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
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
F1 Macro                                                          0.23043
F1 Micro                                                          0.35
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
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
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          
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           
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          
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}

Load

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
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
F1 Macro                                                          0.23043
F1 Micro                                                          0.35
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
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
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          
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           
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          
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], "Digit": 5, "Transpose": false, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 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]]]], "Sample-Weight": null}
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], "Digit": 5, "Transpose": false, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Class-Stat": {"OC": {"200": 0.8571428571428571, "500": 0.5, "100": "None", "600": "None"}, "dInd": {"200": 0.673145600891813, "500": 0.6692567908186672, "100": "None", "600": 1.0}, "NLR": {"200": 0.8333333333333334, "500": 0.7083333333333334, "100": "None", "600": 1.0}, "MCC": {"200": 0.10482848367219183, "500": 0.32673201960653564, "100": "None", "600": "None"}, "RACCU": {"200": 0.33062499999999995, "500": 0.015625, "100": 0.07562500000000001, "600": 0.0006250000000000001}, "IS": {"200": 0.09953567355091428, "500": 1.736965594166206, "100": "None", "600": "None"}, "MK": {"200": 0.08791208791208782, "500": 0.38888888888888884, "100": 0.0, "600": "None"}, "ACC": {"200": 0.45, "500": 0.85, "100": 0.45, "600": 0.95}, "FOR": {"200": 0.7692307692307692, "500": 0.11111111111111116, "100": 0.0, "600": 0.050000000000000044}, "FDR": {"200": 0.1428571428571429, "500": 0.5, "100": 1.0, "600": "None"}, "Y": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "PLRI": {"200": "Poor", "500": "Fair", "100": "None", "600": "None"}, "G": {"200": 0.5669467095138409, "500": 0.408248290463863, "100": "None", "600": "None"}, "TOP": {"200": 7, "500": 2, "100": 11, "600": 0}, "TON": {"200": 13, "500": 18, "100": 9, "600": 20}, "FNR": {"200": 0.625, "500": 0.6666666666666667, "100": "None", "600": 1.0}, "F0.5": {"200": 0.6818181818181818, "500": 0.45454545454545453, "100": 0.0, "600": 0.0}, "DOR": {"200": 1.7999999999999998, "500": 7.999999999999997, "100": "None", "600": "None"}, "sInd": {"200": 0.5240141808835057, "500": 0.5267639848569737, "100": "None", "600": 0.29289321881345254}, "RACC": {"200": 0.28, "500": 0.015, "100": 0.0, "600": 0.0}, "DPI": {"200": "Poor", "500": "Poor", "100": "None", "600": "None"}, "NLRI": {"200": "Negligible", "500": "Negligible", "100": "None", "600": "Negligible"}, "FP": {"200": 1, "100": 11, "500": 1, "600": 0}, "TP": {"200": 6, "100": 0, "500": 1, "600": 0}, "MCEN": {"200": 0.3739448088748241, "500": 0.5802792108518123, "100": 0.3349590631259315, "600": 0.0}, "GI": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "AGM": {"200": 0.5669417382415922, "500": 0.7351956938438939, "100": "None", "600": 0}, "ERR": {"200": 0.55, "500": 0.15000000000000002, "100": 0.55, "600": 0.050000000000000044}, "OOC": {"200": 0.5669467095138409, "500": 0.4082482904638631, "100": "None", "600": "None"}, "PRE": {"200": 0.8, "500": 0.15, "100": 0.0, "600": 0.05}, "AGF": {"200": 0.33642097801219245, "500": 0.5665926996700735, "100": 0.0, "600": 0.0}, "J": {"200": 0.35294117647058826, "500": 0.25, "100": 0.0, "600": 0.0}, "PPV": {"200": 0.8571428571428571, "500": 0.5, "100": 0.0, "600": "None"}, "DP": {"200": 0.1407391082701595, "500": 0.49789960499474867, "100": "None", "600": "None"}, "FPR": {"200": 0.25, "500": 0.05882352941176472, "100": 0.55, "600": 0.0}, "MCCI": {"200": "Negligible", "500": "Weak", "100": "None", "600": "None"}, "AUCI": {"200": "Poor", "500": "Fair", "100": "None", "600": "Poor"}, "IBA": {"200": 0.17578125, "500": 0.1230296039984621, "100": "None", "600": 0.0}, "PLR": {"200": 1.5, "500": 5.666666666666665, "100": "None", "600": "None"}, "AUC": {"200": 0.5625, "500": 0.6372549019607843, "100": "None", "600": 0.5}, "TPR": {"200": 0.375, "500": 0.3333333333333333, "100": "None", "600": 0.0}, "LS": {"200": 1.0714285714285714, "500": 3.3333333333333335, "100": "None", "600": "None"}, "BM": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "AM": {"200": -9, "500": -1, "100": 11, "600": -1}, "POP": {"200": 20, "500": 20, "100": 20, "600": 20}, "P": {"200": 16, "500": 3, "100": 0, "600": 1}, "BCD": {"200": 0.225, "500": 0.025, "100": 0.275, "600": 0.025}, "Q": {"200": 0.28571428571428575, "500": 0.7777777777777778, "100": "None", "600": "None"}, "TNR": {"200": 0.75, "500": 0.9411764705882353, "100": 0.45, "600": 1.0}, "N": {"200": 4, "500": 17, "100": 20, "600": 19}, "F2": {"200": 0.4225352112676056, "500": 0.35714285714285715, "100": 0.0, "600": 0.0}, "NPV": {"200": 0.23076923076923078, "500": 0.8888888888888888, "100": 1.0, "600": 0.95}, "GM": {"200": 0.5303300858899106, "500": 0.5601120336112039, "100": "None", "600": 0.0}, "TN": {"200": 3, "100": 9, "500": 16, "600": 19}, "OP": {"200": 0.1166666666666667, "500": 0.373076923076923, "100": "None", "600": -0.050000000000000044}, "F1": {"200": 0.5217391304347826, "500": 0.4, "100": 0.0, "600": 0.0}, "CEN": {"200": 0.3570795472009597, "500": 0.5389466410223563, "100": 0.3349590631259315, "600": 0.0}, "FN": {"200": 10, "100": 0, "500": 2, "600": 1}}, "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]]]], "Sample-Weight": null, "Overall-Stat": {"NIR": 0.8, "Gwet AC1": 0.19504643962848295, "Phi-Squared": "None", "SOA1(Landis & Koch)": "Slight", "AUNU": "None", "Zero-one Loss": 13, "Kappa No Prevalence": -0.30000000000000004, "Kappa Standard Error": 0.15128176601206766, "Cramer V": "None", "SOA6(Matthews)": "Negligible", "Mutual Information": 0.10087710767390168, "PPV Macro": "None", "Overall ACC": 0.35, "AUNP": "None", "Overall RACC": 0.29500000000000004, "Chi-Squared": "None", "TPR Micro": 0.35, "Bennett S": 0.1333333333333333, "Standard Error": 0.1066536450385077, "Kappa": 0.07801418439716304, "ACC Macro": 0.675, "Conditional Entropy": 1.235789374242786, "CBA": 0.17708333333333331, "Kappa Unbiased": -0.12554112554112543, "SOA5(Cramer)": "None", "Overall J": [0.6029411764705883, 0.15073529411764708], "RR": 5.0, "Overall MCEN": 0.3746281299595305, "Overall MCC": 0.1264200803632855, "PPV Micro": 0.35, "Cross Entropy": 1.709947752496911, "Overall CEN": 0.3648028121279775, "P-Value": 0.9999981549942787, "Pearson C": "None", "KL Divergence": "None", "Hamming Loss": 0.65, "Kappa 95% CI": [-0.21849807698648957, 0.3745264457808156], "Lambda A": 0.0, "SOA3(Altman)": "Poor", "95% CI": [0.14095885572452488, 0.559041144275475], "Lambda B": 0.0, "SOA4(Cicchetti)": "Poor", "SOA2(Fleiss)": "Poor", "TPR Macro": "None", "Response Entropy": 1.3366664819166876, "F1 Macro": 0.23043478260869565, "RCI": 0.11409066398451011, "Joint Entropy": 2.119973094021975, "F1 Micro": 0.35, "Chi-Squared DF": 9, "Reference Entropy": 0.8841837197791889, "Overall RACCU": 0.42249999999999993, "Scott PI": -0.12554112554112543}}
In [11]:
print(open(os.path.join("Example4_Files","cm_no_vectors.obj"),"r").read())
{"Actual-Vector": null, "Digit": 5, "Transpose": false, "Predict-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]]]], "Sample-Weight": null}
  • 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