⚡️ Speed up method Recall._make_empty_content by 8%

[codeflash-ai]

📄 8% (0.08x) speedup for Recall._make_empty_content in supervision/metrics/recall.py

⏱️ Runtime : 10.2 microseconds → 9.43 microseconds (best of 31 runs)

📝 Explanation and details

2. Efficient Use of Conditions: Used elif to avoid multiple if checks in _make_empty_content.

This optimized approach should run faster by avoiding redundant checks and using efficient numpy array operations.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 5 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	57.1%

🌀 Generated Regression Tests Details

from __future__ import annotations

from enum import Enum
from typing import List

import numpy as np
# imports
import pytest  # used for our unit tests
from supervision.detection.core import Detections
from supervision.metrics.core import AveragingMethod, Metric
from supervision.metrics.recall import Recall


class MetricTarget(Enum):
    """
    Specifies what type of detection is used to compute the metric.

    Attributes:
        BOXES: xyxy bounding boxes
        MASKS: Binary masks
        ORIENTED_BOUNDING_BOXES: Oriented bounding boxes (OBB)
    """

    BOXES = "boxes"
    MASKS = "masks"
    ORIENTED_BOUNDING_BOXES = "obb"
from supervision.metrics.recall import Recall

# unit tests

# Test cases for basic valid inputs



def test_make_empty_content_invalid_target():
    recall = Recall()
    recall._metric_target = "invalid"
    with pytest.raises(ValueError, match="Invalid metric target: invalid"):
        recall._make_empty_content()

# Edge cases

def test_make_empty_content_empty_metric_target():
    recall = Recall()
    recall._metric_target = None
    with pytest.raises(ValueError, match="Invalid metric target: None"):
        recall._make_empty_content()

# Performance and scalability



def test_make_empty_content_error_message():
    recall = Recall()
    recall._metric_target = "invalid"
    with pytest.raises(ValueError) as excinfo:
        recall._make_empty_content()

# Rare or unexpected edge cases











from __future__ import annotations

from enum import Enum
from typing import List

import numpy as np
# imports
import pytest  # used for our unit tests
from supervision.detection.core import Detections
from supervision.metrics.core import AveragingMethod, Metric
from supervision.metrics.recall import Recall


class MetricTarget(Enum):
    """
    Specifies what type of detection is used to compute the metric.

    Attributes:
        BOXES: xyxy bounding boxes
        MASKS: Binary masks
        ORIENTED_BOUNDING_BOXES: Oriented bounding boxes (OBB)
    """

    BOXES = "boxes"
    MASKS = "masks"
    ORIENTED_BOUNDING_BOXES = "obb"
from supervision.metrics.recall import Recall

# unit tests




def test_make_empty_content_invalid_metric_target():
    # Test when metric target is an invalid string
    recall = Recall()
    recall._metric_target = "invalid"
    with pytest.raises(ValueError, match="Invalid metric target: invalid"):
        recall._make_empty_content()

def test_make_empty_content_none_metric_target():
    # Test when metric target is None
    recall = Recall()
    recall._metric_target = None
    with pytest.raises(ValueError, match="Invalid metric target: None"):
        recall._make_empty_content()