How to use random seed in Numpy

I’ll explain how to use the random seed in NumPy, a widely used Python library for scientific computing. Setting a random seed allows you to control the randomness in NumPy’s random number generators, which are essential for tasks like generating random data, shuffling arrays, sampling from distributions, and more.

Why use a random seed?

Randomness plays a key role in many scientific and statistical applications, helping to model uncertainty, variability, and noise. However, there are situations where you may want to reproduce the same random results. For instance, when debugging, testing, or comparing algorithms, it’s useful to get the same random output each time you run your code.

A common example is in machine learning, where algorithms often rely on random initialization or stochastic optimization. Setting a seed ensures that you can repeat the same experiments with identical data and parameters, which is crucial for comparison and evaluation.

This is where the random seed comes in. By setting a seed, you ensure that NumPy’s random number generator produces the same sequence of random numbers every time, allowing you to eliminate randomness as a factor and focus on other variables.

How to use random seed?

Setting a random seed in NumPy is simple. You just call np.random.seed with an integer value as the seed. Here’s an example:

import numpy as np
np.random.seed(42) # set the seed to 42
a = np.random.rand(3) # generate a random array of size 3
print(a) # print the array

The output will be:

[0.37454012 0.95071431 0.73199394]

If you run this code again with the same seed value, you’ll get the same output every time. However, if you change the seed value or don’t set a seed, the output will be different.

Note that setting a seed only affects the current instance of NumPy’s random number generator. If you create separate instances using np.random.RandomState or np.random.default_rng(), they will each have their own independent state and seed. If you need to control the randomness across multiple instances, you’ll need to set the seed for each one individually.

Also note that setting a random seed does not guarantee identical results across different versions of NumPy or different platforms. This is because NumPy’s random number generators might use different algorithms or implementations in different environments. For full reproducibility, especially in shared projects or research, it’s a good practice to specify the exact version of NumPy and document the random number generator you’re using.