PyCM 是一个用 Python 编写的多类混淆矩阵库，支持输入数据向量和矩阵，是支持大多数类和统计参数的模型评估工具.zip

<div align="center"> <img src="https://github.com/sepandhaghighi/pycm/raw/master/Otherfiles/logo.png" width="550"> <h1>PyCM: Python Confusion Matrix</h1> <br/> <a href="https://www.python.org/"><img src="https://img.shields.io/badge/built%20with-Python3-green.svg" alt="built with Python3" /></a> <a href="/Document"><img src="https://img.shields.io/badge/doc-latest-orange.svg"></a> <a href="https://codecov.io/gh/sepandhaghighi/pycm"> <img src="https://codecov.io/gh/sepandhaghighi/pycm/branch/master/graph/badge.svg" /> </a> <a href="https://badge.fury.io/py/pycm"><img src="https://badge.fury.io/py/pycm.svg" alt="PyPI version" height="18"></a> <a href="https://anaconda.org/sepandhaghighi/pycm"><img src="https://anaconda.org/sepandhaghighi/pycm/badges/version.svg"></a> <a href="https://colab.research.google.com/github/sepandhaghighi/pycm/blob/master"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Document"/> </a> <a href="https://discord.com/invite/zqpU2b3J3f"> <img src="https://img.shields.io/discord/901883546162065408.svg" alt="Discord Channel"> </a> </div> ## Table of contents * [Overview](https://github.com/sepandhaghighi/pycm#overview) * [Installation](https://github.com/sepandhaghighi/pycm#installation) * [Usage](https://github.com/sepandhaghighi/pycm#usage) * [Document](https://github.com/sepandhaghighi/pycm/tree/master/Document) * [Try PyCM in Your Browser](https://github.com/sepandhaghighi/pycm#try-pycm-in-your-browser) * [Issues & Bug Reports](https://github.com/sepandhaghighi/pycm#issues--bug-reports) * [Todo](https://github.com/sepandhaghighi/pycm/blob/master/TODO.md) * [Contribution](https://github.com/sepandhaghighi/pycm/blob/master/.github/CONTRIBUTING.md) * [Acknowledgments](https://github.com/sepandhaghighi/pycm#acknowledgments) * [Cite](https://github.com/sepandhaghighi/pycm#cite) * [Authors](https://github.com/sepandhaghighi/pycm/blob/master/AUTHORS.md) * [License](https://github.com/sepandhaghighi/pycm/blob/master/LICENSE) * [Show Your Support](https://github.com/sepandhaghighi/pycm#show-your-support) * [Changelog](https://github.com/sepandhaghighi/pycm/blob/master/CHANGELOG.md) * [Code of Conduct](https://github.com/sepandhaghighi/pycm/blob/master/.github/CODE_OF_CONDUCT.md) ## Overview <p align="justify"> PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and accurate evaluation of a large variety of classifiers. </p> <div align="center"> <img src="https://github.com/sepandhaghighi/pycm/raw/master/Otherfiles/block_diagram.jpg"> <p>Fig1. ConfusionMatrix Block Diagram</p> </div> <table> <tr> <td align="center">Open Hub</td> <td align="center"><a href="https://www.openhub.net/p/pycm"><img src="https://www.openhub.net/p/pycm/widgets/project_thin_badge.gif"></a></td> </tr> <tr> <td align="center">PyPI Counter</td> <td align="center"><a href="http://pepy.tech/project/pycm"><img src="http://pepy.tech/badge/pycm"></a></td> </tr> <tr> <td align="center">Github Stars</td> <td align="center"><a href="https://github.com/sepandhaghighi/pycm"><img src="https://img.shields.io/github/stars/sepandhaghighi/pycm.svg?style=social&label=Stars"></a></td> </tr> </table> <table> <tr> <td align="center">Branch</td> <td align="center">master</td> <td align="center">dev</td> </tr> <tr> <td align="center">CI</td> <td align="center"><img src="https://github.com/sepandhaghighi/pycm/workflows/CI/badge.svg?branch=master"></td> <td align="center"><img src="https://github.com/sepandhaghighi/pycm/workflows/CI/badge.svg?branch=dev"></td> </tr> </table> <table> <tr> <td align="center">Code Quality</td> <td align="center"><a class="badge-align" href="https://www.codacy.com/app/sepand-haghighi/pycm?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=sepandhaghighi/pycm&amp;utm_campaign=Badge_Grade"><img src="https://api.codacy.com/project/badge/Grade/5d9463998a0040d09afc2b80c389365c"/></a></td> <td align="center"><a href="https://www.codefactor.io/repository/github/sepandhaghighi/pycm/overview/dev"><img src="https://www.codefactor.io/repository/github/sepandhaghighi/pycm/badge/dev" alt="CodeFactor" /></a></td> <td align="center"><a href="https://codebeat.co/projects/github-com-sepandhaghighi-pycm-dev"><img alt="codebeat badge" src="https://codebeat.co/badges/f6642af1-c343-48c2-bd3e-eee802facf39" /></a></td> </tr> </table> ## Installation ⚠️ PyCM 3.9 is the last version to support **Python 3.5** ⚠️ PyCM 2.4 is the last version to support **Python 2.7** & **Python 3.4** ⚠️ Plotting capability requires **Matplotlib (>= 3.0.0)** or **Seaborn (>= 0.9.1)** ### Source code - Download [Version 4.0](https://github.com/sepandhaghighi/pycm/archive/v4.0.zip) or [Latest Source ](https://github.com/sepandhaghighi/pycm/archive/dev.zip) - Run `pip install -r requirements.txt` or `pip3 install -r requirements.txt` (Need root access) - Run `python3 setup.py install` or `python setup.py install` (Need root access) ### PyPI - Check [Python Packaging User Guide](https://packaging.python.org/installing/) - Run `pip install pycm==4.0` or `pip3 install pycm==4.0` (Need root access) ### Conda - Check [Conda Managing Package](https://conda.io/) - Update Conda using `conda update conda` (Need root access) - Run `conda install -c sepandhaghighi pycm` (Need root access) ### Easy install - Run `easy_install --upgrade pycm` (Need root access) ### MATLAB - Download and install [MATLAB](https://www.mathworks.com/products/matlab.html) (>=8.5, 64/32 bit) - Download and install [Python3.x](https://www.python.org/downloads/) (>=3.6, 64/32 bit) - [x] Select `Add to PATH` option - [x] Select `Install pip` option - Run `pip install pycm` or `pip3 install pycm` (Need root access) - Configure Python interpreter ```matlab >> pyversion PYTHON_EXECUTABLE_FULL_PATH ``` - Visit [MATLAB Examples](https://github.com/sepandhaghighi/pycm/tree/master/MATLAB) ## Usage ### From vector ```pycon >>> from pycm import * >>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2] >>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2] >>> cm = ConfusionMatrix(actual_vector=y_actu, predict_vector=y_pred) >>> cm.classes [0, 1, 2] >>> cm.table {0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}} >>> cm.print_matrix() Predict 0 1 2 Actual 0 3 0 0 1 0 1 2 2 2 1 3 >>> cm.print_normalized_matrix() Predict 0 1 2 Actual 0 1.0 0.0 0.0 1 0.0 0.33333 0.66667 2 0.33333 0.16667 0.5 >>> cm.stat(summary=True) Overall Statistics : ACC Macro 0.72222 F1 Macro 0.56515 FPR Macro 0.22222 Kappa 0.35484 Overall ACC 0.58333 PPV Macro 0.56667 SOA1(Landis & Koch) Fair TPR Macro 0.61111 Zero-one Loss 5 Class Statistics : Classes 0 1 2 ACC(Accuracy) 0.83333 0.75