| |
|
|
| import unittest |
|
|
| from evaluation.run_benchmark import ( |
| dataset_sha256, |
| parse_decisions, |
| score_case, |
| summarize, |
| ) |
|
|
|
|
| class ScoreCaseTest(unittest.TestCase): |
| def test_points_rubric_normalizes_and_applies_penalty(self): |
| case = { |
| "grading_method": "points_rubric", |
| "rubric_items": [ |
| {"item_id": "a", "points": 2}, |
| {"item_id": "b", "points": 1}, |
| {"item_id": "c", "points": -1}, |
| ], |
| } |
| score, components = score_case(case, {"a": True, "b": False, "c": True}) |
| self.assertAlmostEqual(score, 1 / 3) |
| self.assertEqual(components["matched_signed_points"], 1) |
| self.assertEqual(components["available_positive_points"], 3) |
|
|
| def test_points_rubric_clamps_below_zero(self): |
| case = { |
| "grading_method": "points_rubric", |
| "rubric_items": [ |
| {"item_id": "a", "points": 1}, |
| {"item_id": "b", "points": -2}, |
| ], |
| } |
| score, _ = score_case(case, {"a": False, "b": True}) |
| self.assertEqual(score, 0) |
|
|
| def test_f1_weighted_rubric(self): |
| case = { |
| "grading_method": "f1_weighted_rubric", |
| "rubric_items": [ |
| {"item_id": "a", "points": 3}, |
| {"item_id": "b", "points": 1}, |
| {"item_id": "c", "points": -2}, |
| {"item_id": "d", "points": 0}, |
| ], |
| } |
| score, components = score_case( |
| case, {"a": True, "b": False, "c": True, "d": True} |
| ) |
| |
| |
| recall = 72 / 75 |
| precision = 72 / 97 |
| expected = 2 * precision * recall / (precision + recall) |
| self.assertAlmostEqual(score, round(expected, 4)) |
| self.assertEqual(components["severe_rate"], 0.0) |
| self.assertAlmostEqual(components["recall_weighted"], round(recall, 4)) |
| self.assertAlmostEqual(components["precision_weighted"], round(precision, 4)) |
|
|
| def test_f1_weighted_rubric_severe_omission_is_reported_not_gated(self): |
| case = { |
| "grading_method": "f1_weighted_rubric", |
| "rubric_items": [ |
| {"item_id": "a", "points": 3}, |
| {"item_id": "b", "points": 2}, |
| ], |
| } |
| score, components = score_case(case, {"a": False, "b": True}) |
| |
| |
| recall = 24 / 96 |
| precision = 1.0 |
| expected = 2 * precision * recall / (precision + recall) |
| self.assertAlmostEqual(score, round(expected, 4)) |
| self.assertEqual(components["severe_rate"], 1.0) |
|
|
| def test_f1_weighted_rubric_severe_commission_is_reported_not_gated(self): |
| case = { |
| "grading_method": "f1_weighted_rubric", |
| "rubric_items": [ |
| {"item_id": "a", "points": 2}, |
| {"item_id": "b", "points": -3}, |
| ], |
| } |
| score, components = score_case(case, {"a": True, "b": True}) |
| |
| |
| recall = 1.0 |
| precision = 24 / 96 |
| expected = 2 * precision * recall / (precision + recall) |
| self.assertAlmostEqual(score, round(expected, 4)) |
| self.assertEqual(components["severe_rate"], 1.0) |
|
|
| def test_judge_decisions_must_cover_exact_item_ids(self): |
| case = { |
| "rubric_items": [ |
| {"item_id": "a"}, |
| {"item_id": "b"}, |
| ] |
| } |
| with self.assertRaises(ValueError): |
| parse_decisions( |
| case, |
| '{"decisions":[{"item_id":"a","matched":true}]}', |
| ) |
|
|
| def test_dataset_digest_uses_canonical_case_content(self): |
| first = [{"case_id": "a", "prompt": "test", "rubric_items": []}] |
| reordered = [{"rubric_items": [], "prompt": "test", "case_id": "a"}] |
| self.assertEqual(dataset_sha256(first), dataset_sha256(reordered)) |
|
|
| def test_summary_includes_reproducibility_metadata(self): |
| rows = [ |
| { |
| "case_id": "a", |
| "benchmark": "example", |
| "score": 0.5, |
| "answer_model": "answer", |
| "judge_model": "judge", |
| "answer_temperature": 0, |
| "judge_temperature": None, |
| "started_at": "2026-01-01T00:00:00+00:00", |
| "finished_at": "2026-01-01T00:01:00+00:00", |
| } |
| ] |
| summary = summarize(rows, 10, 42, {"a"}, "digest") |
| self.assertEqual(summary["dataset_sha256"], "digest") |
| self.assertEqual(summary["answer_temperature"], 0) |
| self.assertEqual(summary["judge_passes_per_answer"], 1) |
| self.assertEqual(summary["errors"], 0) |
|
|
|
|
| if __name__ == "__main__": |
| unittest.main() |
|
|
