/
test.py
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test.py
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# -*- coding: utf-8 -*-
'''
>>> from pycm import *
>>> import os
>>> import json
>>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
>>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
>>> cm = ConfusionMatrix(y_actu, y_pred)
>>> cm
pycm.ConfusionMatrix(classes: [0, 1, 2])
>>> len(cm)
3
>>> print(cm)
Predict 0 1 2
Actual
0 3 0 0
<BLANKLINE>
1 0 1 2
<BLANKLINE>
2 2 1 3
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
Overall Statistics :
<BLANKLINE>
95% CI (0.30439,0.86228)
AUNP 0.66667
AUNU 0.69444
Bennett S 0.375
CBA 0.47778
Chi-Squared 6.6
Chi-Squared DF 4
Conditional Entropy 0.95915
Cramer V 0.5244
Cross Entropy 1.59352
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
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
Scott PI 0.34426
Standard Error 0.14232
TPR Macro 0.61111
TPR Micro 0.58333
Zero-one Loss 5
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 0 1 2
ACC(Accuracy) 0.83333 0.75 0.58333
AUC(Area under the roc curve) 0.88889 0.61111 0.58333
AUCI(Auc value interpretation) Very Good Fair Poor
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
IS(Information score) 1.26303 1.0 0.26303
J(Jaccard index) 0.6 0.25 0.375
MCC(Matthews correlation coefficient) 0.68313 0.2582 0.16903
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
NPV(Negative predictive value) 1.0 0.8 0.57143
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
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
<BLANKLINE>
>>> cm.relabel({0:"L1",1:"L2",2:"L3"})
>>> print(cm)
Predict L1 L2 L3
Actual
L1 3 0 0
<BLANKLINE>
L2 0 1 2
<BLANKLINE>
L3 2 1 3
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
Overall Statistics :
<BLANKLINE>
95% CI (0.30439,0.86228)
AUNP 0.66667
AUNU 0.69444
Bennett S 0.375
CBA 0.47778
Chi-Squared 6.6
Chi-Squared DF 4
Conditional Entropy 0.95915
Cramer V 0.5244
Cross Entropy 1.59352
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
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
Scott PI 0.34426
Standard Error 0.14232
TPR Macro 0.61111
TPR Micro 0.58333
Zero-one Loss 5
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes L1 L2 L3
ACC(Accuracy) 0.83333 0.75 0.58333
AUC(Area under the roc curve) 0.88889 0.61111 0.58333
AUCI(Auc value interpretation) Very Good Fair Poor
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
IS(Information score) 1.26303 1.0 0.26303
J(Jaccard index) 0.6 0.25 0.375
MCC(Matthews correlation coefficient) 0.68313 0.2582 0.16903
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
NPV(Negative predictive value) 1.0 0.8 0.57143
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
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
<BLANKLINE>
>>> cm.Y["L2"]
0.2222222222222221
>>> cm_2 = ConfusionMatrix(y_actu, 2)
Traceback (most recent call last):
...
pycm.pycm_obj.pycmVectorError: Input Vectors Must Be List
>>> cm_3 = ConfusionMatrix(y_actu, [1,2])
Traceback (most recent call last):
...
pycm.pycm_obj.pycmVectorError: Input Vectors Must Be The Same Length
>>> cm_4 = ConfusionMatrix([], [])
Traceback (most recent call last):
...
pycm.pycm_obj.pycmVectorError: Input Vectors Are Empty
>>> cm_5 = ConfusionMatrix([1,1,1,], [1,1,1,1])
Traceback (most recent call last):
...
pycm.pycm_obj.pycmVectorError: Input Vectors Must Be The Same Length
>>> pycm_help()
<BLANKLINE>
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 an accurate evaluation of large variety of classifiers.
<BLANKLINE>
Repo : https://github.com/sepandhaghighi/pycm
Webpage : http://pycm.shaghighi.ir
<BLANKLINE>
<BLANKLINE>
>>> TTPN_calc(0,0)
'None'
>>> TTPN_calc(1,4)
0.2
>>> FXR_calc(None)
'None'
>>> FXR_calc(0.2)
0.8
>>> ACC_calc(0,0,0,0)
'None'
>>> ACC_calc(1,1,3,4)
0.2222222222222222
>>> MCC_calc(0,2,0,2)
'None'
>>> MCC_calc(1,2,3,4)
-0.408248290463863
>>> LR_calc(1,2)
0.5
>>> LR_calc(1,0)
'None'
>>> MK_BM_calc(2,"None")
'None'
>>> MK_BM_calc(1,2)
2
>>> PRE_calc(None,2)
'None'
>>> PRE_calc(1,5)
0.2
>>> PRE_calc(1,0)
'None'
>>> G_calc(None,2)
'None'
>>> G_calc(1,2)
1.4142135623730951
>>> RACC_calc(2,3,4)
0.375
>>> reliability_calc(1,None)
'None'
>>> reliability_calc(2,0.3)
1.7
>>> micro_calc({1:2,2:3},{1:1,2:4})
0.5
>>> micro_calc({1:2,2:3},None)
'None'
>>> macro_calc(None)
'None'
>>> macro_calc({1:2,2:3})
2.5
>>> F_calc(TP=0,FP=0,FN=0,Beta=1)
'None'
>>> F_calc(TP=3,FP=2,FN=1,Beta=5)
0.7428571428571429
>>> save_stat=cm.save_stat("test",address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_stat("test_filtered",address=False,overall_param=["Kappa","Scott PI"],class_param=["TPR","TNR","ACC","AUC"])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_stat("/asdasd,qweqwe.eo/",address=True)
>>> save_stat=={'Status': False, 'Message': "[Errno 2] No such file or directory: '/asdasd,qweqwe.eo/.pycm'"}
True
>>> ERR_calc(None)
'None'
>>> ERR_calc(0.1)
0.9
>>> cm.F_beta(4)["L1"]
0.9622641509433962
>>> cm.F_beta(4)["L2"]
0.34
>>> cm.F_beta(4)["L3"]
0.504950495049505
>>> import numpy as np
>>> y_test = np.array([600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200])
>>> y_pred = np.array([100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200])
>>> cm=ConfusionMatrix(y_test, y_pred)
>>> print(cm)
Predict 100 200 500 600
Actual
100 0 0 0 0
<BLANKLINE>
200 9 6 1 0
<BLANKLINE>
500 1 1 1 0
<BLANKLINE>
600 1 0 0 0
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
Overall Statistics :
<BLANKLINE>
95% CI (0.14096,0.55904)
AUNP None
AUNU None
Bennett S 0.13333
CBA 0.17708
Chi-Squared None
Chi-Squared DF 9
Conditional Entropy 1.23579
Cramer V None
Cross Entropy 1.70995
Gwet AC1 0.19505
Hamming Loss 0.65
Joint Entropy 2.11997
KL Divergence None
Kappa 0.07801
Kappa 95% CI (-0.2185,0.37453)
Kappa No Prevalence -0.3
Kappa Standard Error 0.15128
Kappa Unbiased -0.12554
Lambda A 0.0
Lambda B 0.0
Mutual Information 0.10088
NIR 0.8
Overall ACC 0.35
Overall CEN 0.3648
Overall J (0.60294,0.15074)
Overall MCC 0.12642
Overall MCEN 0.37463
Overall RACC 0.295
Overall RACCU 0.4225
P-Value 1.0
PPV Macro None
PPV Micro 0.35
Phi-Squared None
RCI 0.11409
RR 5.0
Reference Entropy 0.88418
Response Entropy 1.33667
SOA1(Landis & Koch) Slight
SOA2(Fleiss) Poor
SOA3(Altman) Poor
SOA4(Cicchetti) Poor
Scott PI -0.12554
Standard Error 0.10665
TPR Macro None
TPR Micro 0.35
Zero-one Loss 13
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 100 200 500 600
ACC(Accuracy) 0.45 0.45 0.85 0.95
AUC(Area under the roc curve) None 0.5625 0.63725 0.5
AUCI(Auc value interpretation) None Poor Fair Poor
BM(Informedness or bookmaker informedness) None 0.125 0.27451 0.0
CEN(Confusion entropy) 0.33496 0.35708 0.53895 0.0
DOR(Diagnostic odds ratio) None 1.8 8.0 None
DP(Discriminant power) None 0.14074 0.4979 None
DPI(Discriminant power interpretation) None Poor Poor None
ERR(Error rate) 0.55 0.55 0.15 0.05
F0.5(F0.5 score) 0.0 0.68182 0.45455 0.0
F1(F1 score - harmonic mean of precision and sensitivity) 0.0 0.52174 0.4 0.0
F2(F2 score) 0.0 0.42254 0.35714 0.0
FDR(False discovery rate) 1.0 0.14286 0.5 None
FN(False negative/miss/type 2 error) 0 10 2 1
FNR(Miss rate or false negative rate) None 0.625 0.66667 1.0
FOR(False omission rate) 0.0 0.76923 0.11111 0.05
FP(False positive/type 1 error/false alarm) 11 1 1 0
FPR(Fall-out or false positive rate) 0.55 0.25 0.05882 0.0
G(G-measure geometric mean of precision and sensitivity) None 0.56695 0.40825 None
IS(Information score) None 0.09954 1.73697 None
J(Jaccard index) 0.0 0.35294 0.25 0.0
MCC(Matthews correlation coefficient) None 0.10483 0.32673 None
MCEN(Modified confusion entropy) 0.33496 0.37394 0.58028 0.0
MK(Markedness) 0.0 0.08791 0.38889 None
N(Condition negative) 20 4 17 19
NLR(Negative likelihood ratio) None 0.83333 0.70833 1.0
NPV(Negative predictive value) 1.0 0.23077 0.88889 0.95
P(Condition positive or support) 0 16 3 1
PLR(Positive likelihood ratio) None 1.5 5.66667 None
PLRI(Positive likelihood ratio interpretation) None Poor Fair None
POP(Population) 20 20 20 20
PPV(Precision or positive predictive value) 0.0 0.85714 0.5 None
PRE(Prevalence) 0.0 0.8 0.15 0.05
RACC(Random accuracy) 0.0 0.28 0.015 0.0
RACCU(Random accuracy unbiased) 0.07563 0.33062 0.01562 0.00063
TN(True negative/correct rejection) 9 3 16 19
TNR(Specificity or true negative rate) 0.45 0.75 0.94118 1.0
TON(Test outcome negative) 9 13 18 20
TOP(Test outcome positive) 11 7 2 0
TP(True positive/hit) 0 6 1 0
TPR(Sensitivity, recall, hit rate, or true positive rate) None 0.375 0.33333 0.0
Y(Youden index) None 0.125 0.27451 0.0
dInd(Distance index) None 0.67315 0.66926 1.0
sInd(Similarity index) None 0.52401 0.52676 0.29289
<BLANKLINE>
>>> cm.stat()
Overall Statistics :
<BLANKLINE>
95% CI (0.14096,0.55904)
AUNP None
AUNU None
Bennett S 0.13333
CBA 0.17708
Chi-Squared None
Chi-Squared DF 9
Conditional Entropy 1.23579
Cramer V None
Cross Entropy 1.70995
Gwet AC1 0.19505
Hamming Loss 0.65
Joint Entropy 2.11997
KL Divergence None
Kappa 0.07801
Kappa 95% CI (-0.2185,0.37453)
Kappa No Prevalence -0.3
Kappa Standard Error 0.15128
Kappa Unbiased -0.12554
Lambda A 0.0
Lambda B 0.0
Mutual Information 0.10088
NIR 0.8
Overall ACC 0.35
Overall CEN 0.3648
Overall J (0.60294,0.15074)
Overall MCC 0.12642
Overall MCEN 0.37463
Overall RACC 0.295
Overall RACCU 0.4225
P-Value 1.0
PPV Macro None
PPV Micro 0.35
Phi-Squared None
RCI 0.11409
RR 5.0
Reference Entropy 0.88418
Response Entropy 1.33667
SOA1(Landis & Koch) Slight
SOA2(Fleiss) Poor
SOA3(Altman) Poor
SOA4(Cicchetti) Poor
Scott PI -0.12554
Standard Error 0.10665
TPR Macro None
TPR Micro 0.35
Zero-one Loss 13
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 100 200 500 600
ACC(Accuracy) 0.45 0.45 0.85 0.95
AUC(Area under the roc curve) None 0.5625 0.63725 0.5
AUCI(Auc value interpretation) None Poor Fair Poor
BM(Informedness or bookmaker informedness) None 0.125 0.27451 0.0
CEN(Confusion entropy) 0.33496 0.35708 0.53895 0.0
DOR(Diagnostic odds ratio) None 1.8 8.0 None
DP(Discriminant power) None 0.14074 0.4979 None
DPI(Discriminant power interpretation) None Poor Poor None
ERR(Error rate) 0.55 0.55 0.15 0.05
F0.5(F0.5 score) 0.0 0.68182 0.45455 0.0
F1(F1 score - harmonic mean of precision and sensitivity) 0.0 0.52174 0.4 0.0
F2(F2 score) 0.0 0.42254 0.35714 0.0
FDR(False discovery rate) 1.0 0.14286 0.5 None
FN(False negative/miss/type 2 error) 0 10 2 1
FNR(Miss rate or false negative rate) None 0.625 0.66667 1.0
FOR(False omission rate) 0.0 0.76923 0.11111 0.05
FP(False positive/type 1 error/false alarm) 11 1 1 0
FPR(Fall-out or false positive rate) 0.55 0.25 0.05882 0.0
G(G-measure geometric mean of precision and sensitivity) None 0.56695 0.40825 None
IS(Information score) None 0.09954 1.73697 None
J(Jaccard index) 0.0 0.35294 0.25 0.0
MCC(Matthews correlation coefficient) None 0.10483 0.32673 None
MCEN(Modified confusion entropy) 0.33496 0.37394 0.58028 0.0
MK(Markedness) 0.0 0.08791 0.38889 None
N(Condition negative) 20 4 17 19
NLR(Negative likelihood ratio) None 0.83333 0.70833 1.0
NPV(Negative predictive value) 1.0 0.23077 0.88889 0.95
P(Condition positive or support) 0 16 3 1
PLR(Positive likelihood ratio) None 1.5 5.66667 None
PLRI(Positive likelihood ratio interpretation) None Poor Fair None
POP(Population) 20 20 20 20
PPV(Precision or positive predictive value) 0.0 0.85714 0.5 None
PRE(Prevalence) 0.0 0.8 0.15 0.05
RACC(Random accuracy) 0.0 0.28 0.015 0.0
RACCU(Random accuracy unbiased) 0.07563 0.33062 0.01562 0.00063
TN(True negative/correct rejection) 9 3 16 19
TNR(Specificity or true negative rate) 0.45 0.75 0.94118 1.0
TON(Test outcome negative) 9 13 18 20
TOP(Test outcome positive) 11 7 2 0
TP(True positive/hit) 0 6 1 0
TPR(Sensitivity, recall, hit rate, or true positive rate) None 0.375 0.33333 0.0
Y(Youden index) None 0.125 0.27451 0.0
dInd(Distance index) None 0.67315 0.66926 1.0
sInd(Similarity index) None 0.52401 0.52676 0.29289
<BLANKLINE>
>>> cm.stat(overall_param=["Kappa","Scott PI"],class_param=["TPR","TNR","ACC","AUC"])
Overall Statistics :
<BLANKLINE>
Kappa 0.07801
Scott PI -0.12554
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 100 200 500 600
ACC(Accuracy) 0.45 0.45 0.85 0.95
AUC(Area under the roc curve) None 0.5625 0.63725 0.5
TNR(Specificity or true negative rate) 0.45 0.75 0.94118 1.0
TPR(Sensitivity, recall, hit rate, or true positive rate) None 0.375 0.33333 0.0
<BLANKLINE>
>>> cm.print_normalized_matrix()
Predict 100 200 500 600
Actual
100 0.0 0.0 0.0 0.0
200 0.5625 0.375 0.0625 0.0
500 0.33333 0.33333 0.33333 0.0
600 1.0 0.0 0.0 0.0
<BLANKLINE>
>>> cm.print_matrix()
Predict 100 200 500 600
Actual
100 0 0 0 0
200 9 6 1 0
500 1 1 1 0
600 1 0 0 0
<BLANKLINE>
>>> cm.print_matrix(one_vs_all=True,class_name=200)
Predict 200 ~
Actual
200 6 10
~ 1 3
<BLANKLINE>
>>> cm.print_normalized_matrix(one_vs_all=True,class_name=200)
Predict 200 ~
Actual
200 0.375 0.625
~ 0.25 0.75
<BLANKLINE>
>>> kappa_analysis_koch(-0.1)
'Poor'
>>> kappa_analysis_koch(0)
'Slight'
>>> kappa_analysis_koch(0.2)
'Fair'
>>> kappa_analysis_koch(0.4)
'Moderate'
>>> kappa_analysis_koch(0.6)
'Substantial'
>>> kappa_analysis_koch(0.8)
'Almost Perfect'
>>> kappa_analysis_koch(1.2)
'None'
>>> kappa_analysis_fleiss(0.4)
'Intermediate to Good'
>>> kappa_analysis_fleiss(0.75)
'Excellent'
>>> kappa_analysis_fleiss(1.2)
'Excellent'
>>> kappa_analysis_altman(-0.2)
'Poor'
>>> kappa_analysis_altman(0.2)
'Fair'
>>> kappa_analysis_altman(0.4)
'Moderate'
>>> kappa_analysis_altman(0.6)
'Good'
>>> kappa_analysis_altman(0.8)
'Very Good'
>>> kappa_analysis_altman(1.2)
'None'
>>> kappa_analysis_fleiss(0.2)
'Poor'
>>> kappa_analysis_cicchetti(0.3)
'Poor'
>>> kappa_analysis_cicchetti(0.5)
'Fair'
>>> kappa_analysis_cicchetti(0.65)
'Good'
>>> kappa_analysis_cicchetti(0.8)
'Excellent'
>>> PLR_analysis(1)
'Negligible'
>>> PLR_analysis(3)
'Poor'
>>> PLR_analysis(7)
'Fair'
>>> PLR_analysis(11)
'Good'
>>> DP_analysis(0.2)
'Poor'
>>> DP_analysis(1.5)
'Limited'
>>> DP_analysis(2.5)
'Fair'
>>> DP_analysis(10)
'Good'
>>> AUC_analysis(0.5)
'Poor'
>>> AUC_analysis(0.65)
'Fair'
>>> AUC_analysis(0.75)
'Good'
>>> AUC_analysis(0.86)
'Very Good'
>>> AUC_analysis(0.97)
'Excellent'
>>> AUC_analysis(1.0)
'Excellent'
>>> PC_PI_calc(1,1,1)
'None'
>>> PC_PI_calc({1:12},{1:6},{1:45})
0.04000000000000001
>>> PC_AC1_calc(1,1,1)
'None'
>>> PC_AC1_calc({1:123,2:2},{1:120,2:5},{1:125,2:125})
0.05443200000000002
>>> y_act=[0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2]
>>> y_pre=[0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,1,1,1,1,1,2,0,1,2,2,2,2]
>>> cm2=ConfusionMatrix(y_act,y_pre)
>>> chi_squared=chi_square_calc(cm2.classes,cm2.table,cm2.TOP,cm2.P,cm2.POP)
>>> chi_squared
15.525641025641026
>>> phi_squared=phi_square_calc(chi_squared,cm2.POP)
>>> phi_squared
0.5750237416904084
>>> V=cramers_V_calc(phi_squared,cm2.classes)
>>> V
0.5362013342441477
>>> DF=DF_calc(cm2.classes)
>>> DF
4
>>> SE=se_calc(cm2.Overall_ACC,cm2.POP)
>>> SE
0.09072184232530289
>>> CI=CI_calc(cm2.Overall_ACC,SE)
>>> CI
(0.48885185570907297, 0.8444814776242603)
>>> response_entropy=entropy_calc(cm2.TOP,cm2.POP)
>>> response_entropy
1.486565953154142
>>> reference_entropy=entropy_calc(cm2.P,cm2.POP)
>>> reference_entropy
1.5304930567574824
>>> cross_entropy = cross_entropy_calc(cm2.TOP,cm2.P,cm2.POP)
>>> cross_entropy
1.5376219392005763
>>> join_entropy = joint_entropy_calc(cm2.classes,cm2.table,cm2.POP)
>>> join_entropy
2.619748965432189
>>> conditional_entropy = conditional_entropy_calc(cm2.classes,cm2.table,cm2.P,cm2.POP)
>>> conditional_entropy
1.089255908674706
>>> kl_divergence=kl_divergence_calc(cm2.P,cm2.TOP,cm2.POP)
>>> kl_divergence
0.007128882443093773
>>> lambda_B=lambda_B_calc(cm2.classes,cm2.table,cm2.TOP,cm2.POP)
>>> lambda_B
0.35714285714285715
>>> lambda_A=lambda_A_calc(cm2.classes,cm2.table,cm2.P,cm2.POP)
>>> lambda_A
0.4
>>> IS_calc(13,0,0,38)
1.5474877953024933
>>> kappa_no_prevalence_calc(cm2.Overall_ACC)
0.33333333333333326
>>> reliability_calc(cm2.Overall_RACC,cm2.Overall_ACC)
0.4740259740259741
>>> mutual_information_calc(cm2.ResponseEntropy,cm2.ConditionalEntropy)
0.39731004447943596
>>> cm3=ConfusionMatrix(matrix=cm2.table)
>>> cm3
pycm.ConfusionMatrix(classes: [0, 1, 2])
>>> cm3.CI
(0.48885185570907297, 0.8444814776242603)
>>> cm3.Chi_Squared
15.525641025641026
>>> cm3.Phi_Squared
0.5750237416904084
>>> cm3.V
0.5362013342441477
>>> cm3.DF
4
>>> cm3.ResponseEntropy
1.486565953154142
>>> cm3.ReferenceEntropy
1.5304930567574824
>>> cm3.CrossEntropy
1.5376219392005763
>>> cm3.JointEntropy
2.619748965432189
>>> cm3.ConditionalEntropy
1.089255908674706
>>> cm3.KL
0.007128882443093773
>>> cm3.LambdaA
0.4
>>> cm3.LambdaB
0.35714285714285715
>>> cm3=ConfusionMatrix(matrix={})
Traceback (most recent call last):
...
pycm.pycm_obj.pycmMatrixError: Input Confusion Matrix Format Error
>>> cm_4=ConfusionMatrix(matrix={1:{1:2,"1":2},"1":{1:2,"1":3}})
Traceback (most recent call last):
...
pycm.pycm_obj.pycmMatrixError: Input Matrix Classes Must Be Same Type
>>> cm_5=ConfusionMatrix(matrix={1:{1:2}})
Traceback (most recent call last):
...
pycm.pycm_obj.pycmVectorError: Number Of Classes < 2
>>> save_stat=cm.save_html("test",address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_filtered",address=False,overall_param=["Kappa","Scott PI"],class_param=["TPR","TNR","ACC","AUC"])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test",address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test_filtered",address=False,class_param=["TPR","TNR","ACC","AUC"])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("/asdasd,qweqwe.eo/",address=True)
>>> save_stat=={'Status': False, 'Message': "[Errno 2] No such file or directory: '/asdasd,qweqwe.eo/.html'"}
True
>>> save_stat=cm.save_csv("/asdasd,qweqwe.eo/",address=True)
>>> save_stat=={'Status': False, 'Message': "[Errno 2] No such file or directory: '/asdasd,qweqwe.eo/.csv'"}
True
>>> def activation(i):
... if i<0.7:
... return 1
... else:
... return 0
>>> cm_6 = ConfusionMatrix([0,0,1,0],[0.87,0.34,0.9,0.12],threshold=activation)
>>> cm_6.print_matrix()
Predict 0 1
Actual
0 1 2
1 1 0
>>> save_obj=cm.save_obj("test",address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> cm_file=ConfusionMatrix(file=open("test.obj","r"))
>>> print(cm_file)
Predict 100 200 500 600
Actual
100 0 0 0 0
<BLANKLINE>
200 9 6 1 0
<BLANKLINE>
500 1 1 1 0
<BLANKLINE>
600 1 0 0 0
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
Overall Statistics :
<BLANKLINE>
95% CI (0.14096,0.55904)
AUNP None
AUNU None
Bennett S 0.13333
CBA 0.17708
Chi-Squared None
Chi-Squared DF 9
Conditional Entropy 1.23579
Cramer V None
Cross Entropy 1.70995
Gwet AC1 0.19505
Hamming Loss 0.65
Joint Entropy 2.11997
KL Divergence None
Kappa 0.07801
Kappa 95% CI (-0.2185,0.37453)
Kappa No Prevalence -0.3
Kappa Standard Error 0.15128
Kappa Unbiased -0.12554
Lambda A 0.0
Lambda B 0.0
Mutual Information 0.10088
NIR 0.8
Overall ACC 0.35
Overall CEN 0.3648
Overall J (0.60294,0.15074)
Overall MCC 0.12642
Overall MCEN 0.37463
Overall RACC 0.295
Overall RACCU 0.4225
P-Value 1.0
PPV Macro None
PPV Micro 0.35
Phi-Squared None
RCI 0.11409
RR 5.0
Reference Entropy 0.88418
Response Entropy 1.33667
SOA1(Landis & Koch) Slight
SOA2(Fleiss) Poor
SOA3(Altman) Poor
SOA4(Cicchetti) Poor
Scott PI -0.12554
Standard Error 0.10665
TPR Macro None
TPR Micro 0.35
Zero-one Loss 13
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 100 200 500 600
ACC(Accuracy) 0.45 0.45 0.85 0.95
AUC(Area under the roc curve) None 0.5625 0.63725 0.5
AUCI(Auc value interpretation) None Poor Fair Poor
BM(Informedness or bookmaker informedness) None 0.125 0.27451 0.0
CEN(Confusion entropy) 0.33496 0.35708 0.53895 0.0
DOR(Diagnostic odds ratio) None 1.8 8.0 None
DP(Discriminant power) None 0.14074 0.4979 None
DPI(Discriminant power interpretation) None Poor Poor None
ERR(Error rate) 0.55 0.55 0.15 0.05
F0.5(F0.5 score) 0.0 0.68182 0.45455 0.0
F1(F1 score - harmonic mean of precision and sensitivity) 0.0 0.52174 0.4 0.0
F2(F2 score) 0.0 0.42254 0.35714 0.0
FDR(False discovery rate) 1.0 0.14286 0.5 None
FN(False negative/miss/type 2 error) 0 10 2 1
FNR(Miss rate or false negative rate) None 0.625 0.66667 1.0
FOR(False omission rate) 0.0 0.76923 0.11111 0.05
FP(False positive/type 1 error/false alarm) 11 1 1 0
FPR(Fall-out or false positive rate) 0.55 0.25 0.05882 0.0
G(G-measure geometric mean of precision and sensitivity) None 0.56695 0.40825 None
IS(Information score) None 0.09954 1.73697 None
J(Jaccard index) 0.0 0.35294 0.25 0.0
MCC(Matthews correlation coefficient) None 0.10483 0.32673 None
MCEN(Modified confusion entropy) 0.33496 0.37394 0.58028 0.0
MK(Markedness) 0.0 0.08791 0.38889 None
N(Condition negative) 20 4 17 19
NLR(Negative likelihood ratio) None 0.83333 0.70833 1.0
NPV(Negative predictive value) 1.0 0.23077 0.88889 0.95
P(Condition positive or support) 0 16 3 1
PLR(Positive likelihood ratio) None 1.5 5.66667 None
PLRI(Positive likelihood ratio interpretation) None Poor Fair None
POP(Population) 20 20 20 20
PPV(Precision or positive predictive value) 0.0 0.85714 0.5 None
PRE(Prevalence) 0.0 0.8 0.15 0.05
RACC(Random accuracy) 0.0 0.28 0.015 0.0
RACCU(Random accuracy unbiased) 0.07563 0.33062 0.01562 0.00063
TN(True negative/correct rejection) 9 3 16 19
TNR(Specificity or true negative rate) 0.45 0.75 0.94118 1.0
TON(Test outcome negative) 9 13 18 20
TOP(Test outcome positive) 11 7 2 0
TP(True positive/hit) 0 6 1 0
TPR(Sensitivity, recall, hit rate, or true positive rate) None 0.375 0.33333 0.0
Y(Youden index) None 0.125 0.27451 0.0
dInd(Distance index) None 0.67315 0.66926 1.0
sInd(Similarity index) None 0.52401 0.52676 0.29289
<BLANKLINE>
>>> save_obj=cm_6.save_obj("test2",address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> cm_file_2=ConfusionMatrix(file=open("test2.obj","r"))
>>> cm_file_2.print_matrix()
Predict 0 1
Actual
0 1 2
1 1 0
>>> cm = ConfusionMatrix(matrix={"Class1":{"Class1":9,"Class2":3,"Class3":0},"Class2":{"Class1":3,"Class2":5,"Class3":1},"Class3":{"Class1":1,"Class2":1,"Class3":4}})
>>> print(cm)
Predict Class1 Class2 Class3
Actual
Class1 9 3 0
<BLANKLINE>
Class2 3 5 1
<BLANKLINE>
Class3 1 1 4
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
Overall Statistics :
<BLANKLINE>
95% CI (0.48885,0.84448)
AUNP 0.73175
AUNU 0.73929
Bennett S 0.5
CBA 0.63818
Chi-Squared 15.52564
Chi-Squared DF 4
Conditional Entropy 1.08926
Cramer V 0.5362
Cross Entropy 1.53762
Gwet AC1 0.51229
Hamming Loss 0.33333
Joint Entropy 2.61975
KL Divergence 0.00713
Kappa 0.47403
Kappa 95% CI (0.19345,0.7546)
Kappa No Prevalence 0.33333
Kappa Standard Error 0.14315
Kappa Unbiased 0.47346
Lambda A 0.4
Lambda B 0.35714
Mutual Information 0.39731
NIR 0.44444
Overall ACC 0.66667
Overall CEN 0.52986
Overall J (1.51854,0.50618)
Overall MCC 0.47511
Overall MCEN 0.65286
Overall RACC 0.36626
Overall RACCU 0.36694
P-Value 0.01667
PPV Macro 0.68262
PPV Micro 0.66667
Phi-Squared 0.57502
RCI 0.2596
RR 9.0
Reference Entropy 1.53049
Response Entropy 1.48657
SOA1(Landis & Koch) Moderate
SOA2(Fleiss) Intermediate to Good
SOA3(Altman) Moderate
SOA4(Cicchetti) Fair
Scott PI 0.47346
Standard Error 0.09072
TPR Macro 0.65741
TPR Micro 0.66667
Zero-one Loss 9
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes Class1 Class2 Class3
ACC(Accuracy) 0.74074 0.7037 0.88889
AUC(Area under the roc curve) 0.74167 0.66667 0.80952
AUCI(Auc value interpretation) Good Fair Very Good
BM(Informedness or bookmaker informedness) 0.48333 0.33333 0.61905
CEN(Confusion entropy) 0.45994 0.66249 0.47174
DOR(Diagnostic odds ratio) 8.25 4.375 40.0
DP(Discriminant power) 0.50527 0.35339 0.88326
DPI(Discriminant power interpretation) Poor Poor Poor
ERR(Error rate) 0.25926 0.2963 0.11111
F0.5(F0.5 score) 0.70312 0.55556 0.76923