pytest
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from app.ml.evaluation import Evaluator
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from app.ml.feature_store import FeatureStore, FeatureVector
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from app.ml.training import TrainingPipeline
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def _vector(sensor_id: str, temperature: float, label: str | None = None) -> FeatureVector:
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return FeatureVector(sensor_id=sensor_id, values={"temperature": temperature}, label=label)
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def evaluator_factory() -> Evaluator:
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store = FeatureStore()
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store.add_batch([_vector("sensor.kitchen", 19.0), _vector("sensor.bedroom", 18.5)])
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pipeline = TrainingPipeline(store)
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pipeline.run("artifact_v1")
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return Evaluator(pipeline)
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def test_evaluate_returns_report_with_metrics() -> None:
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evaluator = evaluator_factory()
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report = evaluator.evaluate(
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"artifact_v1",
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[
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"artifact_v1:sensor.kitchen:{'temperature': 21.0}",
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"artifact_v1:sensor.bedroom:{'temperature': 18.5}",
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],
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)
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assert report.artifact_id == "artifact_v1"
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assert report.sample_size == 2
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assert {metric.name for metric in report.metrics} == {"coverage", "unknown_rate"}
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assert next(metric.value for metric in report.metrics if metric.name == "coverage") == 1.0
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def test_evaluate_without_training_raises_value_error() -> None:
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evaluator = Evaluator(TrainingPipeline(FeatureStore()))
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with pytest.raises(ValueError):
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evaluator.evaluate("artifact_v1", [])
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def test_coverage_is_bounded_and_requires_exact_sensor_match() -> None:
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evaluator = evaluator_factory()
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report = evaluator.evaluate(
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"artifact_v1",
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[
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"artifact_v1:sensor.kitchen:{'note': 'sensor.bedroom'}",
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"artifact_v1:sensor.kitchen_extra:{}",
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"malformed",
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],
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)
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metrics = {metric.name: metric.value for metric in report.metrics}
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assert metrics == {"coverage": pytest.approx(1 / 3), "unknown_rate": pytest.approx(2 / 3)}
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