In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one (in unsupervised learning it is usually called a matching matrix). Each row of the matrix represents the instances in a predicted class while each column represents the instances in an actual class (or vice versa) pycm(python confusion matrix) is a multi class confusion matrix library in python.
- Download Version 0.1 or Latest Source
- Run
pip install -r requirements.txtorpip3 install -r requirements.txt(Need root access) - Run
python3 setup.py installorpython setup.py install(Need root access)
- Check Python Packaging User Guide
- Run
pip install pycmorpip3 install pycm(Need root access)
>>> 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(y_actu, y_pred)
>>> print(cm)
Predict 0 1 2
Actual
0 3 0 0
1 0 1 2
2 2 1 3
Classes 0 1 2
ACC(accuracy) 0.83333 0.75 0.58333
BM(Informedness or Bookmaker Informedness) 0.77778 0.22222 0.16667
DOR(Diagnostic odds ratio) None 4.0 2.00003
F1(F1 Score - harmonic mean of precision and sensitivity) 0.75 0.4 0.54545
FDR(false discovery rate) 0.4 0.5 0.4
FN(false negative/miss/Type II error) 0 2 3
FNR(miss rate or false negative rate) 0.0 0.66667 0.5
FOR(false omission rate) 0.0 0.2 0.42857
FP(false positive/Type I error/false alarm) 2 1 2
FPR(fall-out or false positive rate) 0.22222 0.11111 0.33333
LR+(Positive likelihood ratio) 4.50005 3.0 1.50002
LR-(Negative likelihood ratio) 0.0 0.75 0.75
MCC(Matthews correlation coefficient) 0.68313 0.2582 0.16903
MK(Markedness) 0.6 0.3 0.17143
NPV(negative predictive value) 1.0 0.8 0.57143
PPV(precision or positive predictive value) 0.6 0.5 0.6
TN(true negative/correct rejection) 7 8 4
TNR(specificity or true negative rate) 0.77778 0.88889 0.66667
TP(true positive/hit) 3 1 3
TPR(sensitivity, recall, hit rate, or true positive rate) 1.0 0.33333 0.5 For more information visit here
Just fill an issue and describe it. We'll check it ASAP! or send an email to [email protected].
- Class Statistics
- ACC
- BM
- DOR
- F1-Score
- FDR
- FNR
- FOR
- FPR
- LR+
- LR-
- MCC
- MK
- NPV
- PPV
- TNR
- TPR
- Outputs
- CSV File
- HTML File
- Table
- Overall Statistics
- Kappa
- 95% CI
You can fork the repository, improve or fix some part of it and then send the pull requests back if you want to see them here. I really appreciate that. ❤️
Remember to write a few tests for your code before sending pull requests.
Sepand Haghighi. “Pycm : Multi Class Confusion Matrix Library in Python”. Zenodo, January 22, 2018. doi:10.5281/zenodo.1157173.
If you use pycm in your research , please cite this :
Sepand Haghighi. “Pycm : Multi Class Confusion Matrix Library in Python”. Zenodo, January 22, 2018. doi:10.5281/zenodo.1157173.
@misc{https://doi.org/10.5281/zenodo.1157173,
doi = {10.5281/zenodo.1157173},
author = {Sepand Haghighi},
keywords = {Machine Learning, statistics, confusion-matrix, statistical-analysis, matrices, matrix},
title = {Pycm : Multi Class Confusion Matrix Library in Python},
pages = {--},
publisher = {Zenodo},
year = {2018}
}
12Xm1qL4MXYWiY9sRMoa3VpfTfw6su3vNq
