refactor: convert docstring examples to doctest format (#2173)
* migrate docstrings (doctest - pycon) * Refine doctests in `mean_average_recall.py` and `f1_score.py` for consistent formatting * Expand doctests across metrics modules for detailed result output * Apply suggestions from code review --------- Co-authored-by: Jirka Borovec <6035284+Borda@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
This commit is contained in:
Omkar Kabde
GitHub
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600099e8ab
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cee411689d
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@ -520,21 +520,39 @@ class F1ScoreResult:
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Format as a pretty string.
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Example:
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```python
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print(f1_result)
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# F1ScoreResult:
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# Metric target: MetricTarget.BOXES
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# Averaging method: AveragingMethod.WEIGHTED
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# F1 @ 50: 0.7618
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# F1 @ 75: 0.7487
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# F1 @ thresh: [0.76175 0.76068 0.76068]
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# IoU thresh: [0.5 0.55 0.6 ...]
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# F1 per class:
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# 0: [0.70968 0.70968 0.70968 ...]
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# ...
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# Small objects: ...
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# Medium objects: ...
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# Large objects: ...
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```pycon
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>>> import numpy as np
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>>> import supervision as sv
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>>> from supervision.metrics import F1Score
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>>> predictions = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0]),
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... confidence=np.array([0.9])
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... )
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>>> targets = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0])
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... )
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>>> f1_metric = F1Score()
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>>> f1_result = f1_metric.update(predictions, targets).compute()
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>>> print(f1_result) # doctest: +ELLIPSIS
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F1ScoreResult:
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Metric target: MetricTarget.BOXES
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Averaging method: AveragingMethod.WEIGHTED
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F1 @ 50: 1.0000
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F1 @ 75: 1.0000
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F1 @ thresh: [1. ... 1.]
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IoU thresh: [0.5 0.55 ... 0.95]
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F1 per class:
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0: [1. ... 1.]
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...
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Medium objects:
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F1ScoreResult:
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Metric target: MetricTarget.BOXES
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Averaging method: AveragingMethod.WEIGHTED
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F1 @ 50: 0.0000
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...
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```
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"""
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out_str = (
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@ -88,15 +88,29 @@ class MeanAveragePrecisionResult:
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Formats the evaluation output metrics to match the structure used by pycocotools
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Example:
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```python
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print(map_result)
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# MeanAveragePrecisionResult:
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.464
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.637
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.203
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.284
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.497
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.629
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```pycon
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>>> import numpy as np
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>>> import supervision as sv
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>>> from supervision.metrics import MeanAveragePrecision
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>>> predictions = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0]),
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... confidence=np.array([0.9])
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... )
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>>> targets = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0])
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... )
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>>> map_metric = MeanAveragePrecision()
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>>> map_result = map_metric.update(predictions, targets).compute()
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>>> print(map_result) # doctest: +ELLIPSIS
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = ...
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = ...
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = ...
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = ...
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = ...
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = ...
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```
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"""
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if (
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@ -87,21 +87,38 @@ class MeanAverageRecallResult:
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Format as a pretty string.
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Example:
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```python
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print(mar_results)
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# MeanAverageRecallResult:
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# Metric target: MetricTarget.BOXES
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# mAR @ 1: 0.1362
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# mAR @ 10: 0.4239
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# mAR @ 100: 0.5241
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# max detections: [1 10 100]
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# IoU thresh: [0.5 0.55 0.6 ...]
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# mAR per class:
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# 0: [0.78571 0.78571 0.78571 ...]
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# ...
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# Small objects: ...
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# Medium objects: ...
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# Large objects: ...
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```pycon
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>>> import numpy as np
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>>> import supervision as sv
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>>> from supervision.metrics import MeanAverageRecall
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>>> predictions = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0]),
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... confidence=np.array([0.9])
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... )
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>>> targets = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0])
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... )
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>>> mar_metric = MeanAverageRecall()
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>>> mar_result = mar_metric.update(predictions, targets).compute()
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>>> print(mar_result) # doctest: +ELLIPSIS
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MeanAverageRecallResult:
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Metric target: MetricTarget.BOXES
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mAR @ 1: 1.0000
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mAR @ 10: 1.0000
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mAR @ 100: 1.0000
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max detections: [ 1 10 100]
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IoU thresh: [0.5 0.55 ... 0.95]
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mAR per class:
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0: [1. ... 1.]
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...
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Medium objects:
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MeanAverageRecallResult:
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Metric target: MetricTarget.BOXES
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mAR @ 1: 0.0000
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...
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```
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"""
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out_str = (
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@ -531,21 +531,41 @@ class PrecisionResult:
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Format as a pretty string.
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Example:
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```python
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print(precision_result)
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# PrecisionResult:
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# Metric target: MetricTarget.BOXES
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# Averaging method: AveragingMethod.WEIGHTED
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# P @ 50: 0.8099
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# P @ 75: 0.7969
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# P @ thresh: [0.80992 0.80905 0.80905 ...]
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# IoU thresh: [0.5 0.55 0.6 ...]
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# Precision per class:
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# 0: [0.64706 0.64706 0.64706 ...]
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# ...
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# Small objects: ...
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# Medium objects: ...
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# Large objects: ...
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```pycon
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>>> import numpy as np
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>>> import supervision as sv
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>>> from supervision.metrics import Precision
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>>> predictions = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0]),
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... confidence=np.array([0.9])
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... )
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>>> targets = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0])
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... )
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>>> precision_metric = Precision()
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>>> precision_result = precision_metric.update(
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... predictions, targets
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... ).compute()
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>>> print(precision_result) # doctest: +ELLIPSIS
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PrecisionResult:
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Metric target: MetricTarget.BOXES
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Averaging method: AveragingMethod.WEIGHTED
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P @ 50: 1.0000
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P @ 75: 1.0000
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P @ thresh: [1. ... 1.]
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IoU thresh: [0.5 0.55 ... 0.95]
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Precision per class:
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0: [1. ... 1.]
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...
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Medium objects:
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PrecisionResult:
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Metric target: MetricTarget.BOXES
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Averaging method: AveragingMethod.WEIGHTED
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P @ 50: 0.0000
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...
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```
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"""
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out_str = (
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@ -529,21 +529,39 @@ class RecallResult:
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Format as a pretty string.
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Example:
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```python
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print(recall_result)
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# RecallResult:
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# Metric target: MetricTarget.BOXES
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# Averaging method: AveragingMethod.WEIGHTED
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# R @ 50: 0.7615
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# R @ 75: 0.7462
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# R @ thresh: [0.76151 0.76011 0.76011 0.75732 ...]
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# IoU thresh: [0.5 0.55 0.6 ...]
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# Recall per class:
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# 0: [0.78571 0.78571 0.78571 ...]
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# ...
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# Small objects: ...
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# Medium objects: ...
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# Large objects: ...
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```pycon
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>>> import numpy as np
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>>> import supervision as sv
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>>> from supervision.metrics import Recall
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>>> predictions = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0]),
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... confidence=np.array([0.9])
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... )
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>>> targets = sv.Detections(
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... xyxy=np.array([[0, 0, 10, 10]]),
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... class_id=np.array([0])
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... )
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>>> recall_metric = Recall()
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>>> recall_result = recall_metric.update(predictions, targets).compute()
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>>> print(recall_result) # doctest: +ELLIPSIS
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RecallResult:
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Metric target: MetricTarget.BOXES
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Averaging method: AveragingMethod.WEIGHTED
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R @ 50: 1.0000
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R @ 75: 1.0000
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R @ thresh: [1. ... 1.]
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IoU thresh: [0.5 0.55 ... 0.95]
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Recall per class:
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0: [1. ... 1.]
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...
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Medium objects:
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RecallResult:
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Metric target: MetricTarget.BOXES
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Averaging method: AveragingMethod.WEIGHTED
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R @ 50: 0.0000
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...
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```
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"""
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out_str = (
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@ -24,12 +24,15 @@ def _get_logger(name: str = "supervision", level: int | None = None) -> logging.
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A configured `logging.Logger` instance.
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Example:
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```python
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from supervision.utils.logger import _get_logger
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```pycon
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>>> from supervision.utils.logger import _get_logger
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>>> import logging
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>>> logger = _get_logger("test_logger", level=logging.INFO)
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>>> logger.name
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'test_logger'
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>>> logger.level == logging.INFO
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True
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logger = _get_logger(__name__)
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logger.info("Processing started")
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logger.warning("File not found, using default")
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```
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"""
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if level is None:
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