Challenge: Implementing Benchmarking

Task

Swipe to start coding

Let's practice benchmarking by comparing two approaches to squaring the elements of a NumPy array. The first approach, the slower one, uses a for loop to square each element individually, while the second approach leverages vectorization. Don't worry if this concept sounds unfamiliar—we'll discuss it later in the course.

Your task for now is the following:

Define two functions:
- The first, named square_array_slow, should take a single parameter array;
- The second, named square_array_fast, should also take the same parameter.
Decorate both functions with the timeit_decorator and set number to 100.

Solution

# Import the benchmarking decorator

import os

os.system('wget https://content-media-cdn.codefinity.com/courses/8d21890f-d960-4129-bc88-096e24211d53/section_1/chapter_3/decorators.py 2>/dev/null')

from decorators import timeit_decorator

import numpy as np

from copy import deepcopy

numbers = np.arange(100000)

# Define and decorate the first function

@timeit_decorator(number=100)

def square_array_slow(array):

array = deepcopy(array)

for i in range(len(array)):

# Square the element

array[i] = array[i] ** 2

return array

# Define and decorate the second function

@timeit_decorator(number=100)

def square_array_fast(array):

array = deepcopy(array)

# Square each element using vectorization

return array ** 2

squares_array_1 = square_array_slow(numbers)

squares_array_2 = square_array_fast(numbers)

# Check if the arrays are equal

if np.array_equal(squares_array_1, squares_array_2):

print('The arrays are equal')

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Section 1. Chapter 4

# Import the benchmarking decorator

import os

os.system('wget https://content-media-cdn.codefinity.com/courses/8d21890f-d960-4129-bc88-096e24211d53/section_1/chapter_3/decorators.py 2>/dev/null')

from decorators import timeit_decorator

import numpy as np

from copy import deepcopy

numbers = np.arange(100000)

# Define and decorate the first function

___

___:

array = deepcopy(array)

for i in range(len(array)):

# Square the element

array[i] = array[i] **2

return array

# Define and decorate the second function

___

___:

array = deepcopy(array)

# Square each element using vectorization

return array ** 2

squares_array_1 = square_array_slow(numbers)

squares_array_2 = square_array_fast(numbers)

# Check if the arrays are equal

if np.array_equal(squares_array_1, squares_array_2):

print('The arrays are equal')

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