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## 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)
* [Issues & Bug Reports](https://github.com/sepandhaghighi/pycm#issues--bug-reports)
* [Todo](https://github.com/sepandhaghighi/pycm/blob/master/TODO.md)
* [Outputs](https://github.com/sepandhaghighi/pycm#outputs)
* [Dependencies](https://github.com/sepandhaghighi/pycm#dependencies)
* [Contribution](https://github.com/sepandhaghighi/pycm#contribution)
* [References](https://github.com/sepandhaghighi/pycm#references)
* [Cite](https://github.com/sepandhaghighi/pycm#cite)
* [Authors](https://github.com/sepandhaghighi/pycm/blob/master/AUTHORS.md)
* [License](https://github.com/sepandhaghighi/pycm#license)
* [Donate](https://github.com/sepandhaghighi/pycm#donate-to-our-project)
* [Changelog](https://github.com/sepandhaghighi/pycm/blob/master/CHANGELOG.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 an accurate evaluation of large variety of classifiers.
</p>
<div align="center">
<img src="https://github.com/sepandhaghighi/pycm/raw/master/Otherfiles/block_diagram.jpg">
<p>Fig1. PyCM Block Diagram</p>
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<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>
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<td align="center">PyPI Counter</td>
<td align="center"><a href="http://pepy.tech/count/pycm"><img src="http://pepy.tech/badge/pycm"></a></td>
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<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>
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</html>
## Installation
### Source Code
- Download [Version 1.1](https://github.com/sepandhaghighi/pycm/archive/v1.1.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 --upgrade` or `pip3 install pycm --upgrade` (Need root access)
### Easy Install
- Run `easy_install --upgrade pycm` (Need root access)
## Usage
### From Vector
```python
>>> from pycm import *
>>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2] # or y_actu = numpy.array([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] # or y_pred = numpy.array([0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2])
>>> cm = ConfusionMatrix(actual_vector=y_actu, predict_vector=y_pred) # Create CM From Data
>>> 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}}
>>> print(cm)
Predict 0 1 2
Actual
0 3 0 0
1 0 1 2
2 2 1 3
Overall Statistics :
95% CI (0.30439,0.86228)
Bennett_S 0.375
Chi-Squared 6.6
Chi-Squared DF 4
Conditional Entropy 0.95915
Cramer_V 0.5244
Cross Entropy 1.59352
Gwet_AC1 0.38931
Hamming Loss 0.41667
Joint Entropy 2.45915
KL Divergence 0.09352
Kappa 0.35484
Kappa 95% CI (-0.07708,0.78675)
Kappa No Prevalence 0.16667
Kappa Standard Error 0.22036
Kappa Unbiased 0.34426
Lambda A 0.16667
Lambda B 0.42857
Mutual Information 0.52421
Overall_ACC 0.58333
Overall_J (1.225,0.40833)
Overall_RACC 0.35417
Overall_RACCU 0.36458
PPV_Macro 0.56667
PPV_Micro 0.58333
Phi-Squared 0.55
Reference Entropy 1.5
Response Entropy 1.48336
Scott_PI 0.34426
Standard Error 0.14232
Strength_Of_Agreement(Altman) Fair
Strength_Of_Agreement(Cicchetti) Poor
Strength_Of_Agreement(Fleiss) Poor
Strength_Of_Agreement(Landis and Koch) Fair
TPR_Macro 0.61111
TPR_Micro 0.58333
Zero-one Loss 5
Class Statistics :
Classes 0 1 2
ACC(Accuracy) 0.83333 0.75 0.58333
BM(Informedness or bookmaker informedness) 0.77778 0.22222
pycm-1.1.tar.gz （34个子文件） 
pycm-1.1 
MANIFEST.in 455B .travis.yml 235B PKG-INFO 38KB AUTHORS.md 467B paper 
10.21105.joss.00729.pdf 123KB paper.md 2KB paper.bib 6KB pycm pycm_obj.py 12KB test.py 39KB __main__.py 632B pycm_param.py 8KB __init__.py 126B pycm_output.py 13KB pycm_func.py 31KB pytest.ini 126B Otherfiles 
test.html 34KB
block_diagram.png 224KB
block_diagram.jpg 56KB test.pycm 7KB logo.png 27KB test.csv 640B LICENSE 1KB TODO.md 1KB appveyor.yml 904B pycm.egg-info PKG-INFO 38KB
requires.txt 71B SOURCES.txt 594B top_level.txt 5B dependency_links.txt 1B setup.cfg 67B requirements.txt 73B setup.py 3KB CHANGELOG.md 4KB README.md 27KB资源评论
