mloptimizer 0.5.3

mloptimizer is a Python library for optimizing hyperparameters of machine learning algorithms using genetic algorithms.

mloptimizer

mloptimizer is a Python library for optimizing hyperparameters of machine learning algorithms using genetic algorithms. With mloptimizer, you can find the optimal set of hyperparameters for a given machine learning model and dataset, which can significantly improve the performance of the model. The library supports several popular machine learning algorithms, including decision trees, random forests, and gradient boosting classifiers. The genetic algorithm used in mloptimizer provides an efficient and flexible approach to search for the optimal hyperparameters in a large search space.

Installation

It is recommended to create a virtual environment using the venv package. To learn more about how to use venv, check out the official Python documentation at https://docs.python.org/3/library/venv.html.

# Create the virtual environment
python -m venv myenv
# Activate the virtual environment
source myenv/bin/activate

To install mloptimizer, run:

pip install mloptimizer

Quickstart

Here's a simple example of how to optimize hyperparameters in a decision tree classifier using the iris dataset:

from mloptimizer.genoptimizer import TreeOptimizer
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)

# The log file will be created in the current directory and will have info about optimizations performed
opt = TreeOptimizer(X, y, "output_log_file.log")

clf = opt.optimize_clf(10, 10)

The las line of code will create a directory in the current folder with a name similar to YYYYMMDD_nnnnnnnnnn_TreeOptimizer. This folder contains a backup file for each generation and an opt.log inform with all the steps, the best estimator and the result of the optimization.

Dependencies

The following dependencies are used in mloptimizer:

• Deap - Genetic Algorithms
• XGBoost - Gradient boosting classifier
• Scikit-Learn - Usado para generar RSS

Documentation

The documentation for mloptimizer can be found in the project's [wiki](DOCUMENTATION TODO)

Authors

• Antonio Caparrini - Owner - caparrini

License

This project is licensed under the MIT License.