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import sys
import tempfile
import unittest
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
from evaluation.tools import paired_bootstrap
def _write(path, payload):
path.write_text(json.dumps(payload), encoding="utf-8")
class PairedBootstrapTests(unittest.TestCase):
def _toy_gt(self):
return {
"images": [{"id": 1}, {"id": 2}, {"id": 3}],
"categories": [
{"id": 1, "name": "Button"},
{"id": 2, "name": "Sphere"},
],
"annotations": [
{"id": 1, "image_id": 1, "category_id": 1, "bbox": [0, 0, 10, 10]},
{"id": 2, "image_id": 2, "category_id": 2, "bbox": [0, 0, 10, 10]},
],
}
def test_greedy_matching_uses_score_order_and_complete_images(self):
gt = self._toy_gt()
preds = [
{"image_id": 1, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 0.9},
{"image_id": 1, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 0.2},
{"image_id": 2, "category_id": 2, "bbox": [0, 0, 10, 10], "score": 0.8},
]
prepared_gt = paired_bootstrap.prepare_ground_truth(gt)
evaluated = paired_bootstrap.evaluate_predictions(
prepared_gt,
preds,
dimension="interactable",
iou_threshold=0.75,
score_threshold=0.0,
)
self.assertEqual(evaluated.counts["tp"], 2)
self.assertEqual(evaluated.counts["fp"], 1)
self.assertEqual(evaluated.counts["fn"], 0)
self.assertEqual(len(evaluated.per_image), 3)
self.assertEqual(evaluated.per_image[3].support, 0)
def test_auto_threshold_maximizes_micro_f1(self):
gt = self._toy_gt()
preds = [
{"image_id": 1, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 0.4},
{"image_id": 2, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 0.9},
{"image_id": 2, "category_id": 2, "bbox": [0, 0, 10, 10], "score": 0.8},
{"image_id": 3, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 0.4},
{"image_id": 3, "category_id": 1, "bbox": [1, 1, 10, 10], "score": 0.4},
{"image_id": 3, "category_id": 2, "bbox": [2, 2, 10, 10], "score": 0.4},
{"image_id": 3, "category_id": 2, "bbox": [3, 3, 10, 10], "score": 0.4},
]
prepared_gt = paired_bootstrap.prepare_ground_truth(gt)
threshold, evaluated = paired_bootstrap.select_best_threshold(
prepared_gt,
preds,
dimension="interactable",
iou_threshold=0.75,
)
self.assertEqual(threshold, 0.8)
self.assertAlmostEqual(evaluated.metrics["f1"], 0.5)
def test_fast_auto_threshold_matches_bruteforce_selection(self):
gt = self._toy_gt()
preds = [
{"image_id": 1, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 0.91},
{"image_id": 1, "category_id": 1, "bbox": [1, 1, 10, 10], "score": 0.42},
{"image_id": 2, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 0.88},
{"image_id": 2, "category_id": 2, "bbox": [0, 0, 10, 10], "score": 0.73},
{"image_id": 3, "category_id": 2, "bbox": [0, 0, 10, 10], "score": 0.11},
{"image_id": 99, "category_id": 2, "bbox": [0, 0, 10, 10], "score": 0.72},
]
prepared_gt = paired_bootstrap.prepare_ground_truth(gt)
fast_threshold = paired_bootstrap.select_best_threshold_fast(
prepared_gt,
preds,
dimension="interactable",
iou_threshold=0.75,
)
brute_threshold, brute_result = self._select_best_threshold_bruteforce(prepared_gt, preds)
self.assertEqual(fast_threshold, brute_threshold)
self.assertEqual(fast_threshold, 0.72)
fast_result = paired_bootstrap.evaluate_predictions(
prepared_gt,
preds,
dimension="interactable",
iou_threshold=0.75,
score_threshold=fast_threshold,
)
self.assertEqual(fast_result.metrics, brute_result.metrics)
def _select_best_threshold_bruteforce(self, prepared_gt, preds):
best_threshold = None
best_result = None
for threshold in sorted({float(pred["score"]) for pred in preds}, reverse=True):
result = paired_bootstrap.evaluate_predictions(
prepared_gt,
preds,
dimension="interactable",
iou_threshold=0.75,
score_threshold=threshold,
)
if best_result is None or (
result.metrics["f1"],
result.metrics["precision"],
result.metrics["recall"],
-threshold,
) > (
best_result.metrics["f1"],
best_result.metrics["precision"],
best_result.metrics["recall"],
-best_threshold,
):
best_threshold = threshold
best_result = result
return best_threshold, best_result
def test_semantic_cache_match_maps_numeric_categories(self):
with tempfile.TemporaryDirectory() as tmpdir:
tmp = Path(tmpdir)
cache = tmp / "embedding.json"
_write(cache, {"Button": [1.0, 0.0], "Control": [0.9, 0.1]})
matcher = paired_bootstrap.SemanticMatcher(cache)
gt = self._toy_gt()
preds = [
{"image_id": 1, "category_id": "control", "bbox": [0, 0, 10, 10], "score": 1.0}
]
prepared_gt = paired_bootstrap.prepare_ground_truth(gt)
evaluated = paired_bootstrap.evaluate_predictions(
prepared_gt,
preds,
dimension="semantics",
iou_threshold=0.75,
score_threshold=0.0,
semantic_matcher=matcher,
)
self.assertEqual(evaluated.counts["tp"], 1)
def test_semantic_cache_preserves_historical_camelcase_keys(self):
with tempfile.TemporaryDirectory() as tmpdir:
cache = Path(tmpdir) / "embedding.json"
_write(cache, {"QuitButton": [1.0, 0.0], "ConfirmButton": [0.9, 0.1]})
matcher = paired_bootstrap.SemanticMatcher(cache)
self.assertTrue(matcher.matches("quit_button", "confirm_button"))
def test_semantic_match_defaults_to_historical_raw_dot_product(self):
with tempfile.TemporaryDirectory() as tmpdir:
cache = Path(tmpdir) / "embedding.json"
_write(cache, {"A": [1.0, 0.0], "B": [0.84, 0.20]})
historical = paired_bootstrap.SemanticMatcher(cache)
cosine = paired_bootstrap.SemanticMatcher(cache, similarity_mode="cosine")
self.assertFalse(historical.matches("a", "b"))
self.assertTrue(cosine.matches("a", "b"))
def test_semantic_cache_miss_is_hard_error(self):
with tempfile.TemporaryDirectory() as tmpdir:
cache = Path(tmpdir) / "embedding.json"
_write(cache, {"Button": [1.0, 0.0]})
matcher = paired_bootstrap.SemanticMatcher(cache)
with self.assertRaisesRegex(KeyError, "Missing frozen embedding cache entries"):
matcher.matches("button", "unknown")
def test_cli_writes_json_report(self):
with tempfile.TemporaryDirectory() as tmpdir:
tmp = Path(tmpdir)
gt_path = tmp / "gt.json"
a_path = tmp / "a.json"
b_path = tmp / "b.json"
out_path = tmp / "report.json"
_write(gt_path, self._toy_gt())
_write(a_path, [{"image_id": 1, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 1.0}])
_write(
b_path,
[
{"image_id": 1, "category_id": 1, "bbox": [0, 0, 10, 10], "score": 1.0},
{"image_id": 2, "category_id": 2, "bbox": [0, 0, 10, 10], "score": 0.9},
],
)
paired_bootstrap.main(
[
"--gt",
str(gt_path),
"--method-a",
str(a_path),
"--method-b",
str(b_path),
"--dimension",
"interactable",
"--auto-threshold",
"--replicates",
"100",
"--seed",
"7",
"--output",
str(out_path),
]
)
report = json.loads(out_path.read_text(encoding="utf-8"))
self.assertEqual(report["protocol"]["dimension"], "interactable")
self.assertEqual(report["protocol"]["bootstrap_unit"], "image_id")
self.assertGreater(report["methods"]["method_b"]["point"]["micro"]["f1"], report["methods"]["method_a"]["point"]["micro"]["f1"])
self.assertIn("mean_per_all_images", report["methods"]["method_a"]["point"])
self.assertIn("mean_per_positive_support_images", report["methods"]["method_a"]["point"])
self.assertIn("mean_per_all_images_delta", report["bootstrap"])
self.assertIn("mean_per_positive_support_images_delta", report["bootstrap"])
self.assertEqual(report["bootstrap"]["delta_direction"], "method_b_minus_method_a")
if __name__ == "__main__":
unittest.main()
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