Multi-class confusion matrix library in Python.
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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

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Installation

Source Code

  • Download Version 1.3 or Latest Source
  • 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

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
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
NIR                                                              0.5
Overall_ACC                                                      0.58333
Overall_CEN                                                      0.46381
Overall_J                                                        (1.225,0.40833)
Overall_MCEN                                                     0.51894
Overall_RACC                                                     0.35417
Overall_RACCU                                                    0.36458
P-Value                                                          0.38721
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                 0.16667                 
CEN(Confusion entropy)                                           0.25                    0.49658                 0.60442                 
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                 
IS(Information score)                                            1.26303                 1.0                     0.26303                 
J(Jaccard index)                                                 0.6                     0.25                    0.375                   
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                 
MCEN(Modified confusion entropy)                                 0.26439                 0.5                     0.6875                  
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 or support)                                 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                                                      1.90476
Chi-Squared DF                                                   1
Conditional Entropy                                              0.34436
Cramer_V                                                         0.48795
Cross Entropy                                                    1.2454
Gwet_AC1                                                         0.6
Hamming Loss                                                     0.25
Joint Entropy                                                    1.29879
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                                                         0.33333
Lambda B                                                         0.0
Mutual Information                                               0.1992
NIR                                                              0.625
Overall_ACC                                                      0.75
Overall_CEN                                                      0.44812
Overall_J                                                        (1.04762,0.52381)
Overall_MCEN                                                     0.29904
Overall_RACC                                                     0.59375
Overall_RACCU                                                    0.625
P-Value                                                          0.36974
PPV_Macro                                                        0.85714
PPV_Micro                                                        0.75
Phi-Squared                                                      0.2381
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
Zero-one Loss                                                    2

Class Statistics :

Classes                                                          Class1                  Class2                  
ACC(Accuracy)                                                    0.75                    0.75                    
BM(Informedness or bookmaker informedness)                       0.33333                 0.33333                 
CEN(Confusion entropy)                                           0.5                     0.43083                 
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                 
IS(Information score)                                            1.41504                 0.19265                 
J(Jaccard index)                                                 0.33333                 0.71429                 
LR+(Positive likelihood ratio)                                   None                    1.5                     
LR-(Negative likelihood ratio)                                   0.66667                 0.0                     
MCC(Matthews correlation coefficient)                            0.48795                 0.48795                 
MCEN(Modified confusion entropy)                                 0.38998                 0.51639                 
MK(Markedness)                                                   0.71429                 0.71429                 
N(Condition negative)                                            5                       3                       
NPV(Negative predictive value)                                   0.71429                 1.0                     
P(Condition positive or support)                                 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

>>> cm3 = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":0}, "Class2": {"Class1": 2, "Class2": 5}},transpose=True) # Transpose Matrix      
>>> cm3.matrix()
Predict          Class1    Class2    
Actual
Class1           1         2         
Class2           0         5         
                   

Activation Threshold

threshold is added in Version 0.9 for real value prediction.

For more information visit Example3

Load From File

file is added in Version 0.9.5 in order to load saved confusion matrix with .obj format generated by save_obj method.

For more information visit Example4

Sample Weights

sample_weight is added in Version 1.2

For more information visit Example5

Transpose

transpose is added in Version 1.2 in order to transpose input matrix (only in Direct CM mode)

Online Help

online_help function is added in Version 1.1 in order to open each statistics definition in web browser

>>> from pycm import online_help
>>> online_help("J")
>>> online_help("Strength_Of_Agreement(Landis and Koch)")
>>> online_help(2)
  • list of items are available by calling online_help() (without argument)

Acceptable Data Types

  1. actual_vector : python list or numpy array of any stringable objects
  2. predict_vector : python list or numpy array of any stringable objects
  3. matrix : dict
  4. digit: int
  5. threshold : FunctionType (function or lambda)
  6. file : File object
  7. sample_weight : python list or numpy array of any stringable objects
  8. transpose : bool
  • run help(ConfusionMatrix) for ConfusionMatrix object 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

Moved here

Outputs

  1. HTML
  2. CSV
  3. PyCM
  4. OBJ

Dependencies

Requirements Status

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.
17- Wei, J.-M., Yuan, X.-Y., Hu, Q.-H., Wang, S.-Q.: A novel measure for evaluating classifiers. Expert Systems with Applications, Vol 37, 3799–3809 (2010).
18- Kononenko I. and Bratko I. Information-based evaluation criterion for classifier’s performance. Machine Learning, 6:67–80, 1991.
19- Delgado R., Núñez-González J.D. (2019) Enhancing Confusion Entropy as Measure for Evaluating Classifiers. In: Graña M. et al. (eds) International Joint Conference SOCO’18-CISIS’18-ICEUTE’18. SOCO’18-CISIS’18-ICEUTE’18 2018. Advances in Intelligent Systems and Computing, vol 771. Springer, Cham

Cite

If you use PyCM in your research , please cite this JOSS 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}
}


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