⚡️ Speed up method Recall.update by 18%

[codeflash-ai]

📄 18% (0.18x) speedup for Recall.update in supervision/metrics/recall.py

⏱️ Runtime : 18.5 microseconds → 15.7 microseconds (best of 121 runs)

📝 Explanation and details

Here is the optimized version of the provided Python code. The main focus is to improve the efficiency of the update function.

Changes.

1. Combined the checks and conversions to list for predictions and targets into a more concise and direct manner to reduce redundant checks and improve readability.
2. Directly converted predictions and targets to lists if they were not already.
3. Ensured that list length validation and extension operations are efficient and straightforward.

These changes ensure that the update function performs fewer checks and operations, optimizing the runtime slightly, while still maintaining the same functionality and integrity of the original code.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 12 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

from __future__ import annotations

from typing import List, Union
from unittest.mock import MagicMock

# imports
import pytest  # used for our unit tests
from supervision.metrics.core import AveragingMethod, Metric, MetricTarget
from supervision.metrics.recall import Recall


class Detections:
    pass  # Mock class for Detections
from supervision.metrics.recall import Recall

# unit tests

# Basic Functionality
def test_single_prediction_and_target():
    recall = Recall()
    prediction = Detections()
    target = Detections()
    recall.update(prediction, target)

def test_multiple_predictions_and_targets():
    recall = Recall()
    prediction1 = Detections()
    prediction2 = Detections()
    target1 = Detections()
    target2 = Detections()
    recall.update([prediction1, prediction2], [target1, target2])

# Edge Cases
def test_empty_inputs():
    recall = Recall()
    recall.update([], [])

def test_mismatched_lengths():
    recall = Recall()
    prediction = Detections()
    target1 = Detections()
    target2 = Detections()
    with pytest.raises(ValueError):
        recall.update([prediction], [target1, target2])

# Data Types and Structures



def test_large_scale_performance():
    recall = Recall()
    large_predictions = [Detections() for _ in range(1000)]
    large_targets = [Detections() for _ in range(1000)]
    recall.update(large_predictions, large_targets)

# State Management
def test_multiple_updates():
    recall = Recall()
    prediction1 = Detections()
    target1 = Detections()
    prediction2 = Detections()
    target2 = Detections()
    recall.update([prediction1], [target1])
    recall.update([prediction2], [target2])

# Method Chaining
def test_method_chaining():
    recall = Recall()
    prediction = Detections()
    target = Detections()
    codeflash_output = recall.update(prediction, target)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from __future__ import annotations

from typing import List, Union

# imports
import pytest  # used for our unit tests
from supervision.metrics.recall import Recall


# Mocked classes for testing
class Detections:
    pass

class AveragingMethod:
    WEIGHTED = "weighted"

class MetricTarget:
    BOXES = "boxes"

class Metric:
    pass
from supervision.metrics.recall import Recall

# unit tests

# Basic Functionality
def test_single_prediction_and_target():
    recall = Recall()
    prediction = Detections()
    target = Detections()
    recall.update(prediction, target)

def test_multiple_predictions_and_targets():
    recall = Recall()
    predictions = [Detections(), Detections()]
    targets = [Detections(), Detections()]
    recall.update(predictions, targets)

# Type Handling
def test_prediction_single_target_list():
    recall = Recall()
    prediction = Detections()
    targets = [Detections()]
    recall.update(prediction, targets)

def test_prediction_list_target_single():
    recall = Recall()
    predictions = [Detections()]
    target = Detections()
    recall.update(predictions, target)

# Edge Cases
def test_empty_lists():
    recall = Recall()
    predictions = []
    targets = []
    recall.update(predictions, targets)

def test_mismatched_lengths():
    recall = Recall()
    predictions = [Detections()]
    targets = [Detections(), Detections()]
    with pytest.raises(ValueError):
        recall.update(predictions, targets)

# Large Scale Test Cases
def test_large_number_of_predictions_and_targets():
    recall = Recall()
    predictions = [Detections() for _ in range(1000)]
    targets = [Detections() for _ in range(1000)]
    recall.update(predictions, targets)

# Invalid Inputs


def test_multiple_updates():
    recall = Recall()
    recall.update([Detections()], [Detections()])
    recall.update([Detections()], [Detections()])

# Exception Handling