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 Random Search

Random Search is one of the variants of the Exhaustive Search hyperparameter tuning group (see Chapter 3) that the NNI package can implement. Let’s use the same data, pipeline, and hyperparameter space as in the example in the previous section to show you how to implement Random Search with NNI using pure Python code.

The following code shows how to implement Random Search with the NNI package. Here, we’ll use pure Python code instead of using nnictl as in the previous section. You can find the more detailed code in the GitHub repository mentioned in the Technical requirements section:

  1. Prepare the model to be tuned in a script. We’ll use the same script as in the previous section.
  2. Define the hyperparameter space in the form of a Python dictionary:
    hyperparameter_space = { 
        'model__n_estimators': {'_type': 'randint', '_value': [5, 200]},