Please cite us if you use the software

Example-4 (File)¶

In [1]:
from pycm import ConfusionMatrix
import numpy as np
import os
if "Example4_Files" not in os.listdir():
os.mkdir("Example4_Files")
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])

In [2]:
cm=ConfusionMatrix(y_test, y_pred)
cm

Out[2]:
pycm.ConfusionMatrix(classes: [100, 200, 500, 600])
In [3]:
print(cm)

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

Overall Statistics :

95% CI                                                            (0.14096,0.55904)
ACC Macro                                                         0.675
ARI                                                               0.02298
AUNP                                                              None
AUNU                                                              None
Bangdiwala B                                                      0.31356
Bennett S                                                         0.13333
CBA                                                               0.17708
CSI                                                               None
Chi-Squared                                                       None
Chi-Squared DF                                                    9
Conditional Entropy                                               1.23579
Cramer V                                                          None
Cross Entropy                                                     1.70995
F1 Macro                                                          0.23043
F1 Micro                                                          0.35
FNR Macro                                                         None
FNR Micro                                                         0.65
FPR Macro                                                         0.21471
FPR Micro                                                         0.21667
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
Krippendorff Alpha                                                -0.0974
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
Pearson C                                                         None
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
SOA5(Cramer)                                                      None
SOA6(Matthews)                                                    Negligible
Scott PI                                                          -0.12554
Standard Error                                                    0.10665
TNR Macro                                                         0.78529
TNR Micro                                                         0.78333
TPR Macro                                                         None
TPR Micro                                                         0.35
Zero-one Loss                                                     13

Class Statistics :

Classes                                                           100           200           500           600
ACC(Accuracy)                                                     0.45          0.45          0.85          0.95
AGF(Adjusted F-score)                                             0.0           0.33642       0.56659       0.0
AGM(Adjusted geometric mean)                                      None          0.56694       0.7352        0
AM(Difference between automatic and manual classification)        11            -9            -1            -1
AUC(Area under the ROC curve)                                     None          0.5625        0.63725       0.5
AUCI(AUC value interpretation)                                    None          Poor          Fair          Poor
AUPR(Area under the PR curve)                                     None          0.61607       0.41667       None
BCD(Bray-Curtis dissimilarity)                                    0.275         0.225         0.025         0.025
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
GI(Gini index)                                                    None          0.125         0.27451       0.0
GM(G-mean geometric mean of specificity and sensitivity)          None          0.53033       0.56011       0.0
IBA(Index of balanced accuracy)                                   None          0.17578       0.12303       0.0
ICSI(Individual classification success index)                     None          0.23214       -0.16667      None
IS(Information score)                                             None          0.09954       1.73697       None
J(Jaccard index)                                                  0.0           0.35294       0.25          0.0
LS(Lift score)                                                    None          1.07143       3.33333       None
MCC(Matthews correlation coefficient)                             None          0.10483       0.32673       None
MCCI(Matthews correlation coefficient interpretation)             None          Negligible    Weak          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
NLRI(Negative likelihood ratio interpretation)                    None          Negligible    Negligible    Negligible
NPV(Negative predictive value)                                    1.0           0.23077       0.88889       0.95
OC(Overlap coefficient)                                           None          0.85714       0.5           None
OOC(Otsuka-Ochiai coefficient)                                    None          0.56695       0.40825       None
OP(Optimized precision)                                           None          0.11667       0.37308       -0.05
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
Q(Yule Q - coefficient of colligation)                            None          0.28571       0.77778       None
QI(Yule Q interpretation)                                         None          Weak          Strong        None
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



Save¶

In [4]:
cm.save_obj(os.path.join("Example4_Files","cm"))

Out[4]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Example4_Files\\cm.obj',
'Status': True}
In [5]:
cm.save_obj(os.path.join("Example4_Files","cm_stat"),save_stat=True)

Out[5]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Example4_Files\\cm_stat.obj',
'Status': True}
In [6]:
cm.save_obj(os.path.join("Example4_Files","cm_no_vectors"),save_vector=False)

Out[6]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Example4_Files\\cm_no_vectors.obj',
'Status': True}

In [7]:
cm_load = ConfusionMatrix(file=open(os.path.join("Example4_Files","cm.obj"),"r"))
cm

Out[7]:
pycm.ConfusionMatrix(classes: [100, 200, 500, 600])
In [8]:
print(cm)

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

Overall Statistics :

95% CI                                                            (0.14096,0.55904)
ACC Macro                                                         0.675
ARI                                                               0.02298
AUNP                                                              None
AUNU                                                              None
Bangdiwala B                                                      0.31356
Bennett S                                                         0.13333
CBA                                                               0.17708
CSI                                                               None
Chi-Squared                                                       None
Chi-Squared DF                                                    9
Conditional Entropy                                               1.23579
Cramer V                                                          None
Cross Entropy                                                     1.70995
F1 Macro                                                          0.23043
F1 Micro                                                          0.35
FNR Macro                                                         None
FNR Micro                                                         0.65
FPR Macro                                                         0.21471
FPR Micro                                                         0.21667
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
Krippendorff Alpha                                                -0.0974
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
Pearson C                                                         None
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
SOA5(Cramer)                                                      None
SOA6(Matthews)                                                    Negligible
Scott PI                                                          -0.12554
Standard Error                                                    0.10665
TNR Macro                                                         0.78529
TNR Micro                                                         0.78333
TPR Macro                                                         None
TPR Micro                                                         0.35
Zero-one Loss                                                     13

Class Statistics :

Classes                                                           100           200           500           600
ACC(Accuracy)                                                     0.45          0.45          0.85          0.95
AGF(Adjusted F-score)                                             0.0           0.33642       0.56659       0.0
AGM(Adjusted geometric mean)                                      None          0.56694       0.7352        0
AM(Difference between automatic and manual classification)        11            -9            -1            -1
AUC(Area under the ROC curve)                                     None          0.5625        0.63725       0.5
AUCI(AUC value interpretation)                                    None          Poor          Fair          Poor
AUPR(Area under the PR curve)                                     None          0.61607       0.41667       None
BCD(Bray-Curtis dissimilarity)                                    0.275         0.225         0.025         0.025
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
GI(Gini index)                                                    None          0.125         0.27451       0.0
GM(G-mean geometric mean of specificity and sensitivity)          None          0.53033       0.56011       0.0
IBA(Index of balanced accuracy)                                   None          0.17578       0.12303       0.0
ICSI(Individual classification success index)                     None          0.23214       -0.16667      None
IS(Information score)                                             None          0.09954       1.73697       None
J(Jaccard index)                                                  0.0           0.35294       0.25          0.0
LS(Lift score)                                                    None          1.07143       3.33333       None
MCC(Matthews correlation coefficient)                             None          0.10483       0.32673       None
MCCI(Matthews correlation coefficient interpretation)             None          Negligible    Weak          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
NLRI(Negative likelihood ratio interpretation)                    None          Negligible    Negligible    Negligible
NPV(Negative predictive value)                                    1.0           0.23077       0.88889       0.95
OC(Overlap coefficient)                                           None          0.85714       0.5           None
OOC(Otsuka-Ochiai coefficient)                                    None          0.56695       0.40825       None
OP(Optimized precision)                                           None          0.11667       0.37308       -0.05
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
Q(Yule Q - coefficient of colligation)                            None          0.28571       0.77778       None
QI(Yule Q interpretation)                                         None          Weak          Strong        None
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



Obj File¶

In [9]:
print(open(os.path.join("Example4_Files","cm.obj"),"r").read())

{"Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Matrix": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], "Digit": 5, "Sample-Weight": null, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Transpose": false}

In [10]:
print(open(os.path.join("Example4_Files","cm_stat.obj"),"r").read())

{"Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Matrix": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], "Class-Stat": {"PPV": {"200": 0.8571428571428571, "500": 0.5, "100": 0.0, "600": "None"}, "GI": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "AGM": {"200": 0.5669417382415922, "500": 0.7351956938438939, "100": "None", "600": 0}, "ACC": {"200": 0.45, "500": 0.85, "100": 0.45, "600": 0.95}, "RACCU": {"200": 0.33062499999999995, "500": 0.015625, "100": 0.07562500000000001, "600": 0.0006250000000000001}, "LS": {"200": 1.0714285714285714, "500": 3.3333333333333335, "100": "None", "600": "None"}, "AUCI": {"200": "Poor", "500": "Fair", "100": "None", "600": "Poor"}, "NLR": {"200": 0.8333333333333334, "500": 0.7083333333333334, "100": "None", "600": 1.0}, "J": {"200": 0.35294117647058826, "500": 0.25, "100": 0.0, "600": 0.0}, "AGF": {"200": 0.33642097801219245, "500": 0.5665926996700735, "100": 0.0, "600": 0.0}, "TNR": {"200": 0.75, "500": 0.9411764705882353, "100": 0.45, "600": 1.0}, "F0.5": {"200": 0.6818181818181818, "500": 0.45454545454545453, "100": 0.0, "600": 0.0}, "IBA": {"200": 0.17578125, "500": 0.1230296039984621, "100": "None", "600": 0.0}, "MCEN": {"200": 0.3739448088748241, "500": 0.5802792108518123, "100": 0.3349590631259315, "600": 0.0}, "TPR": {"200": 0.375, "500": 0.3333333333333333, "100": "None", "600": 0.0}, "ERR": {"200": 0.55, "500": 0.15000000000000002, "100": 0.55, "600": 0.050000000000000044}, "FPR": {"200": 0.25, "500": 0.05882352941176472, "100": 0.55, "600": 0.0}, "DPI": {"200": "Poor", "500": "Poor", "100": "None", "600": "None"}, "G": {"200": 0.5669467095138409, "500": 0.408248290463863, "100": "None", "600": "None"}, "sInd": {"200": 0.5240141808835057, "500": 0.5267639848569737, "100": "None", "600": 0.29289321881345254}, "F2": {"200": 0.4225352112676056, "500": 0.35714285714285715, "100": 0.0, "600": 0.0}, "TP": {"200": 6, "100": 0, "500": 1, "600": 0}, "PLR": {"200": 1.5, "500": 5.666666666666665, "100": "None", "600": "None"}, "dInd": {"200": 0.673145600891813, "500": 0.6692567908186672, "100": "None", "600": 1.0}, "POP": {"200": 20, "500": 20, "100": 20, "600": 20}, "GM": {"200": 0.5303300858899106, "500": 0.5601120336112039, "100": "None", "600": 0.0}, "FP": {"200": 1, "100": 11, "500": 1, "600": 0}, "N": {"200": 4, "500": 17, "100": 20, "600": 19}, "CEN": {"200": 0.3570795472009597, "500": 0.5389466410223563, "100": 0.3349590631259315, "600": 0.0}, "OP": {"200": 0.1166666666666667, "500": 0.373076923076923, "100": "None", "600": -0.050000000000000044}, "TN": {"200": 3, "100": 9, "500": 16, "600": 19}, "NPV": {"200": 0.23076923076923078, "500": 0.8888888888888888, "100": 1.0, "600": 0.95}, "MK": {"200": 0.08791208791208782, "500": 0.38888888888888884, "100": 0.0, "600": "None"}, "AUPR": {"200": 0.6160714285714286, "500": 0.41666666666666663, "100": "None", "600": "None"}, "NLRI": {"200": "Negligible", "500": "Negligible", "100": "None", "600": "Negligible"}, "TON": {"200": 13, "500": 18, "100": 9, "600": 20}, "FOR": {"200": 0.7692307692307692, "500": 0.11111111111111116, "100": 0.0, "600": 0.050000000000000044}, "DP": {"200": 0.1407391082701595, "500": 0.49789960499474867, "100": "None", "600": "None"}, "BM": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "MCCI": {"200": "Negligible", "500": "Weak", "100": "None", "600": "None"}, "DOR": {"200": 1.7999999999999998, "500": 7.999999999999997, "100": "None", "600": "None"}, "Y": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "P": {"200": 16, "500": 3, "100": 0, "600": 1}, "FDR": {"200": 0.1428571428571429, "500": 0.5, "100": 1.0, "600": "None"}, "ICSI": {"200": 0.2321428571428572, "500": -0.16666666666666674, "100": "None", "600": "None"}, "BCD": {"200": 0.225, "500": 0.025, "100": 0.275, "600": 0.025}, "F1": {"200": 0.5217391304347826, "500": 0.4, "100": 0.0, "600": 0.0}, "PLRI": {"200": "Poor", "500": "Fair", "100": "None", "600": "None"}, "OOC": {"200": 0.5669467095138409, "500": 0.4082482904638631, "100": "None", "600": "None"}, "Q": {"200": 0.28571428571428575, "500": 0.7777777777777778, "100": "None", "600": "None"}, "RACC": {"200": 0.28, "500": 0.015, "100": 0.0, "600": 0.0}, "QI": {"200": "Weak", "500": "Strong", "100": "None", "600": "None"}, "MCC": {"200": 0.10482848367219183, "500": 0.32673201960653564, "100": "None", "600": "None"}, "TOP": {"200": 7, "500": 2, "100": 11, "600": 0}, "AUC": {"200": 0.5625, "500": 0.6372549019607843, "100": "None", "600": 0.5}, "PRE": {"200": 0.8, "500": 0.15, "100": 0.0, "600": 0.05}, "OC": {"200": 0.8571428571428571, "500": 0.5, "100": "None", "600": "None"}, "FNR": {"200": 0.625, "500": 0.6666666666666667, "100": "None", "600": 1.0}, "AM": {"200": -9, "500": -1, "100": 11, "600": -1}, "IS": {"200": 0.09953567355091428, "500": 1.736965594166206, "100": "None", "600": "None"}, "FN": {"200": 10, "100": 0, "500": 2, "600": 1}}, "Digit": 5, "Overall-Stat": {"TNR Macro": 0.7852941176470588, "Bangdiwala B": 0.3135593220338983, "RR": 5.0, "Mutual Information": 0.10087710767390168, "SOA4(Cicchetti)": "Poor", "Hamming Loss": 0.65, "SOA2(Fleiss)": "Poor", "ARI": 0.02298247455136956, "Bennett S": 0.1333333333333333, "NIR": 0.8, "F1 Micro": 0.35, "Joint Entropy": 2.119973094021975, "Standard Error": 0.1066536450385077, "Cross Entropy": 1.709947752496911, "Lambda B": 0.0, "Chi-Squared DF": 9, "Cramer V": "None", "Zero-one Loss": 13, "Kappa No Prevalence": -0.30000000000000004, "CBA": 0.17708333333333331, "Response Entropy": 1.3366664819166876, "Lambda A": 0.0, "AUNU": "None", "Kappa 95% CI": [-0.21849807698648957, 0.3745264457808156], "CSI": "None", "FPR Micro": 0.21666666666666667, "PPV Macro": "None", "Phi-Squared": "None", "P-Value": 0.9999981549942787, "Kappa Standard Error": 0.15128176601206766, "TNR Micro": 0.7833333333333333, "Overall ACC": 0.35, "RCI": 0.11409066398451011, "TPR Micro": 0.35, "Pearson C": "None", "95% CI": [0.14095885572452488, 0.559041144275475], "PPV Micro": 0.35, "Gwet AC1": 0.19504643962848295, "SOA6(Matthews)": "Negligible", "Overall MCC": 0.1264200803632855, "SOA1(Landis & Koch)": "Slight", "FPR Macro": 0.2147058823529412, "Kappa Unbiased": -0.12554112554112543, "AUNP": "None", "Kappa": 0.07801418439716304, "Chi-Squared": "None", "Overall RACC": 0.29500000000000004, "KL Divergence": "None", "Scott PI": -0.12554112554112543, "SOA5(Cramer)": "None", "Overall CEN": 0.3648028121279775, "Overall J": [0.6029411764705883, 0.15073529411764708], "SOA3(Altman)": "Poor", "FNR Macro": "None", "TPR Macro": "None", "Conditional Entropy": 1.235789374242786, "Overall RACCU": 0.42249999999999993, "Overall MCEN": 0.3746281299595305, "Reference Entropy": 0.8841837197791889, "ACC Macro": 0.675, "F1 Macro": 0.23043478260869565, "Krippendorff Alpha": -0.09740259740259723, "FNR Micro": 0.65}, "Sample-Weight": null, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Transpose": false}

In [11]:
print(open(os.path.join("Example4_Files","cm_no_vectors.obj"),"r").read())

{"Actual-Vector": null, "Matrix": [[100, [[200, 0], [500, 0], [100, 0], [600, 0]]], [200, [[200, 6], [500, 1], [100, 9], [600, 0]]], [500, [[200, 1], [500, 1], [100, 1], [600, 0]]], [600, [[200, 0], [500, 0], [100, 1], [600, 0]]]], "Digit": 5, "Sample-Weight": null, "Predict-Vector": null, "Transpose": false}

• Notice : Matrix save method changed in version 1.5
• Notice : save_vector and save_stat, new in version 2.3
• Notice : output format is JSON