Metadata-Version: 2.1
Name: pyProcessAutom
Version: 0.2
Summary: A Train Test Val Split library
Home-page: UNKNOWN
Author: Aryan Sakhala
Author-email: ryansakhala@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: numpy


# 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:

```python
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.


