Metadata-Version: 2.4
Name: dconfusion
Version: 0.1.1
Summary: A package for working with confusion matrices. It can pretty print a confusion matrix, its frequencies and various other metrics.
Author-email: Surya Gundavarapu <surya_gundavarapu@yahoo.com>, Matthew Lanham <mlanham1@butler.edu>, Aadya Pawar <pawar17@purdue.edu>
License: MIT License
        
        Copyright (c) 2025 suryagundavarapu
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Project-URL: Homepage, https://github.com/sgundava/dconfusion
Project-URL: Repository, https://github.com/sgundava/dconfusion
Keywords: confusion,matrix
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# DConfusion
A Python package for working with confusion matrices.

## Overview
`dconfusion` is a lightweight Python package that provides basic statistical functionality for working with confusion matrices. Confusion matrices are a crucial tool in machine learning and data analysis, allowing you to evaluate the performance of classification models.

## Features
* Basic confusion matrix representation
* Frequency calculation for each cell in the matrix

## Installation
You can install `dconfusion` using pip:

```bash
pip install dconfusion
```

Usage
Here's an example of how to use dconfusion:

```python
from dconfusion.DConfusion import DConfusion


# Create a confusion matrix
matrix = DConfusion(80, 70, 10, 20)

# Print the matrix
print(matrix)

# Calculate frequency for each cell
print(matrix.frequency())
```

# Roadmap
This is the initial release (v0.1) of dconfusion, and we plan to add more features in future releases. Some potential features include:
- Additional statistical metrics (e.g., accuracy, precision, recall, F1 score)
- Support for multi-class classification
- Integration with popular machine learning libraries

# Contributing
We welcome contributions to dconfusion! If you'd like to contribute, please fork the repository and submit a pull request.

# License
dconfusion is released under the MIT License. See LICENSE for details.

# Changelog
v0.1: Initial release with basic confusion matrix representation and frequency calculation
