⚡️ Speed up method AlexNet._extract_features by 754%

[codeflash-ai]

📄 754% (7.54x) speedup for AlexNet._extract_features in code_to_optimize/code_directories/simple_tracer_e2e/workload.py

⏱️ Runtime : 91.8 microseconds → 10.7 microseconds (best of 137 runs)

📝 Explanation and details

Here’s an optimized version of your code. The original _extract_features method is an (empty) O(N) for-loop, which is essentially a waste if the real feature extraction logic is not provided. For demonstration, I'll keep the functionally-correct placeholder, but will replace the loop with a slice if you intend to return an empty list of the same (zero) behavior, improving speed.

Explanation:

• The for loop, as written, does nothing except iterate and burn time.
• Returning an empty list is equivalent to the old function.
• No for-loop is needed, yielding fastest runtime and memory usage for this specific logic.
• All comments are preserved unless modified for clarity or due to code change.

If you do have real feature extraction logic, paste that for further optimization of the computational part. As profiled, your bottleneck was the unnecessary for-loop.

This is fully optimal for the code as posted.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 54 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 AlexNet

# unit tests

# 1. Basic Test Cases

def test_extract_features_single_sample():
    # Test with a single sample of positive integers
    net = AlexNet()
    x = [[1, 2, 3, 4, 5]]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.28μs -> 371ns (246% faster)

def test_extract_features_multiple_samples():
    # Test with multiple samples of different ranges
    net = AlexNet()
    x = [
        [1, 2, 3],
        [10, 20, 30],
        [5]
    ]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.18μs -> 371ns (219% faster)

def test_extract_features_negative_numbers():
    # Test with negative numbers
    net = AlexNet()
    x = [[-1, -2, -3, -4]]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.22μs -> 351ns (248% faster)

def test_extract_features_floats():
    # Test with floating point numbers
    net = AlexNet()
    x = [[1.5, 2.5, 3.5]]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.14μs -> 311ns (268% faster)

# 2. Edge Test Cases

def test_extract_features_empty_input():
    # Test with empty input list
    net = AlexNet()
    x = []
    codeflash_output = net._extract_features(x); out = codeflash_output # 951ns -> 301ns (216% faster)

def test_extract_features_empty_sample():
    # Test with a sample that is an empty list
    net = AlexNet()
    x = [[]]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.22μs -> 320ns (282% faster)

def test_extract_features_all_empty_samples():
    # Test with multiple empty samples
    net = AlexNet()
    x = [[], [], []]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.23μs -> 321ns (284% faster)
    for features in out:
        pass

def test_extract_features_mixed_empty_and_nonempty():
    # Test with a mix of empty and non-empty samples
    net = AlexNet()
    x = [[1, 2, 3], [], [4, 5]]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.16μs -> 341ns (241% faster)




def test_extract_features_large_numbers():
    # Test with very large numbers
    net = AlexNet()
    big = 10**18
    x = [[big, big, big]]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.43μs -> 471ns (204% faster)

def test_extract_features_single_element_samples():
    # Test with samples containing only one element
    net = AlexNet()
    x = [[42], [-7], [0]]
    codeflash_output = net._extract_features(x); out = codeflash_output # 1.17μs -> 351ns (234% faster)

# 3. Large Scale Test Cases

def test_extract_features_many_samples():
    # Test with a large number of samples (up to 1000)
    net = AlexNet()
    x = [[i, i+1, i+2] for i in range(1000)]
    codeflash_output = net._extract_features(x); out = codeflash_output # 16.0μs -> 401ns (3895% faster)

def test_extract_features_long_samples():
    # Test with a single sample of 1000 elements
    net = AlexNet()
    x = [list(range(1000))]
    codeflash_output = net._extract_features(x); out = codeflash_output # 982ns -> 390ns (152% faster)
    expected_sum = sum(range(1000))
    expected_mean = expected_sum / 1000


def test_extract_features_performance():
    # Performance test: ensure function runs in reasonable time for 1000 samples of 100 elements
    import time
    net = AlexNet()
    x = [list(range(100)) for _ in range(1000)]
    start = time.time()
    codeflash_output = net._extract_features(x); out = codeflash_output # 15.4μs -> 481ns (3112% faster)
    end = time.time()
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import random
import string

# imports
import pytest  # used for our unit tests
from workload import AlexNet

# unit tests

# 1. BASIC TEST CASES

def test_single_sample_basic():
    # Single sample, positive integers
    model = AlexNet()
    x = [[1, 2, 3, 4]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.29μs -> 381ns (239% faster)

def test_multiple_samples_basic():
    # Multiple samples, mixed positive and negative numbers
    model = AlexNet()
    x = [
        [1, 2, 3],
        [-1, -2, -3],
        [0, 5, 10]
    ]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.18μs -> 351ns (237% faster)

def test_single_sample_floats():
    # Sample with float values
    model = AlexNet()
    x = [[1.5, 2.5, 3.0]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.19μs -> 330ns (261% faster)

def test_empty_outer_list():
    # No samples at all
    model = AlexNet()
    x = []
    codeflash_output = model._extract_features(x); features = codeflash_output # 922ns -> 341ns (170% faster)

def test_single_empty_sample():
    # Single sample, but it's empty
    model = AlexNet()
    x = [[]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.26μs -> 330ns (282% faster)

def test_multiple_samples_some_empty():
    # Some samples are empty, some are not
    model = AlexNet()
    x = [[1, 2], [], [3, 4, 5], []]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.13μs -> 310ns (265% faster)

# 2. EDGE TEST CASES

def test_sample_with_zeroes():
    # Sample contains all zeroes
    model = AlexNet()
    x = [[0, 0, 0, 0]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.11μs -> 330ns (237% faster)

def test_sample_with_large_numbers():
    # Sample with very large numbers
    model = AlexNet()
    x = [[1e10, 2e10, -1e10]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.13μs -> 321ns (253% faster)

def test_sample_with_single_element():
    # Each sample has only one element
    model = AlexNet()
    x = [[5], [-2], [0]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.21μs -> 310ns (291% faster)



def test_sample_with_bool_values():
    # bools are subclasses of int, so should be accepted
    model = AlexNet()
    x = [[True, False, 1]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.41μs -> 420ns (236% faster)

def test_sample_with_nan_inf():
    # Sample contains float('nan') and float('inf')
    model = AlexNet()
    x = [[float('nan'), 1, 2], [float('inf'), 3, 4]]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.24μs -> 351ns (254% faster)
    import math



def test_large_number_of_samples():
    # 1000 samples, each with 10 numbers
    model = AlexNet()
    num_samples = 1000
    x = [[i for i in range(10)] for _ in range(num_samples)]
    codeflash_output = model._extract_features(x); features = codeflash_output # 15.4μs -> 470ns (3183% faster)
    expected = {'sum': 45, 'mean': 4.5, 'min': 0, 'max': 9}
    for f in features:
        pass


def test_large_samples_some_empty():
    # 1000 samples, every 10th sample is empty
    model = AlexNet()
    num_samples = 1000
    x = []
    for i in range(num_samples):
        if i % 10 == 0:
            x.append([])
        else:
            x.append([i, i+1, i+2])
    codeflash_output = model._extract_features(x); features = codeflash_output
    for i in range(num_samples):
        if i % 10 == 0:
            pass
        else:
            vals = [i, i+1, i+2]

def test_large_sample_size():
    # One sample with 1000 numbers
    model = AlexNet()
    x = [list(range(1000))]
    codeflash_output = model._extract_features(x); features = codeflash_output # 1.02μs -> 380ns (169% faster)
    expected_sum = sum(range(1000))
    expected_mean = expected_sum / 1000
    expected_min = 0
    expected_max = 999
# 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-AlexNet._extract_features-mccvtdob and push.

[codeflash-ai]

This PR has been automatically closed because the original PR #414 by codeflash-ai[bot] was closed.