docs: convert fenced examples to doctests in dataset/formats/createml.py (#2475)

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

mkdocs.yml

@@ -20,7 +20,7 @@ extra:
       link: https://discord.gg/GbfgXGJ8Bk
   analytics:
     provider: google
-    property: G-P7ZG0Y19G5
+    property: G-SEKT4K1EWR
   version:
     provider: mike
 

pyproject.toml

@@ -4,7 +4,7 @@ requires = [ "setuptools>=61" ]
 
 [project]
 name = "supervision"
-version = "0.30.0"
+version = "0.31.0.dev0"
 description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
 readme = "README.md"
 keywords = [
@@ -78,6 +78,7 @@ dev = [
   "pre-commit>=3.8",
   "pytest>=7.2.2,<10",
   "pytest-cov>=4,<8",
+  "scikit-learn>=1.7",
   "tox>=4.11.4",
   "types-tqdm",
 ]

src/supervision/dataset/formats/coco.py

@@ -65,15 +65,13 @@ def classes_to_coco_categories(classes: list[str]) -> list[CocoDict]:
     Returns:
         A list of COCO category dictionaries with 1-indexed ``id`` values.
 
-    Examples:
-        ```python
-        from supervision.dataset.formats.coco import classes_to_coco_categories
+    Example:
+        ```pycon
+        >>> from supervision.dataset.formats.coco import classes_to_coco_categories
+        >>> classes_to_coco_categories(classes=["cat", "dog"])
+        [{'id': 1, 'name': 'cat', 'supercategory': 'common-objects'},
+         {'id': 2, 'name': 'dog', 'supercategory': 'common-objects'}]
 
-        classes_to_coco_categories(classes=["cat", "dog"])
-        # [
-        #     {"id": 1, "name": "cat", "supercategory": "common-objects"},
-        #     {"id": 2, "name": "dog", "supercategory": "common-objects"},
-        # ]
         ```
     """
     return [
@@ -280,23 +278,25 @@ def detections_to_coco_annotations(
         (``iscrowd=0``). Supply ``data={"iscrowd": np.array([0])}`` to
         force polygon output regardless of mask topology.
 
-    Examples:
-        ```python
-        import numpy as np
-        from supervision import Detections
-        from supervision.dataset.formats.coco import (
-            detections_to_coco_annotations,
-        )
+    Example:
+        ```pycon
+        >>> import numpy as np
+        >>> from supervision import Detections
+        >>> from supervision.dataset.formats.coco import (
+        ...     detections_to_coco_annotations,
+        ... )
+        >>> detections = Detections(
+        ...     xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
+        ...     class_id=np.array([0], dtype=int),
+        ... )
+        >>> annotations, next_id = detections_to_coco_annotations(
+        ...     detections=detections, image_id=1, annotation_id=1
+        ... )
+        >>> annotations[0]["category_id"]
+        1
+        >>> next_id
+        2
 
-        detections = Detections(
-            xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
-            class_id=np.array([0], dtype=int),
-        )
-        annotations, next_id = detections_to_coco_annotations(
-            detections=detections, image_id=1, annotation_id=1
-        )
-        annotations[0]["category_id"]
-        # 1
         ```
     """
     coco_annotations: list[CocoDict] = []

src/supervision/dataset/formats/createml.py

@@ -77,21 +77,23 @@ def createml_annotations_to_detections(
             ``"label"``, or any coordinate sub-key), or if a coordinate value
             cannot be converted to float.
 
-    Examples:
-        ```python
-        import supervision as sv
-        from supervision.dataset.formats.createml import (
-            createml_annotations_to_detections,
-        )
+    Example:
+        ```pycon
+        >>> from supervision.dataset.formats.createml import (
+        ...     createml_annotations_to_detections,
+        ... )
+        >>> annotations = [
+        ...     {
+        ...         "label": "dog",
+        ...         "coordinates": {"x": 50, "y": 50, "width": 20, "height": 20},
+        ...     }
+        ... ]
+        >>> detections = createml_annotations_to_detections(annotations, {"dog": 0})
+        >>> detections.xyxy
+        array([[40., 40., 60., 60.]], dtype=float32)
+        >>> detections.class_id
+        array([0])
 
-        annotations = [
-            {
-                "label": "dog",
-                "coordinates": {"x": 50, "y": 50, "width": 20, "height": 20},
-            }
-        ]
-        detections = createml_annotations_to_detections(annotations, {"dog": 0})
-        # detections.xyxy → [[40, 40, 60, 60]]
         ```
     """
     if not image_annotations:
@@ -240,20 +242,21 @@ def detections_to_createml_annotations(
     Raises:
         ValueError: If ``detections.class_id`` is ``None``.
 
-    Examples:
-        ```python
-        import numpy as np
-        import supervision as sv
-        from supervision.dataset.formats.createml import (
-            detections_to_createml_annotations,
-        )
+    Example:
+        ```pycon
+        >>> import numpy as np
+        >>> import supervision as sv
+        >>> from supervision.dataset.formats.createml import (
+        ...     detections_to_createml_annotations,
+        ... )
+        >>> detections = sv.Detections(
+        ...     xyxy=np.array([[40, 40, 60, 60]], dtype=np.float32),
+        ...     class_id=np.array([0], dtype=int),
+        ... )
+        >>> detections_to_createml_annotations(detections, classes=["dog"])
+        [{'label': 'dog', 'coordinates':
+          {'x': 50.0, 'y': 50.0, 'width': 20.0, 'height': 20.0}}]
 
-        detections = sv.Detections(
-            xyxy=np.array([[40, 40, 60, 60]], dtype=np.float32),
-            class_id=np.array([0], dtype=int),
-        )
-        detections_to_createml_annotations(detections, classes=["dog"])
-        # [{"label": "dog", "coordinates": {"x": 50.0, "y": 50.0, ...}}]
         ```
     """
     class_ids = detections.class_id
@@ -301,7 +304,7 @@ def save_createml_annotations(
         ValueError: If two image paths share the same basename and would map to
             the same CreateML ``image`` entry.
 
-    Examples:
+    Example:
         ```python
         import supervision as sv
         from supervision.dataset.formats.createml import save_createml_annotations

tests/metrics/test_sklearn_parity.py

@@ -0,0 +1,286 @@
+"""Compare detection-style metric outcomes with live scikit-learn results.
+
+Unmatched targets and predictions are represented by ``BACKGROUND_CLASS_ID`` in
+the sklearn event stream, while evaluated labels contain only foreground classes.
+This produces the same per-class TP, FP, and FN counts as Supervision's metrics.
+"""
+
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Literal
+
+import numpy as np
+import pytest
+from sklearn.metrics import f1_score, precision_score, recall_score
+
+from supervision.detection.core import Detections
+from supervision.metrics.core import AveragingMethod
+from supervision.metrics.f1_score import F1Score
+from supervision.metrics.precision import Precision
+from supervision.metrics.recall import Recall
+
+BACKGROUND_CLASS_ID = -1
+
+
+@dataclass(frozen=True)
+class ParityCase:
+    """Define equivalent Supervision detection batches and sklearn label events."""
+
+    name: str
+    prediction_batches: tuple[tuple[int, ...], ...]
+    target_batches: tuple[tuple[int, ...], ...]
+    sklearn_y_true: tuple[int, ...]
+    sklearn_y_pred: tuple[int, ...]
+    sklearn_labels: tuple[int, ...]
+    matched_classes: tuple[int, ...]
+
+
+@dataclass(frozen=True)
+class MetricSpec:
+    """Connect a Supervision metric result to its scikit-learn function."""
+
+    name: Literal["precision", "recall", "f1"]
+    metric_class: type[Precision] | type[Recall] | type[F1Score]
+    sklearn_function: Callable[..., float | np.ndarray]
+    scores_attribute: str
+    per_class_attribute: str
+
+
+PARITY_CASES = (
+    ParityCase(
+        name="balanced",
+        prediction_batches=((0, 2, 2, 5, 0),),
+        target_batches=((0, 0, 2, 5, 5),),
+        sklearn_y_true=(0, 0, 2, 5, 5),
+        sklearn_y_pred=(0, 2, 2, 5, 0),
+        sklearn_labels=(0, 2, 5),
+        matched_classes=(0, 2, 5),
+    ),
+    ParityCase(
+        name="prediction-only-label",
+        prediction_batches=((0, 0, 7),),
+        target_batches=((0, 0, 0),),
+        sklearn_y_true=(0, 0, 0),
+        sklearn_y_pred=(0, 0, 7),
+        sklearn_labels=(0, 7),
+        matched_classes=(0, 7),
+    ),
+    ParityCase(
+        name="target-only-label",
+        prediction_batches=((0, 0, 0),),
+        target_batches=((0, 0, 9),),
+        sklearn_y_true=(0, 0, 9),
+        sklearn_y_pred=(0, 0, 0),
+        sklearn_labels=(0, 9),
+        matched_classes=(0, 9),
+    ),
+    ParityCase(
+        name="non-contiguous-ids",
+        prediction_batches=((2, 11, 11, 42, 2, 42),),
+        target_batches=((2, 2, 11, 42, 42, 42),),
+        sklearn_y_true=(2, 2, 11, 42, 42, 42),
+        sklearn_y_pred=(2, 11, 11, 42, 2, 42),
+        sklearn_labels=(2, 11, 42),
+        matched_classes=(2, 11, 42),
+    ),
+    ParityCase(
+        name="background-image",
+        prediction_batches=((0, 2), (0, 11, 11)),
+        target_batches=((0, 2), ()),
+        sklearn_y_true=(
+            0,
+            2,
+            BACKGROUND_CLASS_ID,
+            BACKGROUND_CLASS_ID,
+            BACKGROUND_CLASS_ID,
+        ),
+        sklearn_y_pred=(0, 2, 0, 11, 11),
+        sklearn_labels=(0, 2, 11),
+        matched_classes=(0, 2, 11),
+    ),
+    ParityCase(
+        name="targets-only",
+        prediction_batches=((),),
+        target_batches=((3, 10),),
+        sklearn_y_true=(3, 10),
+        sklearn_y_pred=(BACKGROUND_CLASS_ID, BACKGROUND_CLASS_ID),
+        sklearn_labels=(3, 10),
+        matched_classes=(3, 10),
+    ),
+    ParityCase(
+        name="predictions-only",
+        prediction_batches=((3, 10),),
+        target_batches=((),),
+        sklearn_y_true=(BACKGROUND_CLASS_ID, BACKGROUND_CLASS_ID),
+        sklearn_y_pred=(3, 10),
+        sklearn_labels=(3, 10),
+        matched_classes=(3, 10),
+    ),
+)
+
+METRIC_SPECS = (
+    MetricSpec(
+        name="precision",
+        metric_class=Precision,
+        sklearn_function=precision_score,
+        scores_attribute="precision_scores",
+        per_class_attribute="precision_per_class",
+    ),
+    MetricSpec(
+        name="recall",
+        metric_class=Recall,
+        sklearn_function=recall_score,
+        scores_attribute="recall_scores",
+        per_class_attribute="recall_per_class",
+    ),
+    MetricSpec(
+        name="f1",
+        metric_class=F1Score,
+        sklearn_function=f1_score,
+        scores_attribute="f1_scores",
+        per_class_attribute="f1_per_class",
+    ),
+)
+
+AVERAGING_CASES = (
+    pytest.param(AveragingMethod.MICRO, "micro", id="micro"),
+    pytest.param(AveragingMethod.MACRO, "macro", id="macro"),
+    pytest.param(AveragingMethod.WEIGHTED, "weighted", id="weighted"),
+)
+
+
+def _detections_from_classes(
+    class_ids: tuple[int, ...], *, predictions: bool
+) -> Detections:
+    """Build isolated boxes whose equal indices are the only possible matches."""
+    if not class_ids:
+        return Detections.empty()
+
+    left = np.arange(len(class_ids), dtype=np.float32) * 30
+    zeros = np.zeros(len(class_ids), dtype=np.float32)
+    xyxy = np.column_stack((left, zeros, left + 10, zeros + 10))
+    confidence = (
+        np.full(len(class_ids), 0.99, dtype=np.float32) if predictions else None
+    )
+    return Detections(
+        xyxy=xyxy,
+        class_id=np.asarray(class_ids, dtype=np.int32),
+        confidence=confidence,
+    )
+
+
+def _build_detection_batches(
+    case: ParityCase,
+) -> tuple[list[Detections], list[Detections]]:
+    """Convert a parity case's class batches into Supervision inputs."""
+    predictions = [
+        _detections_from_classes(class_ids, predictions=True)
+        for class_ids in case.prediction_batches
+    ]
+    targets = [
+        _detections_from_classes(class_ids, predictions=False)
+        for class_ids in case.target_batches
+    ]
+    return predictions, targets
+
+
+@pytest.mark.parametrize(
+    "case", [pytest.param(case, id=case.name) for case in PARITY_CASES]
+)
+@pytest.mark.parametrize(
+    "metric_spec",
+    [pytest.param(spec, id=spec.name) for spec in METRIC_SPECS],
+)
+class TestSklearnParity:
+    """Verify metric outputs against independently computed sklearn results."""
+
+    @pytest.mark.parametrize(("averaging_method", "average_name"), AVERAGING_CASES)
+    def test_aggregate_scores_match_sklearn(
+        self,
+        case: ParityCase,
+        metric_spec: MetricSpec,
+        averaging_method: AveragingMethod,
+        average_name: Literal["micro", "macro", "weighted"],
+    ) -> None:
+        """Aggregate scores match live sklearn results at every IoU threshold."""
+        predictions, targets = _build_detection_batches(case)
+        metric = metric_spec.metric_class(averaging_method=averaging_method)
+
+        result = metric.update(predictions, targets).compute()
+
+        expected_value = metric_spec.sklearn_function(
+            case.sklearn_y_true,
+            case.sklearn_y_pred,
+            labels=case.sklearn_labels,
+            average=average_name,
+            zero_division=0,
+        )
+        expected = np.full(result.iou_thresholds.shape, expected_value)
+        actual = getattr(result, metric_spec.scores_attribute)
+        np.testing.assert_allclose(actual, expected, rtol=1e-12, atol=1e-12)
+
+    def test_per_class_scores_match_sklearn(
+        self, case: ParityCase, metric_spec: MetricSpec
+    ) -> None:
+        """Per-class scores and class ordering match live sklearn results."""
+        predictions, targets = _build_detection_batches(case)
+        metric = metric_spec.metric_class(averaging_method=AveragingMethod.MACRO)
+
+        result = metric.update(predictions, targets).compute()
+
+        expected_values = metric_spec.sklearn_function(
+            case.sklearn_y_true,
+            case.sklearn_y_pred,
+            labels=case.matched_classes,
+            average=None,
+            zero_division=0,
+        )
+        expected = np.repeat(
+            np.asarray(expected_values, dtype=np.float64)[:, None],
+            result.iou_thresholds.size,
+            axis=1,
+        )
+        actual = getattr(result, metric_spec.per_class_attribute)
+        np.testing.assert_array_equal(result.matched_classes, case.matched_classes)
+        np.testing.assert_allclose(actual, expected, rtol=1e-12, atol=1e-12)
+
+
+@pytest.mark.parametrize(
+    "metric_spec",
+    [pytest.param(spec, id=spec.name) for spec in METRIC_SPECS],
+)
+class TestEmptyInput:
+    """Verify Supervision's defined behavior when no detections exist."""
+
+    @pytest.mark.parametrize(
+        "averaging_method",
+        [
+            pytest.param(AveragingMethod.MICRO, id="micro"),
+            pytest.param(AveragingMethod.MACRO, id="macro"),
+            pytest.param(AveragingMethod.WEIGHTED, id="weighted"),
+        ],
+    )
+    def test_returns_zero_aggregate_scores(
+        self, metric_spec: MetricSpec, averaging_method: AveragingMethod
+    ) -> None:
+        """Empty batches produce zero aggregate scores at every IoU threshold."""
+        predictions = [Detections.empty()]
+        targets = [Detections.empty()]
+        metric = metric_spec.metric_class(averaging_method=averaging_method)
+
+        result = metric.update(predictions, targets).compute()
+
+        actual = getattr(result, metric_spec.scores_attribute)
+        np.testing.assert_array_equal(actual, np.zeros_like(result.iou_thresholds))
+
+    def test_returns_no_per_class_scores(self, metric_spec: MetricSpec) -> None:
+        """Empty batches produce no matched classes or per-class score rows."""
+        predictions = [Detections.empty()]
+        targets = [Detections.empty()]
+        metric = metric_spec.metric_class(averaging_method=AveragingMethod.MACRO)
+
+        result = metric.update(predictions, targets).compute()
+
+        actual = getattr(result, metric_spec.per_class_attribute)
+        np.testing.assert_array_equal(result.matched_classes, np.array([]))
+        assert actual.shape == (0, result.iou_thresholds.size)

uv.lock

@@ -334,7 +334,7 @@ resolution-markers = [
     "python_full_version < '3.11' and sys_platform == 'darwin'",
 ]
 dependencies = [
-    { name = "pycparser", marker = "sys_platform == 'darwin'" },
+    { name = "pycparser" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/fc/97/c783634659c2920c3fc70419e3af40972dbaf758daa229a7d6ea6135c90d/cffi-1.17.1.tar.gz", hash = "sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824", size = 516621, upload-time = "2024-09-04T20:45:21.852Z" }
 wheels = [
@@ -361,7 +361,7 @@ resolution-markers = [
     "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')",
 ]
 dependencies = [
-    { name = "pycparser", marker = "implementation_name != 'PyPy' and sys_platform != 'darwin'" },
+    { name = "pycparser", marker = "implementation_name != 'PyPy'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/eb/56/b1ba7935a17738ae8453301356628e8147c79dbb825bcbc73dc7401f9846/cffi-2.0.0.tar.gz", hash = "sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529", size = 523588, upload-time = "2025-09-08T23:24:04.541Z" }
 wheels = [
@@ -522,7 +522,7 @@ name = "click-plugins"
 version = "1.1.1.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "click", marker = "python_full_version < '3.12'" },
+    { name = "click" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/c3/a4/34847b59150da33690a36da3681d6bbc2ec14ee9a846bc30a6746e5984e4/click_plugins-1.1.1.2.tar.gz", hash = "sha256:d7af3984a99d243c131aa1a828331e7630f4a88a9741fd05c927b204bcf92261", size = 8343, upload-time = "2025-06-25T00:47:37.555Z" }
 wheels = [
@@ -736,45 +736,45 @@ toml = [
 
 [[package]]
 name = "cryptography"
-version = "48.0.1"
+version = "50.0.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cffi", version = "2.0.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation != 'PyPy' and sys_platform != 'darwin'" },
-    { name = "typing-extensions", marker = "python_full_version < '3.11' and sys_platform != 'darwin'" },
+    { name = "cffi", version = "2.0.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_python_implementation != 'PyPy'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
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