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⚡️ Speed up _one_of_ours() by 7% in scanpy/_utils/__init__.py #39

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@codeflash-ai codeflash-ai bot commented Mar 9, 2024

📄 _one_of_ours() in scanpy/_utils/__init__.py

📈 Performance improved by 7% (0.07x faster)

⏱️ Runtime went down from 1.60μs to 1.50μs

Explanation and details

(click to show)

When optimizing a program for speed, you generally want to minimize expensive operations and remove any unnecessary ones. In this function, we can achieve this by using the walrus operator ':=' (assignment expression) introduced in Python 3.8 and rearranging the conditions for early returning when possible.

Also, accessing attributes of an object using dot notation is generally faster than using the getattr function. Here is a refactored function.

The advantage of using this technique is that the function will return as soon as it finds that one of the conditions is not met without needing to check all the conditions. Furthermore, it reduces the need to call functions (like getattr) multiple times, which helps in improving the function's speed.

Correctness verification

The new optimized code was tested for correctness. The results are listed below.

🔘 (none found) − ⚙️ Existing Unit Tests

✅ 1 Passed − 🌀 Generated Regression Tests

(click to show generated tests)
# imports
import pytest
from scanpy._utils.__init__ import _one_of_ours

# unit tests

# Test with an object that has a __name__ attribute
def test_with_name_attribute():
    def sample_function():
        pass
    
    assert _one_of_ours(sample_function, "test") == sample_function.__module__.startswith("test")

# Test with an object without a __name__ attribute
def test_without_name_attribute():
    sample_object = object()
    assert not _one_of_ours(sample_object, "test")

# Test with an object with __name__ starting with an underscore
def test_name_starts_with_underscore():
    class _PrivateClass:
        pass
    
    assert not _one_of_ours(_PrivateClass, "test")

# Test with an object with __module__ attribute
def test_with_module_attribute():
    class SampleClass:
        pass
    
    assert _one_of_ours(SampleClass, SampleClass.__module__.split(".")[0])

# Test with an object without __module__ attribute but with __qualname__
def test_without_module_with_qualname():
    class SampleClass:
        class NestedClass:
            pass
    
    assert _one_of_ours(SampleClass.NestedClass, SampleClass.__module__.split(".")[0])

# Test with an object without __module__ and __qualname__ attributes
def test_without_module_and_qualname():
    SampleType = type("SampleType", (), {})
    assert not _one_of_ours(SampleType, "test")

# Test with an object with __module__ not starting with the specified root
def test_module_not_starting_with_root():
    import json  # standard library module, unlikely to start with "test"
    assert not _one_of_ours(json, "test")

# Test with an object with __module__ starting with the specified root
def test_module_starting_with_root():
    class SampleClass:
        pass
    
    root = SampleClass.__module__.split(".")[0]
    assert _one_of_ours(SampleClass, root)

# Test edge case with empty string for __name__
def test_empty_name_attribute():
    class SampleClass:
        __name__ = ""
    
    assert not _one_of_ours(SampleClass, "test")

# Test edge case with empty string for __module__ or __qualname__
def test_empty_module_or_qualname_attribute():
    class SampleClass:
        __module__ = ""
    
    assert not _one_of_ours(SampleClass, "test")

# Test edge case with None for __module__ or __qualname__
def test_none_module_or_qualname_attribute():
    class SampleClass:
        __module__ = None
    
    assert not _one_of_ours(SampleClass, "test")

When optimizing a program for speed, you generally want to minimize expensive operations and remove any unnecessary ones. In this function, we can achieve this by using the walrus operator ':=' (assignment expression) introduced in Python 3.8 and rearranging the conditions for early returning when possible.

Also, accessing attributes of an object using dot notation is generally faster than using the getattr function. Here is a refactored function.



The advantage of using this technique is that the function will return as soon as it finds that one of the conditions is not met without needing to check all the conditions. Furthermore, it reduces the need to call functions (like getattr) multiple times, which helps in improving the function's speed.
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by CodeFlash AI label Mar 9, 2024
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