ttsvsplit - Train, Test, Validation Splitter

A simple utility designed to seamlessly split datasets into train, test, and validation sets, inspired by sklearn's train_test_split.

Installation

To install ttvsplit, use pip:

pip install ttsvsplit

Usage

Using ttvsplit is straightforward. Import the train_test_val_split function and apply it on your features (X) and labels (y).

Here's a basic example:

import numpy as np
from ttsvsplit import train_test_val_split

# Sample data
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])
y = np.array([1, 0, 1, 0, 1, 0])

X_train, y_train, X_test, y_test, X_val, y_val = train_test_val_split(X, y)

Functions

The library provides the following function:

• train_test_val_split(X, y, train_size=0.6, test_size=0.2, random_state=None):
  • X: Features to be split.
  • y: Labels corresponding to the features.
  • train_size: Proportion of the data to be used as training data (default: 0.6).
  • test_size: Proportion of the data to be used as testing data (default: 0.2).
  • random_state: Seed for reproducibility.

License

This project is licensed under the MIT License - see the LICENSE.txt file for details.