⚡️ Speed up method GeneticAlgorithm.mutation by 280%

[codeflash-ai]

📄 280% (2.80x) speedup for GeneticAlgorithm.mutation in GeneticAlgorithm.py

⏱️ Runtime : 847 milliseconds → 223 milliseconds (best of 23 runs)

📝 Explanation and details

To optimize this code for better performance, we can reduce the number of calls to the rand() function and avoid repeatedly accessing the length of the bitstring in each iteration. One approach to do this is to generate all the random values at once and to transform the bitstring using vectorized operations, which are typically faster in Python due to reduced overhead from looping in the interpreter.

In this optimized version.

1. We generate an array of random values (random_values) at once for all the bits in the bitstring.
2. We create a mask (mutation_mask) that indicates which bits should be mutated based on self.mutation_rate.
3. We use the mask to efficiently iterate and mutate the corresponding bits in the bitstring.

This approach minimizes the number of calls to rand() and enhances the overall speed by utilizing efficient array operations.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 38 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

import numpy as np  # used to set random seed
# imports
import pytest  # used for our unit tests
from GeneticAlgorithm import GeneticAlgorithm
# function to test
from numpy.random import rand

# unit tests

def test_single_bit_flip():
    ga = GeneticAlgorithm(1, 1, 1, 0.0, 1.0, 1.0)
    codeflash_output = ga.mutation([0])
    codeflash_output = ga.mutation([1])

def test_no_mutation():
    ga = GeneticAlgorithm(1, 1, 4, 0.0, 0.0, 1.0)
    codeflash_output = ga.mutation([0, 1, 0, 1])
    codeflash_output = ga.mutation([1, 1, 1, 1])

def test_full_mutation():
    ga = GeneticAlgorithm(1, 1, 4, 0.0, 1.0, 1.0)
    codeflash_output = ga.mutation([0, 0, 0, 0])
    codeflash_output = ga.mutation([1, 1, 1, 1])

def test_partial_mutation():
    np.random.seed(0)  # Set a fixed seed for deterministic behavior
    ga = GeneticAlgorithm(1, 1, 4, 0.0, 0.5, 1.0)
    codeflash_output = ga.mutation([0, 1, 0, 1])

def test_empty_bitstring():
    ga = GeneticAlgorithm(1, 1, 0, 0.0, 0.5, 1.0)
    codeflash_output = ga.mutation([])

def test_single_bit_bitstring():
    np.random.seed(0)  # Set a fixed seed for deterministic behavior
    ga = GeneticAlgorithm(1, 1, 1, 0.0, 0.5, 1.0)
    codeflash_output = ga.mutation([0])  # With seed 0, rand() < 0.5 is False

def test_large_bitstring():
    np.random.seed(0)  # Set a fixed seed for deterministic behavior
    ga = GeneticAlgorithm(1, 1, 10000, 0.0, 0.1, 1.0)
    bitstring = [0] * 10000
    codeflash_output = ga.mutation(bitstring)

def test_fixed_random_seed():
    np.random.seed(0)  # Set a fixed seed for deterministic behavior
    ga = GeneticAlgorithm(1, 1, 4, 0.0, 0.5, 1.0)
    codeflash_output = ga.mutation([0, 1, 0, 1])

def test_performance():
    ga = GeneticAlgorithm(1, 1, 1000000, 0.0, 0.01, 1.0)
    bitstring = [0] * 1000000
    codeflash_output = ga.mutation(bitstring)

def test_boundary_mutation_rates():
    ga_zero = GeneticAlgorithm(1, 1, 4, 0.0, 0.0, 1.0)
    codeflash_output = ga_zero.mutation([0, 1, 0, 1])
    
    ga_one = GeneticAlgorithm(1, 1, 4, 0.0, 1.0, 1.0)
    codeflash_output = ga_one.mutation([0, 1, 0, 1])




import numpy as np
# imports
import pytest  # used for our unit tests
from GeneticAlgorithm import GeneticAlgorithm
# function to test
from numpy.random import rand

# unit tests

def test_single_bit_flip():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 1.0, 5)
    codeflash_output = ga.mutation([0])
    codeflash_output = ga.mutation([1])

def test_no_mutation():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.0, 5)
    codeflash_output = ga.mutation([0, 1, 0, 1])
    codeflash_output = ga.mutation([1, 1, 1, 0])

def test_partial_mutation():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.5, 5)
    np.random.seed(0)  # Set seed for reproducibility
    codeflash_output = ga.mutation([0, 1, 0, 1])
    np.random.seed(0)  # Reset seed for reproducibility
    codeflash_output = ga.mutation([1, 0, 1, 0])

def test_empty_bitstring():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.5, 5)
    codeflash_output = ga.mutation([])

def test_all_zeros():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 1.0, 5)
    codeflash_output = ga.mutation([0, 0, 0, 0])
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.5, 5)
    np.random.seed(0)
    codeflash_output = ga.mutation([0, 0, 0, 0])

def test_all_ones():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 1.0, 5)
    codeflash_output = ga.mutation([1, 1, 1, 1])
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.5, 5)
    np.random.seed(0)
    codeflash_output = ga.mutation([1, 1, 1, 1])

def test_high_mutation_rate():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 1.0, 5)
    codeflash_output = ga.mutation([0, 1, 0, 1, 0])
    codeflash_output = ga.mutation([1, 0, 1, 0, 1])

def test_low_mutation_rate():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.01, 5)
    np.random.seed(0)
    codeflash_output = ga.mutation([0, 1, 0, 1, 0, 1, 0, 1])
    np.random.seed(0)
    codeflash_output = ga.mutation([1, 0, 1, 0, 1, 0, 1, 0])

def test_large_bitstring():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.5, 5)
    bitstring = [0, 1] * 1000
    np.random.seed(0)
    codeflash_output = ga.mutation(bitstring)

def test_consistent_results_with_seed():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.5, 5)
    np.random.seed(0)
    codeflash_output = ga.mutation([0, 1, 0, 1, 0, 1, 0, 1])
    np.random.seed(0)
    codeflash_output = ga.mutation([1, 0, 1, 0, 1, 0, 1, 0])

def test_performance_with_very_large_bitstring():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.5, 5)
    bitstring = [0, 1] * 100000
    np.random.seed(0)
    codeflash_output = ga.mutation(bitstring)


def test_non_list_input():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.5, 5)
    with pytest.raises(TypeError):
        ga.mutation(None)
    with pytest.raises(TypeError):
        ga.mutation(12345)
    with pytest.raises(TypeError):
        ga.mutation("bitstring")

def test_mutation_rate_boundaries():
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.999999, 5)
    np.random.seed(0)
    codeflash_output = ga.mutation([0, 1, 0, 1])
    ga = GeneticAlgorithm(10, 10, 10, 0.5, 0.000001, 5)
    np.random.seed(0)
    codeflash_output = ga.mutation([0, 1, 0, 1])
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

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