Multi-class confusion matrix library in Python
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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⚠️ 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)
pip install -r requirements.txtor
pip3 install -r requirements.txt(Need root access)
python3 setup.py installor
python setup.py install(Need root access)
pip install pycm==3.1or
pip3 install pycm==3.1(Need root access)
conda install -c sepandhaghighi pycm(Need root access)
easy_install --upgrade pycm(Need root access)
Add to PATHoption
Install pipoption
pip install pycmor
pip3 install pycm(Need root access)
matlab >> pyversion PYTHON_EXECUTABLE_FULL_PATH
docker pull sepandhaghighi/pycm(Need root access)
>>> 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 01 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 01 0 1 2
2 2 1 3
>>> cm.print_normalized_matrix() Predict 0 1 2
Actual 0 1.0 0.0 0.01 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
>>> 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 2Class2 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 2Class2 0 5
matrix()and
normalized_matrix()renamed to
print_matrix()and
print_normalized_matrix()in
version 1.5
thresholdis added in
version 0.9for real value prediction.
For more information visit Example3
fileis added in
version 0.9.5in order to load saved confusion matrix with
.objformat generated by
save_objmethod.
For more information visit Example4
sample_weightis added in
version 1.2
For more information visit Example5
transposeis added in
version 1.2in order to transpose input matrix (only in
Direct CMmode)
relabelmethod is added in
version 1.5in order to change ConfusionMatrix classnames.
>>> cm.relabel(mapping={0:"L1",1:"L2",2:"L3"}) >>> cm pycm.ConfusionMatrix(classes: ['L1', 'L2', 'L3'])
positionmethod is added in
version 2.8in order to find the indexes of observations in
predict_vectorwhich 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_arraymethod is added in
version 2.9in 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]])
combinemethod is added in
version 3.0in 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 4Class2 0 10
plotmethod is added in
version 3.0in 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_helpfunction is added in
version 1.1in 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)
online_help()(without argument)
alt_link = True(new in
version 2.4)
This option has been added in
version 1.9to 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']
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_classboolean 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 : cm2Rank 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'
actual_vector: python
listor numpy
arrayof any stringable objects
predict_vector: python
listor numpy
arrayof any stringable objects
matrix:
dict
digit:
int
threshold:
FunctionType (function or lambda)
file:
File object
sample_weight: python
listor numpy
arrayof numbers
transpose:
bool
help(ConfusionMatrix)for
ConfusionMatrixobject details
cm_dict: python
dictof
ConfusionMatrixobject (
str:
ConfusionMatrix)
by_class:
bool
weight: python
dictof class weights (
class_name:
float)
digit:
int
help(Compare)for
Compareobject details
For more information visit here
PyCM can be used online in interactive Jupyter Notebooks via the Binder service! Try it out now! :
Examplesin
Documentfolder
Just fill an issue and describe it. We'll check it ASAP!
or send an email to [email protected].
master | dev |
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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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