Analysis of Comprehension Optimization

comprehension_optimization_analysis.py

from optimize_comprehensions import optimize_comprehensions
import numpy as np
import matplotlib.pyplot as plt
import timeit
from functools import partial
from time import sleep



def list_comprehension(expensive_func):
    return [
        expensive_func(x)
        for x in [1]
        if expensive_func(x)
    ]


@optimize_comprehensions
def list_comprehension__optimized(expensive_func):
    return [
        expensive_func(x)
        for x in [1]
        if expensive_func(x)
    ]


func_times = [x / 100.0 for x in range(0, 50, 1)]
opt_comp_time_frac = []

for func_time in func_times:
    print(func_time)
    def expensive_func(x):
        sleep(func_time)
        return x
    comp_time = timeit.timeit(
        partial(list_comprehension, expensive_func),
        number=10,
    )
    optimized_comp_time = timeit.timeit(
        partial(list_comprehension__optimized, expensive_func),
        number=10,
    )
    opt_comp_time_frac.append(optimized_comp_time / comp_time)

with plt.rc_context({'axes.edgecolor':'gray', 'xtick.color':'gray', 'ytick.color':'gray', 'figure.facecolor':'white'}):
    plt.plot(func_times, opt_comp_time_frac)
    plt.ylabel('Execution time of optimized comp / original comp',  fontsize=10, wrap=True)
    plt.xlabel('Execution time of function (seconds)', fontsize=10)
    plt.savefig('comprehension_optimization_analysis.svg')