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# -*- coding: utf-8 -*-
"""
>>> from pycm import *
>>> from pycm.utils import deprecated
>>> from pytest import warns
>>> large_cm = ConfusionMatrix(list(range(10)) + [2, 3, 5], list(range(10)) + [1, 7, 2])
>>> with warns(RuntimeWarning, match='Confusion matrix is high-dimensional and may not display properly. Consider using the `sparse` flag in printing functions, or save it as a CSV file for better visualization.'):
... large_cm.print_matrix()
Predict 0 1 2 3 4 5 6 7 8 9
Actual
0 1 0 0 0 0 0 0 0 0 0
<BLANKLINE>
1 0 1 0 0 0 0 0 0 0 0
<BLANKLINE>
2 0 1 1 0 0 0 0 0 0 0
<BLANKLINE>
3 0 0 0 1 0 0 0 1 0 0
<BLANKLINE>
4 0 0 0 0 1 0 0 0 0 0
<BLANKLINE>
5 0 0 1 0 0 1 0 0 0 0
<BLANKLINE>
6 0 0 0 0 0 0 1 0 0 0
<BLANKLINE>
7 0 0 0 0 0 0 0 1 0 0
<BLANKLINE>
8 0 0 0 0 0 0 0 0 1 0
<BLANKLINE>
9 0 0 0 0 0 0 0 0 0 1
<BLANKLINE>
>>> with warns(RuntimeWarning, match='Confusion matrix is high-dimensional and may not display properly. Consider using the `sparse` flag in printing functions, or save it as a CSV file for better visualization.'):
... large_cm.print_normalized_matrix()
Predict 0 1 2 3 4 5 6 7 8 9
Actual
0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
<BLANKLINE>
1 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
<BLANKLINE>
2 0.0 0.5 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0
<BLANKLINE>
3 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.5 0.0 0.0
<BLANKLINE>
4 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0
<BLANKLINE>
5 0.0 0.0 0.5 0.0 0.0 0.5 0.0 0.0 0.0 0.0
<BLANKLINE>
6 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0
<BLANKLINE>
7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0
<BLANKLINE>
8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0
<BLANKLINE>
9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
<BLANKLINE>
>>> with warns(RuntimeWarning, match='Confusion matrix is high-dimensional and may not display properly. Consider using the `sparse` flag in printing functions, or save it as a CSV file for better visualization.'):
... large_cm.stat()
Overall Statistics :
<BLANKLINE>
95% CI (0.5402,0.99827)
ACC Macro 0.95385
ARI -0.04
AUNP 0.87121
AUNU 0.91212
Bangdiwala B 0.58824
Bennett S 0.74359
CBA 0.75
CSI 0.7
Chi-Squared 91.0
Chi-Squared DF 81
Conditional Entropy 0.46154
Cramer V 0.88192
Cross Entropy 3.39275
F1 Macro 0.81667
F1 Micro 0.76923
FNR Macro 0.15
FNR Micro 0.23077
FPR Macro 0.02576
FPR Micro 0.02564
Gwet AC1 0.7438
Hamming Loss 0.23077
Joint Entropy 3.70044
KL Divergence 0.15385
Kappa 0.74342
Kappa 95% CI (0.48877,0.99807)
Kappa No Prevalence 0.53846
Kappa Standard Error 0.12992
Kappa Unbiased 0.74172
Krippendorff Alpha 0.75166
Lambda A 0.72727
Lambda B 0.72727
Mutual Information 2.77736
NIR 0.15385
NPV Macro 0.97424
NPV Micro 0.97436
Overall ACC 0.76923
Overall CEN 0.09537
Overall J (7.33333,0.73333)
Overall MCC 0.75333
Overall MCEN 0.10746
Overall RACC 0.10059
Overall RACCU 0.10651
P-Value 0.0
PPV Macro 0.85
PPV Micro 0.76923
Pearson C 0.93541
Phi-Squared 7.0
RCI 0.8575
RR 1.3
Reference Entropy 3.2389
Response Entropy 3.2389
SOA1(Landis & Koch) Substantial
SOA2(Fleiss) Intermediate to Good
SOA3(Altman) Good
SOA4(Cicchetti) Excellent
SOA5(Cramer) Very Strong
SOA6(Matthews) Strong
SOA7(Lambda A) Strong
SOA8(Lambda B) Strong
SOA9(Krippendorff Alpha) Tentative
SOA10(Pearson C) Strong
Scott PI 0.74172
Standard Error 0.11685
TNR Macro 0.97424
TNR Micro 0.97436
TPR Macro 0.85
TPR Micro 0.76923
Zero-one Loss 3
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 0 1 2 3 4 5 6 7 8 9
ACC(Accuracy) 1.0 0.92308 0.84615 0.92308 1.0 0.92308 1.0 0.92308 1.0 1.0
AGF(Adjusted F-score) 1.0 0.90468 0.6742 0.71965 1.0 0.71965 1.0 0.90468 1.0 1.0
AGM(Adjusted geometric mean) 1.0 0.93786 0.78186 0.84135 1.0 0.84135 1.0 0.93786 1.0 1.0
AM(Difference between automatic and manual classification) 0 1 0 -1 0 -1 0 1 0 0
AUC(Area under the ROC curve) 1.0 0.95833 0.70455 0.75 1.0 0.75 1.0 0.95833 1.0 1.0
AUCI(AUC value interpretation) Excellent Excellent Good Good Excellent Good Excellent Excellent Excellent Excellent
AUPR(Area under the PR curve) 1.0 0.75 0.5 0.75 1.0 0.75 1.0 0.75 1.0 1.0
BB(Braun-Blanquet similarity) 1.0 0.5 0.5 0.5 1.0 0.5 1.0 0.5 1.0 1.0
BCD(Bray-Curtis dissimilarity) 0.0 0.03846 0.0 0.03846 0.0 0.03846 0.0 0.03846 0.0 0.0
BM(Informedness or bookmaker informedness) 1.0 0.91667 0.40909 0.5 1.0 0.5 1.0 0.91667 1.0 1.0
CEN(Confusion entropy) 0 0.1267 0.23981 0.1267 0 0.1267 0 0.1267 0 0
DOR(Diagnostic odds ratio) None None 10.0 None None None None None None None
DP(Discriminant power) None None 0.55133 None None None None None None None
DPI(Discriminant power interpretation) None None Poor None None None None None None None
ERR(Error rate) 0.0 0.07692 0.15385 0.07692 0.0 0.07692 0.0 0.07692 0.0 0.0
F0.5(F0.5 score) 1.0 0.55556 0.5 0.83333 1.0 0.83333 1.0 0.55556 1.0 1.0
F1(F1 score - harmonic mean of precision and sensitivity) 1.0 0.66667 0.5 0.66667 1.0 0.66667 1.0 0.66667 1.0 1.0
F2(F2 score) 1.0 0.83333 0.5 0.55556 1.0 0.55556 1.0 0.83333 1.0 1.0
FDR(False discovery rate) 0.0 0.5 0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0
FN(False negative/miss/type 2 error) 0 0 1 1 0 1 0 0 0 0
FNR(Miss rate or false negative rate) 0.0 0.0 0.5 0.5 0.0 0.5 0.0 0.0 0.0 0.0
FOR(False omission rate) 0.0 0.0 0.09091 0.08333 0.0 0.08333 0.0 0.0 0.0 0.0
FP(False positive/type 1 error/false alarm) 0 1 1 0 0 0 0 1 0 0
FPR(Fall-out or false positive rate) 0.0 0.08333 0.09091 0.0 0.0 0.0 0.0 0.08333 0.0 0.0
G(G-measure geometric mean of precision and sensitivity) 1.0 0.70711 0.5 0.70711 1.0 0.70711 1.0 0.70711 1.0 1.0
GI(Gini index) 1.0 0.91667 0.40909 0.5 1.0 0.5 1.0 0.91667 1.0 1.0
GM(G-mean geometric mean of specificity and sensitivity) 1.0 0.95743 0.6742 0.70711 1.0 0.70711 1.0 0.95743 1.0 1.0
HD(Hamming distance) 0 1 2 1 0 1 0 1 0 0
IBA(Index of balanced accuracy) 1.0 0.99306 0.2686 0.25 1.0 0.25 1.0 0.99306 1.0 1.0
ICSI(Individual classification success index) 1.0 0.5 0.0 0.5 1.0 0.5 1.0 0.5 1.0 1.0
IS(Information score) 3.70044 2.70044 1.70044 2.70044 3.70044 2.70044 3.70044 2.70044 3.70044 3.70044
J(Jaccard index) 1.0 0.5 0.33333 0.5 1.0 0.5 1.0 0.5 1.0 1.0
LS(Lift score) 13.0 6.5 3.25 6.5 13.0 6.5 13.0 6.5 13.0 13.0
MCC(Matthews correlation coefficient) 1.0 0.677 0.40909 0.677 1.0 0.677 1.0 0.677 1.0 1.0
MCCI(Matthews correlation coefficient interpretation) Very Strong Moderate Weak Moderate Very Strong Moderate Very Strong Moderate Very Strong Very Strong
MCEN(Modified confusion entropy) 0 0.11991 0.2534 0.11991 0 0.11991 0 0.11991 0 0
MK(Markedness) 1.0 0.5 0.40909 0.91667 1.0 0.91667 1.0 0.5 1.0 1.0
N(Condition negative) 12 12 11 11 12 11 12 12 12 12
NLR(Negative likelihood ratio) 0.0 0.0 0.55 0.5 0.0 0.5 0.0 0.0 0.0 0.0
NLRI(Negative likelihood ratio interpretation) Good Good Negligible Negligible Good Negligible Good Good Good Good
NPV(Negative predictive value) 1.0 1.0 0.90909 0.91667 1.0 0.91667 1.0 1.0 1.0 1.0
OC(Overlap coefficient) 1.0 1.0 0.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0
OOC(Otsuka-Ochiai coefficient) 1.0 0.70711 0.5 0.70711 1.0 0.70711 1.0 0.70711 1.0 1.0
OP(Optimized precision) 1.0 0.8796 0.55583 0.58974 1.0 0.58974 1.0 0.8796 1.0 1.0
P(Condition positive or support) 1 1 2 2 1 2 1 1 1 1
PLR(Positive likelihood ratio) None 12.0 5.5 None None None None 12.0 None None
PLRI(Positive likelihood ratio interpretation) None Good Fair None None None None Good None None
POP(Population) 13 13 13 13 13 13 13 13 13 13
PPV(Precision or positive predictive value) 1.0 0.5 0.5 1.0 1.0 1.0 1.0 0.5 1.0 1.0
PR(Positive rate) 0.07692 0.07692 0.15385 0.15385 0.07692 0.15385 0.07692 0.07692 0.07692 0.07692
PRE(Prevalence) 0.07692 0.07692 0.15385 0.15385 0.07692 0.15385 0.07692 0.07692 0.07692 0.07692
Q(Yule Q - coefficient of colligation) None None 0.81818 None None None None None None None
QI(Yule Q interpretation) None None Strong None None None None None None None
RACC(Random accuracy) 0.00592 0.01183 0.02367 0.01183 0.00592 0.01183 0.00592 0.01183 0.00592 0.00592
RACCU(Random accuracy unbiased) 0.00592 0.01331 0.02367 0.01331 0.00592 0.01331 0.00592 0.01331 0.00592 0.00592
TN(True negative/correct rejection) 12 11 10 11 12 11 12 11 12 12
TNR(Specificity or true negative rate) 1.0 0.91667 0.90909 1.0 1.0 1.0 1.0 0.91667 1.0 1.0
TON(Test outcome negative) 12 11 11 12 12 12 12 11 12 12
TOP(Test outcome positive) 1 2 2 1 1 1 1 2 1 1
TOPR(Test outcome positive rate) 0.07692 0.15385 0.15385 0.07692 0.07692 0.07692 0.07692 0.15385 0.07692 0.07692
TP(True positive/hit) 1 1 1 1 1 1 1 1 1 1
TPR(Sensitivity, recall, hit rate, or true positive rate) 1.0 1.0 0.5 0.5 1.0 0.5 1.0 1.0 1.0 1.0
Y(Youden index) 1.0 0.91667 0.40909 0.5 1.0 0.5 1.0 0.91667 1.0 1.0
dInd(Distance index) 0.0 0.08333 0.5082 0.5 0.0 0.5 0.0 0.08333 0.0 0.0
sInd(Similarity index) 1.0 0.94107 0.64065 0.64645 1.0 0.64645 1.0 0.94107 1.0 1.0
<BLANKLINE>
>>> with warns(RuntimeWarning, match='Confusion matrix is high-dimensional and may not display properly. Consider using the `sparse` flag in printing functions, or save it as a CSV file for better visualization.'):
... print(large_cm)
Predict 0 1 2 3 4 5 6 7 8 9
Actual
0 1 0 0 0 0 0 0 0 0 0
<BLANKLINE>
1 0 1 0 0 0 0 0 0 0 0
<BLANKLINE>
2 0 1 1 0 0 0 0 0 0 0
<BLANKLINE>
3 0 0 0 1 0 0 0 1 0 0
<BLANKLINE>
4 0 0 0 0 1 0 0 0 0 0
<BLANKLINE>
5 0 0 1 0 0 1 0 0 0 0
<BLANKLINE>
6 0 0 0 0 0 0 1 0 0 0
<BLANKLINE>
7 0 0 0 0 0 0 0 1 0 0
<BLANKLINE>
8 0 0 0 0 0 0 0 0 1 0
<BLANKLINE>
9 0 0 0 0 0 0 0 0 0 1
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
Overall Statistics :
<BLANKLINE>
95% CI (0.5402,0.99827)
ACC Macro 0.95385
ARI -0.04
AUNP 0.87121
AUNU 0.91212
Bangdiwala B 0.58824
Bennett S 0.74359
CBA 0.75
CSI 0.7
Chi-Squared 91.0
Chi-Squared DF 81
Conditional Entropy 0.46154
Cramer V 0.88192
Cross Entropy 3.39275
F1 Macro 0.81667
F1 Micro 0.76923
FNR Macro 0.15
FNR Micro 0.23077
FPR Macro 0.02576
FPR Micro 0.02564
Gwet AC1 0.7438
Hamming Loss 0.23077
Joint Entropy 3.70044
KL Divergence 0.15385
Kappa 0.74342
Kappa 95% CI (0.48877,0.99807)
Kappa No Prevalence 0.53846
Kappa Standard Error 0.12992
Kappa Unbiased 0.74172
Krippendorff Alpha 0.75166
Lambda A 0.72727
Lambda B 0.72727
Mutual Information 2.77736
NIR 0.15385
NPV Macro 0.97424
NPV Micro 0.97436
Overall ACC 0.76923
Overall CEN 0.09537
Overall J (7.33333,0.73333)
Overall MCC 0.75333
Overall MCEN 0.10746
Overall RACC 0.10059
Overall RACCU 0.10651
P-Value 0.0
PPV Macro 0.85
PPV Micro 0.76923
Pearson C 0.93541
Phi-Squared 7.0
RCI 0.8575
RR 1.3
Reference Entropy 3.2389
Response Entropy 3.2389
SOA1(Landis & Koch) Substantial
SOA2(Fleiss) Intermediate to Good
SOA3(Altman) Good
SOA4(Cicchetti) Excellent
SOA5(Cramer) Very Strong
SOA6(Matthews) Strong
SOA7(Lambda A) Strong
SOA8(Lambda B) Strong
SOA9(Krippendorff Alpha) Tentative
SOA10(Pearson C) Strong
Scott PI 0.74172
Standard Error 0.11685
TNR Macro 0.97424
TNR Micro 0.97436
TPR Macro 0.85
TPR Micro 0.76923
Zero-one Loss 3
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 0 1 2 3 4 5 6 7 8 9
ACC(Accuracy) 1.0 0.92308 0.84615 0.92308 1.0 0.92308 1.0 0.92308 1.0 1.0
AGF(Adjusted F-score) 1.0 0.90468 0.6742 0.71965 1.0 0.71965 1.0 0.90468 1.0 1.0
AGM(Adjusted geometric mean) 1.0 0.93786 0.78186 0.84135 1.0 0.84135 1.0 0.93786 1.0 1.0
AM(Difference between automatic and manual classification) 0 1 0 -1 0 -1 0 1 0 0
AUC(Area under the ROC curve) 1.0 0.95833 0.70455 0.75 1.0 0.75 1.0 0.95833 1.0 1.0
AUCI(AUC value interpretation) Excellent Excellent Good Good Excellent Good Excellent Excellent Excellent Excellent
AUPR(Area under the PR curve) 1.0 0.75 0.5 0.75 1.0 0.75 1.0 0.75 1.0 1.0
BB(Braun-Blanquet similarity) 1.0 0.5 0.5 0.5 1.0 0.5 1.0 0.5 1.0 1.0
BCD(Bray-Curtis dissimilarity) 0.0 0.03846 0.0 0.03846 0.0 0.03846 0.0 0.03846 0.0 0.0
BM(Informedness or bookmaker informedness) 1.0 0.91667 0.40909 0.5 1.0 0.5 1.0 0.91667 1.0 1.0
CEN(Confusion entropy) 0 0.1267 0.23981 0.1267 0 0.1267 0 0.1267 0 0
DOR(Diagnostic odds ratio) None None 10.0 None None None None None None None
DP(Discriminant power) None None 0.55133 None None None None None None None
DPI(Discriminant power interpretation) None None Poor None None None None None None None
ERR(Error rate) 0.0 0.07692 0.15385 0.07692 0.0 0.07692 0.0 0.07692 0.0 0.0
F0.5(F0.5 score) 1.0 0.55556 0.5 0.83333 1.0 0.83333 1.0 0.55556 1.0 1.0
F1(F1 score - harmonic mean of precision and sensitivity) 1.0 0.66667 0.5 0.66667 1.0 0.66667 1.0 0.66667 1.0 1.0
F2(F2 score) 1.0 0.83333 0.5 0.55556 1.0 0.55556 1.0 0.83333 1.0 1.0
FDR(False discovery rate) 0.0 0.5 0.5 0.0 0.0 0.0 0.0 0.5 0.0 0.0
FN(False negative/miss/type 2 error) 0 0 1 1 0 1 0 0 0 0
FNR(Miss rate or false negative rate) 0.0 0.0 0.5 0.5 0.0 0.5 0.0 0.0 0.0 0.0
FOR(False omission rate) 0.0 0.0 0.09091 0.08333 0.0 0.08333 0.0 0.0 0.0 0.0
FP(False positive/type 1 error/false alarm) 0 1 1 0 0 0 0 1 0 0
FPR(Fall-out or false positive rate) 0.0 0.08333 0.09091 0.0 0.0 0.0 0.0 0.08333 0.0 0.0
G(G-measure geometric mean of precision and sensitivity) 1.0 0.70711 0.5 0.70711 1.0 0.70711 1.0 0.70711 1.0 1.0
GI(Gini index) 1.0 0.91667 0.40909 0.5 1.0 0.5 1.0 0.91667 1.0 1.0
GM(G-mean geometric mean of specificity and sensitivity) 1.0 0.95743 0.6742 0.70711 1.0 0.70711 1.0 0.95743 1.0 1.0
HD(Hamming distance) 0 1 2 1 0 1 0 1 0 0
IBA(Index of balanced accuracy) 1.0 0.99306 0.2686 0.25 1.0 0.25 1.0 0.99306 1.0 1.0
ICSI(Individual classification success index) 1.0 0.5 0.0 0.5 1.0 0.5 1.0 0.5 1.0 1.0
IS(Information score) 3.70044 2.70044 1.70044 2.70044 3.70044 2.70044 3.70044 2.70044 3.70044 3.70044
J(Jaccard index) 1.0 0.5 0.33333 0.5 1.0 0.5 1.0 0.5 1.0 1.0
LS(Lift score) 13.0 6.5 3.25 6.5 13.0 6.5 13.0 6.5 13.0 13.0
MCC(Matthews correlation coefficient) 1.0 0.677 0.40909 0.677 1.0 0.677 1.0 0.677 1.0 1.0
MCCI(Matthews correlation coefficient interpretation) Very Strong Moderate Weak Moderate Very Strong Moderate Very Strong Moderate Very Strong Very Strong
MCEN(Modified confusion entropy) 0 0.11991 0.2534 0.11991 0 0.11991 0 0.11991 0 0
MK(Markedness) 1.0 0.5 0.40909 0.91667 1.0 0.91667 1.0 0.5 1.0 1.0
N(Condition negative) 12 12 11 11 12 11 12 12 12 12
NLR(Negative likelihood ratio) 0.0 0.0 0.55 0.5 0.0 0.5 0.0 0.0 0.0 0.0
NLRI(Negative likelihood ratio interpretation) Good Good Negligible Negligible Good Negligible Good Good Good Good
NPV(Negative predictive value) 1.0 1.0 0.90909 0.91667 1.0 0.91667 1.0 1.0 1.0 1.0
OC(Overlap coefficient) 1.0 1.0 0.5 1.0 1.0 1.0 1.0 1.0 1.0 1.0
OOC(Otsuka-Ochiai coefficient) 1.0 0.70711 0.5 0.70711 1.0 0.70711 1.0 0.70711 1.0 1.0
OP(Optimized precision) 1.0 0.8796 0.55583 0.58974 1.0 0.58974 1.0 0.8796 1.0 1.0
P(Condition positive or support) 1 1 2 2 1 2 1 1 1 1
PLR(Positive likelihood ratio) None 12.0 5.5 None None None None 12.0 None None
PLRI(Positive likelihood ratio interpretation) None Good Fair None None None None Good None None
POP(Population) 13 13 13 13 13 13 13 13 13 13
PPV(Precision or positive predictive value) 1.0 0.5 0.5 1.0 1.0 1.0 1.0 0.5 1.0 1.0
PR(Positive rate) 0.07692 0.07692 0.15385 0.15385 0.07692 0.15385 0.07692 0.07692 0.07692 0.07692
PRE(Prevalence) 0.07692 0.07692 0.15385 0.15385 0.07692 0.15385 0.07692 0.07692 0.07692 0.07692
Q(Yule Q - coefficient of colligation) None None 0.81818 None None None None None None None
QI(Yule Q interpretation) None None Strong None None None None None None None
RACC(Random accuracy) 0.00592 0.01183 0.02367 0.01183 0.00592 0.01183 0.00592 0.01183 0.00592 0.00592
RACCU(Random accuracy unbiased) 0.00592 0.01331 0.02367 0.01331 0.00592 0.01331 0.00592 0.01331 0.00592 0.00592
TN(True negative/correct rejection) 12 11 10 11 12 11 12 11 12 12
TNR(Specificity or true negative rate) 1.0 0.91667 0.90909 1.0 1.0 1.0 1.0 0.91667 1.0 1.0
TON(Test outcome negative) 12 11 11 12 12 12 12 11 12 12
TOP(Test outcome positive) 1 2 2 1 1 1 1 2 1 1
TOPR(Test outcome positive rate) 0.07692 0.15385 0.15385 0.07692 0.07692 0.07692 0.07692 0.15385 0.07692 0.07692
TP(True positive/hit) 1 1 1 1 1 1 1 1 1 1
TPR(Sensitivity, recall, hit rate, or true positive rate) 1.0 1.0 0.5 0.5 1.0 0.5 1.0 1.0 1.0 1.0
Y(Youden index) 1.0 0.91667 0.40909 0.5 1.0 0.5 1.0 0.91667 1.0 1.0
dInd(Distance index) 0.0 0.08333 0.5082 0.5 0.0 0.5 0.0 0.08333 0.0 0.0
sInd(Similarity index) 1.0 0.94107 0.64065 0.64645 1.0 0.64645 1.0 0.94107 1.0 1.0
<BLANKLINE>
>>> cm = ConfusionMatrix(matrix={1: {1: 22, 0: 54}, 0: {1: 1, 0: 57}}, transpose=True)
>>> with warns(RuntimeWarning):
... cm.CI("TPR", alpha=2)[1][1][1]
1.0398659919971112
>>> with warns(RuntimeWarning):
... cm.CI("TPR", alpha=2, one_sided=True)[1][1][1]
1.0264713799292524
>>> cm = ConfusionMatrix(matrix={"often": {"often": 16, "seldom": 6, "never": 2}, "seldom": {"often": 4, "seldom": 10, "never": 1}, "never": {"often": 3, "seldom": 0, "never": 8}})
>>> with warns(RuntimeWarning):
... cm.weighted_kappa()
0.4959042218021425
>>> with warns(RuntimeWarning):
... cm.weighted_kappa(weight={1: {1: 1, 2: 2}, 2: {1: 2, 2: 1}})
0.4959042218021425
>>> with warns(RuntimeWarning):
... cm.weighted_alpha()
0.5007878978884337
>>> with warns(RuntimeWarning):
... cm.weighted_alpha(weight={1: {1: 1, 2: 2}, 2: {1: 2, 2: 1}})
0.5007878978884337
>>> 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]
>>> with warns(RuntimeWarning):
... cm4 = ConfusionMatrix(y_act, y_pre, classes=[1, 2, 0, 3])
>>> cm4
pycm.ConfusionMatrix(classes: [1, 2, 0, 3])
>>> cm4.classes
[1, 2, 0, 3]
>>> cm4.to_array()
array([[5, 1, 3, 0],
[1, 4, 1, 0],
[3, 0, 9, 0],
[0, 0, 0, 0]])
>>> print(cm4)
Predict 1 2 0 3
Actual
1 5 1 3 0
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2 1 4 1 0
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0 3 0 9 0
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3 0 0 0 0
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Overall Statistics :
<BLANKLINE>
95% CI (0.48885,0.84448)
ACC Macro 0.83333
ARI 0.21053
AUNP None
AUNU None
Bangdiwala B 0.45693
Bennett S 0.55556
CBA None
CSI None
Chi-Squared None
Chi-Squared DF 9
Conditional Entropy 1.08926
Cramer V None
Cross Entropy 1.53762
F1 Macro None
F1 Micro 0.66667
FNR Macro None
FNR Micro 0.33333
FPR Macro 0.13413
FPR Micro 0.11111
Gwet AC1 0.57751
Hamming Loss 0.33333
Joint Entropy 2.61975
KL Divergence None
Kappa 0.47403
Kappa 95% CI (0.19345,0.7546)
Kappa No Prevalence 0.33333
Kappa Standard Error 0.14315
Kappa Unbiased 0.47346
Krippendorff Alpha 0.48321
Lambda A 0.4
Lambda B 0.35714
Mutual Information 0.39731
NIR 0.44444
NPV Macro 0.86815
NPV Micro 0.88889
Overall ACC 0.66667
Overall CEN None
Overall J None
Overall MCC 0.47511
Overall MCEN None
Overall RACC 0.36626
Overall RACCU 0.36694
P-Value 0.01667
PPV Macro None
PPV Micro 0.66667
Pearson C None
Phi-Squared None
RCI 0.2596
RR 6.75
Reference Entropy 1.53049
Response Entropy 1.48657
SOA1(Landis & Koch) Moderate
SOA2(Fleiss) Intermediate to Good
SOA3(Altman) Moderate
SOA4(Cicchetti) Fair
SOA5(Cramer) None
SOA6(Matthews) Weak
SOA7(Lambda A) Moderate
SOA8(Lambda B) Weak
SOA9(Krippendorff Alpha) Low
SOA10(Pearson C) None
Scott PI 0.47346
Standard Error 0.09072
TNR Macro 0.86587
TNR Micro 0.88889
TPR Macro None
TPR Micro 0.66667
Zero-one Loss 9
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 1 2 0 3
ACC(Accuracy) 0.7037 0.88889 0.74074 1.0
AGF(Adjusted F-score) 0.65734 0.79543 0.75595 None
AGM(Adjusted geometric mean) 0.70552 0.86488 0.73866 None
AM(Difference between automatic and manual classification) 0 -1 1 0
AUC(Area under the ROC curve) 0.66667 0.80952 0.74167 None
AUCI(AUC value interpretation) Fair Very Good Good None
AUPR(Area under the PR curve) 0.55556 0.73333 0.72115 None
BB(Braun-Blanquet similarity) 0.55556 0.66667 0.69231 None
BCD(Bray-Curtis dissimilarity) 0.0 0.01852 0.01852 0.0
BM(Informedness or bookmaker informedness) 0.33333 0.61905 0.48333 None
CEN(Confusion entropy) 0.51257 0.36499 0.35586 None
DOR(Diagnostic odds ratio) 4.375 40.0 8.25 None
DP(Discriminant power) 0.35339 0.88326 0.50527 None
DPI(Discriminant power interpretation) Poor Poor Poor None
ERR(Error rate) 0.2963 0.11111 0.25926 0.0
F0.5(F0.5 score) 0.55556 0.76923 0.70312 None
F1(F1 score - harmonic mean of precision and sensitivity) 0.55556 0.72727 0.72 None
F2(F2 score) 0.55556 0.68966 0.7377 None
FDR(False discovery rate) 0.44444 0.2 0.30769 None
FN(False negative/miss/type 2 error) 4 2 3 0
FNR(Miss rate or false negative rate) 0.44444 0.33333 0.25 None
FOR(False omission rate) 0.22222 0.09091 0.21429 0.0
FP(False positive/type 1 error/false alarm) 4 1 4 0
FPR(Fall-out or false positive rate) 0.22222 0.04762 0.26667 0.0
G(G-measure geometric mean of precision and sensitivity) 0.55556 0.7303 0.72058 None
GI(Gini index) 0.33333 0.61905 0.48333 None
GM(G-mean geometric mean of specificity and sensitivity) 0.65734 0.79682 0.74162 None
HD(Hamming distance) 8 3 7 0
IBA(Index of balanced accuracy) 0.33608 0.45351 0.55917 None
ICSI(Individual classification success index) 0.11111 0.46667 0.44231 None
IS(Information score) 0.73697 1.848 0.63941 None
J(Jaccard index) 0.38462 0.57143 0.5625 None
LS(Lift score) 1.66667 3.6 1.55769 None
MCC(Matthews correlation coefficient) 0.33333 0.66254 0.48067 None
MCCI(Matthews correlation coefficient interpretation) Weak Moderate Weak None
MCEN(Modified confusion entropy) 0.59795 0.46544 0.44706 None
MK(Markedness) 0.33333 0.70909 0.47802 None
N(Condition negative) 18 21 15 27
NLR(Negative likelihood ratio) 0.57143 0.35 0.34091 None
NLRI(Negative likelihood ratio interpretation) Negligible Poor Poor None
NPV(Negative predictive value) 0.77778 0.90909 0.78571 1.0
OC(Overlap coefficient) 0.55556 0.8 0.75 None
OOC(Otsuka-Ochiai coefficient) 0.55556 0.7303 0.72058 None
OP(Optimized precision) 0.53704 0.71242 0.7295 None
P(Condition positive or support) 9 6 12 0
PLR(Positive likelihood ratio) 2.5 14.0 2.8125 None
PLRI(Positive likelihood ratio interpretation) Poor Good Poor None
POP(Population) 27 27 27 27
PPV(Precision or positive predictive value) 0.55556 0.8 0.69231 None
PR(Positive rate) 0.33333 0.22222 0.44444 0.0
PRE(Prevalence) 0.33333 0.22222 0.44444 0.0
Q(Yule Q - coefficient of colligation) 0.62791 0.95122 0.78378 None
QI(Yule Q interpretation) Moderate Strong Strong None
RACC(Random accuracy) 0.11111 0.04115 0.21399 0.0
RACCU(Random accuracy unbiased) 0.11111 0.0415 0.21433 0.0
TN(True negative/correct rejection) 14 20 11 27
TNR(Specificity or true negative rate) 0.77778 0.95238 0.73333 1.0
TON(Test outcome negative) 18 22 14 27
TOP(Test outcome positive) 9 5 13 0
TOPR(Test outcome positive rate) 0.33333 0.18519 0.48148 0.0
TP(True positive/hit) 5 4 9 0
TPR(Sensitivity, recall, hit rate, or true positive rate) 0.55556 0.66667 0.75 None
Y(Youden index) 0.33333 0.61905 0.48333 None
dInd(Distance index) 0.4969 0.33672 0.36553 None
sInd(Similarity index) 0.64864 0.7619 0.74153 None
<BLANKLINE>
>>> with warns(RuntimeWarning):
... cm4 = ConfusionMatrix([1, 1, 1, 1], [1, 1, 2, 1], classes=[1, "s"])
>>> cm4.to_array()
array([[3, 0],
[0, 0]])
>>> cm4
pycm.ConfusionMatrix(classes: ['1', 's'])
>>> with warns(RuntimeWarning):
... cm4 = ConfusionMatrix([1, 1, 1, 1], [1, 1, 2, 1], classes=(1, 2))
>>> cm4.to_array()
array([[3, 1],
[0, 0]])
>>> cm4
pycm.ConfusionMatrix(classes: [1, 2])
>>> with warns(RuntimeWarning):
... crv = PRCurve(actual_vector=[1, 1, 2, 2], probs=[[0.1, 0.9], [0.4, 0.6], [0.35, 0.65], [0.8, 0.2]], classes=[2, 1])
>>> crv
pycm.PRCurve(classes: [2, 1])
>>> @deprecated
... def test_deprecated():
... return
>>> with warns(DeprecationWarning, match='`test_deprecated` is deprecated and may be removed in future releases.'):
... test_deprecated()
"""
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