⚡️ Speed up function matrix_sum by 50%

[codeflash-ai]

📄 50% (0.50x) speedup for matrix_sum in src/dsa/various.py

⏱️ Runtime : 27.0 milliseconds → 18.0 milliseconds (best of 61 runs)

📝 Explanation and details

The optimization eliminates redundant computation by using Python's walrus operator (:=) to compute each row sum only once instead of twice.

Key optimization: The original code calls sum(matrix[i]) twice for each row - once in the condition if sum(matrix[i]) > 0 and again in the list comprehension [sum(matrix[i]) for i in range(len(matrix))]. The optimized version uses the walrus operator (row_sum := sum(row)) to compute the sum once, store it in row_sum, and reuse it in both the condition and the result list.

Additional improvements:

• Direct iteration over matrix instead of range(len(matrix)) eliminates index lookups
• More readable and Pythonic code structure

Why this leads to speedup: The optimization reduces time complexity from O(2nm) to O(nm) where n is the number of rows and m is the average row length, since each element is now summed only once instead of twice. This is particularly effective for larger matrices, as shown in the test results where large-scale tests show 80-99% speedups.

Test case performance patterns:

• Small matrices: 15-30% speedup due to reduced function call overhead
• Large matrices: 80-99% speedup where the redundant computation elimination dominates
• Sparse matrices with many zeros: Minimal speedup (~0-1%) since most rows are filtered out anyway, making the redundant computation less significant
• Edge cases (empty matrices, single elements): 30-50% speedup from eliminating indexing overhead

The optimization is most beneficial for matrices with many rows that pass the positive sum filter, where the double computation penalty is most pronounced.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 53 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	✅ 1 Passed
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from src.dsa.various import matrix_sum

# unit tests

# ---------------- Basic Test Cases ----------------

def test_single_element_matrix():
    # Matrix with one row and one column
    codeflash_output = matrix_sum([[5]]) # 375ns -> 291ns (28.9% faster)
    codeflash_output = matrix_sum([[-3]]) # 167ns -> 166ns (0.602% faster)
    codeflash_output = matrix_sum([[0]]) # 125ns -> 125ns (0.000% faster)

def test_single_row_matrix():
    # Matrix with a single row
    codeflash_output = matrix_sum([[1, 2, 3]]) # 375ns -> 291ns (28.9% faster)
    codeflash_output = matrix_sum([[0, 0, 0]]) # 166ns -> 166ns (0.000% faster)
    codeflash_output = matrix_sum([[-1, -2, -3]]) # 167ns -> 167ns (0.000% faster)

def test_single_column_matrix():
    # Matrix with a single column
    codeflash_output = matrix_sum([[1], [2], [3]]) # 541ns -> 416ns (30.0% faster)
    codeflash_output = matrix_sum([[0], [0], [0]]) # 291ns -> 250ns (16.4% faster)
    codeflash_output = matrix_sum([[-5], [5], [0]]) # 250ns -> 208ns (20.2% faster)

def test_regular_matrix():
    # Matrix with multiple rows and columns
    matrix = [
        [1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]
    ]
    codeflash_output = matrix_sum(matrix) # 542ns -> 416ns (30.3% faster)

def test_matrix_with_negative_numbers():
    # Matrix containing negative numbers
    matrix = [
        [-1, -2, -3],
        [4, -5, 6],
        [7, 8, -9]
    ]
    codeflash_output = matrix_sum(matrix) # 583ns -> 500ns (16.6% faster)

def test_matrix_with_zeros():
    # Matrix containing zeros
    matrix = [
        [0, 0, 0],
        [0, 1, 2],
        [3, 0, 4]
    ]
    codeflash_output = matrix_sum(matrix) # 500ns -> 375ns (33.3% faster)

# ---------------- Edge Test Cases ----------------

def test_empty_matrix():
    # Empty matrix should return an empty list
    codeflash_output = matrix_sum([]) # 250ns -> 166ns (50.6% faster)

def test_matrix_with_empty_rows():
    # Matrix with empty rows
    codeflash_output = matrix_sum([[]]) # 333ns -> 291ns (14.4% faster)
    codeflash_output = matrix_sum([[], []]) # 208ns -> 167ns (24.6% faster)

def test_matrix_with_mixed_empty_and_nonempty_rows():
    # Matrix with a mix of empty and non-empty rows
    matrix = [
        [],
        [1, 2, 3],
        [],
        [4],
        []
    ]
    codeflash_output = matrix_sum(matrix) # 541ns -> 459ns (17.9% faster)

def test_matrix_with_large_and_small_numbers():
    # Matrix with very large and very small (negative) numbers
    matrix = [
        [10**9, -10**9, 1],
        [-10**18, 10**18, 0],
        [2**63-1, -2**63+1]
    ]
    codeflash_output = matrix_sum(matrix) # 625ns -> 500ns (25.0% faster)

def test_matrix_with_non_uniform_row_lengths():
    # Matrix with rows of different lengths
    matrix = [
        [1, 2],
        [3],
        [4, 5, 6],
        []
    ]
    codeflash_output = matrix_sum(matrix) # 584ns -> 458ns (27.5% faster)

def test_matrix_all_zeros():
    # Matrix where all elements are zero
    matrix = [
        [0, 0],
        [0, 0],
        [0, 0]
    ]
    codeflash_output = matrix_sum(matrix) # 417ns -> 416ns (0.240% faster)

def test_matrix_with_single_empty_row():
    # Matrix with only one empty row
    codeflash_output = matrix_sum([[]]) # 292ns -> 291ns (0.344% faster)

# ---------------- Large Scale Test Cases ----------------

def test_large_matrix_all_ones():
    # Large matrix (1000x1000) with all elements = 1
    size = 1000
    matrix = [[1]*size for _ in range(size)]
    expected = [size]*size  # Each row sums to 1000
    codeflash_output = matrix_sum(matrix) # 3.78ms -> 2.10ms (80.4% faster)

def test_large_matrix_increasing_rows():
    # Large matrix with increasing row values
    size = 1000
    matrix = [list(range(i, i+size)) for i in range(size)]
    expected = [sum(range(i, i+size)) for i in range(size)]
    codeflash_output = matrix_sum(matrix) # 4.26ms -> 2.22ms (92.1% faster)

def test_large_matrix_sparse():
    # Large matrix with mostly zeros and a few non-zero values
    size = 1000
    matrix = [[0]*size for _ in range(size)]
    # Set a few non-zero values
    matrix[0][0] = 999
    matrix[999][999] = -999
    matrix[500][500] = 12345
    expected = [999] + [0]*499 + [12345] + [0]*498 + [-999]
    codeflash_output = matrix_sum(matrix) # 2.10ms -> 2.08ms (0.797% faster)

def test_large_matrix_alternating_signs():
    # Large matrix with alternating 1 and -1 in each row
    size = 1000
    matrix = [[1 if j % 2 == 0 else -1 for j in range(size)] for _ in range(size)]
    # Each row sum is 0 if size is even, else 1
    expected = [0]*size if size % 2 == 0 else [1]*size
    codeflash_output = matrix_sum(matrix) # 2.04ms -> 2.03ms (0.277% faster)

# ---------------- Invalid Input/Type Edge Cases ----------------


def test_matrix_with_non_list_rows():
    # Matrix with a row that's not a list should raise TypeError
    matrix = [
        [1, 2, 3],
        "not a list",
        [4, 5, 6]
    ]
    with pytest.raises(TypeError):
        matrix_sum(matrix) # 1.17μs -> 1.08μs (7.66% faster)

def test_matrix_with_non_iterable_rows():
    # Matrix with a non-iterable row should raise TypeError
    matrix = [
        [1, 2, 3],
        123,
        [4, 5, 6]
    ]
    with pytest.raises(TypeError):
        matrix_sum(matrix) # 750ns -> 708ns (5.93% faster)

def test_matrix_with_none():
    # Matrix is None should raise TypeError
    with pytest.raises(TypeError):
        matrix_sum(None) # 541ns -> 416ns (30.0% faster)

def test_matrix_with_row_none():
    # Matrix with a row as None should raise TypeError
    matrix = [
        [1, 2, 3],
        None,
        [4, 5, 6]
    ]
    with pytest.raises(TypeError):
        matrix_sum(matrix) # 791ns -> 625ns (26.6% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import pytest  # used for our unit tests
from src.dsa.various import matrix_sum

# unit tests

# -------------------
# 1. Basic Test Cases
# -------------------

def test_single_row_single_column():
    # Single element matrix
    codeflash_output = matrix_sum([[5]]) # 375ns -> 291ns (28.9% faster)

def test_single_row_multiple_columns():
    # Single row, multiple columns
    codeflash_output = matrix_sum([[1, 2, 3, 4]]) # 417ns -> 292ns (42.8% faster)

def test_multiple_rows_single_column():
    # Multiple rows, single column
    codeflash_output = matrix_sum([[1], [2], [3]]) # 541ns -> 416ns (30.0% faster)

def test_multiple_rows_multiple_columns():
    # Multiple rows, multiple columns
    codeflash_output = matrix_sum([[1, 2], [3, 4], [5, 6]]) # 542ns -> 416ns (30.3% faster)

def test_negative_numbers():
    # Matrix with negative numbers
    codeflash_output = matrix_sum([[1, -1], [-2, -3], [4, 5]]) # 500ns -> 417ns (19.9% faster)

def test_zeros_in_matrix():
    # Matrix with zeros
    codeflash_output = matrix_sum([[0, 0, 0], [1, 0, 2], [0, 3, 0]]) # 500ns -> 416ns (20.2% faster)

def test_empty_rows():
    # Matrix with empty rows
    codeflash_output = matrix_sum([[], [], []]) # 375ns -> 375ns (0.000% faster)

def test_empty_matrix():
    # Completely empty matrix
    codeflash_output = matrix_sum([]) # 250ns -> 166ns (50.6% faster)

# -------------------
# 2. Edge Test Cases
# -------------------

def test_large_positive_and_negative_values():
    # Matrix with very large positive and negative values
    codeflash_output = matrix_sum([[10**9, -10**9], [2**31-1, -2**31+1]]) # 417ns -> 416ns (0.240% faster)

def test_row_with_all_negative_numbers():
    # Row with all negative numbers
    codeflash_output = matrix_sum([[-1, -2, -3], [4, 5, 6]]) # 458ns -> 458ns (0.000% faster)

def test_row_with_all_zeros():
    # Row with all zeros
    codeflash_output = matrix_sum([[0, 0, 0], [1, 2, 3]]) # 416ns -> 334ns (24.6% faster)

def test_jagged_matrix():
    # Matrix with jagged (uneven) rows
    codeflash_output = matrix_sum([[1, 2], [3], [4, 5, 6]]) # 500ns -> 416ns (20.2% faster)

def test_matrix_with_empty_and_nonempty_rows():
    # Matrix with a mix of empty and non-empty rows
    codeflash_output = matrix_sum([[], [1, 2, 3], []]) # 458ns -> 375ns (22.1% faster)

def test_matrix_with_one_empty_row():
    # Matrix with only one empty row
    codeflash_output = matrix_sum([[]]) # 292ns -> 291ns (0.344% faster)

def test_matrix_with_single_zero_element():
    # Matrix with a single zero
    codeflash_output = matrix_sum([[0]]) # 375ns -> 291ns (28.9% faster)

def test_matrix_with_large_number_of_zeros():
    # Matrix with a large number of zeros in one row
    codeflash_output = matrix_sum([[0]*1000]) # 2.33μs -> 2.33μs (0.043% faster)

# --------------------------
# 3. Large Scale Test Cases
# --------------------------

def test_large_matrix_all_ones():
    # Large matrix (1000x1000) with all ones
    matrix = [[1]*1000 for _ in range(1000)]
    codeflash_output = matrix_sum(matrix); result = codeflash_output # 4.19ms -> 2.10ms (99.3% faster)

def test_large_matrix_increasing_rows():
    # Large matrix with increasing row lengths and values
    matrix = [list(range(i)) for i in range(1, 1001)]  # 1000 rows
    codeflash_output = matrix_sum(matrix); result = codeflash_output # 2.16ms -> 1.10ms (96.9% faster)
    # Each row sum is sum of 0..i-1 = i*(i-1)//2
    expected = [i*(i-1)//2 for i in range(1, 1001)]

def test_large_matrix_alternating_signs():
    # Large matrix with alternating +1 and -1
    matrix = [[1 if j % 2 == 0 else -1 for j in range(1000)] for _ in range(1000)]
    codeflash_output = matrix_sum(matrix); result = codeflash_output # 2.04ms -> 2.04ms (0.264% faster)
    # Each row should sum to 0 if even length, else 1
    expected = [0]*1000 if 1000 % 2 == 0 else [1]*1000

def test_large_matrix_sparse_nonzero():
    # Large matrix with mostly zeros and a single one in each row
    matrix = [[0]*999 + [1] for _ in range(1000)]
    codeflash_output = matrix_sum(matrix); result = codeflash_output # 4.26ms -> 2.14ms (98.8% faster)

def test_large_matrix_mixed_values():
    # Large matrix with mixed positive and negative numbers
    matrix = [[(-1)**(i+j) for j in range(1000)] for i in range(1000)]
    codeflash_output = matrix_sum(matrix); result = codeflash_output # 2.12ms -> 2.11ms (0.397% faster)

# -------------------
# 4. Additional Robustness
# -------------------

def test_matrix_with_non_integer_values():
    # Matrix with float values (should work with floats as well)
    codeflash_output = matrix_sum([[1.5, 2.5], [3.0, -1.0]]) # 792ns -> 625ns (26.7% faster)

def test_matrix_with_boolean_values():
    # Matrix with boolean values (True=1, False=0)
    codeflash_output = matrix_sum([[True, False, True], [False, False]]) # 458ns -> 334ns (37.1% faster)

def test_matrix_with_nested_empty_lists():
    # Matrix with nested empty lists (should treat as zero)
    codeflash_output = matrix_sum([[], [], []]) # 375ns -> 375ns (0.000% faster)

def test_matrix_with_large_empty_rows_and_nonempty_rows():
    # Large matrix with empty and non-empty rows
    matrix = [[] if i % 2 == 0 else [i] for i in range(1000)]
    expected = [0 if i % 2 == 0 else i for i in range(1000)]
    codeflash_output = matrix_sum(matrix) # 46.0μs -> 29.6μs (55.4% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from src.dsa.various import matrix_sum

def test_matrix_sum():
    matrix_sum([[], [401]])

To edit these changes git checkout codeflash/optimize-matrix_sum-mdpc58n8 and push.