gatree 0.1.4

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GATree

• Free software: MIT license
• Documentation: http://gatree.readthedocs.io
• Python: 3.9.x, 3.10.x
• Operating systems: Windows, Ubuntu, macOS

About 📋

GATree is a Python library designed for implementing evolutionary decision trees using a genetic algorithm approach. The library provides functionalities for selection, mutation, and crossover operations within the decision tree structure, allowing users to evolve and optimise decision trees for various classification tasks. 🌲🧬

The library's core objective is to empower users in creating and fine-tuning decision trees through an evolutionary process, opening avenues for innovative approaches to classification problems. GATree enables the dynamic growth and adaptation of decision trees, offering a flexible and powerful tool for machine learning enthusiasts and practitioners. 🚀🌿

Installation 📦

pip

To install GATree using pip, run the following command:

pip install gatree

Usage 🚀

The following example demonstrates how to perform classification of the iris dataset using GATree. More examples can be found in the examples directory.

import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from gatree.gatree import GATree

# Load the iris dataset
iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = pd.Series(iris.target, name='target')

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=123)

# Create and fit the GATree classifier
gatree = GATree(n_jobs=16, random_state=123)
gatree.fit(X=X_train, y=y_train, population_size=100, max_iter=100)

# Make predictions on the testing set
y_pred = gatree.predict(X_test)

# Evaluate the accuracy of the classifier
print(accuracy_score(y_test, y_pred))

License

This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.

Disclaimer

This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!