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Date: Mon, 3 Feb 2025 07:04:19 +0000
Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=EF=B8=8F=20Speed=20up=20method=20`Rec?=
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---
 supervision/metrics/recall.py | 30 +++++++++++++++++-------------
 1 file changed, 17 insertions(+), 13 deletions(-)

diff --git a/supervision/metrics/recall.py b/supervision/metrics/recall.py
index b3586ff7d..0de53a183 100644
--- a/supervision/metrics/recall.py
+++ b/supervision/metrics/recall.py
@@ -334,27 +334,31 @@ class ids.
         num_thresholds = sorted_matches.shape[1]
         num_classes = unique_classes.shape[0]
 
+        # Initialize confusion matrix
         confusion_matrix = np.zeros((num_classes, num_thresholds, 3))
+
+        # Vectorized approach to handle the class-based confusion matrix updates
         for class_idx, class_id in enumerate(unique_classes):
-            is_class = sorted_prediction_class_ids == class_id
+            class_mask = sorted_prediction_class_ids == class_id
             num_true = class_counts[class_idx]
-            num_predictions = is_class.sum()
+            num_predictions = class_mask.sum()
 
             if num_predictions == 0:
-                true_positives = np.zeros(num_thresholds)
-                false_positives = np.zeros(num_thresholds)
-                false_negatives = np.full(num_thresholds, num_true)
+                confusion_matrix[class_idx, :, 0] = 0  # true_positives
+                confusion_matrix[class_idx, :, 1] = 0  # false_positives
+                confusion_matrix[class_idx, :, 2] = num_true  # false_negatives
             elif num_true == 0:
-                true_positives = np.zeros(num_thresholds)
-                false_positives = np.full(num_thresholds, num_predictions)
-                false_negatives = np.zeros(num_thresholds)
+                confusion_matrix[class_idx, :, 0] = 0  # true_positives
+                confusion_matrix[class_idx, :, 1] = num_predictions  # false_positives
+                confusion_matrix[class_idx, :, 2] = 0  # false_negatives
             else:
-                true_positives = sorted_matches[is_class].sum(0)
-                false_positives = (1 - sorted_matches[is_class]).sum(0)
+                true_positives = sorted_matches[class_mask].sum(axis=0)
+                false_positives = class_mask.sum() - true_positives
                 false_negatives = num_true - true_positives
-            confusion_matrix[class_idx] = np.stack(
-                [true_positives, false_positives, false_negatives], axis=1
-            )
+
+                confusion_matrix[class_idx, :, 0] = true_positives
+                confusion_matrix[class_idx, :, 1] = false_positives
+                confusion_matrix[class_idx, :, 2] = false_negatives
 
         return confusion_matrix