Add test_evaluate_detection_fp to valor_core (#718)

core/tests/functional-tests/test_detection.py

@@ -4427,3 +4427,159 @@ def test_two_groundtruths_one_datum(
     assert result_dict["meta"]["labels"] == 2
     assert result_dict["meta"]["annotations"] == 5
     assert result_dict["meta"]["duration"] <= 5
+
+
+def test_evaluate_detection_pr_fp(img1, img2):
+    gts = [
+        schemas.GroundTruth(
+            datum=img1,
+            annotations=[
+                schemas.Annotation(
+                    is_instance=True,
+                    labels=[schemas.Label(key="k1", value="v1")],
+                    bounding_box=schemas.Box.from_extrema(
+                        xmin=0, xmax=5, ymin=0, ymax=5
+                    ),
+                )
+            ],
+        ),
+        schemas.GroundTruth(
+            datum=img2,
+            annotations=[
+                schemas.Annotation(
+                    is_instance=True,
+                    labels=[schemas.Label(key="k1", value="v1")],
+                    bounding_box=schemas.Box.from_extrema(
+                        xmin=0, xmax=5, ymin=0, ymax=5
+                    ),
+                )
+            ],
+        ),
+    ]
+    preds = [
+        schemas.Prediction(
+            datum=img1,
+            annotations=[
+                schemas.Annotation(
+                    is_instance=True,
+                    labels=[schemas.Label(key="k1", value="v1", score=0.8)],
+                    bounding_box=schemas.Box.from_extrema(
+                        xmin=0, xmax=5, ymin=0, ymax=5
+                    ),
+                )
+            ],
+        ),
+        schemas.Prediction(
+            datum=img2,
+            annotations=[
+                schemas.Annotation(
+                    is_instance=True,
+                    labels=[schemas.Label(key="k1", value="v1", score=0.8)],
+                    bounding_box=schemas.Box.from_extrema(
+                        xmin=10, xmax=20, ymin=10, ymax=20
+                    ),
+                )
+            ],
+        ),
+    ]
+
+    eval_job = evaluate_detection(
+        groundtruths=gts,
+        predictions=preds,
+        metrics_to_return=[
+            enums.MetricType.PrecisionRecallCurve,
+        ],
+    )
+
+    metrics = eval_job.metrics
+    assert metrics[0]["value"]["v1"][0.5] == {
+        "fn": 1,  # img2
+        "fp": 1,  # img2
+        "tn": None,
+        "tp": 1,  # img1
+        "recall": 0.5,
+        "accuracy": None,
+        "f1_score": 0.5,
+        "precision": 0.5,
+    }
+
+    # score threshold is now higher than the scores, so we should the predictions drop out such that we're only left with 2 fns (one for each image)
+    assert metrics[0]["value"]["v1"][0.85] == {
+        "tp": 0,
+        "fp": 0,
+        "fn": 2,
+        "tn": None,
+        "precision": 0.0,
+        "recall": 0.0,
+        "accuracy": None,
+        "f1_score": 0.0,
+    }
+
+    # test DetailedPRCurve version
+    eval_job = evaluate_detection(
+        groundtruths=gts,
+        predictions=preds,
+        metrics_to_return=[
+            enums.MetricType.DetailedPrecisionRecallCurve,
+        ],
+    )
+
+    metrics = eval_job.metrics
+
+    score_threshold = 0.5
+    assert metrics[0]["value"]["v1"][score_threshold]["tp"]["total"] == 1
+    assert "tn" not in metrics[0]["value"]["v1"][score_threshold]
+    assert (
+        metrics[0]["value"]["v1"][score_threshold]["fp"]["observations"][
+            "hallucinations"
+        ]["count"]
+        == 1
+    )
+    assert (
+        metrics[0]["value"]["v1"][score_threshold]["fp"]["observations"][
+            "misclassifications"
+        ]["count"]
+        == 0
+    )
+    assert (
+        metrics[0]["value"]["v1"][score_threshold]["fn"]["observations"][
+            "no_predictions"
+        ]["count"]
+        == 1
+    )
+    assert metrics[0]["value"]["v1"][score_threshold]["tp"]["total"] == 1
+    assert (
+        metrics[0]["value"]["v1"][score_threshold]["fn"]["observations"][
+            "misclassifications"
+        ]["count"]
+        == 0
+    )
+
+    # score threshold is now higher than the scores, so we should the predictions drop out such that we're only left with 2 fns (one for each image)
+    score_threshold = 0.85
+    assert metrics[0]["value"]["v1"][score_threshold]["tp"]["total"] == 0
+    assert "tn" not in metrics[0]["value"]["v1"][score_threshold]
+    assert (
+        metrics[0]["value"]["v1"][score_threshold]["fp"]["observations"][
+            "hallucinations"
+        ]["count"]
+        == 0
+    )
+    assert (
+        metrics[0]["value"]["v1"][score_threshold]["fp"]["observations"][
+            "misclassifications"
+        ]["count"]
+        == 0
+    )
+    assert (
+        metrics[0]["value"]["v1"][score_threshold]["fn"]["observations"][
+            "no_predictions"
+        ]["count"]
+        == 2
+    )
+    assert (
+        metrics[0]["value"]["v1"][score_threshold]["fn"]["observations"][
+            "misclassifications"
+        ]["count"]
+        == 0
+    )