⚡️ Speed up function sorter by 764,385% in bubble_sort.py

[codeflash-ai]

📄 sorter() in bubble_sort.py

📈 Performance improved by 764,385% (7,643.85x faster)

⏱️ Runtime went down from 124 milliseconds to 16.2 microseconds (best of 1154 runs)

Explanation and details

Certainly! The original program uses a bubble sort algorithm, which is inefficient for large datasets. To improve the runtime, we can use Python's built-in sort method, which implements Timsort, an adaptive sorting algorithm that provides better performance. Here's the rewritten program.

This change will significantly reduce the running time of the sorting operation, especially for larger arrays.

Correctness verification

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

🔘 (none found) − ⚙️ Existing Unit Tests

✅ 39 Passed − 🌀 Generated Regression Tests

(click to show generated tests)

import pytest  # used for our unit tests
from bubble_sort import sorter

# unit tests

def test_basic_functionality():
    # Test with a small list of integers
    codeflash_output = sorter([3, 1, 2])
    # Test with a list of floating-point numbers
    codeflash_output = sorter([2.1, 3.3, 1.5])
    # Outputs were verified to be equal to the original implementation

def test_edge_cases():
    # Test with an empty list
    codeflash_output = sorter([])
    # Test with a single-element list
    codeflash_output = sorter([1])
    # Test with a list where all elements are the same
    codeflash_output = sorter([2, 2, 2])
    # Test with a list that is already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5])
    # Test with a list that is sorted in reverse order
    codeflash_output = sorter([5, 4, 3, 2, 1])
    # Outputs were verified to be equal to the original implementation

def test_mixed_data_types():
    # Test with a list of mixed data types that are comparable
    codeflash_output = sorter([1, 2.5, 0.5])
    # Outputs were verified to be equal to the original implementation

def test_negative_and_positive_numbers():
    # Test with a list containing both negative and positive integers
    codeflash_output = sorter([-1, 2, -3, 4])
    # Test with a list containing both negative and positive floating-point numbers
    codeflash_output = sorter([-1.1, 2.2, -3.3, 4.4])
    # Outputs were verified to be equal to the original implementation

def test_large_scale():
    # Test with a large list of random integers to assess performance
    large_list = list(range(10000, 9000, -1))
    codeflash_output = sorter(large_list)
    # Test with a large list of sorted integers to check efficiency
    sorted_large_list = list(range(1000))
    codeflash_output = sorter(sorted_large_list)
    # Outputs were verified to be equal to the original implementation

def test_stability():
    # Test with a list containing duplicate elements to ensure stability
    codeflash_output = sorter([3, 1, 2, 3, 1])
    # Outputs were verified to be equal to the original implementation

def test_boundary_values():
    # Test with the smallest and largest possible integers
    codeflash_output = sorter([0, -2147483648, 2147483647])
    # Outputs were verified to be equal to the original implementation

def test_non_comparable_elements():
    # Test with a list containing elements that cannot be directly compared
    with pytest.raises(TypeError):
        sorter([1, 'a', 3])
    with pytest.raises(TypeError):
        sorter([None, 2, 3])
    # Outputs were verified to be equal to the original implementation


def test_custom_objects():
    # Test with a list of custom objects without comparison methods
    class CustomObject:
        pass
    with pytest.raises(TypeError):
        sorter([CustomObject(), CustomObject()])
    # Outputs were verified to be equal to the original implementation

def test_immutable_vs_mutable_elements():
    # Test with a list containing both mutable and immutable elements
    with pytest.raises(TypeError):
        sorter([1, [2, 3], 4])
    # Outputs were verified to be equal to the original implementation

def test_mixed_case_sensitivity():
    # Test with a list of strings with mixed case sensitivity
    codeflash_output = sorter(['apple', 'Banana', 'cherry'])
    # Outputs were verified to be equal to the original implementation

def test_nan_values():
    # Test with a list containing NaN values
    import math
    nan_list = [1.0, float('nan'), 2.0]
    codeflash_output = sorter(nan_list)
    # Outputs were verified to be equal to the original implementation

def test_infinity_values():
    # Test with a list containing infinity values
    codeflash_output = sorter([1.0, float('inf'), float('-inf')])
    # Outputs were verified to be equal to the original implementation
import pytest  # used for our unit tests
from bubble_sort import sorter

# unit tests

# Basic Functionality
def test_standard_case_integers():
    # Test a list of integers in random order
    codeflash_output = sorter([3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5])
    # Outputs were verified to be equal to the original implementation

def test_standard_case_floats():
    # Test a list of floating-point numbers
    codeflash_output = sorter([3.1, 2.4, 5.6, 1.2, 4.8])
    # Outputs were verified to be equal to the original implementation

def test_standard_case_strings():
    # Test a list of strings
    codeflash_output = sorter(['banana', 'apple', 'cherry', 'date'])
    # Outputs were verified to be equal to the original implementation

# Edge Cases
def test_empty_list():
    # Test an empty list
    codeflash_output = sorter([])
    # Outputs were verified to be equal to the original implementation

def test_single_element():
    # Test a list with a single element
    codeflash_output = sorter([42])
    # Outputs were verified to be equal to the original implementation

def test_two_elements_sorted():
    # Test a list with two elements already sorted
    codeflash_output = sorter([1, 2])
    # Outputs were verified to be equal to the original implementation

def test_two_elements_unsorted():
    # Test a list with two elements unsorted
    codeflash_output = sorter([2, 1])
    # Outputs were verified to be equal to the original implementation

def test_all_identical_elements():
    # Test a list where all elements are identical
    codeflash_output = sorter([7, 7, 7, 7, 7])
    # Outputs were verified to be equal to the original implementation

# Special Cases
def test_already_sorted_list():
    # Test a list that is already sorted
    codeflash_output = sorter([1, 2, 3, 4, 5])
    # Outputs were verified to be equal to the original implementation

def test_reverse_sorted_list():
    # Test a list that is reverse sorted
    codeflash_output = sorter([5, 4, 3, 2, 1])
    # Outputs were verified to be equal to the original implementation

def test_negative_numbers():
    # Test a list with negative numbers
    codeflash_output = sorter([-3, -1, -4, -2, -5])
    # Outputs were verified to be equal to the original implementation

def test_mixed_positive_negative_numbers():
    # Test a list with mixed positive and negative numbers
    codeflash_output = sorter([-1, 2, -3, 4, -5])
    # Outputs were verified to be equal to the original implementation

# Large Scale Test Cases
def test_large_list():
    # Test a large list with 1000 elements in random order
    large_list = list(range(1000, 0, -1))
    codeflash_output = sorter(large_list)
    # Outputs were verified to be equal to the original implementation

def test_large_identical_elements():
    # Test a large list with 1000 identical elements
    large_identical_list = [1] * 1000
    codeflash_output = sorter(large_identical_list)
    # Outputs were verified to be equal to the original implementation

def test_large_reverse_sorted_list():
    # Test a large list with 1000 elements in descending order
    large_reverse_list = list(range(1000, 0, -1))
    codeflash_output = sorter(large_reverse_list)
    # Outputs were verified to be equal to the original implementation

# Robustness
def test_non_comparable_elements():
    # Test a list with non-comparable elements
    with pytest.raises(TypeError):
        sorter([1, 'string', 3.0])
    # Outputs were verified to be equal to the original implementation

# Deterministic Behavior
def test_deterministic_behavior():
    # Test that the same input always yields the same output
    input_list = [3, 1, 4, 1, 5, 9, 2, 6, 5, 3, 5]
    expected_output = [1, 1, 2, 3, 3, 4, 5, 5, 5, 6, 9]
    codeflash_output = sorter(input_list)
    codeflash_output = sorter(input_list)  # Run again to check determinism
    # Outputs were verified to be equal to the original implementation

🔘 (none found) − ⏪ Replay Tests