Multi-class confusion matrix library in Python
Project description
Table of contents
- Overview
- Installation
- Usage
- Document
- Issues & Bug Reports
- Todo
- Outputs
- Dependencies
- Contribution
- References
- Cite
- Authors
- License
- Donate
- Changelog
Overview
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.
Fig1. PyCM Block Diagram
Installation
Source Code
- Download Version 0.8.6 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)
PyPI
- Check Python Packaging User Guide
- Run
pip install pycmorpip3 install pycm(Need root access)
Easy Install
- Run
easy_install --upgrade pycm(Need root access)
Usage
From Vector
>>> 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
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_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
Class Statistics :
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 2.0
ERR(Error rate) 0.16667 0.25 0.41667
F0.5(F0.5 score) 0.65217 0.45455 0.57692
F1(F1 score - harmonic mean of precision and sensitivity) 0.75 0.4 0.54545
F2(F2 score) 0.88235 0.35714 0.51724
FDR(False discovery rate) 0.4 0.5 0.4
FN(False negative/miss/type 2 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 1 error/false alarm) 2 1 2
FPR(Fall-out or false positive rate) 0.22222 0.11111 0.33333
G(G-measure geometric mean of precision and sensitivity) 0.7746 0.40825 0.54772
LR+(Positive likelihood ratio) 4.5 3.0 1.5
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
N(Condition negative) 9 9 6
NPV(Negative predictive value) 1.0 0.8 0.57143
P(Condition positive) 3 3 6
POP(Population) 12 12 12
PPV(Precision or positive predictive value) 0.6 0.5 0.6
PRE(Prevalence) 0.25 0.25 0.5
RACC(Random accuracy) 0.10417 0.04167 0.20833
RACCU(Random accuracy unbiased) 0.11111 0.0434 0.21007
TN(True negative/correct rejection) 7 8 4
TNR(Specificity or true negative rate) 0.77778 0.88889 0.66667
TON(Test outcome negative) 7 10 7
TOP(Test outcome positive) 5 2 5
TP(True positive/hit) 3 1 3
TPR(Sensitivity, recall, hit rate, or true positive rate) 1.0 0.33333 0.5
>>> cm.matrix()
Predict 0 1 2
Actual
0 3 0 0
1 0 1 2
2 2 1 3
>>> cm.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
Direct CM
>>> from pycm import *
>>> cm2 = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}}) # Create CM Directly
>>> cm2
pycm.ConfusionMatrix(classes: ['Class1', 'Class2'])
>>> print(cm2)
Predict Class1 Class2
Actual
Class1 1 2
Class2 0 5
Overall Statistics :
95% CI (0.44994,1.05006)
Bennett_S 0.5
Chi-Squared None
Chi-Squared DF 1
Conditional Entropy None
Cramer_V None
Cross Entropy 1.2454
Gwet_AC1 0.6
Joint Entropy None
KL Divergence 0.29097
Kappa 0.38462
Kappa 95% CI (-0.354,1.12323)
Kappa No Prevalence 0.5
Kappa Standard Error 0.37684
Kappa Unbiased 0.33333
Lambda A None
Lambda B None
Mutual Information None
Overall_ACC 0.75
Overall_RACC 0.59375
Overall_RACCU 0.625
PPV_Macro 0.85714
PPV_Micro 0.75
Phi-Squared None
Reference Entropy 0.95443
Response Entropy 0.54356
Scott_PI 0.33333
Standard Error 0.15309
Strength_Of_Agreement(Altman) Fair
Strength_Of_Agreement(Cicchetti) Poor
Strength_Of_Agreement(Fleiss) Poor
Strength_Of_Agreement(Landis and Koch) Fair
TPR_Macro 0.66667
TPR_Micro 0.75
Class Statistics :
Classes Class1 Class2
ACC(Accuracy) 0.75 0.75
BM(Informedness or bookmaker informedness) 0.33333 0.33333
DOR(Diagnostic odds ratio) None None
ERR(Error rate) 0.25 0.25
F0.5(F0.5 score) 0.71429 0.75758
F1(F1 score - harmonic mean of precision and sensitivity) 0.5 0.83333
F2(F2 score) 0.38462 0.92593
FDR(False discovery rate) 0.0 0.28571
FN(False negative/miss/type 2 error) 2 0
FNR(Miss rate or false negative rate) 0.66667 0.0
FOR(False omission rate) 0.28571 0.0
FP(False positive/type 1 error/false alarm) 0 2
FPR(Fall-out or false positive rate) 0.0 0.66667
G(G-measure geometric mean of precision and sensitivity) 0.57735 0.84515
LR+(Positive likelihood ratio) None 1.5
LR-(Negative likelihood ratio) 0.66667 0.0
MCC(Matthews correlation coefficient) 0.48795 0.48795
MK(Markedness) 0.71429 0.71429
N(Condition negative) 5 3
NPV(Negative predictive value) 0.71429 1.0
P(Condition positive) 3 5
POP(Population) 8 8
PPV(Precision or positive predictive value) 1.0 0.71429
PRE(Prevalence) 0.375 0.625
RACC(Random accuracy) 0.04688 0.54688
RACCU(Random accuracy unbiased) 0.0625 0.5625
TN(True negative/correct rejection) 5 1
TNR(Specificity or true negative rate) 1.0 0.33333
TON(Test outcome negative) 7 1
TOP(Test outcome positive) 1 7
TP(True positive/hit) 1 5
TPR(Sensitivity, recall, hit rate, or true positive rate) 0.33333 1.0
Acceptable Data Types
actual_vector: pythonlistor numpyarrayof any stringable objectspredict_vector: pythonlistor numpyarrayof any stringable objectsmatrix:dictdigit:int
- run
help(ConfusionMatrix)forConfusionMatrixobject details
For more information visit here
Issues & Bug Reports
Just fill an issue and describe it. We'll check it ASAP! or send an email to shaghighi@ce.sharif.edu.
Todo
- Basic
- TP
- FP
- FN
- TN
- Population
- Condition positive
- Condition negative
- Test outcome positive
- Test outcome negative
- Class Statistics
- ACC
- ERR
- BM
- DOR
- F1-Score
- FDR
- FNR
- FOR
- FPR
- LR+
- LR-
- MCC
- MK
- NPV
- PPV
- TNR
- TPR
- Prevalence
- G-measure
- RACC
- Outputs
- CSV File
- HTML File
- Output File
- Table
- Normalized Table
- Overall Statistics
- CI
- Chi-Squared
- Phi-Squared
- Cramer's V
- Kappa
- Kappa Unbiased
- Kappa No Prevalence
- Aickin's alpha
- Bennett S score
- Gwet's AC1
- Scott's pi
- Krippendorff's alpha
- Goodman and Kruskal's lambda A
- Goodman and Kruskal's lambda B
- Kullback-Liebler divergence
- Entropy
- Overall ACC
- Strength of Agreement
- Landis and Koch
- Fleiss
- Altman
- Cicchetti
- TPR Micro/Macro
- PPV Micro/Macro
Outputs
Dependencies
Contribution
Changes and improvements are more than welcome! ❤️ Feel free to fork and open a pull request. Please make your changes in a specific branch and request to pull into dev
Remember to write a few tests for your code before sending pull requests.
References
1- J. R. Landis, G. G. Koch, “The measurement of observer agreement for categorical data. Biometrics,” in International Biometric Society, pp. 159–174, 1977.
2- D. M. W. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness & correlation,” in Journal of Machine Learning Technologies, pp.37-63, 2011.
3- C. Sammut, G. Webb, “Encyclopedia of Machine Learning” in Springer, 2011.
4- J. L. Fleiss, “Measuring nominal scale agreement among many raters,” in Psychological Bulletin, pp. 378-382.
5- D.G. Altman, “Practical Statistics for Medical Research,” in Chapman and Hall, 1990.
6- K. L. Gwet, “Computing inter-rater reliability and its variance in the presence of high agreement,” in The British Journal of Mathematical and Statistical Psychology, pp. 29–48, 2008.”
7- W. A. Scott, “Reliability of content analysis: The case of nominal scaling,” in Public Opinion Quarterly, pp. 321–325, 1955.
8- E. M. Bennett, R. Alpert, and A. C. Goldstein, “Communication through limited response questioning,” in The Public Opinion Quarterly, pp. 303–308, 1954.
9- D. V. Cicchetti, "Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology," in Psychological Assessment, pp. 284–290, 1994.
10- R.B. Davies, "Algorithm AS155: The Distributions of a Linear Combination of χ2 Random Variables," in Journal of the Royal Statistical Society, pp. 323–333, 1980.
11- S. Kullback, R. A. Leibler "On information and sufficiency," in Annals of Mathematical Statistics, pp. 79–86, 1951.
12- L. A. Goodman, W. H. Kruskal, "Measures of Association for Cross Classifications, IV: Simplification of Asymptotic Variances," in Journal of the American Statistical Association, pp. 415–421, 1972.
13- L. A. Goodman, W. H. Kruskal, "Measures of Association for Cross Classifications III: Approximate Sampling Theory," in Journal of the American Statistical Association, pp. 310–364, 1963.
14- T. Byrt, J. Bishop and J. B. Carlin, “Bias, prevalence, and kappa,” in Journal of Clinical Epidemiology pp. 423-429, 1993.
15- M. Shepperd, D. Bowes, and T. Hall, “Researcher Bias: The Use of Machine Learning in Software Defect Prediction,” in IEEE Transactions on Software Engineering, pp. 603-616, 2014.
16- X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem, ” in Information Sciences, pp.250-261, 2016.
Cite
If you use PyCM in your research , please cite this paper :
Haghighi, S., Jasemi, M., Hessabi, S. and Zolanvari, A. (2018). PyCM: Multiclass confusion matrix library in Python. Journal of Open Source Software, 3(25), p.729.
@article{Haghighi2018,
doi = {10.21105/joss.00729},
url = {https://doi.org/10.21105/joss.00729},
year = {2018},
month = {may},
publisher = {The Open Journal},
volume = {3},
number = {25},
pages = {729},
author = {Sepand Haghighi and Masoomeh Jasemi and Shaahin Hessabi and Alireza Zolanvari},
title = {{PyCM}: Multiclass confusion matrix library in Python},
journal = {Journal of Open Source Software}
}
Download PyCM.bib
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Changelog
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog and this project adheres to Semantic Versioning.
Unreleased
0.8.6 - 2018-05-31
Added
- Example section in document
- Python 2.7 CI
- JOSS paper pdf
Changed
- Cite section
- ConfusionMatrix docstring
- round function changed to numpy.around
- README.md
0.8.5 - 2018-05-21
Added
- Example-1 (Comparison of three different classifiers)
- Example-2 (How to plot via matplotlib)
- JOSS paper
- ConfusionMatrix docstring
Changed
- Table size in HTML report
- Test system
- README.md
0.8.1 - 2018-03-22
Added
- Goodman and Kruskal's lambda B
- Goodman and Kruskal's lambda A
- Cross Entropy
- Conditional Entropy
- Joint Entropy
- Reference Entropy
- Response Entropy
- Kullback-Liebler divergence
- Direct ConfusionMatrix
- Kappa Unbiased
- Kappa No Prevalence
- Random Accuracy Unbiased
- pycmVectorError class
- pycmMatrixError class
- Mutual Information
- Support numpy arrays
Changed
- Notebook file updated
Removed
- pycmError class
0.7 - 2018-02-26
Added
- Cramer's V
- 95% Confidence interval
- Chi-Squared
- Phi-Squared
- Chi-Squared DF
- Standard error
- Kappa standard error
- Kappa 95% confidence interval
- Cicchetti benchmark
Changed
- Overall statistics color in HTML report
- Parameters description link in HTML report
0.6 - 2018-02-21
Added
- CSV report
- Changelog
- Output files
- digit parameter to ConfusionMatrix object
Changed
- Confusion matrix color in HTML report
- Parameters description link in HTML report
- Capitalize descriptions
0.5 - 2018-02-17
Added
- Scott's pi
- Gwet's AC1
- Bennett S score
- HTML report
0.4 - 2018-02-05
Added
- TPR Micro/Macro
- PPV Micro/Macro
- RACC overall
- ERR(Error rate)
- FBeta-Score
- F0.5
- F2
- Fleiss benchmark
- Altman benchmark
- Output file(.pycm)
Changed
- Class with zero item
- Normalized matrix
Removed
- Kappa and SOA for each class
0.3 - 2018-01-27
Added
- Kappa
- Random accuracy
- Landis and Koch benchmark
- overall_stat
0.2 - 2018-01-24
Added
- Population
- Condition positive
- Condition negative
- Test outcome positive
- Test outcome negative
- Prevalence
- G-measure
- Matrix method
- Normalized matrix method
- Params method
Changed
- statistic_result to class_stat
- params() to stat()
0.1 - 2018-01-22
Added
- ACC
- BM
- DOR
- F1-Score
- FDR
- FNR
- FOR
- FPR
- LR+
- LR-
- MCC
- MK
- NPV
- PPV
- TNR
- TPR
- documents and README.md
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