⚡️ Speed up method SimpleModel.predict by 6%

[codeflash-ai]

📄 6% (0.06x) speedup for SimpleModel.predict in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 149 milliseconds → 140 milliseconds (best of 52 runs)

📝 Explanation and details

Here’s a significantly faster rewrite of your program, using NumPy for efficient vectorized computation. If you want to avoid any dependencies, let me know, but using NumPy is the most effective way to speed up this type of arithmetic-heavy operation in Python.

Why is this faster?

• Uses NumPy to avoid Python for-loops.
• Fast array broadcasting and memory-efficient computation.
• If data is large, this will be orders of magnitude faster than nested for-loops.

If you must stick with pure Python and lists:

This version is faster than the original due to use of a single flat list comprehension, minimizing intermediate Python bytecode and method call overhead.

Let me know your preference!

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 34 Passed
⏪ Replay Tests	✅ 1 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import pytest  # used for our unit tests
from workload import SimpleModel

# unit tests

# 1. BASIC TEST CASES

def test_predict_with_small_positive_integers():
    # Basic: Small list of positive integers
    data = [1, 2, 3]
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 77.9μs -> 72.6μs (7.31% faster)
    # For each x in data, output will be: [x*0, x*1, x*4, ..., x*249001] (500 values per x)
    # But the function returns [x0*i_sq for i_sq in i_squares] + [x1*i_sq for i_sq in i_squares] + ...
    # Actually, for each i_sq, it does [x*i_sq for x in data], so the output is:
    # [1*0, 2*0, 3*0, 1*1, 2*1, 3*1, ..., 1*249001, 2*249001, 3*249001]
    expected = []
    i_squares = [i * i for i in range(500)]
    for i_sq in i_squares:
        for x in data:
            expected.append(x * i_sq)

def test_predict_with_negative_and_zero():
    # Basic: Negative, zero, and positive values
    data = [-2, 0, 2]
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 78.9μs -> 73.9μs (6.65% faster)
    i_squares = [i * i for i in range(500)]
    expected = []
    for i_sq in i_squares:
        for x in data:
            expected.append(x * i_sq)

def test_predict_with_single_element():
    # Basic: Single element list
    data = [7]
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 51.6μs -> 49.3μs (4.76% faster)
    i_squares = [i * i for i in range(500)]
    expected = [7 * i_sq for i_sq in i_squares]

def test_predict_with_empty_list():
    # Basic: Empty input should return empty list
    data = []
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 35.7μs -> 36.0μs (0.834% slower)

def test_predict_with_floats():
    # Basic: List with float values
    data = [0.5, 2.0]
    i_squares = [i * i for i in range(500)]
    expected = []
    for i_sq in i_squares:
        for x in data:
            expected.append(x * i_sq)
    codeflash_output = SimpleModel.predict(data); out = codeflash_output

# 2. EDGE TEST CASES

def test_predict_with_large_integers():
    # Edge: Very large integers
    data = [10**6, -10**6]
    i_squares = [i * i for i in range(500)]
    expected = []
    for i_sq in i_squares:
        for x in data:
            expected.append(x * i_sq)
    codeflash_output = SimpleModel.predict(data); out = codeflash_output

def test_predict_with_all_zeros():
    # Edge: All zeros
    data = [0, 0, 0, 0]
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 87.6μs -> 79.4μs (10.3% faster)

def test_predict_with_mixed_types():
    # Edge: Mixed int and float types
    data = [1, 2.5, -3]
    i_squares = [i * i for i in range(500)]
    expected = []
    for i_sq in i_squares:
        for x in data:
            expected.append(x * i_sq)
    codeflash_output = SimpleModel.predict(data); out = codeflash_output

def test_predict_with_large_negative_numbers():
    # Edge: Large negative numbers
    data = [-999999]
    i_squares = [i * i for i in range(500)]
    expected = [-999999 * i_sq for i_sq in i_squares]
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 50.1μs -> 48.4μs (3.60% faster)

def test_predict_with_non_integer_floats():
    # Edge: Non-integer floats
    data = [0.1, -0.1]
    i_squares = [i * i for i in range(500)]
    expected = []
    for i_sq in i_squares:
        for x in data:
            expected.append(x * i_sq)
    codeflash_output = SimpleModel.predict(data); out = codeflash_output

# 3. LARGE SCALE TEST CASES

def test_predict_with_large_input_list():
    # Large: Input list with 999 elements
    data = list(range(1, 1000))
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 18.0ms -> 16.8ms (7.01% faster)
    # Check first and last few elements for correctness
    i_squares = [i * i for i in range(500)]

def test_predict_with_large_values_and_large_list():
    # Large: Large values and large list
    data = [10**5] * 500  # 500 elements, all 100000
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 8.63ms -> 8.03ms (7.50% faster)
    # Check a few random spots
    i_squares = [i * i for i in range(500)]

def test_predict_with_maximum_allowed_elements():
    # Large: Maximum allowed input size (999 elements)
    data = [1] * 999
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 17.3ms -> 16.3ms (6.46% faster)
    # All outputs should be equal for each i_sq, since all x=1
    i_squares = [i * i for i in range(500)]
    # For each i_sq, the next 999 values should be i_sq
    for idx, i_sq in enumerate(i_squares):
        start = idx * 999

def test_predict_performance_on_large_input():
    # Large: Test does not measure time, but ensures function completes and output shape is correct
    data = list(range(100, 1100))  # 1000 elements
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 17.8ms -> 16.8ms (5.95% faster)

# Additional edge case: input with repeated numbers
def test_predict_with_repeated_numbers():
    # Edge: Repeated numbers
    data = [5] * 10
    codeflash_output = SimpleModel.predict(data); out = codeflash_output # 170μs -> 154μs (9.81% faster)
    i_squares = [i * i for i in range(500)]
    for idx, i_sq in enumerate(i_squares):
        start = idx * 10
# 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 workload import SimpleModel

# unit tests

# 1. BASIC TEST CASES

def test_predict_empty_input():
    """
    Test that predict returns an empty list when input is empty.
    """
    codeflash_output = SimpleModel.predict([])

def test_predict_single_zero():
    """
    Test with a single zero in input.
    Should return a list of 500 zeros.
    """
    codeflash_output = SimpleModel.predict([0]); output = codeflash_output

def test_predict_single_one():
    """
    Test with a single one in input.
    Should return a list of squares from 0^2 to 499^2.
    """
    codeflash_output = SimpleModel.predict([1]); output = codeflash_output
    expected = [i*i for i in range(500)]

def test_predict_single_negative():
    """
    Test with a single negative number.
    Should return the negative squares.
    """
    codeflash_output = SimpleModel.predict([-2]); output = codeflash_output
    expected = [-2 * (i*i) for i in range(500)]

def test_predict_multiple_small_integers():
    """
    Test with a list of small integers.
    Should return the correct cross product.
    """
    data = [1, 2, 3]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    # For each i in 0..499, we have [1*i^2, 2*i^2, 3*i^2]
    expected = []
    for i in range(500):
        for x in data:
            expected.append(x * i*i)

def test_predict_floats():
    """
    Test with floating point numbers.
    Should multiply floats correctly.
    """
    data = [0.5, -1.5]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    expected = []
    for i in range(500):
        for x in data:
            expected.append(x * i*i)

# 2. EDGE TEST CASES

def test_predict_large_numbers():
    """
    Test with large numbers to check for overflow or precision issues.
    """
    data = [10**6, -10**6]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    expected = []
    for i in range(500):
        for x in data:
            expected.append(x * i*i)

def test_predict_large_input_list():
    """
    Test with a large input list (length 1000).
    Output should have 500*1000 = 500,000 elements.
    """
    data = list(range(1000))
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    # Spot-check a few values for correctness
    for idx in [0, 499, 123456, 499999]:
        i = idx // 1000
        x = idx % 1000

def test_predict_heterogeneous_types():
    """
    Test with a mix of ints and floats.
    """
    data = [1, 2.5, -3]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    expected = []
    for i in range(500):
        for x in data:
            expected.append(x * i*i)

def test_predict_single_element_list():
    """
    Test with a single element list (other than 0 or 1).
    """
    data = [7]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    expected = [7 * (i*i) for i in range(500)]

def test_predict_input_with_duplicates():
    """
    Test with duplicate values in input.
    """
    data = [2, 2, 2]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    expected = []
    for i in range(500):
        for x in data:
            expected.append(x * i*i)
    # Each value in output should be divisible by 2

def test_predict_input_with_all_zeros():
    """
    Test with all zeros in input.
    Should return all zeros in output.
    """
    data = [0] * 10
    codeflash_output = SimpleModel.predict(data); output = codeflash_output

def test_predict_input_with_min_max_int():
    """
    Test with minimum and maximum integer values.
    """
    import sys
    data = [sys.maxsize, -sys.maxsize-1]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    expected = []
    for i in range(500):
        for x in data:
            expected.append(x * i*i)

def test_predict_input_with_non_integer_types():
    """
    Test with types that support multiplication (e.g., bool).
    """
    data = [True, False]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    expected = []
    for i in range(500):
        for x in data:
            expected.append(x * i*i)

def test_predict_input_with_single_boolean():
    """
    Test with a single boolean value.
    """
    data = [True]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    expected = [int(True) * (i*i) for i in range(500)]

# 3. LARGE SCALE TEST CASES

def test_predict_large_data_and_performance():
    """
    Test with the largest allowed data set (1000 elements).
    Checks output length and spot-checks values.
    """
    data = [i for i in range(1000)]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    # Spot check: For i=123, x=456, position = 123*1000 + 456
    idx = 123 * 1000 + 456

def test_predict_performance_with_many_zeros_and_ones():
    """
    Test with a large list of zeros and ones.
    """
    data = [0]*500 + [1]*500
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    # Check a few outputs for i=1 (positions 1000:2000)
    for j in range(1000):
        expected = data[j] * (1*1)

def test_predict_large_negative_and_positive():
    """
    Test with a large array of negative and positive numbers.
    """
    data = [(-1)**i * i for i in range(1000)]
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    # Spot check: i=250, x=500, idx = 250*1000 + 500
    idx = 250*1000 + 500

def test_predict_large_all_same_value():
    """
    Test with a large array where all values are the same.
    """
    data = [42] * 1000
    codeflash_output = SimpleModel.predict(data); output = codeflash_output
    # All values at position k*1000 + j should be 42 * (k*k)
    for k in [0, 10, 499]:
        for j in [0, 999]:
            idx = k*1000 + j
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-SimpleModel.predict-mcl45pmz and push.