Please cite us if you use the software
Example-4 (File)¶
Environment check¶
Checking that the notebook is running on Google Colab or not.
import sys
try:
import google.colab
!{sys.executable} -m pip -q -q install pycm
except:
pass
Run¶
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])
cm=ConfusionMatrix(y_test, y_pred)
cm
pycm.ConfusionMatrix(classes: [100, 200, 500, 600])
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 NPV Macro 0.76741 NPV Micro 0.78333 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 SOA7(Lambda A) None SOA8(Lambda B) None SOA9(Krippendorff Alpha) Low SOA10(Pearson C) None 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 BB(Braun-Blanquet similarity) 0.0 0.375 0.33333 0.0 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 HD(Hamming distance) 11 11 3 1 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¶
cm.save_obj(os.path.join("Example4_files", "cm"))
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\doc_html\\Example4_files\\cm.obj',
'Status': True}
cm.save_obj(os.path.join("Example4_files", "cm_stat"), save_stat=True)
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\doc_html\\Example4_files\\cm_stat.obj',
'Status': True}
cm.save_obj(os.path.join("Example4_files", "cm_no_vectors"), save_vector=False)
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\doc_html\\Example4_files\\cm_no_vectors.obj',
'Status': True}
Load¶
cm_load = ConfusionMatrix(file=open(os.path.join("Example4_files", "cm.obj"), "r"))
cm
pycm.ConfusionMatrix(classes: [100, 200, 500, 600])
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 NPV Macro 0.76741 NPV Micro 0.78333 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 SOA7(Lambda A) None SOA8(Lambda B) None SOA9(Krippendorff Alpha) Low SOA10(Pearson C) None 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 BB(Braun-Blanquet similarity) 0.0 0.375 0.33333 0.0 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 HD(Hamming distance) 11 11 3 1 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¶
print(open(os.path.join("Example4_files", "cm.obj"), "r").read())
{"Imbalanced": true, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Transpose": false, "Sample-Weight": null, "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Prob-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}
print(open(os.path.join("Example4_files", "cm_stat.obj"), "r").read())
{"Imbalanced": true, "Class-Stat": {"NLR": {"200": 0.8333333333333334, "500": 0.7083333333333334, "100": "None", "600": 1.0}, "FN": {"200": 10, "100": 0, "500": 2, "600": 1}, "ERR": {"200": 0.55, "500": 0.15000000000000002, "100": 0.55, "600": 0.050000000000000044}, "F1": {"200": 0.5217391304347826, "500": 0.4, "100": 0.0, "600": 0.0}, "PLR": {"200": 1.5, "500": 5.666666666666665, "100": "None", "600": "None"}, "POP": {"200": 20, "500": 20, "100": 20, "600": 20}, "NLRI": {"200": "Negligible", "500": "Negligible", "100": "None", "600": "Negligible"}, "IBA": {"200": 0.17578125, "500": 0.1230296039984621, "100": "None", "600": 0.0}, "TP": {"200": 6, "100": 0, "500": 1, "600": 0}, "CEN": {"200": 0.3570795472009597, "500": 0.5389466410223563, "100": 0.3349590631259315, "600": 0.0}, "dInd": {"200": 0.673145600891813, "500": 0.6692567908186672, "100": "None", "600": 1.0}, "F2": {"200": 0.4225352112676056, "500": 0.35714285714285715, "100": 0.0, "600": 0.0}, "BCD": {"200": 0.225, "500": 0.025, "100": 0.275, "600": 0.025}, "PRE": {"200": 0.8, "500": 0.15, "100": 0.0, "600": 0.05}, "BM": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "OP": {"200": 0.1166666666666667, "500": 0.373076923076923, "100": "None", "600": -0.050000000000000044}, "OOC": {"200": 0.5669467095138409, "500": 0.4082482904638631, "100": "None", "600": "None"}, "P": {"200": 16, "500": 3, "100": 0, "600": 1}, "GM": {"200": 0.5303300858899106, "500": 0.5601120336112039, "100": "None", "600": 0.0}, "FPR": {"200": 0.25, "500": 0.05882352941176472, "100": 0.55, "600": 0.0}, "PLRI": {"200": "Poor", "500": "Fair", "100": "None", "600": "None"}, "FNR": {"200": 0.625, "500": 0.6666666666666667, "100": "None", "600": 1.0}, "TOP": {"200": 7, "500": 2, "100": 11, "600": 0}, "G": {"200": 0.5669467095138409, "500": 0.408248290463863, "100": "None", "600": "None"}, "N": {"200": 4, "500": 17, "100": 20, "600": 19}, "NPV": {"200": 0.23076923076923078, "500": 0.8888888888888888, "100": 1.0, "600": 0.95}, "RACCU": {"200": 0.33062499999999995, "500": 0.015625, "100": 0.07562500000000001, "600": 0.0006250000000000001}, "TN": {"200": 3, "100": 9, "500": 16, "600": 19}, "Q": {"200": 0.28571428571428575, "500": 0.7777777777777778, "100": "None", "600": "None"}, "AUC": {"200": 0.5625, "500": 0.6372549019607843, "100": "None", "600": 0.5}, "RACC": {"200": 0.28, "500": 0.015, "100": 0.0, "600": 0.0}, "PPV": {"200": 0.8571428571428571, "500": 0.5, "100": 0.0, "600": "None"}, "ACC": {"200": 0.45, "500": 0.85, "100": 0.45, "600": 0.95}, "LS": {"200": 1.0714285714285714, "500": 3.3333333333333335, "100": "None", "600": "None"}, "MK": {"200": 0.08791208791208782, "500": 0.38888888888888884, "100": 0.0, "600": "None"}, "GI": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "DOR": {"200": 1.7999999999999998, "500": 7.999999999999997, "100": "None", "600": "None"}, "MCCI": {"200": "Negligible", "500": "Weak", "100": "None", "600": "None"}, "AGF": {"200": 0.33642097801219245, "500": 0.5665926996700735, "100": 0.0, "600": 0.0}, "MCEN": {"200": 0.3739448088748241, "500": 0.5802792108518123, "100": 0.3349590631259315, "600": 0.0}, "AGM": {"200": 0.5669417382415922, "500": 0.7351956938438939, "100": "None", "600": 0}, "DP": {"200": 0.1407391082701595, "500": 0.49789960499474867, "100": "None", "600": "None"}, "IS": {"200": 0.09953567355091428, "500": 1.736965594166206, "100": "None", "600": "None"}, "TON": {"200": 13, "500": 18, "100": 9, "600": 20}, "TNR": {"200": 0.75, "500": 0.9411764705882353, "100": 0.45, "600": 1.0}, "J": {"200": 0.35294117647058826, "500": 0.25, "100": 0.0, "600": 0.0}, "FDR": {"200": 0.1428571428571429, "500": 0.5, "100": 1.0, "600": "None"}, "OC": {"200": 0.8571428571428571, "500": 0.5, "100": "None", "600": "None"}, "BB": {"200": 0.375, "500": 0.3333333333333333, "100": 0.0, "600": 0.0}, "MCC": {"200": 0.10482848367219183, "500": 0.32673201960653564, "100": "None", "600": "None"}, "AUPR": {"200": 0.6160714285714286, "500": 0.41666666666666663, "100": "None", "600": "None"}, "FOR": {"200": 0.7692307692307692, "500": 0.11111111111111116, "100": 0.0, "600": 0.050000000000000044}, "DPI": {"200": "Poor", "500": "Poor", "100": "None", "600": "None"}, "AM": {"200": -9, "500": -1, "100": 11, "600": -1}, "HD": {"200": 11, "500": 3, "100": 11, "600": 1}, "FP": {"200": 1, "100": 11, "500": 1, "600": 0}, "F0.5": {"200": 0.6818181818181818, "500": 0.45454545454545453, "100": 0.0, "600": 0.0}, "sInd": {"200": 0.5240141808835057, "500": 0.5267639848569737, "100": "None", "600": 0.29289321881345254}, "TPR": {"200": 0.375, "500": 0.3333333333333333, "100": "None", "600": 0.0}, "ICSI": {"200": 0.2321428571428572, "500": -0.16666666666666674, "100": "None", "600": "None"}, "AUCI": {"200": "Poor", "500": "Fair", "100": "None", "600": "Poor"}, "QI": {"200": "Weak", "500": "Strong", "100": "None", "600": "None"}, "Y": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}}, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Transpose": false, "Overall-Stat": {"FNR Micro": 0.65, "F1 Micro": 0.35, "Pearson C": "None", "SOA8(Lambda B)": "None", "Chi-Squared DF": 9, "Joint Entropy": 2.119973094021975, "Phi-Squared": "None", "Gwet AC1": 0.19504643962848295, "Lambda A": 0.0, "FNR Macro": "None", "AUNP": "None", "NPV Macro": 0.7674145299145299, "Lambda B": 0.0, "SOA6(Matthews)": "Negligible", "95% CI": [0.14095885572452488, 0.559041144275475], "Response Entropy": 1.3366664819166876, "TPR Macro": "None", "Overall CEN": 0.3648028121279775, "ACC Macro": 0.675, "RCI": 0.11409066398451011, "TNR Micro": 0.7833333333333333, "Overall ACC": 0.35, "Scott PI": -0.12554112554112543, "Mutual Information": 0.10087710767390168, "Overall MCC": 0.1264200803632855, "SOA5(Cramer)": "None", "Overall J": [0.6029411764705883, 0.15073529411764708], "Standard Error": 0.1066536450385077, "SOA7(Lambda A)": "None", "Kappa Standard Error": 0.15128176601206766, "CSI": "None", "FPR Micro": 0.21666666666666667, "Bennett S": 0.1333333333333333, "Chi-Squared": "None", "NIR": 0.8, "AUNU": "None", "PPV Macro": "None", "P-Value": 0.9999981549942787, "ARI": 0.02298247455136956, "Cross Entropy": 1.709947752496911, "Krippendorff Alpha": -0.09740259740259723, "Kappa Unbiased": -0.12554112554112543, "Reference Entropy": 0.8841837197791889, "Overall RACCU": 0.42249999999999993, "SOA9(Krippendorff Alpha)": "Low", "Bangdiwala B": 0.3135593220338983, "TNR Macro": 0.7852941176470588, "SOA4(Cicchetti)": "Poor", "KL Divergence": "None", "Overall RACC": 0.29500000000000004, "Zero-one Loss": 13, "Cramer V": "None", "SOA10(Pearson C)": "None", "SOA3(Altman)": "Poor", "FPR Macro": 0.2147058823529412, "NPV Micro": 0.7833333333333333, "PPV Micro": 0.35, "Kappa 95% CI": [-0.21849807698648957, 0.3745264457808156], "Conditional Entropy": 1.235789374242786, "F1 Macro": 0.23043478260869565, "TPR Micro": 0.35, "RR": 5.0, "Hamming Loss": 0.65, "CBA": 0.17708333333333331, "Kappa No Prevalence": -0.30000000000000004, "Overall MCEN": 0.3746281299595305, "Kappa": 0.07801418439716304, "SOA2(Fleiss)": "Poor", "SOA1(Landis & Koch)": "Slight"}, "Sample-Weight": null, "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Prob-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}
print(open(os.path.join("Example4_files", "cm_no_vectors.obj"), "r").read())
{"Imbalanced": true, "Predict-Vector": null, "Transpose": false, "Sample-Weight": null, "Actual-Vector": null, "Prob-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}
- Notice :
Matrixsave method changed in version 1.5
- Notice :
save_vectorandsave_stat, new in version 2.3
- Notice : output format is JSON