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
- Support
- Authors
- License
- Donate
- Changelog
- Code of Conduct
Overview
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
orpip3 install -r requirements.txt
(Need root access) - Run
python3 setup.py install
orpython setup.py install
(Need root access)
PyPI
- Check Python Packaging User Guide
- Run
pip install pycm==3.0
orpip3 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)
- [x] Select
Add to PATH
option - [x] Select
Install pip
option
- [x] Select
- Run
pip install pycm
orpip3 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()
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']
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
: pythonlist
or numpyarray
of any stringable objectspredict_vector
: pythonlist
or numpyarray
of any stringable objectsmatrix
:dict
digit
:int
threshold
:FunctionType (function or lambda)
file
:File object
sample_weight
: pythonlist
or numpyarray
of numberstranspose
:bool
- Run
help(ConfusionMatrix)
forConfusionMatrix
object details
Compare
cm_dict
: pythondict
ofConfusionMatrix
object (str
:ConfusionMatrix
)by_class
:bool
weight
: pythondict
of class weights (class_name
:float
)digit
:int
- Run
help(Compare)
forCompare
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
inDocument
folder
Issues & bug reports
Just fill an issue and describe it. We'll check it ASAP! or send an email to info@pycm.ir.
- Please complete the issue template
Outputs
Dependencies
References
Cite
If you use PyCM in your research, we would appreciate citations to the following paper :
Download PyCM.bib
Support
- Website : https://www.pycm.ir
- Mailing List : https://mail.python.org/mailman3/lists/pycm.python.org/
- Email : info@pycm.ir
License
Donate to our project
If you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do ;-) .
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
3.0 - 2020-10-26
Added
plot_test.py
axes_gen
functionadd_number_label
functionplot
methodcombine
methodmatrix_combine
function
Changed
- Document modified
README.md
modified- Example-2 deprecated
- Example-7 deprecated
- Error messages modified
2.9 - 2020-09-23
Added
notebook_check.py
to_array
method__copy__
methodcopy
method
Changed
average
method refactored
2.8 - 2020-07-09
Added
label_map
attributepositions
attributeposition
method- Krippendorff's Alpha
- Aickin's Alpha
weighted_alpha
method
Changed
- Single class bug fixed
CLASS_NUMBER_ERROR
error type changed topycmMatrixError
relabel
method bug fixed- Document modified
README.md
modified
2.7 - 2020-05-11
Added
average
methodweighted_average
methodweighted_kappa
methodpycmAverageError
class- Bangdiwala's B
- MATLAB examples
- Github action
Changed
- Document modified
README.md
modifiedrelabel
method bug fixedsparse_table_print
function bug fixedmatrix_check
function bug fixed- Minor bug in
Compare
class fixed - Class names mismatch bug fixed
2.6 - 2020-03-25
Added
custom_rounder
functioncomplement
functionsparse_matrix
attributesparse_normalized_matrix
attribute- Net benefit (NB)
- Yule's Q interpretation (QI)
- Adjusted Rand index (ARI)
- TNR micro/macro
- FPR micro/macro
- FNR micro/macro
Changed
sparse
parameter added toprint_matrix
,print_normalized_matrix
andsave_stat
methodsheader
parameter added tosave_csv
method- 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- Document modified
print_normalized_matrix
method modifiednormalized_table_calc
function modifiedsetup.py
modified- summary mode updated
- Dockerfile updated
Python 3.8
added to.travis.yaml
andappveyor.yml
Removed
PC_PI_calc
function
2.5 - 2019-10-16
Added
__version__
variable- Individual classification success index (ICSI)
- Classification success index (CSI)
- Example-8 (Confidence interval)
install.sh
autopep8.sh
- Dockerfile
CI
method (supported statistics :ACC
,AUC
,Overall ACC
,Kappa
,TPR
,TNR
,PPV
,NPV
,PLR
,NLR
,PRE
)
Changed
test.sh
moved to.travis
folder- Python 3.4 support dropped
- Python 2.7 support dropped
AUTHORS.md
updatedsave_stat
,save_csv
andsave_html
methods Non-ASCII character bug fixed- Mixed type input vectors bug fixed
CONTRIBUTING.md
updated- Example-3 updated
README.md
modified- Document modified
CI
attribute renamed toCI95
kappa_se_calc
function renamed tokappa_SE_calc
se_calc
function modified and renamed toSE_calc
- CI/SE functions moved to
pycm_ci.py
- Minor bug in
save_html
method fixed
2.4 - 2019-07-31
Added
- Tversky index (TI)
- Area under the PR curve (AUPR)
FUNDING.yml
Changed
AUC_calc
function modified- Document modified
summary
parameter added tosave_html
,save_stat
,save_csv
andstat
methodssample_weight
bug innumpy
array format fixed- Inputs manipulation bug fixed
- Test system modified
- Warning system modified
alt_link
parameter added tosave_html
method andonline_help
functionCompare
class 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_stat
andsave_vector
parameters added tosave_obj
method- Document modified
README.md
modified- Parameters recommendation for imbalance dataset modified
- Minor bug in
Compare
class fixed pycm_help
function 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
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
2.1 - 2019-05-06
Added
- Adjusted geometric mean (AGM)
- Yule's Q (Q)
Compare
class 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)
Compare
class- Parameters recommendation warning
ConfusionMatrix
equal method
Changed
- Document modified
stat_print
function bug fixedtable_print
function bug fixedBeta
parameter renamed tobeta
(F_calc
function &F_beta
method)- Parameters recommendation for imbalance dataset modified
normalize
parameter added tosave_html
methodpycm_func.py
splitted intopycm_class_func.py
andpycm_overall_func.py
vector_filter
,vector_check
,class_check
andmatrix_check
functions moved topycm_util.py
RACC_calc
andRACCU_calc
functions exception handler modified- Docstrings modified
1.9 - 2019-02-25
Added
- Automatic/Manual (AM)
- Bray-Curtis dissimilarity (BCD)
CODE_OF_CONDUCT.md
ISSUE_TEMPLATE.md
PULL_REQUEST_TEMPLATE.md
CONTRIBUTING.md
- X11 color names support for
save_html
method - Parameters recommendation system
- Warning message for high dimension matrix print
- Interactive notebooks section (binder)
Changed
save_matrix
andnormalize
parameters added tosave_csv
methodREADME.md
modified- Document modified
ConfusionMatrix.__init__
optimized- Document and examples output files moved to different folders
- Test system modified
relabel
method bug fixed
1.8 - 2019-01-05
Added
- Lift score (LS)
version_check.py
Changed
color
parameter added tosave_html
method- 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
else
andelif
removed ==
changed tois
1.7 - 2018-12-18
Added
- Gini index (GI)
- Example-7
pycm_profile.py
Changed
class_name
parameter added tostat
,save_stat
,save_csv
andsave_html
methodsoverall_param
andclass_param
parameters empty list bug fixedmatrix_params_calc
,matrix_params_from_table
andvector_filter
functions optimizedoverall_MCC_calc
,CEN_misclassification_calc
andconvex_combination
functions optimized- Document modified
1.6 - 2018-12-06
Added
- AUC value interpretation (AUCI)
- Example-6
- Anaconda cloud package
Changed
overall_param
andclass_param
parameters added tostat
,save_stat
andsave_html
methodsclass_param
parameter added tosave_csv
method_
removed from overall statistics namesREADME.md
modified- 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__
methodrelabel
method__class_stat_init__
function__overall_stat_init__
functionmatrix
attribute as dictnormalized_matrix
attribute as dictnormalized_table
attribute as dict
Changed
README.md
modified- Document modified
LR+
renamed toPLR
LR-
renamed toNLR
normalized_matrix
method renamed toprint_normalized_matrix
matrix
method renamed toprint_matrix
entropy_calc
fixedcross_entropy_calc
fixedconditional_entropy_calc
fixedprint_table
bug for large numbers fixed- JSON key bug in
save_obj
fixed transpose
bug insave_obj
fixedPython 3.7
added to.travis.yaml
andappveyor.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_all
dev-requirements.txt
Changed
README.md
modified- Document modified
save_stat
modifiedrequirements.txt
modified
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.md
modified
1.2 - 2018-10-01
Added
- No information rate (NIR)
- P-Value
sample_weight
transpose
Changed
README.md
modified- Key error in some parameters fixed
OSX
env added to.travis.yml
1.1 - 2018-09-08
Added
- Zero-one loss
- Support
online_help
function
Changed
README.md
modifiedhtml_table
function modifiedtable_print
function modifiednormalized_table_print
function modified
1.0 - 2018-08-30
Added
- Hamming loss
Changed
README.md
modified
0.9.5 - 2018-07-08
Added
- Obj load
- Obj save
- Example-4
Changed
README.md
modified- Block diagram updated
0.9 - 2018-06-28
Added
- Activation threshold
- Example-3
- Jaccard index
- Overall Jaccard index
Changed
README.md
modifiedsetup.py
modified
0.8.6 - 2018-05-31
Added
- Example section in document
- Python 2.7 CI
- JOSS paper pdf
Changed
- Cite section
- ConfusionMatrix docstring
- round function changed to numpy.around
README.md
modified
0.8.5 - 2018-05-21
Added
- Example-1 (Comparison of three different classifiers)
- Example-2 (How to plot via matplotlib)
- JOSS paper
- ConfusionMatrix docstring
Changed
- Table size in HTML report
- Test system
README.md
modified
0.8.1 - 2018-03-22
Added
- Goodman and Kruskal's lambda B
- Goodman and Kruskal's lambda A
- Cross entropy
- Conditional entropy
- Joint entropy
- Reference entropy
- Response entropy
- Kullback-Liebler divergence
- Direct ConfusionMatrix
- Kappa unbiased
- Kappa no prevalence
- Random accuracy unbiased
pycmVectorError
classpycmMatrixError
class- Mutual information
- Support
numpy
arrays
Changed
- Notebook file updated
Removed
pycmError
class
0.7 - 2018-02-26
Added
- Cramer's V
- 95% confidence interval
- Chi-Squared
- Phi-Squared
- Chi-Squared DF
- Standard error
- Kappa standard error
- Kappa 95% confidence interval
- Cicchetti benchmark
Changed
- Overall statistics color in HTML report
- Parameters description link in HTML report
0.6 - 2018-02-21
Added
- CSV report
- Changelog
- Output files
digit
parameter toConfusionMatrix
object
Changed
- Confusion matrix color in HTML report
- Parameters description link in HTML report
- Capitalize descriptions
0.5 - 2018-02-17
Added
- Scott's pi
- Gwet's AC1
- Bennett S score
- HTML report
0.4 - 2018-02-05
Added
- TPR micro/macro
- PPV micro/macro
- 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_result
toclass_stat
params
tostat
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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