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|
# -*- coding: utf-8 -*-
"""
>>> from pycm import *
>>> from math import isclose
>>> import os
>>> import json
>>> import numpy as np
>>> ABS_TOL = 1e-12
>>> REL_TOL = 0
>>> 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)
>>> save_stat=cm.save_stat("test", address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_stat("test_filtered", address=False, overall_param=["Kappa", "Scott PI"], class_param=["TPR", "TNR", "ACC", "AUC"])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_stat("test_summary", address=False, summary=True)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_stat("test_filtered2", address=False, overall_param=["Kappa", "Scott PI"], class_param=["TPR", "TNR", "ACC", "AUC"], class_name=["L1", "L2"])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_stat("test_filtered3", address=False, overall_param=["Kappa", "Scott PI"], class_param=["TPR", "TNR", "ACC", "AUC"], class_name=[])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_stat("test_filtered4", address=False, overall_param=["Wrong_param"], class_param=["Wrong_param"], class_name=["L1", "L2"])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> large_cm = ConfusionMatrix(list(range(20)), list(range(20)))
>>> save_stat = large_cm.save_stat("test_large", address=False)
>>> save_stat == {'Status': True, 'Message': None}
True
>>> save_stat=cm.save_stat("/asdasd, qweqwe.eo/", address=True)
>>> save_stat=={'Status': False, 'Message': "[Errno 2] No such file or directory: '/asdasd, qweqwe.eo/.pycm'"}
True
>>> save_stat=cm.save_html("test", address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_normalized", address=False, normalize=True)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_alt", address=False, normalize=True, alt_link=True)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_summary", address=False, summary=True)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_filtered", address=False, overall_param=["Kappa", "Scott PI"], class_param=["TPR", "TNR", "ACC", "AUC"])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_filtered2", address=False, overall_param=["Kappa", "Scott PI"], class_param=["TPR", "TNR", "ACC", "AUC"], class_name=[100])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_filtered3", address=False, overall_param=["Kappa", "Scott PI"], class_param=["TPR", "TNR", "ACC", "AUC"], class_name=[], color=(-2,-2,-2))
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_filtered4", address=False, overall_param=["Kappa", "Scott PI"], class_param=[], class_name=[100], color={})
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_filtered5", address=False, overall_param=[], class_param=["TPR", "TNR", "ACC", "AUC"], class_name=[100])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_filtered6", address=False, overall_param=["Wrong_param"], class_param=["Wrong_param"], class_name=[100])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_colored", address=False, color=(130, 100, 200))
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("test_colored2", address=False, color="Beige")
>>> save_stat=={'Status': True, 'Message': None}
True
>>> long_name_cm = ConfusionMatrix(matrix={'SVM-Classifier': {'SVM-Classifier': 25, 'NN-Classifier': 2}, 'NN-Classifier': {'SVM-Classifier': 3, 'NN-Classifier': 50}})
>>> save_stat=long_name_cm.save_html("test_long_name", address=False, color="Pink")
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=long_name_cm.save_html("test_shortener", address=False, color="Pink", shortener=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_html("/asdasd, qweqwe.eo/", address=True)
>>> save_stat=={'Status': False, 'Message': "[Errno 2] No such file or directory: '/asdasd, qweqwe.eo/.html'"}
True
>>> save_stat=cm.save_csv("test", address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test_normalized", address=False, normalize=True)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test_summary", address=False, summary=True, matrix_save=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test_filtered", address=False, class_param=["TPR", "TNR", "ACC", "AUC"])
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test_filtered2", address=False, class_param=["TPR", "TNR", "ACC", "AUC"], class_name=[100], matrix_save=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test_filtered3", address=False, class_param=["TPR", "TNR", "ACC", "AUC"], class_name=[], matrix_save=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test_filtered4", address=False, class_param=[], class_name=[100], matrix_save=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("test_filtered5", address=False, class_param=["Wrong_param"], class_name=[100], matrix_save=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat=cm.save_csv("/asdasd, qweqwe.eo/", address=True)
>>> save_stat=={'Status': False, 'Message': "[Errno 2] No such file or directory: '/asdasd, qweqwe.eo/.csv'"}
True
>>> save_obj=cm.save_obj("test", address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> save_obj=cm.save_obj("test_stat", address=False, save_stat=True)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> save_obj=cm.save_obj("test_no_vectors", address=False, save_vector=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> cm_file=ConfusionMatrix(file=open("test.obj", "r"))
>>> print(cm_file)
Predict 100 200 500 600
Actual
100 0 0 0 0
<BLANKLINE>
200 9 6 1 0
<BLANKLINE>
500 1 1 1 0
<BLANKLINE>
600 1 0 0 0
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
Overall Statistics :
<BLANKLINE>
95% CI (0.14096,0.55904)
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
<BLANKLINE>
Class Statistics :
<BLANKLINE>
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
PR(Positive rate) 0.0 0.8 0.15 0.05
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
TOPR(Test outcome positive rate) 0.55 0.35 0.1 0.0
TP(True positive/hit) 0 6 1 0
TPR(Sensitivity, recall, hit rate, or true positive rate) None 0.375 0.33333 0.0
Y(Youden index) None 0.125 0.27451 0.0
dInd(Distance index) None 0.67315 0.66926 1.0
sInd(Similarity index) None 0.52401 0.52676 0.29289
<BLANKLINE>
>>> cm_stat_file=ConfusionMatrix(file=open("test_stat.obj", "r"))
>>> cm_no_vectors_file=ConfusionMatrix(file=open("test_no_vectors.obj", "r"))
>>> cm_stat_file==cm_file
True
>>> cm.imbalance == cm_file.imbalance
True
>>> cm_no_vectors_file==cm_file
True
>>> cm_no_vectors_dict = json.load(open("test_no_vectors.obj", "r"))
>>> cm_no_vectors_dict["Actual-Vector"] == None
True
>>> cm_no_vectors_dict["Predict-Vector"] == None
True
>>> cm_no_vectors_dict["Prob-Vector"] == None
True
>>> cm_stat_dict = json.load(open("test_stat.obj", "r"))
>>> cm_stat_dict["Class-Stat"]["MCC"] != None
True
>>> cm_stat_dict["Overall-Stat"]["Overall MCC"] != None
True
>>> def activation(i):
... if i<0.7:
... return 1
... else:
... return 0
>>> cm_6 = ConfusionMatrix([0, 0, 1, 0], [0.87, 0.34, 0.9, 0.12], threshold=activation)
>>> cm_6.prob_vector
[0.87, 0.34, 0.9, 0.12]
>>> save_obj=cm_6.save_obj("test2", address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> cm_file_2=ConfusionMatrix(file=open("test2.obj", "r"))
>>> cm_file_2.prob_vector
[0.87, 0.34, 0.9, 0.12]
>>> cm_file_2.prob_vector == cm_6.prob_vector
True
>>> cm_file_2.print_matrix()
Predict 0 1
Actual
0 1 2
1 1 0
>>> cm_7 = ConfusionMatrix([0, 0, 1, 0], np.array([0.87, 0.34, 0.9, 0.12]), threshold=activation)
>>> isinstance(cm_7.prob_vector, np.ndarray)
True
>>> save_obj=cm_7.save_obj("test2", address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> cm_file_2=ConfusionMatrix(file=open("test2.obj", "r"))
>>> cm_file_2.prob_vector
[0.87, 0.34, 0.9, 0.12]
>>> cm_file_2.prob_vector == cm_7.prob_vector.tolist()
True
>>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2, 0, 1, 0, 2, 1, 0, 0, 0, 1, 2, 4, 5]
>>> y_pred = [2, 0, 2, 2, 0, 2, 2, 2, 2, 0, 0, 2, 0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 5, 3]
>>> cm = ConfusionMatrix(actual_vector=y_actu, predict_vector=y_pred)
>>> cm.sparse_normalized_matrix
>>> cm.sparse_matrix
>>> save_stat_data = cm.save_stat("test", sparse = True)
>>> save_stat_data['Status']
True
>>> cm.sparse_normalized_matrix
[{0: {0: 1.0, 2: 0.0, 3: 0.0, 5: 0.0}, 1: {0: 0.5, 2: 0.5, 3: 0.0, 5: 0.0}, 2: {0: 0.0, 2: 1.0, 3: 0.0, 5: 0.0}, 4: {0: 0.0, 2: 0.0, 3: 0.0, 5: 1.0}, 5: {0: 0.0, 2: 0.0, 3: 1.0, 5: 0.0}}, [0, 1, 2, 4, 5], [0, 2, 3, 5]]
>>> cm.sparse_matrix
[{0: {0: 8, 2: 0, 3: 0, 5: 0}, 1: {0: 3, 2: 3, 3: 0, 5: 0}, 2: {0: 0, 2: 8, 3: 0, 5: 0}, 4: {0: 0, 2: 0, 3: 0, 5: 1}, 5: {0: 0, 2: 0, 3: 1, 5: 0}}, [0, 1, 2, 4, 5], [0, 2, 3, 5]]
>>> save_stat_data = cm.save_stat("test", sparse = True)
>>> save_stat_data['Status']
True
>>> y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
>>> y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
>>> cm = ConfusionMatrix(y_actu, y_pred, sample_weight=[2, 2, 2, 2, 3, 1, 1, 2, 2, 1, 1, 2])
>>> save_obj=cm.save_obj("test3", address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> cm_file_3=ConfusionMatrix(file=open("test3.obj", "r"))
>>> cm = ConfusionMatrix(y_actu, y_pred, sample_weight=np.array([2, 2, 2, 2, 3, 1, 1, 2, 2, 1, 1, 2]))
>>> save_obj=cm.save_obj("test3_np", address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> cm_file_3_np=ConfusionMatrix(file=open("test3_np.obj", "r"))
>>> cm_file_3_np == cm_file_3
True
>>> cm_file_3.print_matrix()
Predict 0 1 2
Actual
0 6 0 0
1 0 1 2
2 4 2 6
<BLANKLINE>
>>> cm_file_3.stat()
Overall Statistics :
<BLANKLINE>
95% CI (0.41134,0.82675)
ACC Macro 0.74603
ARI 0.15323
AUNP 0.7
AUNU 0.70556
Bangdiwala B 0.44242
Bennett S 0.42857
CBA 0.47778
CSI 0.17222
Chi-Squared 10.44167
Chi-Squared DF 4
Conditional Entropy 0.96498
Cramer V 0.49861
Cross Entropy 1.50249
F1 Macro 0.56111
F1 Micro 0.61905
FNR Macro 0.38889
FNR Micro 0.38095
FPR Macro 0.2
FPR Micro 0.19048
Gwet AC1 0.45277
Hamming Loss 0.38095
Joint Entropy 2.34377
KL Divergence 0.1237
Kappa 0.3913
Kappa 95% CI (0.05943,0.72318)
Kappa No Prevalence 0.2381
Kappa Standard Error 0.16932
Kappa Unbiased 0.37313
Krippendorff Alpha 0.38806
Lambda A 0.22222
Lambda B 0.36364
Mutual Information 0.47618
NIR 0.57143
NPV Macro 0.80912
NPV Micro 0.80952
Overall ACC 0.61905
Overall CEN 0.43947
Overall J (1.22857,0.40952)
Overall MCC 0.41558
Overall MCEN 0.50059
Overall RACC 0.37415
Overall RACCU 0.39229
P-Value 0.41709
PPV Macro 0.56111
PPV Micro 0.61905
Pearson C 0.57628
Phi-Squared 0.49722
RCI 0.34536
RR 7.0
Reference Entropy 1.37878
Response Entropy 1.44117
SOA1(Landis & Koch) Fair
SOA2(Fleiss) Poor
SOA3(Altman) Fair
SOA4(Cicchetti) Poor
SOA5(Cramer) Relatively Strong
SOA6(Matthews) Weak
SOA7(Lambda A) Weak
SOA8(Lambda B) Weak
SOA9(Krippendorff Alpha) Low
SOA10(Pearson C) Strong
Scott PI 0.37313
Standard Error 0.10597
TNR Macro 0.8
TNR Micro 0.80952
TPR Macro 0.61111
TPR Micro 0.61905
Zero-one Loss 8
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 0 1 2
ACC(Accuracy) 0.80952 0.80952 0.61905
AGF(Adjusted F-score) 0.90694 0.54433 0.55442
AGM(Adjusted geometric mean) 0.80509 0.70336 0.66986
AM(Difference between automatic and manual classification) 4 0 -4
AUC(Area under the ROC curve) 0.86667 0.61111 0.63889
AUCI(AUC value interpretation) Very Good Fair Fair
AUPR(Area under the PR curve) 0.8 0.33333 0.625
BB(Braun-Blanquet similarity) 0.6 0.33333 0.5
BCD(Bray-Curtis dissimilarity) 0.09524 0.0 0.09524
BM(Informedness or bookmaker informedness) 0.73333 0.22222 0.27778
CEN(Confusion entropy) 0.25 0.52832 0.56439
DOR(Diagnostic odds ratio) None 4.0 3.5
DP(Discriminant power) None 0.33193 0.29996
DPI(Discriminant power interpretation) None Poor Poor
ERR(Error rate) 0.19048 0.19048 0.38095
F0.5(F0.5 score) 0.65217 0.33333 0.68182
F1(F1 score - harmonic mean of precision and sensitivity) 0.75 0.33333 0.6
F2(F2 score) 0.88235 0.33333 0.53571
FDR(False discovery rate) 0.4 0.66667 0.25
FN(False negative/miss/type 2 error) 0 2 6
FNR(Miss rate or false negative rate) 0.0 0.66667 0.5
FOR(False omission rate) 0.0 0.11111 0.46154
FP(False positive/type 1 error/false alarm) 4 2 2
FPR(Fall-out or false positive rate) 0.26667 0.11111 0.22222
G(G-measure geometric mean of precision and sensitivity) 0.7746 0.33333 0.61237
GI(Gini index) 0.73333 0.22222 0.27778
GM(G-mean geometric mean of specificity and sensitivity) 0.85635 0.54433 0.62361
HD(Hamming distance) 4 4 8
IBA(Index of balanced accuracy) 0.92889 0.13169 0.28086
ICSI(Individual classification success index) 0.6 -0.33333 0.25
IS(Information score) 1.07039 1.22239 0.39232
J(Jaccard index) 0.6 0.2 0.42857
LS(Lift score) 2.1 2.33333 1.3125
MCC(Matthews correlation coefficient) 0.66332 0.22222 0.28307
MCCI(Matthews correlation coefficient interpretation) Moderate Negligible Negligible
MCEN(Modified confusion entropy) 0.26439 0.52877 0.65924
MK(Markedness) 0.6 0.22222 0.28846
N(Condition negative) 15 18 9
NLR(Negative likelihood ratio) 0.0 0.75 0.64286
NLRI(Negative likelihood ratio interpretation) Good Negligible Negligible
NPV(Negative predictive value) 1.0 0.88889 0.53846
OC(Overlap coefficient) 1.0 0.33333 0.75
OOC(Otsuka-Ochiai coefficient) 0.7746 0.33333 0.61237
OP(Optimized precision) 0.65568 0.35498 0.40166
P(Condition positive or support) 6 3 12
PLR(Positive likelihood ratio) 3.75 3.0 2.25
PLRI(Positive likelihood ratio interpretation) Poor Poor Poor
POP(Population) 21 21 21
PPV(Precision or positive predictive value) 0.6 0.33333 0.75
PR(Positive rate) 0.28571 0.14286 0.57143
PRE(Prevalence) 0.28571 0.14286 0.57143
Q(Yule Q - coefficient of colligation) None 0.6 0.55556
QI(Yule Q interpretation) None Moderate Moderate
RACC(Random accuracy) 0.13605 0.02041 0.21769
RACCU(Random accuracy unbiased) 0.14512 0.02041 0.22676
TN(True negative/correct rejection) 11 16 7
TNR(Specificity or true negative rate) 0.73333 0.88889 0.77778
TON(Test outcome negative) 11 18 13
TOP(Test outcome positive) 10 3 8
TOPR(Test outcome positive rate) 0.47619 0.14286 0.38095
TP(True positive/hit) 6 1 6
TPR(Sensitivity, recall, hit rate, or true positive rate) 1.0 0.33333 0.5
Y(Youden index) 0.73333 0.22222 0.27778
dInd(Distance index) 0.26667 0.67586 0.54716
sInd(Similarity index) 0.81144 0.52209 0.6131
>>> cm = ConfusionMatrix(matrix={1: {1: 13182, 2: 30516}, 2: {1: 5108, 2: 295593}}, transpose=True) # Verified Case
>>> save_obj = cm.save_obj("test4", address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> save_obj = cm.save_obj("/asdasd, qweqwe.eo/", address=False)
>>> save_obj=={'Status': False, 'Message': "[Errno 2] No such file or directory: '/asdasd, qweqwe.eo/.obj'"}
True
>>> cm_file=ConfusionMatrix(file=open("test4.obj", "r"))
>>> assert isclose(cm_file.DP[1], 0.770700985610517, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm_file.Y[1], 0.627145631592811, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> assert isclose(cm_file.BM[1], 0.627145631592811, abs_tol=ABS_TOL, rel_tol=REL_TOL)
>>> cm_file.transpose
True
>>> cm.matrix == cm_file.matrix
True
>>> cm.normalized_matrix == cm_file.normalized_matrix
True
>>> json.dump({"Actual-Vector": None, "Digit": 5, "Predict-Vector": None, "Matrix": {"0": {"0": 3, "1": 0, "2": 2}, "1": {"0": 0, "1": 1, "2": 1}, "2": {"0": 0, "1": 2, "2": 3}}, "Transpose": True, "Sample-Weight": None}, open("test5.obj", "w"))
>>> cm_file=ConfusionMatrix(file=open("test5.obj", "r"))
>>> cm_file.transpose
True
>>> cm_file.matrix == {"0": {"0": 3, "1": 0, "2": 2}, "1": {"0": 0, "1": 1, "2": 1}, "2": {"0": 0, "1": 2, "2": 3}}
True
>>> json.dump({"Actual-Vector": None, "Digit": 5, "Predict-Vector": None, "Matrix": {"0": {"0": 3, "1": 0, "2": 2}, "1": {"0": 0, "1": 1, "2": 1}, "2": {"0": 0, "1": 2, "2": 3}}}, open("test6.obj", "w"))
>>> cm_file=ConfusionMatrix(file=open("test6.obj", "r"))
>>> cm_file.weights
>>> cm_file.transpose
False
>>> cm_file.matrix == {'1': {'1': 1, '2': 1, '0': 0}, '2': {'1': 2, '2': 3, '0': 0}, '0': {'1': 0, '2': 2, '0': 3}}
True
>>> json.dump({"Actual-Vector": ['1', '1', '2', '2', '2', '2', '2', '0', '0', '0', '0', '0'], "Digit": 5, "Predict-Vector": ['1', '2', '1', '1', '2', '2', '2', '2', '2', '0', '0', '0'], "Matrix": {"0": {"0": 3, "1": 0, "2": 2}, "1": {"0": 0, "1": 1, "2": 1}, "2": {"0": 0, "1": 2, "2": 3}}}, open("test7.obj", "w"))
>>> cm_file=ConfusionMatrix(file=open("test7.obj", "r"))
>>> cm_file.weights
>>> cm_file.transpose
False
>>> cm_file.imbalance
False
>>> cm_file.matrix == {'1': {'1': 1, '2': 1, '0': 0}, '2': {'1': 2, '2': 3, '0': 0}, '0': {'1': 0, '2': 2, '0': 3}}
True
>>> cm_file.actual_vector == ['1', '1', '2', '2', '2', '2', '2', '0', '0', '0', '0', '0']
True
>>> cm_file.predict_vector == ['1', '2', '1', '1', '2', '2', '2', '2', '2', '0', '0', '0']
True
>>> json.dump({"Actual-Vector": ['1', '1', '2', '2', '2', '2', '2', '0', '0', '0', '0', '0'], "Digit": 5, "Predict-Vector": ['1', '2', '1', '1', '2', '2', '2', '2', '2', '0', '0', '0'], "Matrix": {"0": {"0": 3, "1": 0, "2": 2}, "1": {"0": 0, "1": 1, "2": 1}, "2": {"0": 0, "1": 2, "2": 3}}, "Imbalanced": True}, open("test8.obj", "w"))
>>> cm_file=ConfusionMatrix(file=open("test8.obj", "r"))
>>> cm_file.imbalance
True
>>> cm_comp1 = ConfusionMatrix(matrix={0: {0: 2, 1: 50, 2: 6}, 1: {0: 5, 1: 50, 2: 3}, 2: {0: 1, 1: 7, 2: 50}})
>>> cm_comp2 = ConfusionMatrix(matrix={0: {0: 50, 1: 2, 2: 6}, 1: {0: 50, 1: 5, 2: 3}, 2: {0: 1, 1: 55, 2: 2}})
>>> cp = Compare({"model1": cm_comp1, "model2": cm_comp2})
>>> save_report = cp.save_report("test", address=False)
>>> save_report == {'Status': True, 'Message': None}
True
>>> save_report = cp.save_report("/asdasd, qweqwe.eo/", address=False)
>>> save_report == {'Status': False, 'Message': "[Errno 2] No such file or directory: '/asdasd, qweqwe.eo/.comp'"}
True
>>> cm = ConfusionMatrix(["¢ℓαѕѕ1", "¢ℓαѕѕ2"], ["¢ℓαѕѕ1", "¢ℓαѕѕ2"])
>>> save_stat_data = cm.save_stat("test")
>>> save_stat_data["Status"]
True
>>> save_csv_data = cm.save_csv("test")
>>> save_csv_data["Status"]
True
>>> save_csv_data = cm.save_csv("test", header=False)
>>> save_csv_data["Status"]
True
>>> save_html_data = cm.save_html("test")
>>> save_html_data["Status"]
True
>>> save_csv_data = cm.save_csv("test_header", header=True)
>>> save_csv_data["Status"]
True
>>> save_csv_data = cm.save_csv("test_header", header=True, matrix_save=True)
>>> save_csv_data["Status"]
True
>>> 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, metrics_off=True)
>>> save_stat = cm.save_stat("test_metrics_off", address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat = cm.save_html("test_metrics_off", address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_stat = cm.save_csv("test_metrics_off", address=False)
>>> save_stat=={'Status': True, 'Message': None}
True
>>> save_obj = cm.save_obj("test_metrics_off", address=False)
>>> save_obj=={'Status': True, 'Message': None}
True
>>> cm_file_metrics_off = ConfusionMatrix(file=open("test_metrics_off.obj", "r"), metrics_off=True)
>>> print(cm_file_metrics_off)
Predict 100 200 500 600
Actual
100 0 0 0 0
<BLANKLINE>
200 9 6 1 0
<BLANKLINE>
500 1 1 1 0
<BLANKLINE>
600 1 0 0 0
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
<BLANKLINE>
Overall Statistics :
<BLANKLINE>
95% CI None
ACC Macro None
ARI None
AUNP None
AUNU None
Bangdiwala B None
Bennett S None
CBA None
CSI None
Chi-Squared None
Chi-Squared DF None
Conditional Entropy None
Cramer V None
Cross Entropy None
F1 Macro None
F1 Micro None
FNR Macro None
FNR Micro None
FPR Macro None
FPR Micro None
Gwet AC1 None
Hamming Loss None
Joint Entropy None
KL Divergence None
Kappa None
Kappa 95% CI None
Kappa No Prevalence None
Kappa Standard Error None
Kappa Unbiased None
Krippendorff Alpha None
Lambda A None
Lambda B None
Mutual Information None
NIR None
NPV Macro None
NPV Micro None
Overall ACC None
Overall CEN None
Overall J None
Overall MCC None
Overall MCEN None
Overall RACC None
Overall RACCU None
P-Value None
PPV Macro None
PPV Micro None
Pearson C None
Phi-Squared None
RCI None
RR None
Reference Entropy None
Response Entropy None
SOA1(Landis & Koch) None
SOA2(Fleiss) None
SOA3(Altman) None
SOA4(Cicchetti) None
SOA5(Cramer) None
SOA6(Matthews) None
SOA7(Lambda A) None
SOA8(Lambda B) None
SOA9(Krippendorff Alpha) None
SOA10(Pearson C) None
Scott PI None
Standard Error None
TNR Macro None
TNR Micro None
TPR Macro None
TPR Micro None
Zero-one Loss None
<BLANKLINE>
Class Statistics :
<BLANKLINE>
Classes 100 200 500 600
ACC(Accuracy) None None None None
AGF(Adjusted F-score) None None None None
AGM(Adjusted geometric mean) None None None None
AM(Difference between automatic and manual classification) None None None None
AUC(Area under the ROC curve) None None None None
AUCI(AUC value interpretation) None None None None
AUPR(Area under the PR curve) None None None None
BB(Braun-Blanquet similarity) None None None None
BCD(Bray-Curtis dissimilarity) None None None None
BM(Informedness or bookmaker informedness) None None None None
CEN(Confusion entropy) None None None None
DOR(Diagnostic odds ratio) None None None None
DP(Discriminant power) None None None None
DPI(Discriminant power interpretation) None None None None
ERR(Error rate) None None None None
F0.5(F0.5 score) None None None None
F1(F1 score - harmonic mean of precision and sensitivity) None None None None
F2(F2 score) None None None None
FDR(False discovery rate) None None None None
FN(False negative/miss/type 2 error) None None None None
FNR(Miss rate or false negative rate) None None None None
FOR(False omission rate) None None None None
FP(False positive/type 1 error/false alarm) None None None None
FPR(Fall-out or false positive rate) None None None None
G(G-measure geometric mean of precision and sensitivity) None None None None
GI(Gini index) None None None None
GM(G-mean geometric mean of specificity and sensitivity) None None None None
HD(Hamming distance) None None None None
IBA(Index of balanced accuracy) None None None None
ICSI(Individual classification success index) None None None None
IS(Information score) None None None None
J(Jaccard index) None None None None
LS(Lift score) None None None None
MCC(Matthews correlation coefficient) None None None None
MCCI(Matthews correlation coefficient interpretation) None None None None
MCEN(Modified confusion entropy) None None None None
MK(Markedness) None None None None
N(Condition negative) None None None None
NLR(Negative likelihood ratio) None None None None
NLRI(Negative likelihood ratio interpretation) None None None None
NPV(Negative predictive value) None None None None
OC(Overlap coefficient) None None None None
OOC(Otsuka-Ochiai coefficient) None None None None
OP(Optimized precision) None None None None
P(Condition positive or support) None None None None
PLR(Positive likelihood ratio) None None None None
PLRI(Positive likelihood ratio interpretation) None None None None
POP(Population) None None None None
PPV(Precision or positive predictive value) None None None None
PR(Positive rate) None None None None
PRE(Prevalence) None None None None
Q(Yule Q - coefficient of colligation) None None None None
QI(Yule Q interpretation) None None None None
RACC(Random accuracy) None None None None
RACCU(Random accuracy unbiased) None None None None
TN(True negative/correct rejection) None None None None
TNR(Specificity or true negative rate) None None None None
TON(Test outcome negative) None None None None
TOP(Test outcome positive) None None None None
TOPR(Test outcome positive rate) None None None None
TP(True positive/hit) None None None None
TPR(Sensitivity, recall, hit rate, or true positive rate) None None None None
Y(Youden index) None None None None
dInd(Distance index) None None None None
sInd(Similarity index) None None None None
<BLANKLINE>
>>> os.remove("test.csv")
>>> os.remove("test_matrix.csv")
>>> os.remove("test_normalized.csv")
>>> os.remove("test_normalized_matrix.csv")
>>> os.remove("test.obj")
>>> os.remove("test_stat.obj")
>>> os.remove("test_no_vectors.obj")
>>> os.remove("test.html")
>>> os.remove("test_normalized.html")
>>> os.remove("test_filtered.html")
>>> os.remove("test_filtered.csv")
>>> os.remove("test_filtered_matrix.csv")
>>> os.remove("test_filtered.pycm")
>>> os.remove("test_large.pycm")
>>> os.remove("test_summary.pycm")
>>> os.remove("test_filtered2.html")
>>> os.remove("test_filtered3.html")
>>> os.remove("test_filtered4.html")
>>> os.remove("test_filtered5.html")
>>> os.remove("test_filtered6.html")
>>> os.remove("test_long_name.html")
>>> os.remove("test_shortener.html")
>>> os.remove("test_alt.html")
>>> os.remove("test_summary.html")
>>> os.remove("test_colored.html")
>>> os.remove("test_colored2.html")
>>> os.remove("test_filtered2.csv")
>>> os.remove("test_filtered3.csv")
>>> os.remove("test_filtered4.csv")
>>> os.remove("test_filtered5.csv")
>>> os.remove("test_summary.csv")
>>> os.remove("test_filtered2.pycm")
>>> os.remove("test_filtered3.pycm")
>>> os.remove("test_filtered4.pycm")
>>> os.remove("test2.obj")
>>> os.remove("test3.obj")
>>> os.remove("test3_np.obj")
>>> os.remove("test4.obj")
>>> os.remove("test5.obj")
>>> os.remove("test6.obj")
>>> os.remove("test7.obj")
>>> os.remove("test8.obj")
>>> os.remove("test.pycm")
>>> os.remove("test.comp")
>>> os.remove("test_header.csv")
>>> os.remove("test_header_matrix.csv")
>>> os.remove("test_metrics_off.pycm")
>>> os.remove("test_metrics_off.html")
>>> os.remove("test_metrics_off.csv")
>>> os.remove("test_metrics_off_matrix.csv")
>>> os.remove("test_metrics_off.obj")
"""
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