Book Image

Hyperparameter Tuning with Python

By : Louis Owen
Book Image

Hyperparameter Tuning with Python

By: Louis Owen

Overview of this book

Hyperparameters are an important element in building useful machine learning models. This book curates numerous hyperparameter tuning methods for Python, one of the most popular coding languages for machine learning. Alongside in-depth explanations of how each method works, you will use a decision map that can help you identify the best tuning method for your requirements. You’ll start with an introduction to hyperparameter tuning and understand why it's important. Next, you'll learn the best methods for hyperparameter tuning for a variety of use cases and specific algorithm types. This book will not only cover the usual grid or random search but also other powerful underdog methods. Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. Finally, you will cover hyperparameters of popular algorithms and best practices that will help you efficiently tune your hyperparameter. By the end of this book, you will have the skills you need to take full control over your machine learning models and get the best models for the best results.
Table of Contents (19 chapters)
Section 1:The Methods
Section 2:The Implementation
Section 3:Putting Things into Practice

Implementing Particle Swarm Optimization

PSO is also one of the variants of the Heuristic Search hyperparameter tuning group (see Chapter 5) that can be implemented by the DEAP package. We’ll still use the same example as in the previous section to see how we can implement PSO using the DEAP package.

The following code shows how to implement PSO with the DEAP package. You can find the more detailed code in the GitHub repository mentioned in the Technical requirements section:

  1. Define the PSO parameters and type classes through the creator.create() module:
    N = 50 #swarm size
    w = 0.5 #inertia weight coefficient
    c1 = 0.3 #cognitive coefficient
    c2 = 0.5 #social coefficient
    num_trials = 15 #number of trials

Fix the seed for reproducibility:

import random

Define the type of our fitness function. Here, we are working with a maximization problem and a single objective function, which is why we set weights=(1.0,):

from deap import creator, base...