Hyperopt is a Python library for optimizing over hyperparameters. It's an open-source project that allows you to automate the process of finding the best hyperparameters for your models. In this documentation, we will cover the basics of Hyperopt and how to use it effectively.

Getting Started

Before you start using Hyperopt, make sure you have Python installed on your system. You can install Hyperopt using pip:

pip install hyperopt

Quick Start

Here's a simple example of how to use Hyperopt:

from hyperopt import fmin, tpe, hp

def objective(x):
    return x**2

space = hp.uniform('x', 0, 1)
best = fmin(objective, space, algo=tpe.suggest, max_evals=100)

print("The best value of x is", best['x'], "and the objective value is", objective(best))

In this example, we define a simple objective function that squares its input. We then define the space over which we want to optimize (x in this case is a continuous variable between 0 and 1). We use the Tree of Parzen Estimators (TPE) algorithm to find the best hyperparameters and evaluate the objective function 100 times.

Features

Hyperopt provides a number of features that make it powerful:

  • Objectives: You can define custom objective functions that Hyperopt will optimize.
  • Spaces: Hyperopt supports various types of spaces, including uniform, categorical, and discrete.
  • Algorithms: Hyperopt provides multiple algorithms to find the best hyperparameters, such as TPE, Random Search, and Grid Search.
  • Bayesian Optimization: Hyperopt uses Bayesian optimization to efficiently explore the hyperparameter space.

Resources

For more detailed information, please refer to the following resources:

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