Multi-class confusion matrix library in Python
Project description
Table of contents
- Overview
- Installation
- Usage
- Document
- Try PyCM in Your Browser
- Issues & Bug Reports
- Todo
- Outputs
- Dependencies
- Contribution
- References
- Cite
- Authors
- License
- Show Your Support
- Changelog
- Code of Conduct
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 3.5 or Latest Source
- Run
pip install -r requirements.txtorpip3 install -r requirements.txt(Need root access) - Run
python3 setup.py installorpython setup.py install(Need root access)
PyPI
- Check Python Packaging User Guide
- Run
pip install pycm==3.5orpip3 install pycm==3.5(Need root access)
Conda
- Check Conda Managing Package
- Update Conda using
conda update conda(Need root access) - Run
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 PATHoption - Select
Install pipoption
- Select
- Run
pip install pycmorpip3 install pycm(Need root access) - Configure Python interpreter
>> pyversion PYTHON_EXECUTABLE_FULL_PATH
- Visit MATLAB Examples
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 >>> cm = ConfusionMatrix(y_actu, y_pred, classes=[1,0,2]) # classes, new in version 3.2 >>> cm.print_matrix() Predict 1 0 2 Actual 1 1 0 2 0 0 3 0 2 1 2 3 >>> cm = ConfusionMatrix(y_actu, y_pred, classes=[1,0,4]) # classes, new in version 3.2 >>> cm.print_matrix() Predict 1 0 4 Actual 1 1 0 0 0 0 3 0 4 0 0 0
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()andnormalized_matrix()renamed toprint_matrix()andprint_normalized_matrix()inversion 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 inversion 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']
is_imbalanced parameter has been added in version 3.3, so the user can indicate whether the concerned dataset is imbalanced or not. As long as the user does not provide any information in this regard, the automatic detection algorithm will be used.
>>> cm = ConfusionMatrix(y_actu, y_pred, is_imbalanced = True) >>> cm.imbalance True >>> cm = ConfusionMatrix(y_actu, y_pred, is_imbalanced = False) >>> cm.imbalance False
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 0.50278 0.425 2 cm3 0.33611 0.33056 >>> cp.best pycm.ConfusionMatrix(classes: [0, 1, 2]) >>> cp.sorted ['cm2', 'cm3'] >>> cp.best_name 'cm2'
Acceptable data types
ConfusionMatrix
actual_vector: pythonlistor numpyarrayof any stringable objectspredict_vector: pythonlistor numpyarrayof any stringable objectsmatrix:dictdigit:intthreshold:FunctionType (function or lambda)file:File objectsample_weight: pythonlistor numpyarrayof numberstranspose:boolclasses: pythonlistis_imbalanced:bool
- Run
help(ConfusionMatrix)forConfusionMatrixobject details
Compare
cm_dict: pythondictofConfusionMatrixobject (str:ConfusionMatrix)by_class:boolclass_weight: pythondictof class weights (class_name:float)class_benchmark_weight: pythondictof class benchmark weights (class_benchmark_name:float)overall_benchmark_weight: pythondictof overall benchmark weights (overall_benchmark_name:float)digit:int
- Run
help(Compare)forCompareobject details
For more information visit here
Try PyCM in your browser!
PyCM can be used online in interactive Jupyter Notebooks via the Binder or Colab services! Try it out now! :
- Check
ExamplesinDocumentfolder
Issues & bug reports
- Fill an issue and describe it. We'll check it ASAP!
- Please complete the issue template
- Discord : https://discord.com/invite/zqpU2b3J3f
- Website : https://www.pycm.ir
- Mailing List : https://mail.python.org/mailman3/lists/pycm.python.org/
- Email : info@pycm.ir
Outputs
Dependencies
| master | dev |
References
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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}
}
Download PyCM.bib
| JOSS | |
| Zenodo | |
| Researchgate |
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Changelog
All notable changes to this project will be documented in this file.
The format is based on Keep a Changelog and this project adheres to Semantic Versioning.
Unreleased
3.5 - 2022-04-27
Added
- Anaconda workflow
- Custom iterating setting
- Custom casting setting
Changed
plotmethod updatedclass_statisticsfunction modifiedoverall_statisticsfunction modifiedBCD_calcfunction modifiedCONTRIBUTING.mdupdatedCODE_OF_CONDUCT.mdupdated- Document modified
3.4 - 2022-01-26
Added
- Colab badge
- Discord badge
brier_scoremethod
Changed
J (Jaccard index)section inDocument.ipynbupdatedsave_objmethod updatedPython 3.10added totest.yml- Example-3 updated
- Docstrings of the functions updated
CONTRIBUTING.mdupdated
3.3 - 2021-10-27
Added
__compare_weight_handler__function
Changed
is_imbalancedparameter added to ConfusionMatrix__init__methodclass_benchmark_weightandoverall_benchmark_weightparameters added to Compare__init__methodstatistic_recommendfunction modified- Compare
weightparameter renamed toclass_weight - Document modified
- License updated
AUTHORS.mdupdatedREADME.mdmodified- Block diagrams updated
3.2 - 2021-08-11
Added
classes_filterfunction
Changed
classesparameter added tomatrix_params_calcfunctionclassesparameter added to__obj_vector_handler__functionclassesparameter added to ConfusionMatrix__init__methodnameparameter removed fromhtml_initfunctionshortenerparameter added tohtml_tablefunctionshortenerparameter added tosave_htmlmethod- Document modified
- HTML report modified
3.1 - 2021-03-11
Added
requirements-splitter.pysensitivity_indexmethod
Changed
- Test system modified
overall_statisticsfunction modified- HTML report modified
- Document modified
- References format updated
CONTRIBUTING.mdupdated
3.0 - 2020-10-26
Added
plot_test.pyaxes_genfunctionadd_number_labelfunctionplotmethodcombinemethodmatrix_combinefunction
Changed
- Document modified
README.mdmodified- Example-2 deprecated
- Example-7 deprecated
- Error messages modified
2.9 - 2020-09-23
Added
notebook_check.pyto_arraymethod__copy__methodcopymethod
Changed
averagemethod refactored
2.8 - 2020-07-09
Added
label_mapattributepositionsattributepositionmethod- Krippendorff's Alpha
- Aickin's Alpha
weighted_alphamethod
Changed
- Single class bug fixed
CLASS_NUMBER_ERRORerror type changed topycmMatrixErrorrelabelmethod bug fixed- Document modified
README.mdmodified
2.7 - 2020-05-11
Added
averagemethodweighted_averagemethodweighted_kappamethodpycmAverageErrorclass- Bangdiwala's B
- MATLAB examples
- Github action
Changed
- Document modified
README.mdmodifiedrelabelmethod bug fixedsparse_table_printfunction bug fixedmatrix_checkfunction bug fixed- Minor bug in
Compareclass fixed - Class names mismatch bug fixed
2.6 - 2020-03-25
Added
custom_rounderfunctioncomplementfunctionsparse_matrixattributesparse_normalized_matrixattribute- Net benefit (NB)
- Yule's Q interpretation (QI)
- Adjusted Rand index (ARI)
- TNR micro/macro
- FPR micro/macro
- FNR micro/macro
Changed
sparseparameter added toprint_matrix,print_normalized_matrixandsave_statmethodsheaderparameter added tosave_csvmethod- 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.mdupdated- Namespace optimized
README.mdmodified- Document modified
print_normalized_matrixmethod modifiednormalized_table_calcfunction modifiedsetup.pymodified- summary mode updated
- Dockerfile updated
Python 3.8added to.travis.yamlandappveyor.yml
Removed
PC_PI_calcfunction
2.5 - 2019-10-16
Added
__version__variable- Individual classification success index (ICSI)
- Classification success index (CSI)
- Example-8 (Confidence interval)
install.shautopep8.sh- Dockerfile
CImethod (supported statistics :ACC,AUC,Overall ACC,Kappa,TPR,TNR,PPV,NPV,PLR,NLR,PRE)
Changed
test.shmoved to.travisfolder- Python 3.4 support dropped
- Python 2.7 support dropped
AUTHORS.mdupdatedsave_stat,save_csvandsave_htmlmethods Non-ASCII character bug fixed- Mixed type input vectors bug fixed
CONTRIBUTING.mdupdated- Example-3 updated
README.mdmodified- Document modified
CIattribute renamed toCI95kappa_se_calcfunction renamed tokappa_SE_calcse_calcfunction modified and renamed toSE_calc- CI/SE functions moved to
pycm_ci.py - Minor bug in
save_htmlmethod fixed
2.4 - 2019-07-31
Added
- Tversky index (TI)
- Area under the PR curve (AUPR)
FUNDING.yml
Changed
AUC_calcfunction modified- Document modified
summaryparameter added tosave_html,save_stat,save_csvandstatmethodssample_weightbug innumpyarray format fixed- Inputs manipulation bug fixed
- Test system modified
- Warning system modified
alt_linkparameter added tosave_htmlmethod andonline_helpfunctionCompareclass tests moved tocompare_test.py- Warning tests moved to
warning_test.py
2.3 - 2019-06-27
Added
- Adjusted F-score (AGF)
- Overlap coefficient (OC)
- Otsuka-Ochiai coefficient (OOC)
Changed
save_statandsave_vectorparameters added tosave_objmethod- Document modified
README.mdmodified- Parameters recommendation for imbalance dataset modified
- Minor bug in
Compareclass fixed pycm_helpfunction modified- Benchmarks color modified
2.2 - 2019-05-30
Added
- Negative likelihood ratio interpretation (NLRI)
- Cramer's benchmark (SOA5)
- Matthews correlation coefficient interpretation (MCCI)
- Matthews's benchmark (SOA6)
- F1 macro
- F1 micro
- Accuracy macro
Changed
Compareclass score calculation modified- Parameters recommendation for multi-class dataset modified
- Parameters recommendation for imbalance dataset modified
README.mdmodified- Document modified
- Logo updated
2.1 - 2019-05-06
Added
- Adjusted geometric mean (AGM)
- Yule's Q (Q)
Compareclass and parameters recommendation system block diagrams
Changed
- Document links bug fixed
- Document modified
2.0 - 2019-04-15
Added
- G-Mean (GM)
- Index of balanced accuracy (IBA)
- Optimized precision (OP)
- Pearson's C (C)
Compareclass- Parameters recommendation warning
ConfusionMatrixequal method
Changed
- Document modified
stat_printfunction bug fixedtable_printfunction bug fixedBetaparameter renamed tobeta(F_calcfunction &F_betamethod)- Parameters recommendation for imbalance dataset modified
normalizeparameter added tosave_htmlmethodpycm_func.pysplitted intopycm_class_func.pyandpycm_overall_func.pyvector_filter,vector_check,class_checkandmatrix_checkfunctions moved topycm_util.pyRACC_calcandRACCU_calcfunctions exception handler modified- Docstrings modified
1.9 - 2019-02-25
Added
- Automatic/Manual (AM)
- Bray-Curtis dissimilarity (BCD)
CODE_OF_CONDUCT.mdISSUE_TEMPLATE.mdPULL_REQUEST_TEMPLATE.mdCONTRIBUTING.md- X11 color names support for
save_htmlmethod - Parameters recommendation system
- Warning message for high dimension matrix print
- Interactive notebooks section (binder)
Changed
save_matrixandnormalizeparameters added tosave_csvmethodREADME.mdmodified- Document modified
ConfusionMatrix.__init__optimized- Document and examples output files moved to different folders
- Test system modified
relabelmethod bug fixed
1.8 - 2019-01-05
Added
- Lift score (LS)
version_check.py
Changed
colorparameter added tosave_htmlmethod- Error messages modified
- Document modified
- Website changed to http://www.pycm.ir
- Interpretation functions moved to
pycm_interpret.py - Utility functions moved to
pycm_util.py - Unnecessary
elseandelifremoved ==changed tois
1.7 - 2018-12-18
Added
- Gini index (GI)
- Example-7
pycm_profile.py
Changed
class_nameparameter added tostat,save_stat,save_csvandsave_htmlmethodsoverall_paramandclass_paramparameters empty list bug fixedmatrix_params_calc,matrix_params_from_tableandvector_filterfunctions optimizedoverall_MCC_calc,CEN_misclassification_calcandconvex_combinationfunctions optimized- Document modified
1.6 - 2018-12-06
Added
- AUC value interpretation (AUCI)
- Example-6
- Anaconda cloud package
Changed
overall_paramandclass_paramparameters added tostat,save_statandsave_htmlmethodsclass_paramparameter added tosave_csvmethod_removed from overall statistics namesREADME.mdmodified- Document modified
1.5 - 2018-11-26
Added
- Relative classifier information (RCI)
- Discriminator power (DP)
- Youden's index (Y)
- Discriminant power interpretation (DPI)
- Positive likelihood ratio interpretation (PLRI)
__len__methodrelabelmethod__class_stat_init__function__overall_stat_init__functionmatrixattribute as dictnormalized_matrixattribute as dictnormalized_tableattribute as dict
Changed
README.mdmodified- Document modified
LR+renamed toPLRLR-renamed toNLRnormalized_matrixmethod renamed toprint_normalized_matrixmatrixmethod renamed toprint_matrixentropy_calcfixedcross_entropy_calcfixedconditional_entropy_calcfixedprint_tablebug for large numbers fixed- JSON key bug in
save_objfixed transposebug insave_objfixedPython 3.7added to.travis.yamlandappveyor.yml
1.4 - 2018-11-12
Added
- Area under curve (AUC)
- AUNU
- AUNP
- Class balance accuracy (CBA)
- Global performance index (RR)
- Overall MCC
- Distance index (dInd)
- Similarity index (sInd)
one_vs_alldev-requirements.txt
Changed
README.mdmodified- Document modified
save_statmodifiedrequirements.txtmodified
1.3 - 2018-10-10
Added
- Confusion entropy (CEN)
- Overall confusion entropy (Overall CEN)
- Modified confusion entropy (MCEN)
- Overall modified confusion entropy (Overall MCEN)
- Information score (IS)
Changed
README.mdmodified
1.2 - 2018-10-01
Added
- No information rate (NIR)
- P-Value
sample_weighttranspose
Changed
README.mdmodified- Key error in some parameters fixed
OSXenv added to.travis.yml
1.1 - 2018-09-08
Added
- Zero-one loss
- Support
online_helpfunction
Changed
README.mdmodifiedhtml_tablefunction modifiedtable_printfunction modifiednormalized_table_printfunction modified
1.0 - 2018-08-30
Added
- Hamming loss
Changed
README.mdmodified
0.9.5 - 2018-07-08
Added
- Obj load
- Obj save
- Example-4
Changed
README.mdmodified- Block diagram updated
0.9 - 2018-06-28
Added
- Activation threshold
- Example-3
- Jaccard index
- Overall Jaccard index
Changed
README.mdmodifiedsetup.pymodified
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.mdmodified
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.mdmodified
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
pycmVectorErrorclasspycmMatrixErrorclass- Mutual information
- Support
numpyarrays
Changed
- Notebook file updated
Removed
pycmErrorclass
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
digitparameter toConfusionMatrixobject
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
- Overall RACC
- Error rate (ERR)
- 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_resulttoclass_statparamstostat
0.1 - 2018-01-22
Added
- ACC
- BM
- DOR
- F1-Score
- FDR
- FNR
- FOR
- FPR
- LR+
- LR-
- MCC
- MK
- NPV
- PPV
- TNR
- TPR
- documents and
README.md
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