Table of contents
- Overview
- Installation
- Usage
- Document
- Try PyCM in Your Browser
- Issues & Bug Reports
- Todo
- Outputs
- Dependencies
- Contribution
- References
- Cite
- Authors
- License
- Donate
- Changelog
- Code of Conduct
Overview
Installation
⚠️ PyCM 2.4 is the last version to support Python 2.7 & Python 3.4
Source code
- Download Version 2.5 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==2.5orpip3 install pycm==2.5(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)
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
AUNP 0.66667
AUNU 0.69444
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
Gwet AC1 0.38931
Hamming Loss 0.41667
Joint Entropy 2.45915
KL Divergence 0.09352
Kappa 0.35484
Kappa 95% CI (-0.07708,0.78675)
Kappa No Prevalence 0.16667
Kappa Standard Error 0.22036
Kappa Unbiased 0.34426
Lambda A 0.16667
Lambda B 0.42857
Mutual Information 0.52421
NIR 0.5
Overall ACC 0.58333
Overall CEN 0.46381
Overall J (1.225,0.40833)
Overall MCC 0.36667
Overall MCEN 0.51894
Overall RACC 0.35417
Overall RACCU 0.36458
P-Value 0.38721
PPV Macro 0.56667
PPV Micro 0.58333
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
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
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
AUNP 0.66667
AUNU 0.66667
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
Gwet AC1 0.6
Hamming Loss 0.25
Joint Entropy 1.29879
KL Divergence 0.29097
Kappa 0.38462
Kappa 95% CI (-0.354,1.12323)
Kappa No Prevalence 0.5
Kappa Standard Error 0.37684
Kappa Unbiased 0.33333
Lambda A 0.33333
Lambda B 0.0
Mutual Information 0.1992
NIR 0.625
Overall ACC 0.75
Overall CEN 0.44812
Overall J (1.04762,0.52381)
Overall MCC 0.48795
Overall MCEN 0.29904
Overall RACC 0.59375
Overall RACCU 0.625
P-Value 0.36974
PPV Macro 0.85714
PPV Micro 0.75
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
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
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
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
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'])
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 in order to recommend most related parameters considering the characteristics of the input dataset. The characteristics according to which the parameters are suggested are balance/imbalance and binary/multiclass. 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 performance is 1 and for the poor performance is 0.
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. And with a same manner, the class based score is the average of the score of five class-based benchmarks which are Positive Likelihood Ratio Interpretation, Negative Likelihood Ratio Interpretation, Discriminant Power Interpretation, AUC value Interpretation, and Matthews Correlation Coefficient Interpretation. It should be notice that if one of the benchmarks returns none for one of the classes, that benchmarks will be eliminate in total averaging. If user set weights for the classes, the averaging over the value of class-based benchmark scores will transform to a weighted average.
If the user set 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 obtain the maximum of the both overall and class-based score, that will be the reported as the best confusion matrix but in any other cases the compare object doesn’t select 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 4.15 1.48333
2 cm3 2.75 0.95
>>> 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:bool
- Run
help(ConfusionMatrix)forConfusionMatrixobject details
Compare
cm_dict: pythondictofConfusionMatrixobject (str:ConfusionMatrix)by_class:boolweight: pythondictof class weights (class_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 service! Try it out now! :
- Check
ExamplesinDocumentfolder
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
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 ;-) .