Metadata-Version: 2.1
Name: pycm
Version: 1.4
Summary: Multi-class confusion matrix library in Python
Home-page: https://github.com/sepandhaghighi/pycm
Author: Sepand Haghighi
Author-email: sepand@qpage.ir
License: MIT
Download-URL: https://github.com/sepandhaghighi/pycm/tarball/v1.4
Project-URL: Source, https://github.com/sepandhaghighi/pycm
Project-URL: Webpage, http://pycm.shaghighi.ir
Description: 
        <div align="center">
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        <br/>
        <br/>
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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>
        </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/count/pycm"><img src="http://pepy.tech/badge/pycm"></a></td>
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        	<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">Travis</td>
        		<td align="center"><a href="https://travis-ci.org/sepandhaghighi/pycm"><img src="https://travis-ci.org/sepandhaghighi/pycm.svg?branch=master"></a></td>
        		<td align="center"><a href="https://travis-ci.org/sepandhaghighi/pycm"><img src="https://travis-ci.org/sepandhaghighi/pycm.svg?branch=dev"></a></td>
        	</tr>
        	<tr>
        		<td align="center">AppVeyor</td>
        		<td align="center"><a href="https://ci.appveyor.com/project/sepandhaghighi/pycm"><img src="https://ci.appveyor.com/api/projects/status/nbe96d7gk2693ju0/branch/master?svg=true"></a></td>
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        	</tr>
        </table>
        
        
        
        ## Installation		
        
        ### Source Code
        - Download [Version 1.4](https://github.com/sepandhaghighi/pycm/archive/v1.4.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)
        AUNP                                                             0.66667
        AUNU                                                             0.69444
        Bennett_S                                                        0.375
        CBA                                                              0.47778
        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_MCC                                                      0.36667
        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
        RR                                                               4.0
        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                 
        AUC(Area under the roc curve)                                    0.88889                 0.61111                 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                     
        dInd(Distance index)                                             0.22222                 0.67586                 0.60093                 
        sInd(Similarity index)                                           0.84287                 0.52209                 0.57508                 
                                                                                             
        >>> 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            
        
        >>> cm.matrix(one_vs_all=True,class_name=0)   # One-Vs-All, new in version 1.4
        Predict          0    ~    
        Actual
        0                3    0    
        ~                2    7    
        
        ```
        ### Direct CM
        ```python
        >>> 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)
        AUNP                                                             0.66667
        AUNU                                                             0.66667
        Bennett_S                                                        0.5
        CBA                                                              0.52381
        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_MCC                                                      0.48795
        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
        RR                                                               4.0
        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                    
        AUC(Area under the roc curve)                                    0.66667                 0.66667                 
        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                     
        dInd(Distance index)                                             0.66667                 0.66667                 
        sInd(Similarity index)                                           0.5286                  0.5286                  
        
        >>> 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](http://www.shaghighi.ir/pycm/doc/Example3.html "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](http://www.shaghighi.ir/pycm/doc/Example4.html "Example4")
        
        ### Sample Weights
        `sample_weight` is added in `Version 1.2`
        
        For more information visit [Example5](http://www.shaghighi.ir/pycm/doc/Example5.html "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
        
        
        ```python
        
        >>> 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](https://github.com/sepandhaghighi/pycm/tree/master/Document "Document")
        
        <div align="center">
        
        <a href="https://asciinema.org/a/171863" target="_blank"><img src="https://asciinema.org/a/171863.png" /></a>
        </div>
        
        ## Issues & Bug Reports			
        
        Just fill an issue and describe it. We'll check it ASAP!							
        or send an email to [shaghighi@ce.sharif.edu](mailto:shaghighi@ce.sharif.edu "shaghighi@ce.sharif.edu"). 
        
        
        ## Todo	
        
        Moved [here](https://github.com/sepandhaghighi/pycm/blob/master/TODO.md)
        
        ## Outputs	
        
        1. [HTML](http://www.shaghighi.ir/pycm/test.html)
        2. [CSV](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.csv)
        3. [PyCM](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.pycm)			
        4. [OBJ](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.obj)	
        
        
        ## Dependencies
        
        <a href="https://requires.io/github/sepandhaghighi/pycm/requirements/?branch=dev"><img src="https://requires.io/github/sepandhaghighi/pycm/requirements.svg?branch=dev" alt="Requirements Status" /></a>
        
        ## 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			
        
        <blockquote>1- J. R. Landis, G. G. Koch, “The measurement of observer agreement for categorical data. Biometrics,” in International Biometric Society,  pp. 159–174, 1977. </blockquote>
        
        <blockquote>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.</blockquote>
        
        
        <blockquote>3-  C. Sammut, G. Webb, “Encyclopedia of Machine Learning” in Springer, 2011.</blockquote>
        
        <blockquote>4- J. L. Fleiss, “Measuring nominal scale agreement among many raters,” in Psychological Bulletin, pp. 378-382. </blockquote>
        
        <blockquote>5- D.G. Altman, “Practical Statistics for Medical Research,” in Chapman and Hall, 1990.</blockquote>
        
        <blockquote>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.”</blockquote>
        
        <blockquote>7- W. A. Scott, “Reliability of content analysis: The case of nominal scaling,” in Public Opinion Quarterly, pp. 321–325, 1955.</blockquote>
        
        <blockquote>8- E. M. Bennett, R. Alpert, and A. C. Goldstein, “Communication through limited response questioning,” in The Public Opinion Quarterly, pp. 303–308, 1954.</blockquote>
        
        <blockquote>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.</blockquote>
        
        <blockquote>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.</blockquote>
        
        <blockquote>11- S. Kullback, R. A. Leibler "On information and sufficiency," in Annals of Mathematical Statistics, pp. 79–86, 1951.</blockquote>
        
        <blockquote>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.</blockquote>
        
        <blockquote>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. </blockquote>
        
        <blockquote>14- T. Byrt, J. Bishop and J. B. Carlin, “Bias, prevalence, and kappa,” in Journal of Clinical Epidemiology pp. 423-429, 1993.</blockquote>
        
        <blockquote>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.</blockquote>
        
        <blockquote>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.</blockquote>
        
        <blockquote>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).</blockquote>
        
        <blockquote>18- Kononenko I. and Bratko I. Information-based evaluation criterion for classifier’s
        performance. Machine Learning, 6:67–80, 1991.</blockquote>
        
        <blockquote>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 </blockquote>
        
        
        <blockquote>20- Gorodkin J (2004) Comparing two K-category assignments by a K-category
        correlation coefficient. Computational Biology and Chemistry 28: 367–374 </blockquote>
        
        <blockquote>21- Freitas C.O.A., de Carvalho J.M., Oliveira J., Aires S.B.K., Sabourin R. (2007) Confusion Matrix Disagreement for Multiple Classifiers. In: Rueda L., Mery D., Kittler J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg</blockquote>
        
        <blockquote>22- Branco P., Torgo L., Ribeiro R.P. (2017) Relevance-Based Evaluation Metrics for Multi-class Imbalanced Domains. In: Kim J., Shim K., Cao L., Lee JG., Lin X., Moon YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, vol 10234. Springer, Cham</blockquote>
        
        <blockquote>23- Ballabio, D., Grisoni, F. and Todeschini, R. (2018). Multivariate comparison of classification performance measures. Chemometrics and Intelligent Laboratory Systems, 174, pp.33-44.</blockquote>
        
        <blockquote>24- Cohen, Jacob. 1960. A coefficient of agreement for nominal scales. Educational And Psychological Measurement 20:37-46</blockquote>
        
        <blockquote>25- Siegel, Sidney and N. John Castellan, Jr. 1988. Nonparametric Statistics for the Behavioral Sciences. McGraw Hill.</blockquote>
        
        <blockquote>26- Cramér, Harald. 1946. Mathematical Methods of Statistics. Princeton: Princeton University Press, page 282 (Chapter 21. The two-dimensional case)</blockquote>
        
        <blockquote>27- Matthews, B. W. (1975). "Comparison of the predicted and observed secondary structure of T4 phage lysozyme". Biochimica et Biophysica Acta (BBA) - Protein Structure. 405 (2): 442–451.</blockquote>
        
        <blockquote>28- Swets JA. (1973). "The relative operating characteristic in Psychology". Science. 182 (14116): 990–1000.</blockquote> 
        
        <blockquote>29- Jaccard, Paul (1901), "Étude comparative de la distribution florale dans une portion des Alpes et des Jura", Bulletin de la Société Vaudoise des Sciences Naturelles, 37: 547–579.</blockquote> 
        
        <blockquote>30- Thomas M. Cover and Joy A. Thomas. 2006. Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience, New York, NY, USA.</blockquote> 
        
        <blockquote>31- Keeping, E.S. (1962) Introduction to Statistical Inference. D. Van Nostrand, Princeton, NJ.</blockquote>
        
        
        ## Cite
        
        If you use PyCM in your research , please cite this JOSS paper :
        
        <pre>
        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.
        </pre>
        <pre>
        
        @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}
        }
        
        
        </pre>
        
        Download [PyCM.bib](http://www.shaghighi.ir/pycm/PYCM.bib)	
        
        
        <table>
        	<tr> 
        		<td align="center">JOSS</td>
        		<td align="center"><a href="https://doi.org/10.21105/joss.00729"><img src="http://joss.theoj.org/papers/10.21105/joss.00729/status.svg"></a></td>	
        	</tr>
        	<tr>
        		<td align="center">Zenodo</td>
        		<td align="center"><a href="https://doi.org/10.5281/zenodo.1157173"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.1157173.svg" alt="DOI"></a></td>
        	</tr>
        	<tr>
        		<td align="center">Researchgate</td>
        		<td align="center"><a href="https://www.researchgate.net/project/PYCM-python-confusion-matrix"><img src="https://img.shields.io/badge/Researchgate-PyCM-yellow.svg"></a></td>
        	</tr>
        </table>
        				
        
        
        ## License
        
        [![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2Fsepandhaghighi%2Fpycm.svg?type=large)](https://app.fossa.io/projects/git%2Bgithub.com%2Fsepandhaghighi%2Fpycm?ref=badge_large)
        
        
        ## Donate to our project		
        
        If you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do ;-) .
        
        <a href="http://www.shaghighi.ir/pycm/donate.html" target="_blank"><img src="http://www.shaghighi.ir/pycm/images/Donate-Button.png" height="90px" width="270px" alt="PyCM Donation"></a>
        
        
        
        # Changelog
        All notable changes to this project will be documented in this file.
        
        The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/)
        and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html).
        
        ## [Unreleased]
        ## [1.4] - 2018-11-12
        ### Added
        - Area Under Curve
        - AUNU
        - AUNP
        - Class Balance Accuracy
        - Global Performance Index (RR)
        - Overall MCC
        - Distance index (dInd)
        - Similarity index (sInd)
        - `one_vs_all`
        - `dev-requirements.txt`
        
        ### Changed
        - `README.md` modified
        - Document modified
        - `save_stat` modified
        - `requirements.txt` modified
        
        ## [1.3] - 2018-10-10
        ### Added
        - Confusion Entropy
        - Overall Confusion Entropy
        - Modified Confusion Entropy
        - Overall Modified Confusion Entropy
        - Information Score
        
        ### Changed
        - `README.md` modified
        
        ## [1.2] - 2018-10-01
        ### Added
        - NIR (No Information Rate)
        - P-Value
        - `sample_weight`
        - `transpose`
        
        ### Changed
        - `README.md` modified
        - Key error in some parameters fixed
        - `OSX` env added to `.travis.yml`
        
        ## [1.1] - 2018-09-08
        ### Added
        - Zero-one loss
        - Support
        - `online_help` function
        
        ### Changed
        - `README.md` modified
        - `html_table` function modified
        - `table_print` function modified
        - `normalized_table_print` function modified
        
        ## [1.0] - 2018-08-30
        ### Added
        - Hamming loss
        
        ### Changed
        - `README.md` modified
        
        ## [0.9.5] - 2018-07-08
        ### Added
        - Obj load
        - Obj save
        - Example-4
        
        ### Changed
        - `README.md` modified
        - Block diagram updated
        
        ## [0.9] - 2018-06-28
        ### Added
        - Activation Threshold
        - Example-3
        - Jaccard index
        - Overall Jaccard index
        
        ### Changed
        - `README.md` modified
        - `setup.py` modified
        
        ## [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` modified
        
        ## [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` modified
        
        ## [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`
        
        [Unreleased]: https://github.com/sepandhaghighi/pycm/compare/v1.4...HEAD
        [1.4]: https://github.com/sepandhaghighi/pycm/compare/v1.3...v1.4
        [1.3]: https://github.com/sepandhaghighi/pycm/compare/v1.2...v1.3
        [1.2]: https://github.com/sepandhaghighi/pycm/compare/v1.1...v1.2
        [1.1]: https://github.com/sepandhaghighi/pycm/compare/v1.0...v1.1
        [1.0]: https://github.com/sepandhaghighi/pycm/compare/v0.9.5...v1.0
        [0.9.5]: https://github.com/sepandhaghighi/pycm/compare/v0.9...v0.9.5
        [0.9]: https://github.com/sepandhaghighi/pycm/compare/v0.8.6...v0.9
        [0.8.6]: https://github.com/sepandhaghighi/pycm/compare/v0.8.5...v0.8.6
        [0.8.5]: https://github.com/sepandhaghighi/pycm/compare/v0.8.1...v0.8.5
        [0.8.1]: https://github.com/sepandhaghighi/pycm/compare/v0.7...v0.8.1
        [0.7]: https://github.com/sepandhaghighi/pycm/compare/v0.6...v0.7
        [0.6]: https://github.com/sepandhaghighi/pycm/compare/v0.5...v0.6
        [0.5]: https://github.com/sepandhaghighi/pycm/compare/v0.4...v0.5
        [0.4]: https://github.com/sepandhaghighi/pycm/compare/v0.3...v0.4
        [0.3]: https://github.com/sepandhaghighi/pycm/compare/v0.2...v0.3
        [0.2]: https://github.com/sepandhaghighi/pycm/compare/v0.1...v0.2
        [0.1]: https://github.com/sepandhaghighi/pycm/compare/1e238cd...v0.1
        
        
        
        
Keywords: confusion-matrix python3 python machine_learning ML
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Manufacturing
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Physics
Requires-Python: >=2.7
Description-Content-Type: text/markdown
