246 lines
9.2 KiB
Python
246 lines
9.2 KiB
Python
import numpy as np
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import pytest
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from supervision.detection.core import Detections
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from supervision.metrics.core import AveragingMethod, MetricTarget
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from supervision.metrics.recall import Recall
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from tests.helpers import assert_almost_equal
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class TestRecall:
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@pytest.fixture
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def predictions_multiple_classes(self):
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return Detections(
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xyxy=np.array(
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[
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[10, 10, 50, 50], # class 0, matches first target
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[200, 200, 240, 240], # class 1, matches target
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],
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dtype=np.float32,
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),
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confidence=np.array([0.9, 0.8]),
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class_id=np.array([0, 1]),
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)
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@pytest.fixture
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def targets_multiple_classes(self):
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return Detections(
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xyxy=np.array(
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[
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[10, 10, 50, 50], # class 0, matched
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[60, 60, 100, 100], # class 0, missed
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[200, 200, 240, 240], # class 1, matched
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],
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dtype=np.float32,
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),
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class_id=np.array([0, 0, 1]),
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)
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def test_initialization_default(self):
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"""Test that Recall can be initialized with default parameters"""
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metric = Recall()
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assert metric._metric_target == MetricTarget.BOXES
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assert metric.averaging_method == AveragingMethod.WEIGHTED
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assert metric._predictions_list == []
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assert metric._targets_list == []
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def test_initialization_custom(self):
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"""Test that Recall can be initialized with custom parameters"""
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metric = Recall(
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metric_target=MetricTarget.MASKS,
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averaging_method=AveragingMethod.MACRO,
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)
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assert metric._metric_target == MetricTarget.MASKS
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assert metric.averaging_method == AveragingMethod.MACRO
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def test_reset(self, dummy_prediction):
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"""Test that reset() clears all stored data"""
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metric = Recall()
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# Add some dummy data
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metric.update(dummy_prediction, dummy_prediction)
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# Verify data was added
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assert len(metric._predictions_list) == 1
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assert len(metric._targets_list) == 1
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# Reset and verify lists are empty
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metric.reset()
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assert metric._predictions_list == []
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assert metric._targets_list == []
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def test_perfect_match(self, detections_50_50, targets_50_50):
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"""Test recall with perfect matching predictions and targets"""
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metric = Recall()
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result = metric.update(detections_50_50, targets_50_50).compute()
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# Perfect match should give recall = 1.0
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# TP = 1, FN = 0 -> recall = TP / (TP + FN) = 1 / 1 = 1.0
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assert result.recall_at_50 == 1.0
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assert result.recall_at_75 == 1.0
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assert len(result.matched_classes) == 1
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assert result.matched_classes[0] == 0
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def test_no_overlap(self, predictions_no_overlap, targets_no_overlap):
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"""Test recall with predictions that don't overlap with targets"""
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metric = Recall()
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result = metric.update(predictions_no_overlap, targets_no_overlap).compute()
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# No overlap means no TP, only FN
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# TP = 0, FN = 1 -> recall = TP / (TP + FN) = 0 / 1 = 0.0
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assert result.recall_at_50 == 0.0
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assert result.recall_at_75 == 0.0
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def test_empty_predictions(self, targets_50_50):
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"""Test recall with empty predictions but existing targets"""
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predictions = Detections.empty()
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metric = Recall()
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result = metric.update(predictions, targets_50_50).compute()
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# No predictions means TP = 0, FN = 1 -> recall = 0 / 1 = 0.0
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assert result.recall_at_50 == 0.0
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assert result.recall_at_75 == 0.0
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def test_empty_targets(self, detections_50_50):
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"""Test recall with predictions but no targets"""
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targets = Detections.empty()
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metric = Recall()
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result = metric.update(detections_50_50, targets).compute()
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# No targets means TP = 0, FN = 0 -> recall = 0 / 0 = 0
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assert result.recall_at_50 == 0.0
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assert result.recall_at_75 == 0.0
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def test_single_class_missed_detections(
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self, detections_50_50, targets_two_objects_class_0
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):
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"""Test recall calculation with some missed detections"""
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metric = Recall()
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result = metric.update(detections_50_50, targets_two_objects_class_0).compute()
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# TP = 1 (first target matched), FN = 1 (second target missed)
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# recall = TP / (TP + FN) = 1 / 2 = 0.5
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assert_almost_equal(result.recall_at_50, 0.5)
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assert_almost_equal(result.recall_at_75, 0.5)
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def test_multiple_classes(
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self, predictions_multiple_classes, targets_multiple_classes
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):
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"""Test recall calculation for multiple classes"""
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metric = Recall()
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result = metric.update(
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predictions_multiple_classes, targets_multiple_classes
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).compute()
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# Class 0: TP=1, FN=1 -> recall=0.5 (weight=2)
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# Class 1: TP=1, FN=0 -> recall=1.0 (weight=1)
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# Weighted avg: (2*0.5 + 1*1.0) / (2+1) = 2.0/3 = 0.6667
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expected_recall = (2 * 0.5 + 1 * 1.0) / (2 + 1)
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assert_almost_equal(result.recall_at_50, expected_recall)
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assert_almost_equal(result.recall_at_75, expected_recall)
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assert len(result.matched_classes) == 2
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assert 0 in result.matched_classes
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assert 1 in result.matched_classes
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def test_different_iou_thresholds(self, predictions_iou_064, targets_iou_064):
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"""Test recall at different IoU thresholds"""
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metric = Recall()
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result = metric.update(predictions_iou_064, targets_iou_064).compute()
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# IoU = 0.64 > 0.5 but < 0.75
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# Should match at IoU 0.5 but not at 0.75
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assert result.recall_at_50 == 1.0 # TP=1, FN=0
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assert result.recall_at_75 == 0.0 # TP=0, FN=1
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def test_confidence_ranking(self, predictions_confidence_ranking, targets_50_50):
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"""Test that higher confidence predictions are preferred for matching"""
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metric = Recall()
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result = metric.update(predictions_confidence_ranking, targets_50_50).compute()
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# Target should be matched (by higher confidence prediction)
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# TP = 1, FN = 0 -> recall = 1.0
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assert result.recall_at_50 == 1.0
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def test_multiple_predictions_one_target(
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self, predictions_confidence_ranking, targets_50_50
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):
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"""Test recall when multiple predictions compete for one target"""
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metric = Recall()
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result = metric.update(predictions_confidence_ranking, targets_50_50).compute()
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# Target should be matched exactly once
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# TP = 1, FN = 0 -> recall = 1.0
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assert result.recall_at_50 == 1.0
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def test_list_inputs(
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self, detections_50_50, targets_50_50, prediction_class_1, target_class_1
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):
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"""Test recall with list inputs"""
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metric = Recall()
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result = metric.update(
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[detections_50_50, prediction_class_1], [targets_50_50, target_class_1]
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).compute()
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# Perfect matches for both
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assert result.recall_at_50 == 1.0
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assert result.recall_at_75 == 1.0
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def test_mismatched_list_lengths(self, detections_50_50, targets_50_50):
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"""Test that mismatched prediction/target list lengths raise error"""
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metric = Recall()
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# Should raise ValueError for mismatched lengths
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with pytest.raises(ValueError, match="number of predictions"):
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metric.update([detections_50_50], [targets_50_50, targets_50_50])
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@pytest.mark.parametrize(
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"averaging_method",
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[AveragingMethod.MACRO, AveragingMethod.MICRO, AveragingMethod.WEIGHTED],
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)
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def test_averaging_methods(self, averaging_method, detections_50_50, targets_50_50):
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"""Test different averaging methods"""
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metric = Recall(averaging_method=averaging_method)
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result = metric.update(detections_50_50, targets_50_50).compute()
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# Perfect match should give 1.0 regardless of averaging method
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assert result.recall_at_50 == 1.0
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assert result.averaging_method == averaging_method
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def test_macro_averaging(self):
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"""Test MACRO averaging with specific example"""
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# Class 0: 1/2 targets matched -> recall = 0.5
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# Class 1: 1/1 targets matched -> recall = 1.0
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# Macro average: (0.5 + 1.0) / 2 = 0.75
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predictions = Detections(
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xyxy=np.array(
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[
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[10, 10, 50, 50], # matches class 0 target 1
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[200, 200, 240, 240], # matches class 1 target
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],
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dtype=np.float32,
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),
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confidence=np.array([0.9, 0.8]),
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class_id=np.array([0, 1]),
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)
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targets = Detections(
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xyxy=np.array(
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[
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[10, 10, 50, 50], # class 0, matched
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[60, 60, 100, 100], # class 0, missed
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[200, 200, 240, 240], # class 1, matched
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],
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dtype=np.float32,
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),
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class_id=np.array([0, 0, 1]),
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)
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metric = Recall(averaging_method=AveragingMethod.MACRO)
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result = metric.update(predictions, targets).compute()
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# Macro average: (0.5 + 1.0) / 2 = 0.75
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assert result.recall_at_50 == 0.75
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