Multi-class confusion matrix library in Python

Sepand Haghighi

Last update: Dec 31, 2022

Overview

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 accurate evaluation of a large variety of classifiers.

Fig1. ConfusionMatrix Block Diagram

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Installation

⚠️ PyCM 2.4 is the last version to support Python 2.7 & Python 3.4

⚠️ Plotting capability requires Matplotlib (>= 3.0.0) or Seaborn (>= 0.9.1)

Source code

Download Version 3.0 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

Check Python Packaging User Guide
Run pip install pycm==3.0 or pip3 install pycm==3.0 (Need root access)

Conda

Check Conda Managing Package
conda install -c sepandhaghighi pycm (Need root access)

Easy install

Run easy_install --upgrade pycm (Need root access)

MATLAB

Download and install MATLAB (>=8.5, 64/32 bit)
Download and install Python3.x (>=3.5, 64/32 bit)
- Select Add to PATH option
- Select Install pip option
Run pip install pycm or pip3 install pycm (Need root access)
Configure Python interpreter

>> pyversion PYTHON_EXECUTABLE_FULL_PATH

Visit MATLAB Examples

Docker

Run docker pull sepandhaghighi/pycm (Need root access)
Configuration :
- Ubuntu 16.04
- Python 3.6

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)
ACC Macro                                                         0.72222
ARI                                                               0.09206
AUNP                                                              0.66667
AUNU                                                              0.69444
Bangdiwala B                                                      0.37255
Bennett S                                                         0.375
CBA                                                               0.47778
CSI                                                               0.17778
Chi-Squared                                                       6.6
Chi-Squared DF                                                    4
Conditional Entropy                                               0.95915
Cramer V                                                          0.5244
Cross Entropy                                                     1.59352
F1 Macro                                                          0.56515
F1 Micro                                                          0.58333
FNR Macro                                                         0.38889
FNR Micro                                                         0.41667
FPR Macro                                                         0.22222
FPR Micro                                                         0.20833
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
Krippendorff Alpha                                                0.37158
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
Pearson C                                                         0.59568
Phi-Squared                                                       0.55
RCI                                                               0.34947
RR                                                                4.0
Reference Entropy                                                 1.5
Response Entropy                                                  1.48336
SOA1(Landis & Koch)                                               Fair
SOA2(Fleiss)                                                      Poor
SOA3(Altman)                                                      Fair
SOA4(Cicchetti)                                                   Poor
SOA5(Cramer)                                                      Relatively Strong
SOA6(Matthews)                                                    Weak
Scott PI                                                          0.34426
Standard Error                                                    0.14232
TNR Macro                                                         0.77778
TNR Micro                                                         0.79167
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       
AGF(Adjusted F-score)                                             0.9136        0.53995       0.5516        
AGM(Adjusted geometric mean)                                      0.83729       0.692         0.60712       
AM(Difference between automatic and manual classification)        2             -1            -1            
AUC(Area under the ROC curve)                                     0.88889       0.61111       0.58333       
AUCI(AUC value interpretation)                                    Very Good     Fair          Poor          
AUPR(Area under the PR curve)                                     0.8           0.41667       0.55          
BCD(Bray-Curtis dissimilarity)                                    0.08333       0.04167       0.04167       
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           
DP(Discriminant power)                                            None          0.33193       0.16597       
DPI(Discriminant power interpretation)                            None          Poor          Poor          
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       
GI(Gini index)                                                    0.77778       0.22222       0.16667       
GM(G-mean geometric mean of specificity and sensitivity)          0.88192       0.54433       0.57735       
IBA(Index of balanced accuracy)                                   0.95062       0.13169       0.27778       
ICSI(Individual classification success index)                     0.6           -0.16667      0.1           
IS(Information score)                                             1.26303       1.0           0.26303       
J(Jaccard index)                                                  0.6           0.25          0.375         
LS(Lift score)                                                    2.4           2.0           1.2           
MCC(Matthews correlation coefficient)                             0.68313       0.2582        0.16903       
MCCI(Matthews correlation coefficient interpretation)             Moderate      Negligible    Negligible    
MCEN(Modified confusion entropy)                                  0.26439       0.5           0.6875        
MK(Markedness)                                                    0.6           0.3           0.17143       
N(Condition negative)                                             9             9             6             
NLR(Negative likelihood ratio)                                    0.0           0.75          0.75          
NLRI(Negative likelihood ratio interpretation)                    Good          Negligible    Negligible    
NPV(Negative predictive value)                                    1.0           0.8           0.57143       
OC(Overlap coefficient)                                           1.0           0.5           0.6           
OOC(Otsuka-Ochiai coefficient)                                    0.7746        0.40825       0.54772       
OP(Optimized precision)                                           0.70833       0.29545       0.44048       
P(Condition positive or support)                                  3             3             6             
PLR(Positive likelihood ratio)                                    4.5           3.0           1.5           
PLRI(Positive likelihood ratio interpretation)                    Poor          Poor          Poor          
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           
Q(Yule Q - coefficient of colligation)                            None          0.6           0.33333       
QI(Yule Q interpretation)                                         None          Moderate      Weak          
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           
Y(Youden index)                                                   0.77778       0.22222       0.16667       
dInd(Distance index)                                              0.22222       0.67586       0.60093       
sInd(Similarity index)                                            0.84287       0.52209       0.57508

>>> cm.print_matrix()
Predict          0    1    2    
Actual
0                3    0    0    

1                0    1    2    

2                2    1    3    

>>> cm.print_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.print_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

>>> 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)
ACC Macro                                                         0.75
ARI                                                               0.17241
AUNP                                                              0.66667
AUNU                                                              0.66667
Bangdiwala B                                                      0.68421
Bennett S                                                         0.5
CBA                                                               0.52381
CSI                                                               0.52381
Chi-Squared                                                       1.90476
Chi-Squared DF                                                    1
Conditional Entropy                                               0.34436
Cramer V                                                          0.48795
Cross Entropy                                                     1.2454
F1 Macro                                                          0.66667
F1 Micro                                                          0.75
FNR Macro                                                         0.33333
FNR Micro                                                         0.25
FPR Macro                                                         0.33333
FPR Micro                                                         0.25
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
Krippendorff Alpha                                                0.375
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
Pearson C                                                         0.43853
Phi-Squared                                                       0.2381
RCI                                                               0.20871
RR                                                                4.0
Reference Entropy                                                 0.95443
Response Entropy                                                  0.54356
SOA1(Landis & Koch)                                               Fair
SOA2(Fleiss)                                                      Poor
SOA3(Altman)                                                      Fair
SOA4(Cicchetti)                                                   Poor
SOA5(Cramer)                                                      Relatively Strong
SOA6(Matthews)                                                    Weak
Scott PI                                                          0.33333
Standard Error                                                    0.15309
TNR Macro                                                         0.66667
TNR Micro                                                         0.75
TPR Macro                                                         0.66667
TPR Micro                                                         0.75
Zero-one Loss                                                     2

Class Statistics :

Classes                                                           Class1        Class2        
ACC(Accuracy)                                                     0.75          0.75          
AGF(Adjusted F-score)                                             0.53979       0.81325       
AGM(Adjusted geometric mean)                                      0.73991       0.5108        
AM(Difference between automatic and manual classification)        -2            2             
AUC(Area under the ROC curve)                                     0.66667       0.66667       
AUCI(AUC value interpretation)                                    Fair          Fair          
AUPR(Area under the PR curve)                                     0.66667       0.85714       
BCD(Bray-Curtis dissimilarity)                                    0.125         0.125         
BM(Informedness or bookmaker informedness)                        0.33333       0.33333       
CEN(Confusion entropy)                                            0.5           0.43083       
DOR(Diagnostic odds ratio)                                        None          None          
DP(Discriminant power)                                            None          None          
DPI(Discriminant power interpretation)                            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       
GI(Gini index)                                                    0.33333       0.33333       
GM(G-mean geometric mean of specificity and sensitivity)          0.57735       0.57735       
IBA(Index of balanced accuracy)                                   0.11111       0.55556       
ICSI(Individual classification success index)                     0.33333       0.71429       
IS(Information score)                                             1.41504       0.19265       
J(Jaccard index)                                                  0.33333       0.71429       
LS(Lift score)                                                    2.66667       1.14286       
MCC(Matthews correlation coefficient)                             0.48795       0.48795       
MCCI(Matthews correlation coefficient interpretation)             Weak          Weak          
MCEN(Modified confusion entropy)                                  0.38998       0.51639       
MK(Markedness)                                                    0.71429       0.71429       
N(Condition negative)                                             5             3             
NLR(Negative likelihood ratio)                                    0.66667       0.0           
NLRI(Negative likelihood ratio interpretation)                    Negligible    Good          
NPV(Negative predictive value)                                    0.71429       1.0           
OC(Overlap coefficient)                                           1.0           1.0           
OOC(Otsuka-Ochiai coefficient)                                    0.57735       0.84515       
OP(Optimized precision)                                           0.25          0.25          
P(Condition positive or support)                                  3             5             
PLR(Positive likelihood ratio)                                    None          1.5           
PLRI(Positive likelihood ratio interpretation)                    None          Poor          
POP(Population)                                                   8             8             
PPV(Precision or positive predictive value)                       1.0           0.71429       
PRE(Prevalence)                                                   0.375         0.625         
Q(Yule Q - coefficient of colligation)                            None          None          
QI(Yule Q interpretation)                                         None          None          
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           
Y(Youden index)                                                   0.33333       0.33333       
dInd(Distance index)                                              0.66667       0.66667       
sInd(Similarity index)                                            0.5286        0.5286
   
>>> cm2.stat(summary=True)
Overall Statistics : 

ACC Macro                                                         0.75
F1 Macro                                                          0.66667
FPR Macro                                                         0.33333
Kappa                                                             0.38462
Overall ACC                                                       0.75
PPV Macro                                                         0.85714
SOA1(Landis & Koch)                                               Fair
TPR Macro                                                         0.66667
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       
AUCI(AUC value interpretation)                                    Fair          Fair          
F1(F1 score - harmonic mean of precision and sensitivity)         0.5           0.83333       
FN(False negative/miss/type 2 error)                              2             0             
FP(False positive/type 1 error/false alarm)                       0             2             
FPR(Fall-out or false positive rate)                              0.0           0.66667       
N(Condition negative)                                             5             3             
P(Condition positive or support)                                  3             5             
POP(Population)                                                   8             8             
PPV(Precision or positive predictive value)                       1.0           0.71429       
TN(True negative/correct rejection)                               5             1             
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.print_matrix()
Predict          Class1    Class2    
Actual
Class1           1         2         

Class2           0         5

matrix() and normalized_matrix() renamed to print_matrix() and print_normalized_matrix() in version 1.5

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)

Relabel

relabel method is added in version 1.5 in order to change ConfusionMatrix classnames.

>>> cm.relabel(mapping={0:"L1",1:"L2",2:"L3"})
>>> cm
pycm.ConfusionMatrix(classes: ['L1', 'L2', 'L3'])

Position

position method is added in version 2.8 in order to find the indexes of observations in predict_vector which made TP, TN, FP, FN.

>>> cm.position()
{0: {'FN': [], 'FP': [0, 7], 'TP': [1, 4, 9], 'TN': [2, 3, 5, 6, 8, 10, 11]}, 1: {'FN': [5, 10], 'FP': [3], 'TP': [6], 'TN': [0, 1, 2, 4, 7, 8, 9, 11]}, 2: {'FN': [0, 3, 7], 'FP': [5, 10], 'TP': [2, 8, 11], 'TN': [1, 4, 6, 9]}}

To array

to_array method is added in version 2.9 in order to returns the confusion matrix in the form of a NumPy array. This can be helpful to apply different operations over the confusion matrix for different purposes such as aggregation, normalization, and combination.

>>> cm.to_array()
array([[3, 0, 0],
       [0, 1, 2],
       [2, 1, 3]])
>>> cm.to_array(normalized=True)
array([[1.     , 0.     , 0.     ],
       [0.     , 0.33333, 0.66667],
       [0.33333, 0.16667, 0.5    ]])
>>> cm.to_array(normalized=True,one_vs_all=True, class_name="L1")
array([[1.     , 0.     ],
       [0.22222, 0.77778]])

Combine

combine method is added in version 3.0 in order to merge two confusion matrices. This option will be useful in mini-batch learning.

>>> cm_combined = cm2.combine(cm3)
>>> cm_combined.print_matrix()
Predict      Class1       Class2       
Actual
Class1       2            4            

Class2       0            10

Plot

plot method is added in version 3.0 in order to plot a confusion matrix using Matplotlib or Seaborn.

>>> cm.plot()

>>> from matplotlib import pyplot as plt
>>> cm.plot(cmap=plt.cm.Greens,number_label=True,plot_lib="matplotlib")

>>> cm.plot(cmap=plt.cm.Reds,normalized=True,number_label=True,plot_lib="seaborn")

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("SOA1(Landis & Koch)")
>>> online_help(2)

List of items are available by calling online_help() (without argument)
If PyCM website is not available, set alt_link = True (new in version 2.4)

Parameter recommender

This option has been added in version 1.9 to recommend the most related parameters considering the characteristics of the input dataset. The suggested parameters are selected according to some characteristics of the input such as being balance/imbalance and binary/multi-class. All suggestions can be categorized into three main groups: imbalanced dataset, binary classification for a balanced dataset, and multi-class classification for a balanced dataset. The recommendation lists have been gathered according to the respective paper of each parameter and the capabilities which had been claimed by the paper.

>>> cm.imbalance
False
>>> cm.binary
False
>>> cm.recommended_list
['MCC', 'TPR Micro', 'ACC', 'PPV Macro', 'BCD', 'Overall MCC', 'Hamming Loss', 'TPR Macro', 'Zero-one Loss', 'ERR', 'PPV Micro', 'Overall ACC']

Compare

In version 2.0, a method for comparing several confusion matrices is introduced. This option is a combination of several overall and class-based benchmarks. Each of the benchmarks evaluates the performance of the classification algorithm from good to poor and give them a numeric score. The score of good and poor performances are 1 and 0, respectively.

After that, two scores are calculated for each confusion matrices, overall and class-based. The overall score is the average of the score of six overall benchmarks which are Landis & Koch, Fleiss, Altman, Cicchetti, Cramer, and Matthews. In the same manner, the class-based score is the average of the score of six class-based benchmarks which are Positive Likelihood Ratio Interpretation, Negative Likelihood Ratio Interpretation, Discriminant Power Interpretation, AUC value Interpretation, Matthews Correlation Coefficient Interpretation and Yule's Q Interpretation. It should be noticed that if one of the benchmarks returns none for one of the classes, that benchmarks will be eliminated in total averaging. If the user sets weights for the classes, the averaging over the value of class-based benchmark scores will transform to a weighted average.

If the user sets the value of by_class boolean input True, the best confusion matrix is the one with the maximum class-based score. Otherwise, if a confusion matrix obtains the maximum of both overall and class-based scores, that will be reported as the best confusion matrix, but in any other case, the compared object doesn’t select the best confusion matrix.

>>> cm2 = ConfusionMatrix(matrix={0:{0:2,1:50,2:6},1:{0:5,1:50,2:3},2:{0:1,1:7,2:50}})
>>> cm3 = ConfusionMatrix(matrix={0:{0:50,1:2,2:6},1:{0:50,1:5,2:3},2:{0:1,1:55,2:2}})
>>> cp = Compare({"cm2":cm2,"cm3":cm3})
>>> print(cp)
Best : cm2

Rank  Name   Class-Score    Overall-Score
1     cm2    9.05           2.55
2     cm3    6.05           1.98333

>>> cp.best
pycm.ConfusionMatrix(classes: [0, 1, 2])
>>> cp.sorted
['cm2', 'cm3']
>>> cp.best_name
'cm2'

Acceptable data types

ConfusionMatrix

actual_vector : python list or numpy array of any stringable objects
predict_vector : python list or numpy array of any stringable objects
matrix : dict
digit: int
threshold : FunctionType (function or lambda)
file : File object
sample_weight : python list or numpy array of numbers
transpose : bool

Run help(ConfusionMatrix) for ConfusionMatrix object details

Compare

cm_dict : python dict of ConfusionMatrix object (str : ConfusionMatrix)
by_class : bool
weight : python dict of class weights (class_name : float)
digit: int

Run help(Compare) for Compare object details

For more information visit here

Try PyCM in your browser!

PyCM can be used online in interactive Jupyter Notebooks via the Binder service! Try it out now! :

Check Examples in Document folder

Issues & bug reports

Just fill an issue and describe it. We'll check it ASAP! or send an email to [email protected].

Please complete the issue template

Outputs

Dependencies

master	dev

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, 1971.

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.”

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Cite

If you use PyCM in your research, we would appreciate citations to the following 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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Support

Website : https://www.pycm.ir
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Comments

CM Aggregation

Description

Because the datasets are usually huge, more often than not we do the training and testing in batches. So we have to calculate the confusion matrix of each batch, and then we should be able to aggregate them integrally. So I think it's useful to have a method like update_cm(total_cm, new_cm) or something like that.
new feature

opened by fstackgeek 12

Imbalance dataset detection enhancement

Description

PyCM imbalance detection is weak, example :

>>> cm = ConfusionMatrix(matrix={0:{0:60,1:0,2:0,3:0},1:{0:0,1:20,2:0,3:0},2:{0:0,1:0,2:20,3:0},3:{0:0,1:0,2:0,3:20}})
>>> cm.print_matrix()
Predict  0        1        2        3        
Actual
0        60       0        0        0        

1        0        20       0        0        

2        0        0        20       0        

3        0        0        0        20       


>>> cm.imbalance
False

enhancement question discussion

opened by sepandhaghighi 12

Class labels

Reference Issues/PRs

#358

What does this implement/fix? Explain your changes.

classes optional parameter added to ConfusionMatrix, now you can use:

>>> y_act=[0,0,0,0,0,1,1,1,1,1,1,2,2,2,2]
>>> y_pre=[0,0,0,1,1,1,1,1,0,0,0,1,2,0,1]
>>> cm = ConfusionMatrix(y_act,y_pre,classes=[1,2,0])
>>> cm
pycm.ConfusionMatrix(classes: [1, 2, 0])
>>> cm.classes
[1, 2, 0]
>>> cm.to_array()
array([[3, 0, 3],
       [2, 1, 1],
       [2, 0, 3]])
>>> print(cm)
Predict 1       2       0       
Actual
1       3       0       3       

2       2       1       1       

0       2       0       3       
...
>>> cm = ConfusionMatrix(y_act,y_pre,classes=[1,2])
>>> cm
pycm.ConfusionMatrix(classes: [1, 2])
>>> cm.classes
[1, 2]
>>> cm.to_array()
array([[3, 0],
       [2, 1]])
>>> print(cm)
Predict 1       2       
Actual
1       3       0       

2       2       1       
...
>>> cm = ConfusionMatrix(y_act,y_pre,classes=[0,1,2,3])
/home/user/Programing/Git/pycm/pycm/pycm_util.py:340: RuntimeWarning: Used classes is not a subset of classes in actual and predict vectors.
  warn(CLASSES_WARNING, RuntimeWarning)
>>> cm
pycm.ConfusionMatrix(classes: [0, 1, 2, 3])
>>> cm.classes
[0, 1, 2, 3]
>>> cm.to_array()
array([[3, 2, 0, 0],
       [3, 3, 0, 0],
       [1, 2, 1, 0],
       [0, 0, 0, 0]])
>>> print(cm)
Predict 0       1       2       3       
Actual
0       3       2       0       0       

1       3       3       0       0       

2       1       2       1       0       

3       0       0       0       0       
...

opened by sadrasabouri 11

Micro F1 equals accuracy

This is not really a bug per se, but micro-averaged F1 is mathematically identical to overall accuracy (assuming you use the definition of Özgür, Özgür and Güngör, 2005), and hence should not be reported separately.
bug enhancement

opened by wiedenhoeft 8
Plot method
Reference Issues/PRs

#330

What does this implement/fix? Explain your changes.

In this PR:

plot method added to ConfusionMatrix's methods.

add_number_label function added to pycm_utils.py.

plot method is compatible with matplotlib and seaborn. this method may seems a little complex and it may be disturbed into several functions.

@sepandhaghighi please help me with adding proper tests.

And I think it will be great if we add a instance of plot method output in README.md too.
opened by sadrasabouri 8

relabel bug

Description

relabel method mapping order bug

Steps/Code to Reproduce

>>> 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)
>>> cm.relabel({0:"L1",1:"L2",2:"L3"})
>>> cm.relabel({'L3': 6, 'L1': 3, 'L2': 3})

Expected Behavior

Relabeling

Actual Behavior

Traceback (most recent call last):
  File "<pyshell#12>", line 1, in <module>
    cm.relabel({'L3': 6, 'L1': 3, 'L2': 3})
  File "C:\Users\Sepkjaer\AppData\Local\Programs\Python\Python35-32\lib\site-packages\pycm-2.6-py3.5.egg\pycm\pycm_obj.py", line 633, in relabel
    raise pycmMatrixError(MAPPING_CLASS_NAME_ERROR)
pycm.pycm_error.pycmMatrixError: Mapping class names error

Operating System

Windows 10

Python Version

Python 3.5

PyCM Version (Use : `pycm.version`)

2.6

bug

opened by sepandhaghighi 7

normalized_table_calc inefficiency
Description

Recently I found that normalized_table_calc function is too slow (more than 30% of the total execution time!!!) and it seems that the reason is the poor performance of numpy.around and built-in round function in scalar mode!!

I think, we should define our custom rounder function, something like this :

def custom_rounder(input_number,digit): p = float(10**digit) return int(input_number * p + 0.5)/p

Take a look at this discussion
performance
opened by sepandhaghighi 6
QI
Reference Issues/PRs

#267

What does this implement/fix? Explain your changes.

Yule's Q interpretation

Minor edits in interpret functions

Any other comments?
enhancement new feature
opened by sepandhaghighi 6
Net benefit
Reference Issues/PRs

#264 #266

What does this implement/fix? Explain your changes.

Handler functions moved to pycm_handler.py

Error objects moved to pycm_error.py

Verified tests references updated

Verified tests moved to verified_test.py

Test system modified

CONTRIBUTING.md updated

Namespace optimized

Conda Managing Package broken link fixed

.gitattributes updated

Any other comments?
enhancement new feature
opened by sepandhaghighi 6
Bump numpy from 1.23.2 to 1.23.3
Bumps numpy from 1.23.2 to 1.23.3.

Release notes

Sourced from numpy's releases.

v1.23.3

NumPy 1.23.3 Release Notes

NumPy 1.23.3 is a maintenance release that fixes bugs discovered after the 1.23.2 release. There is no major theme for this release, the main improvements are for some downstream builds and some annotation corner cases. The Python versions supported for this release are 3.8-3.11.

Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the 10.15 version currently used will no longer be supported by our build infrastructure at that point.

Contributors

A total of 16 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

Aaron Meurer

Bas van Beek

Charles Harris

Ganesh Kathiresan

Gavin Zhang +

Iantra Solari+

Jyn Spring 琴春 +

Matti Picus

Rafael Cardoso Fernandes Sousa

Rafael Sousa +

Ralf Gommers

Rin Cat (鈴猫) +

Saransh Chopra +

Sayed Adel

Sebastian Berg

Serge Guelton

Pull requests merged

A total of 14 pull requests were merged for this release.

#22136: BLD: Add Python 3.11 wheels to aarch64 build

#22148: MAINT: Update setup.py for Python 3.11.

#22155: CI: Test NumPy build against old versions of GCC(6, 7, 8)

#22156: MAINT: support IBM i system

#22195: BUG: Fix circleci build

#22214: BUG: Expose heapsort algorithms in a shared header

#22215: BUG: Support using libunwind for backtrack

#22216: MAINT: fix an incorrect pointer type usage in f2py

#22220: BUG: change overloads to play nice with pyright.

#22221: TST,BUG: Use fork context to fix MacOS savez test

#22222: TYP,BUG: Reduce argument validation in C-based __class_getitem__

... (truncated)

Commits

e47cbb6 Merge pull request #22234 from charris/prepare-1.23.3-release

e0e8991 REL: Prepare for the NumPy 1.23.3 release.

9bf22bb BUG: Better report integer division overflow (backport) (#22230)

0f0d355 Merge pull request #22224 from charris/backport-21928

55a5ca7 Merge pull request #22223 from charris/backport-21981

6cc4183 BUG: Fix the implementation of numpy.array_api.vecdot (#21928)

fb9666f BUG: Replace assert with correct error

fc0d627 TST: ensure np.equal.reduce raises a TypeError

754ec89 Merge pull request #22222 from charris/backport-22212

63ab75d Merge pull request #22221 from charris/backport-22204

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dependencies
opened by dependabot[bot] 5
Refs unification

Reference Issues/PRs

#173

What does this implement/fix? Explain your changes.

The references are unified with IEEE format

Any other comments?

Please carefully compare the new references' titles with the previous ones. Be sure that the formats are correct.

opened by alirezazolanvari 5
New Minor Features
What does this implement/fix? Explain your changes.

Two new magic method added:

__contains__ method

>>> cm = ConfusionMatrix([1,1,1,1,0],[1,1,1,1,1]) >>> 1 in cm True

__getitem__ method

>>> cm = ConfusionMatrix([1,1,1,1,0],[1,1,1,1,1]) >>> cm[0] array([0, 1]) >>> cm[1][1] 4
enhancement
opened by sadrasabouri 1
Packaging on conda-forge

Hi! It would be great to see this package on conda-forge, for people who may have only certain channels approved for use, though there's already a 'pycm' using the namespace there.

I'd be happy to put in the feed-stock and list myself (and anyone else who would want) as a recipe maintainer - I'd started a WIP PR here (https://github.com/conda-forge/staged-recipes/pull/20795) though it will clearly need a rename to not clash with the ligo package pycm there. Sadly they're on first-come-first-serve. Any name request/suggestions would be great. Thanks!

opened by dillonroach 4
Decimal points in plot method

Description

百分比的混淆矩阵计算出来的位数太长，保存下来的图片数据是重合的，请问是否有办法设置一下小数点位数？真的十分感谢解答！！！

Steps/Code to Reproduce

这是我的代码： cm = ConfusionMatrix(actual_vector=y_trueLabel, predict_vector=y_predictLabel, digit=4) cm.plot(cmap=plt.cm.Reds, normalized=True, number_label=True, plot_lib="matplotlib")
plt.xticks(rotation=45) plt.savefig(fname="C:\Users\Graduate01\Desktop\222\%s" % '235')

Expected Behavior

Actual Behavior

Operating System

Python Version

PyCM Version (Use : pycm.__version__)
bug

opened by tanjiu 9
NPV Micro/Macro missed

Description

I received an email from Dr. Aonghus Lawlor (@aonghus) and it seems we forgot to add NPV Micro/Macro to stat. Currently, we support all other similar metrics (FPR, FNR, TPR, TNR, PPV) both micro and macro average.
enhancement new feature

opened by sepandhaghighi 1

Number of decimal points

Description

Control the number of decimal points in normalized mode.

print_normalized_matrix and plot methods

Steps/Code to Reproduce

>>> from pycm import *
>>> cm = ConfusionMatrix(matrix={"Class1": {"Class1": 1, "Class2":2}, "Class2": {"Class1": 0, "Class2": 5}}, digit=2)
>>> cm.print_normalized_matrix()

Expected Behavior

Predict       Class1        Class2        
Actual
Class1        0.33          0.66      

Class2        0.0           1.0

Actual Behavior

Predict       Class1        Class2        
Actual
Class1        0.33333       0.66667       

Class2        0.0           1.0

Operating System

Windows 10

Python Version

Python 3.8.0

PyCM Version (Use : `pycm.version`)

3.4

bug

opened by sepandhaghighi 0

Adding string classes directly in ConfusionMatrix removes all stats.

Description

I was going to add my own custom labels in ConfusionMatrix. However, this blanked out all other values.

Steps/Code to Reproduce

Input:

cm1 = ConfusionMatrix(
    y_true, y_pred, digit=5, classes=["normal", "lgg", "hgg"]
)

Output:

/Users/lewiuberg/Library/Caches/pypoetry/virtualenvs/tumorclass.info-OrbyvotM-py3.9/lib/python3.9/site-packages/pycm/pycm_util.py:387: RuntimeWarning: Used classes is not a subset of classes in actual and predict vectors.
  warn(CLASSES_WARNING, RuntimeWarning)

Input:

cm1

Output:

pycm.ConfusionMatrix(classes: ['normal', 'lgg', 'hgg'])

Input:

print(cm1)

Output:

Predict      normal       lgg          hgg          
Actual
normal       0            0            0            

lgg          0            0            0            

hgg          0            0            0            





Overall Statistics : 

95% CI                                                            (None,None)
ACC Macro                                                         None
ARI                                                               None
AUNP                                                              None
AUNU                                                              None
Bangdiwala B                                                      None
...

Operating System

macOS Monterey V 12.0.1

Python Version

3.9

PyCM Version (Use : `pycm.version`)

3.3

opened by lewiuberg 4

Releases(v3.7)

v3.7(Dec 15, 2022)
Curve class added #355

ROCCurve class added #355

PRCurve class added #355

pycmCurveError class added #355

CONTRIBUTING.md updated

matrix_params_calc function optimized

README.md modified

Document modified

Test system modified

Python 3.11 added to test.yml

Acknowledgment

This version has been funded by the NLnet foundation and Python Software Foundation (PSF)
Source code(tar.gz)
Source code(zip)
v3.6(Aug 17, 2022)
Hamming distance added #350

Braun-Blanquet similarity added #349

classes parameter added to matrix_params_from_table function

Matrices with numpy.integer elements are now accepted

Arrays added to matrix parameter accepting formats #402

Website changed to http://www.pycm.io

Document modified

README.md modified

Acknowledgment

This version has been funded by the NLnet foundation
Source code(tar.gz)
Source code(zip)
v3.5(Apr 27, 2022)
Anaconda workflow added

Custom iterating setting added #409

Custom casting setting added #409

plot method updated #408

class_statistics function modified

overall_statistics function modified

BCD_calc function modified

CONTRIBUTING.md updated

CODE_OF_CONDUCT.md updated #399

Document modified

Source code(tar.gz)
Source code(zip)
v3.4(Jan 26, 2022)
Colab badge added #389

Discord badge added #397

brier_score method added #219

J (Jaccard index) section in Document.ipynb updated #401

save_obj method updated #219

Python 3.10 added to test.yml #391

Example-3 updated #405

Docstrings of the functions updated #345

CONTRIBUTING.md updated #345

Source code(tar.gz)
Source code(zip)
v3.3(Oct 27, 2021)
__compare_weight_handler__ function added #347

is_imbalanced parameter added to ConfusionMatrix __init__ method #276

class_benchmark_weight and overall_benchmark_weight parameters added to Compare __init__ method #347

statistic_recommend function modified #276

Compare weight parameter renamed to class_weight #347

Document modified

License updated

AUTHORS.md updated

README.md modified

Block diagrams updated

Source code(tar.gz)
Source code(zip)
v3.2(Aug 11, 2021)
classes_filter function added #358

classes parameter added to matrix_params_calc function #358

classes parameter added to __obj_vector_handler__ function #358

classes parameter added to ConfusionMatrix __init__ method #358

name parameter removed from html_init function

shortener parameter added to html_table function #364

shortener parameter added to save_html method #364

Document modified

HTML report modified

Source code(tar.gz)
Source code(zip)
v3.1(Mar 11, 2021)
requirements-splitter.py added

sensitivity_index method added #357

Test system modified #361

overall_statistics function modified #346

HTML report modified #367

Document modified

References format updated #173

CONTRIBUTING.md updated

Source code(tar.gz)
Source code(zip)
v3.0(Oct 26, 2020)
plot_test.py added #330

axes_gen function added #330

add_number_label function added #330

plot method added #330

combine method added #326

matrix_combine function added #326

Document modified

README.md modified

Example-2 deprecated #330

Example-7 deprecated #330

Error messages modified

Source code(tar.gz)
Source code(zip)
v2.9(Sep 23, 2020)
notebook_check.py added #335

to_array method added #328

__copy__ method added #327

copy method added #327

average method refactored #332

Source code(tar.gz)
Source code(zip)
v2.8(Jul 9, 2020)
label_map attribute added #256 #323

positions attribute added #256

position method added #256

Krippendorff's Alpha added #313

Aickin's Alpha added #317

weighted_alpha method added #313

Single class bug fixed #310

CLASS_NUMBER_ERROR error type changed to pycmMatrixError

relabel method bug fixed #323

Document modified

README.md modified

Source code(tar.gz)
Source code(zip)
v2.7(May 11, 2020)
average method added #299

weighted_average method added #274

weighted_kappa method added #243

pycmAverageError class added #274 #299

Bangdiwala's B added #303

MATLAB examples added #268

Github action added #297

Document modified

README.md modified

relabel method bug fixed #293

sparse_table_print function bug fixed #293

matrix_check function bug fixed

Minor bug in Compare class fixed #294

Class names mismatch bug fixed #293

Source code(tar.gz)
Source code(zip)
v2.6(Mar 25, 2020)
custom_rounder function added #279

complement function added

sparse_matrix attribute added #185

sparse_normalized_matrix attribute added #185

Net benefit (NB) added #264

Yule's Q interpretation (QI) added #267

Adjusted Rand index (ARI) added #270

TNR micro/macro added #284

FPR micro/macro added #284

FNR micro/macro added #284

sparse parameter added to print_matrix,print_normalized_matrix and save_stat methods #185

header parameter added to save_csv method #282

Handler functions moved to pycm_handler.py

Error objects moved to pycm_error.py

Verified tests references updated

Verified tests moved to verified_test.py

Test system modified

CONTRIBUTING.md updated

Namespace optimized

README.md modified #266

Document modified

print_normalized_matrix method modified #279

normalized_table_calc function modified #279

setup.py modified

summary mode updated #284

Dockerfile updated

Python 3.8 added to .travis.yaml and appveyor.yml

Source code(tar.gz)
Source code(zip)
v2.5(Oct 16, 2019)
__version__ variable added #241

Individual classification success index (ICSI) added #238

Classification success index (CSI) added #238

Example-8 (Confidence interval) added #237

install.sh added

autopep8.sh added

Dockerfile added

CI method added (supported statistics : ACC,AUC,Overall ACC,Kappa,TPR,TNR,PPV,NPV,PLR,NLR,PRE) #237

test.sh moved to .travis folder

Python 3.4 support dropped

Python 2.7 support dropped

AUTHORS.md updated

save_stat,save_csv and save_html methods Non-ASCII character bug fixed #246

Mixed type input vectors bug fixed #240

CONTRIBUTING.md updated #245

Example-3 updated #239

README.md modified #248

Document modified #248

CI attribute renamed to CI95 #237

kappa_se_calc function renamed to kappa_SE_calc #237

se_calc function modified and renamed to SE_calc #237

CI/SE functions moved to pycm_ci.py #237

Minor bug in save_html method fixed

Source code(tar.gz)
Source code(zip)
v2.4(Jul 31, 2019)
Tversky index (TI) added #214

Area under the PR curve (AUPR) added #216

FUNDING.yml added

AUC_calc function modified

Document modified #225

summary parameter added to save_html,save_stat,save_csv and stat methods #217

sample_weight bug in numpy array format fixed #227

Inputs manipulation bug fixed #226

Test system modified #229 @sadrasabouri

Warning system modified #228

alt_link parameter added to save_html method and online_help function #232

Compare class tests moved to compare_test.py

Warning tests moved to warning_test.py

Source code(tar.gz)
Source code(zip)
v2.3(Jun 26, 2019)
Adjusted F-score (AGF) added #209

Overlap coefficient (OC) added #212

Otsuka-Ochiai coefficient (OOC) added #213

save_stat and save_vector parameters added to save_obj method #210

Document modified #221

README.md modified

Parameters recommendation for imbalance dataset modified

Minor bug in Compare class fixed

pycm_help function modified

Benchmarks color modified #221

Source code(tar.gz)
Source code(zip)
v2.2(May 29, 2019)
Negative likelihood ratio interpretation (NLRI) added

Cramer's benchmark (SOA5) added

Matthews correlation coefficient interpretation (MCCI) added #204

Matthews's benchmark (SOA6) added #204

F1 macro added

F1 micro added

Accuracy macro added #205

Compare class score calculation modified

Parameters recommendation for multi-class dataset modified

Parameters recommendation for imbalance dataset modified

README.md modified

Document modified

Logo updated

Source code(tar.gz)
Source code(zip)
v2.1(May 6, 2019)
Adjusted geometric mean (AGM) added #177

Yule's Q (Q) added #193

Compare class and parameters recommendation system block diagrams added #192

Document links bug fixed #194

Document modified #173

Source code(tar.gz)
Source code(zip)
v2.0(Apr 15, 2019)
G-Mean (GM) added #178

Index of balanced accuracy (IBA) added #176

Optimized precision (OP) added #152

Pearson's C (C) added #180

Compare class added #111

Parameters recommendation warning added #174

ConfusionMatrix equal method added #181

Document modified #173

stat_print function bug fixed

table_print function bug fixed

Beta parameter renamed to beta (F_calc function & F_beta method)

Parameters recommendation for imbalance dataset modified

normalize parameter added to save_html method #183

pycm_func.py splitted into pycm_class_func.py and pycm_overall_func.py

vector_filter,vector_check,class_check and matrix_check functions moved to pycm_util.py

RACC_calc and RACCU_calc functions exception handler modified #187

Docstrings modified

Source code(tar.gz)
Source code(zip)
v1.9(Feb 25, 2019)
Automatic/Manual (AM) added #144

Bray-Curtis dissimilarity (BCD) added #143

CODE_OF_CONDUCT.md added #151

ISSUE_TEMPLATE.md added #156

PULL_REQUEST_TEMPLATE.md added #156

CONTRIBUTING.md added #155

X11 color names support for save_html method added #146

Parameters recommendation system added #112

Warning message for high dimension matrix print added #120

Interactive notebooks section (binder) added #142

save_matrix and normalize arguments added to save_csv method #120

README.md modified

Document modified #149 #164

ConfusionMatrix.__init__ optimized #145

Document and examples output files moved to different folders #149

Test system modified #138

relabel method bug fixed #167

Source code(tar.gz)
Source code(zip)
v1.8(Jan 5, 2019)
Lift Score (LS) added #128

color argument added to save_html method #130

version_check.py added

Error messages modified #132

Document modified #131

Website changed to http://www.pycm.ir

Interpretation functions moved to pycm_interpret.py

Utility functions moved to pycm_util.py

Unnecessary else and elif removed

== changed to is

Source code(tar.gz)
Source code(zip)
v1.7(Dec 18, 2018)
Gini Index (GI) added #119

Example-7 added #121

pycm_profile.py added

class_name argument added to stat,save_stat,save_csv and save_html methods #118

overall_param and class_param arguments empty list bug fixed #118

matrix_params_calc, matrix_params_from_table and vector_filter functions optimized #118

overall_MCC_calc, CEN_misclassification_calc and convex_combination functions optimized #118

Document modified #124

Source code(tar.gz)
Source code(zip)
v1.6(Dec 5, 2018)
AUC Value Interpretation (AUCI) added #114

Example-6 added #113

Anaconda cloud package added #90

overall_param and class_param arguments added to stat,save_stat and save_html methods

class_param argument added to save_csv method

_ removed from overall statistics names

README.md modified

Document modified

Source code(tar.gz)
Source code(zip)
v1.5(Nov 26, 2018)
Relative Classifier Information (RCI) added #82

Discriminator Power (DP) added #104

Youden's Index (Y) added #105

Discriminant Power Interpretation (DPI) added #107

Positive Likelihood Ratio Interpretation (PLRI) added #106

__len__ method added

relabel method added #98

__class_stat_init__ function added

__overall_stat_init__ function added

matrix attribute added #99

normalized_matrix attribute added #99

normalized_table attribute added #99

Python 3.7 added to .travis.yaml & appveyor.yml

README.md modified

Document modified

LR+ renamed to PLR

LR- renamed to NLR

normalized_matrix method renamed to print_normalized_matrix #99

matrix method renamed to print_matrix #99

entropy_calc fixed

cross_entropy_calc fixed

conditional_entropy_calc fixed

print_table bug for large numbers fixed

JSON key bug in save_obj fixed #108

transpose bug in save_obj fixed #108

Source code(tar.gz)
Source code(zip)
v1.4(Nov 11, 2018)
Area Under Curve added #83

AUNU added #83

AUNP added #83

Class Balance Accuracy added #81

Global Performance Index (RR) added #80

Overall MCC added #75

Distance index (dInd) added #84

Similarity index (sInd) added #84

one_vs_all added #76

dev-requirements.txt added

README.md modified #85

Document modified #85

save_stat modified #78

requirements.txt modified

Source code(tar.gz)
Source code(zip)
v1.3(Oct 9, 2018)
Confusion Entropy added #72

Overall Confusion Entropy added #72

Modified Confusion Entropy added #73

Overall Modified Confusion Entropy added #73

Information Score added #71

README.md modified

Source code(tar.gz)
Source code(zip)
v1.2(Oct 1, 2018)
NIR (No Information Rate) added

P-Value added

sample_weight added

transpose added

README.md modified

Key error in some parameters fixed

OSX env added to .travis.yml

Source code(tar.gz)
Source code(zip)
v1.1(Sep 8, 2018)
Zero-one loss added

Support added

online_help function added

README.md modified

html_table function modified

table_print function modified

normalized_table_print function modified

Source code(tar.gz)
Source code(zip)
v1.0(Aug 30, 2018)
Hamming loss added

README.md modified

Source code(tar.gz)
Source code(zip)
v0.9.5(Jul 8, 2018)
Obj load added

Obj save added

Example-4 added

README.md modified

Block diagram updated

Source code(tar.gz)
Source code(zip)
v0.9(Jun 27, 2018)
Activation Threshold added

Example-3 added

Jaccard index added

Overall Jaccard index added

README.md modified

setup.py modified

Source code(tar.gz)
Source code(zip)

Multi-class confusion matrix library in Python

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v1.23.3

NumPy 1.23.3 Release Notes

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