Initial public release

backend/tests/test_detection_precision_recall_curve.py

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+"""A single F1 at one arbitrary confidence cut cannot compare two models.
+
+Reporting F1 at whichever threshold the operator happened to type makes two
+models look better or worse depending on their calibration rather than their
+detection quality. The sweep produces the standard curve instead: precision
+and recall at every operating point, average precision, and the threshold
+where F1 actually peaks.
+"""
+
+from __future__ import annotations
+
+import pytest
+from shapely.geometry import box
+
+from app.services.detection_metrics_service import DetectionMetricsService
+
+
+def _candidate(name: str, geometry, confidence: float):
+    return ({"id": name, "confidence": confidence}, geometry)
+
+
+def _reference(name: str, geometry):
+    return ({"id": name}, geometry)
+
+
+def test_perfect_detector_reaches_average_precision_one() -> None:
+    references = [_reference("r1", box(0, 0, 1, 1)), _reference("r2", box(5, 5, 6, 6))]
+    candidates = [
+        _candidate("c1", box(0, 0, 1, 1), 0.9),
+        _candidate("c2", box(5, 5, 6, 6), 0.8),
+    ]
+
+    curve = DetectionMetricsService.precision_recall_curve(candidates, references, iou_threshold=0.5)
+
+    assert curve["average_precision"] == pytest.approx(1.0)
+    assert curve["best_f1"] == pytest.approx(1.0)
+    assert curve["reference_count"] == 2
+
+
+def test_low_confidence_false_positive_is_only_penalised_below_its_threshold() -> None:
+    references = [_reference("r1", box(0, 0, 1, 1))]
+    candidates = [
+        _candidate("hit", box(0, 0, 1, 1), 0.9),
+        _candidate("junk", box(20, 20, 21, 21), 0.2),
+    ]
+
+    curve = DetectionMetricsService.precision_recall_curve(candidates, references, iou_threshold=0.5)
+
+    # Cutting at 0.2 admits the junk box, so precision there is 0.5.
+    low = next(point for point in curve["points"] if point["confidence_threshold"] == pytest.approx(0.2))
+    assert low["precision"] == pytest.approx(0.5)
+    assert low["recall"] == pytest.approx(1.0)
+
+    # The optimum simply drops it.
+    assert curve["best_f1"] == pytest.approx(1.0)
+    assert curve["best_f1_threshold"] == pytest.approx(0.9)
+    # AP is computed over the ranking, so one trailing false positive after
+    # full recall does not reduce it.
+    assert curve["average_precision"] == pytest.approx(1.0)
+
+
+def test_ranking_quality_is_visible_in_average_precision() -> None:
+    """A detector that ranks its mistake above its hit scores worse."""
+
+    references = [_reference("r1", box(0, 0, 1, 1))]
+    good = DetectionMetricsService.precision_recall_curve(
+        [_candidate("hit", box(0, 0, 1, 1), 0.9), _candidate("junk", box(20, 20, 21, 21), 0.1)],
+        references,
+        iou_threshold=0.5,
+    )
+    bad = DetectionMetricsService.precision_recall_curve(
+        [_candidate("hit", box(0, 0, 1, 1), 0.1), _candidate("junk", box(20, 20, 21, 21), 0.9)],
+        references,
+        iou_threshold=0.5,
+    )
+
+    assert good["average_precision"] > bad["average_precision"]
+    assert bad["average_precision"] == pytest.approx(0.5)
+
+
+def test_missed_reference_caps_recall_and_average_precision() -> None:
+    references = [_reference("r1", box(0, 0, 1, 1)), _reference("r2", box(9, 9, 10, 10))]
+    candidates = [_candidate("hit", box(0, 0, 1, 1), 0.9)]
+
+    curve = DetectionMetricsService.precision_recall_curve(candidates, references, iou_threshold=0.5)
+
+    assert curve["points"][0]["recall"] == pytest.approx(0.5)
+    assert curve["average_precision"] == pytest.approx(0.5)
+    assert curve["best_f1"] == pytest.approx(2 / 3)
+
+
+def test_curve_is_independent_of_input_order() -> None:
+    references = [_reference("r1", box(0, 0, 1, 1)), _reference("r2", box(5, 5, 6, 6))]
+    candidates = [
+        _candidate("c1", box(0, 0, 1, 1), 0.9),
+        _candidate("c2", box(5, 5, 6, 6), 0.4),
+        _candidate("c3", box(30, 30, 31, 31), 0.6),
+    ]
+
+    forward = DetectionMetricsService.precision_recall_curve(candidates, references, iou_threshold=0.5)
+    reverse = DetectionMetricsService.precision_recall_curve(
+        list(reversed(candidates)), list(reversed(references)), iou_threshold=0.5
+    )
+
+    assert forward == reverse
+
+
+def test_empty_candidate_population_is_reported_not_crashed() -> None:
+    curve = DetectionMetricsService.precision_recall_curve(
+        [], [_reference("r1", box(0, 0, 1, 1))], iou_threshold=0.5
+    )
+
+    assert curve["average_precision"] == 0.0
+    assert curve["best_f1"] == 0.0
+    assert curve["best_f1_threshold"] is None
+    assert curve["points"] == []