diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md index 9610f145..597ebeab 100644 --- a/.github/CODE_OF_CONDUCT.md +++ b/.github/CODE_OF_CONDUCT.md @@ -58,7 +58,7 @@ representative at an online or offline event. Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at -info@pycm.ir. +info@pycm.io. All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml index 8fba89bb..de804cad 100644 --- a/.github/FUNDING.yml +++ b/.github/FUNDING.yml @@ -1 +1 @@ -custom: https://www.pycm.ir/donate.html \ No newline at end of file +custom: https://www.pycm.io/donate.html \ No newline at end of file diff --git a/CHANGELOG.md b/CHANGELOG.md index 0b947a40..dcfb6c4b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/) and this project adheres to [Semantic Versioning](http://semver.org/spec/v2.0.0.html). ## [Unreleased] +### Changed +- Website changed to [http://www.pycm.io](http://www.pycm.io) ## [3.5] - 2022-04-27 ### Added - Anaconda workflow diff --git a/CITATION.cff b/CITATION.cff index 7517dd5e..abca6d04 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -12,7 +12,7 @@ authors: version: 3.3 date-released: 2021-10-27 repository-code: "https://github.com/sepandhaghighi/pycm" -url: "https://www.pycm.ir" +url: "https://www.pycm.io" license: MIT keywords: - "confusion matrix" diff --git a/Document/Document.ipynb b/Document/Document.ipynb index b99a442b..1c49faa6 100644 --- a/Document/Document.ipynb +++ b/Document/Document.ipynb @@ -1820,7 +1820,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 46, @@ -1852,7 +1852,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 47, @@ -1884,7 +1884,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 48, @@ -1916,7 +1916,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 49, @@ -1948,7 +1948,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 50, @@ -13469,42 +13469,42 @@ "### Example-1 (Comparison of three different classifiers)\t\n", "\n", "- [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example1.ipynb)\n", - "- [HTML](http://www.pycm.ir/doc/Example1.html)\n", + "- [HTML](http://www.pycm.io/doc/Example1.html)\n", "\n", "### Example-2 (How to plot via matplotlib)\n", "\n", "- [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example2.ipynb)\n", - "- [HTML](http://www.pycm.ir/doc/Example2.html)\n", + "- [HTML](http://www.pycm.io/doc/Example2.html)\n", "\n", "### Example-3 (Activation threshold)\n", "\n", "- [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example3.ipynb)\n", - "- [HTML](http://www.pycm.ir/doc/Example3.html)\n", + "- [HTML](http://www.pycm.io/doc/Example3.html)\n", "\n", "### Example-4 (File)\n", "\n", "- [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example4.ipynb)\n", - "- [HTML](http://www.pycm.ir/doc/Example4.html)\n", + "- [HTML](http://www.pycm.io/doc/Example4.html)\n", "\n", "### Example-5 (Sample weights)\n", "\n", "- [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example5.ipynb)\n", - "- [HTML](http://www.pycm.ir/doc/Example5.html)\n", + "- [HTML](http://www.pycm.io/doc/Example5.html)\n", "\n", "### Example-6 (Unbalanced data)\n", "\n", "- [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example6.ipynb)\n", - "- [HTML](http://www.pycm.ir/doc/Example6.html)\n", + "- [HTML](http://www.pycm.io/doc/Example6.html)\n", "\n", "### Example-7 (How to plot via seaborn+pandas)\n", "\n", "- [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example7.ipynb)\n", - "- [HTML](http://www.pycm.ir/doc/Example7.html)\n", + "- [HTML](http://www.pycm.io/doc/Example7.html)\n", "\n", "### Example-8 (Confidence interval)\n", "\n", "- [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example8.ipynb)\n", - "- [HTML](http://www.pycm.ir/doc/Example8.html)" + "- [HTML](http://www.pycm.io/doc/Example8.html)" ] }, { @@ -13553,7 +13553,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Download PyCM.bib" + "Download PyCM.bib" ] }, { diff --git a/Document/Document_Files/cm1.html b/Document/Document_Files/cm1.html index af8f9502..7bfc5ea1 100644 --- a/Document/Document_Files/cm1.html +++ b/Document/Document_Files/cm1.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,255 +60,255 @@

Confusion Matrix :

Overall Statistics :

- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
95% CI95% CI (0.30439,0.86228)
ACC MacroACC Macro 0.72222
ARIARI 0.09206
AUNPAUNP 0.68571
AUNUAUNU 0.67857
Bangdiwala BBangdiwala B 0.37255
Bennett SBennett S 0.375
CBACBA 0.47778
CSICSI 0.17778
Chi-SquaredChi-Squared 6.6
Chi-Squared DFChi-Squared DF 4
Conditional EntropyConditional Entropy 0.97579
Cramer VCramer V 0.5244
Cross EntropyCross Entropy 1.58333
F1 MacroF1 Macro 0.56515
F1 MicroF1 Micro 0.58333
FNR MacroFNR Macro 0.43333
FNR MicroFNR Micro 0.41667
FPR MacroFPR Macro 0.20952
FPR MicroFPR Micro 0.20833
Gwet AC1Gwet AC1 0.38931
Hamming LossHamming Loss 0.41667
Joint EntropyJoint Entropy 2.45915
KL DivergenceKL Divergence 0.09998
KappaKappa 0.35484
Kappa 95% CIKappa 95% CI (-0.07708,0.78675)
Kappa No PrevalenceKappa No Prevalence 0.16667
Kappa Standard ErrorKappa Standard Error 0.22036
Kappa UnbiasedKappa Unbiased 0.34426
Krippendorff AlphaKrippendorff Alpha 0.37158
Lambda ALambda A 0.42857
Lambda BLambda B 0.16667
Mutual InformationMutual Information 0.52421
NIRNIR 0.41667
Overall ACCOverall ACC 0.58333
Overall CENOverall CEN 0.46381
Overall JOverall J (1.225,0.40833)
Overall MCCOverall MCC 0.36667
Overall MCENOverall MCEN 0.51894
Overall RACCOverall RACC 0.35417
Overall RACCUOverall RACCU 0.36458
P-ValueP-Value 0.18926
PPV MacroPPV Macro 0.61111
PPV MicroPPV Micro 0.58333
Pearson CPearson C 0.59568
Phi-SquaredPhi-Squared 0.55
RCIRCI 0.35339
RRRR 4.0
Reference EntropyReference Entropy 1.48336
Response EntropyResponse Entropy 1.5
SOA1(Landis & Koch)SOA1(Landis & Koch) Fair
SOA2(Fleiss)SOA2(Fleiss) Poor
SOA3(Altman)SOA3(Altman) Fair
SOA4(Cicchetti)SOA4(Cicchetti) Poor
SOA5(Cramer)SOA5(Cramer) Relatively Strong
SOA6(Matthews)SOA6(Matthews) Weak
Scott PIScott PI 0.34426
Standard ErrorStandard Error 0.14232
TNR MacroTNR Macro 0.79048
TNR MicroTNR Micro 0.79167
TPR MacroTPR Macro 0.56667
TPR MicroTPR Micro 0.58333
Zero-one LossZero-one Loss 5
@@ -322,433 +322,433 @@

Class Statistics :

Description -ACC +ACC 0.83333 0.75 0.58333 Accuracy -AGF +AGF 0.72859 0.62869 0.61009 Adjusted F-score -AGM +AGM 0.85764 0.70861 0.58034 Adjusted geometric mean -AM +AM -2 1 1 Difference between automatic and manual classification -AUC +AUC 0.8 0.65 0.58571 Area under the ROC curve -AUCI +AUCI Very Good Fair Poor AUC value interpretation -AUPR +AUPR 0.8 0.41667 0.55 Area under the PR curve -BCD +BCD 0.08333 0.04167 0.04167 Bray-Curtis dissimilarity -BM +BM 0.6 0.3 0.17143 Informedness or bookmaker informedness -CEN +CEN 0.25 0.49658 0.60442 Confusion entropy -DOR +DOR None 4.0 2.0 Diagnostic odds ratio -DP +DP None 0.33193 0.16597 Discriminant power -DPI +DPI None Poor Poor Discriminant power interpretation -ERR +ERR 0.16667 0.25 0.41667 Error rate -F0.5 +F0.5 0.88235 0.35714 0.51724 F0.5 score -F1 +F1 0.75 0.4 0.54545 F1 score - harmonic mean of precision and sensitivity -F2 +F2 0.65217 0.45455 0.57692 F2 score -FDR +FDR 0.0 0.66667 0.5 False discovery rate -FN +FN 2 1 2 False negative/miss/type 2 error -FNR +FNR 0.4 0.5 0.4 Miss rate or false negative rate -FOR +FOR 0.22222 0.11111 0.33333 False omission rate -FP +FP 0 2 3 False positive/type 1 error/false alarm -FPR +FPR 0.0 0.2 0.42857 Fall-out or false positive rate -G +G 0.7746 0.40825 0.54772 G-measure geometric mean of precision and sensitivity -GI +GI 0.6 0.3 0.17143 Gini index -GM +GM 0.7746 0.63246 0.58554 G-mean geometric mean of specificity and sensitivity -IBA +IBA 0.36 0.28 0.35265 Index of balanced accuracy -ICSI +ICSI 0.6 -0.16667 0.1 Individual classification success index -IS +IS 1.26303 1.0 0.26303 Information score -J +J 0.6 0.25 0.375 Jaccard index -LS +LS 2.4 2.0 1.2 Lift score -MCC +MCC 0.68313 0.2582 0.16903 Matthews correlation coefficient -MCCI +MCCI Moderate Negligible Negligible Matthews correlation coefficient interpretation -MCEN +MCEN 0.26439 0.5 0.6875 Modified confusion entropy -MK +MK 0.77778 0.22222 0.16667 Markedness -N +N 7 10 7 Condition negative -NLR +NLR 0.4 0.625 0.7 Negative likelihood ratio -NLRI +NLRI Poor Negligible Negligible Negative likelihood ratio interpretation -NPV +NPV 0.77778 0.88889 0.66667 Negative predictive value -OC +OC 1.0 0.5 0.6 Overlap coefficient -OOC +OOC 0.7746 0.40825 0.54772 Otsuka-Ochiai coefficient -OP +OP 0.58333 0.51923 0.55894 Optimized precision -P +P 5 2 5 Condition positive or support -PLR +PLR None 2.5 1.4 Positive likelihood ratio -PLRI +PLRI None Poor Poor Positive likelihood ratio interpretation -POP +POP 12 12 12 Population -PPV +PPV 1.0 0.33333 0.5 Precision or positive predictive value -PRE +PRE 0.41667 0.16667 0.41667 Prevalence -Q +Q None 0.6 0.33333 Yule Q - coefficient of colligation -QI +QI None Moderate Weak Yule Q interpretation -RACC +RACC 0.10417 0.04167 0.20833 Random accuracy -RACCU +RACCU 0.11111 0.0434 0.21007 Random accuracy unbiased -TN +TN 7 8 4 True negative/correct rejection -TNR +TNR 1.0 0.8 0.57143 Specificity or true negative rate -TON +TON 9 9 6 Test outcome negative -TOP +TOP 3 3 6 Test outcome positive -TP +TP 3 1 3 True positive/hit -TPR +TPR 0.6 0.5 0.6 Sensitivity, recall, hit rate, or true positive rate -Y +Y 0.6 0.3 0.17143 Youden index -dInd +dInd 0.4 0.53852 0.58624 Distance index -sInd +sInd 0.71716 0.61921 0.58547 Similarity index -

Generated By PyCM Version 3.5

+

Generated By PyCM Version 3.5

diff --git a/Document/Document_Files/cm1.obj b/Document/Document_Files/cm1.obj index 7913b36b..4408ba5b 100644 --- a/Document/Document_Files/cm1.obj +++ b/Document/Document_Files/cm1.obj @@ -1 +1 @@ -{"Predict-Vector": null, "Prob-Vector": null, "Transpose": true, "Digit": 5, "Sample-Weight": null, "Imbalanced": false, "Actual-Vector": null, "Matrix": [["L1", [["L2", 0], ["L1", 3], ["L3", 2]]], ["L2", [["L2", 1], ["L1", 0], ["L3", 1]]], ["L3", [["L2", 2], ["L1", 0], ["L3", 3]]]]} \ No newline at end of file +{"Prob-Vector": null, "Predict-Vector": null, "Transpose": true, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Imbalanced": false, "Actual-Vector": null, "Digit": 5, "Sample-Weight": null} \ No newline at end of file diff --git a/Document/Document_Files/cm1_colored.html b/Document/Document_Files/cm1_colored.html index ba0d1a23..3b4c3c8a 100644 --- a/Document/Document_Files/cm1_colored.html +++ b/Document/Document_Files/cm1_colored.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,255 +60,255 @@

Confusion Matrix :

Overall Statistics :

- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
95% CI95% CI (0.30439,0.86228)
ACC MacroACC Macro 0.72222
ARIARI 0.09206
AUNPAUNP 0.68571
AUNUAUNU 0.67857
Bangdiwala BBangdiwala B 0.37255
Bennett SBennett S 0.375
CBACBA 0.47778
CSICSI 0.17778
Chi-SquaredChi-Squared 6.6
Chi-Squared DFChi-Squared DF 4
Conditional EntropyConditional Entropy 0.97579
Cramer VCramer V 0.5244
Cross EntropyCross Entropy 1.58333
F1 MacroF1 Macro 0.56515
F1 MicroF1 Micro 0.58333
FNR MacroFNR Macro 0.43333
FNR MicroFNR Micro 0.41667
FPR MacroFPR Macro 0.20952
FPR MicroFPR Micro 0.20833
Gwet AC1Gwet AC1 0.38931
Hamming LossHamming Loss 0.41667
Joint EntropyJoint Entropy 2.45915
KL DivergenceKL Divergence 0.09998
KappaKappa 0.35484
Kappa 95% CIKappa 95% CI (-0.07708,0.78675)
Kappa No PrevalenceKappa No Prevalence 0.16667
Kappa Standard ErrorKappa Standard Error 0.22036
Kappa UnbiasedKappa Unbiased 0.34426
Krippendorff AlphaKrippendorff Alpha 0.37158
Lambda ALambda A 0.42857
Lambda BLambda B 0.16667
Mutual InformationMutual Information 0.52421
NIRNIR 0.41667
Overall ACCOverall ACC 0.58333
Overall CENOverall CEN 0.46381
Overall JOverall J (1.225,0.40833)
Overall MCCOverall MCC 0.36667
Overall MCENOverall MCEN 0.51894
Overall RACCOverall RACC 0.35417
Overall RACCUOverall RACCU 0.36458
P-ValueP-Value 0.18926
PPV MacroPPV Macro 0.61111
PPV MicroPPV Micro 0.58333
Pearson CPearson C 0.59568
Phi-SquaredPhi-Squared 0.55
RCIRCI 0.35339
RRRR 4.0
Reference EntropyReference Entropy 1.48336
Response EntropyResponse Entropy 1.5
SOA1(Landis & Koch)SOA1(Landis & Koch) Fair
SOA2(Fleiss)SOA2(Fleiss) Poor
SOA3(Altman)SOA3(Altman) Fair
SOA4(Cicchetti)SOA4(Cicchetti) Poor
SOA5(Cramer)SOA5(Cramer) Relatively Strong
SOA6(Matthews)SOA6(Matthews) Weak
Scott PIScott PI 0.34426
Standard ErrorStandard Error 0.14232
TNR MacroTNR Macro 0.79048
TNR MicroTNR Micro 0.79167
TPR MacroTPR Macro 0.56667
TPR MicroTPR Micro 0.58333
Zero-one LossZero-one Loss 5
@@ -322,433 +322,433 @@

Class Statistics :

Description -ACC +ACC 0.83333 0.75 0.58333 Accuracy -AGF +AGF 0.72859 0.62869 0.61009 Adjusted F-score -AGM +AGM 0.85764 0.70861 0.58034 Adjusted geometric mean -AM +AM -2 1 1 Difference between automatic and manual classification -AUC +AUC 0.8 0.65 0.58571 Area under the ROC curve -AUCI +AUCI Very Good Fair Poor AUC value interpretation -AUPR +AUPR 0.8 0.41667 0.55 Area under the PR curve -BCD +BCD 0.08333 0.04167 0.04167 Bray-Curtis dissimilarity -BM +BM 0.6 0.3 0.17143 Informedness or bookmaker informedness -CEN +CEN 0.25 0.49658 0.60442 Confusion entropy -DOR +DOR None 4.0 2.0 Diagnostic odds ratio -DP +DP None 0.33193 0.16597 Discriminant power -DPI +DPI None Poor Poor Discriminant power interpretation -ERR +ERR 0.16667 0.25 0.41667 Error rate -F0.5 +F0.5 0.88235 0.35714 0.51724 F0.5 score -F1 +F1 0.75 0.4 0.54545 F1 score - harmonic mean of precision and sensitivity -F2 +F2 0.65217 0.45455 0.57692 F2 score -FDR +FDR 0.0 0.66667 0.5 False discovery rate -FN +FN 2 1 2 False negative/miss/type 2 error -FNR +FNR 0.4 0.5 0.4 Miss rate or false negative rate -FOR +FOR 0.22222 0.11111 0.33333 False omission rate -FP +FP 0 2 3 False positive/type 1 error/false alarm -FPR +FPR 0.0 0.2 0.42857 Fall-out or false positive rate -G +G 0.7746 0.40825 0.54772 G-measure geometric mean of precision and sensitivity -GI +GI 0.6 0.3 0.17143 Gini index -GM +GM 0.7746 0.63246 0.58554 G-mean geometric mean of specificity and sensitivity -IBA +IBA 0.36 0.28 0.35265 Index of balanced accuracy -ICSI +ICSI 0.6 -0.16667 0.1 Individual classification success index -IS +IS 1.26303 1.0 0.26303 Information score -J +J 0.6 0.25 0.375 Jaccard index -LS +LS 2.4 2.0 1.2 Lift score -MCC +MCC 0.68313 0.2582 0.16903 Matthews correlation coefficient -MCCI +MCCI Moderate Negligible Negligible Matthews correlation coefficient interpretation -MCEN +MCEN 0.26439 0.5 0.6875 Modified confusion entropy -MK +MK 0.77778 0.22222 0.16667 Markedness -N +N 7 10 7 Condition negative -NLR +NLR 0.4 0.625 0.7 Negative likelihood ratio -NLRI +NLRI Poor Negligible Negligible Negative likelihood ratio interpretation -NPV +NPV 0.77778 0.88889 0.66667 Negative predictive value -OC +OC 1.0 0.5 0.6 Overlap coefficient -OOC +OOC 0.7746 0.40825 0.54772 Otsuka-Ochiai coefficient -OP +OP 0.58333 0.51923 0.55894 Optimized precision -P +P 5 2 5 Condition positive or support -PLR +PLR None 2.5 1.4 Positive likelihood ratio -PLRI +PLRI None Poor Poor Positive likelihood ratio interpretation -POP +POP 12 12 12 Population -PPV +PPV 1.0 0.33333 0.5 Precision or positive predictive value -PRE +PRE 0.41667 0.16667 0.41667 Prevalence -Q +Q None 0.6 0.33333 Yule Q - coefficient of colligation -QI +QI None Moderate Weak Yule Q interpretation -RACC +RACC 0.10417 0.04167 0.20833 Random accuracy -RACCU +RACCU 0.11111 0.0434 0.21007 Random accuracy unbiased -TN +TN 7 8 4 True negative/correct rejection -TNR +TNR 1.0 0.8 0.57143 Specificity or true negative rate -TON +TON 9 9 6 Test outcome negative -TOP +TOP 3 3 6 Test outcome positive -TP +TP 3 1 3 True positive/hit -TPR +TPR 0.6 0.5 0.6 Sensitivity, recall, hit rate, or true positive rate -Y +Y 0.6 0.3 0.17143 Youden index -dInd +dInd 0.4 0.53852 0.58624 Distance index -sInd +sInd 0.71716 0.61921 0.58547 Similarity index -

Generated By PyCM Version 3.5

+

Generated By PyCM Version 3.5

diff --git a/Document/Document_Files/cm1_colored2.html b/Document/Document_Files/cm1_colored2.html index 936b21c6..43c66449 100644 --- a/Document/Document_Files/cm1_colored2.html +++ b/Document/Document_Files/cm1_colored2.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,255 +60,255 @@

Confusion Matrix :

Overall Statistics :

- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
95% CI95% CI (0.30439,0.86228)
ACC MacroACC Macro 0.72222
ARIARI 0.09206
AUNPAUNP 0.68571
AUNUAUNU 0.67857
Bangdiwala BBangdiwala B 0.37255
Bennett SBennett S 0.375
CBACBA 0.47778
CSICSI 0.17778
Chi-SquaredChi-Squared 6.6
Chi-Squared DFChi-Squared DF 4
Conditional EntropyConditional Entropy 0.97579
Cramer VCramer V 0.5244
Cross EntropyCross Entropy 1.58333
F1 MacroF1 Macro 0.56515
F1 MicroF1 Micro 0.58333
FNR MacroFNR Macro 0.43333
FNR MicroFNR Micro 0.41667
FPR MacroFPR Macro 0.20952
FPR MicroFPR Micro 0.20833
Gwet AC1Gwet AC1 0.38931
Hamming LossHamming Loss 0.41667
Joint EntropyJoint Entropy 2.45915
KL DivergenceKL Divergence 0.09998
KappaKappa 0.35484
Kappa 95% CIKappa 95% CI (-0.07708,0.78675)
Kappa No PrevalenceKappa No Prevalence 0.16667
Kappa Standard ErrorKappa Standard Error 0.22036
Kappa UnbiasedKappa Unbiased 0.34426
Krippendorff AlphaKrippendorff Alpha 0.37158
Lambda ALambda A 0.42857
Lambda BLambda B 0.16667
Mutual InformationMutual Information 0.52421
NIRNIR 0.41667
Overall ACCOverall ACC 0.58333
Overall CENOverall CEN 0.46381
Overall JOverall J (1.225,0.40833)
Overall MCCOverall MCC 0.36667
Overall MCENOverall MCEN 0.51894
Overall RACCOverall RACC 0.35417
Overall RACCUOverall RACCU 0.36458
P-ValueP-Value 0.18926
PPV MacroPPV Macro 0.61111
PPV MicroPPV Micro 0.58333
Pearson CPearson C 0.59568
Phi-SquaredPhi-Squared 0.55
RCIRCI 0.35339
RRRR 4.0
Reference EntropyReference Entropy 1.48336
Response EntropyResponse Entropy 1.5
SOA1(Landis & Koch)SOA1(Landis & Koch) Fair
SOA2(Fleiss)SOA2(Fleiss) Poor
SOA3(Altman)SOA3(Altman) Fair
SOA4(Cicchetti)SOA4(Cicchetti) Poor
SOA5(Cramer)SOA5(Cramer) Relatively Strong
SOA6(Matthews)SOA6(Matthews) Weak
Scott PIScott PI 0.34426
Standard ErrorStandard Error 0.14232
TNR MacroTNR Macro 0.79048
TNR MicroTNR Micro 0.79167
TPR MacroTPR Macro 0.56667
TPR MicroTPR Micro 0.58333
Zero-one LossZero-one Loss 5
@@ -322,433 +322,433 @@

Class Statistics :

Description -ACC +ACC 0.83333 0.75 0.58333 Accuracy -AGF +AGF 0.72859 0.62869 0.61009 Adjusted F-score -AGM +AGM 0.85764 0.70861 0.58034 Adjusted geometric mean -AM +AM -2 1 1 Difference between automatic and manual classification -AUC +AUC 0.8 0.65 0.58571 Area under the ROC curve -AUCI +AUCI Very Good Fair Poor AUC value interpretation -AUPR +AUPR 0.8 0.41667 0.55 Area under the PR curve -BCD +BCD 0.08333 0.04167 0.04167 Bray-Curtis dissimilarity -BM +BM 0.6 0.3 0.17143 Informedness or bookmaker informedness -CEN +CEN 0.25 0.49658 0.60442 Confusion entropy -DOR +DOR None 4.0 2.0 Diagnostic odds ratio -DP +DP None 0.33193 0.16597 Discriminant power -DPI +DPI None Poor Poor Discriminant power interpretation -ERR +ERR 0.16667 0.25 0.41667 Error rate -F0.5 +F0.5 0.88235 0.35714 0.51724 F0.5 score -F1 +F1 0.75 0.4 0.54545 F1 score - harmonic mean of precision and sensitivity -F2 +F2 0.65217 0.45455 0.57692 F2 score -FDR +FDR 0.0 0.66667 0.5 False discovery rate -FN +FN 2 1 2 False negative/miss/type 2 error -FNR +FNR 0.4 0.5 0.4 Miss rate or false negative rate -FOR +FOR 0.22222 0.11111 0.33333 False omission rate -FP +FP 0 2 3 False positive/type 1 error/false alarm -FPR +FPR 0.0 0.2 0.42857 Fall-out or false positive rate -G +G 0.7746 0.40825 0.54772 G-measure geometric mean of precision and sensitivity -GI +GI 0.6 0.3 0.17143 Gini index -GM +GM 0.7746 0.63246 0.58554 G-mean geometric mean of specificity and sensitivity -IBA +IBA 0.36 0.28 0.35265 Index of balanced accuracy -ICSI +ICSI 0.6 -0.16667 0.1 Individual classification success index -IS +IS 1.26303 1.0 0.26303 Information score -J +J 0.6 0.25 0.375 Jaccard index -LS +LS 2.4 2.0 1.2 Lift score -MCC +MCC 0.68313 0.2582 0.16903 Matthews correlation coefficient -MCCI +MCCI Moderate Negligible Negligible Matthews correlation coefficient interpretation -MCEN +MCEN 0.26439 0.5 0.6875 Modified confusion entropy -MK +MK 0.77778 0.22222 0.16667 Markedness -N +N 7 10 7 Condition negative -NLR +NLR 0.4 0.625 0.7 Negative likelihood ratio -NLRI +NLRI Poor Negligible Negligible Negative likelihood ratio interpretation -NPV +NPV 0.77778 0.88889 0.66667 Negative predictive value -OC +OC 1.0 0.5 0.6 Overlap coefficient -OOC +OOC 0.7746 0.40825 0.54772 Otsuka-Ochiai coefficient -OP +OP 0.58333 0.51923 0.55894 Optimized precision -P +P 5 2 5 Condition positive or support -PLR +PLR None 2.5 1.4 Positive likelihood ratio -PLRI +PLRI None Poor Poor Positive likelihood ratio interpretation -POP +POP 12 12 12 Population -PPV +PPV 1.0 0.33333 0.5 Precision or positive predictive value -PRE +PRE 0.41667 0.16667 0.41667 Prevalence -Q +Q None 0.6 0.33333 Yule Q - coefficient of colligation -QI +QI None Moderate Weak Yule Q interpretation -RACC +RACC 0.10417 0.04167 0.20833 Random accuracy -RACCU +RACCU 0.11111 0.0434 0.21007 Random accuracy unbiased -TN +TN 7 8 4 True negative/correct rejection -TNR +TNR 1.0 0.8 0.57143 Specificity or true negative rate -TON +TON 9 9 6 Test outcome negative -TOP +TOP 3 3 6 Test outcome positive -TP +TP 3 1 3 True positive/hit -TPR +TPR 0.6 0.5 0.6 Sensitivity, recall, hit rate, or true positive rate -Y +Y 0.6 0.3 0.17143 Youden index -dInd +dInd 0.4 0.53852 0.58624 Distance index -sInd +sInd 0.71716 0.61921 0.58547 Similarity index -

Generated By PyCM Version 3.5

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Generated By PyCM Version 3.5

diff --git a/Document/Document_Files/cm1_filtered.html b/Document/Document_Files/cm1_filtered.html index aad00c5a..60dceb4e 100644 --- a/Document/Document_Files/cm1_filtered.html +++ b/Document/Document_Files/cm1_filtered.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,7 +60,7 @@

Confusion Matrix :

Overall Statistics :

- +
KappaKappa 0.35484
@@ -74,27 +74,27 @@

Class Statistics :

Description -ACC +ACC 0.83333 0.75 0.58333 Accuracy -AUC +AUC 0.8 0.65 0.58571 Area under the ROC curve -TPR +TPR 0.6 0.5 0.6 Sensitivity, recall, hit rate, or true positive rate -

Generated By PyCM Version 3.5

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Generated By PyCM Version 3.5

diff --git a/Document/Document_Files/cm1_filtered2.html b/Document/Document_Files/cm1_filtered2.html index b1fafa04..dd0f9065 100644 --- a/Document/Document_Files/cm1_filtered2.html +++ b/Document/Document_Files/cm1_filtered2.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,7 +60,7 @@

Confusion Matrix :

Overall Statistics :

- +
KappaKappa 0.35484
@@ -72,21 +72,21 @@

Class Statistics :

Description -ACC +ACC 0.83333 Accuracy -AUC +AUC 0.8 Area under the ROC curve -TPR +TPR 0.6 Sensitivity, recall, hit rate, or true positive rate -

Generated By PyCM Version 3.5

+

Generated By PyCM Version 3.5

diff --git a/Document/Document_Files/cm1_no_vectors.obj b/Document/Document_Files/cm1_no_vectors.obj index 7913b36b..4408ba5b 100644 --- a/Document/Document_Files/cm1_no_vectors.obj +++ b/Document/Document_Files/cm1_no_vectors.obj @@ -1 +1 @@ -{"Predict-Vector": null, "Prob-Vector": null, "Transpose": true, "Digit": 5, "Sample-Weight": null, "Imbalanced": false, "Actual-Vector": null, "Matrix": [["L1", [["L2", 0], ["L1", 3], ["L3", 2]]], ["L2", [["L2", 1], ["L1", 0], ["L3", 1]]], ["L3", [["L2", 2], ["L1", 0], ["L3", 3]]]]} \ No newline at end of file +{"Prob-Vector": null, "Predict-Vector": null, "Transpose": true, "Matrix": [["L1", [["L1", 3], ["L2", 0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Imbalanced": false, "Actual-Vector": null, "Digit": 5, "Sample-Weight": null} \ No newline at end of file diff --git a/Document/Document_Files/cm1_normalized.html b/Document/Document_Files/cm1_normalized.html index 5e01ebcf..e5d16d04 100644 --- a/Document/Document_Files/cm1_normalized.html +++ b/Document/Document_Files/cm1_normalized.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,255 +60,255 @@

Confusion Matrix (Normalized):

Overall Statistics :

- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
95% CI95% CI (0.30439,0.86228)
ACC MacroACC Macro 0.72222
ARIARI 0.09206
AUNPAUNP 0.68571
AUNUAUNU 0.67857
Bangdiwala BBangdiwala B 0.37255
Bennett SBennett S 0.375
CBACBA 0.47778
CSICSI 0.17778
Chi-SquaredChi-Squared 6.6
Chi-Squared DFChi-Squared DF 4
Conditional EntropyConditional Entropy 0.97579
Cramer VCramer V 0.5244
Cross EntropyCross Entropy 1.58333
F1 MacroF1 Macro 0.56515
F1 MicroF1 Micro 0.58333
FNR MacroFNR Macro 0.43333
FNR MicroFNR Micro 0.41667
FPR MacroFPR Macro 0.20952
FPR MicroFPR Micro 0.20833
Gwet AC1Gwet AC1 0.38931
Hamming LossHamming Loss 0.41667
Joint EntropyJoint Entropy 2.45915
KL DivergenceKL Divergence 0.09998
KappaKappa 0.35484
Kappa 95% CIKappa 95% CI (-0.07708,0.78675)
Kappa No PrevalenceKappa No Prevalence 0.16667
Kappa Standard ErrorKappa Standard Error 0.22036
Kappa UnbiasedKappa Unbiased 0.34426
Krippendorff AlphaKrippendorff Alpha 0.37158
Lambda ALambda A 0.42857
Lambda BLambda B 0.16667
Mutual InformationMutual Information 0.52421
NIRNIR 0.41667
Overall ACCOverall ACC 0.58333
Overall CENOverall CEN 0.46381
Overall JOverall J (1.225,0.40833)
Overall MCCOverall MCC 0.36667
Overall MCENOverall MCEN 0.51894
Overall RACCOverall RACC 0.35417
Overall RACCUOverall RACCU 0.36458
P-ValueP-Value 0.18926
PPV MacroPPV Macro 0.61111
PPV MicroPPV Micro 0.58333
Pearson CPearson C 0.59568
Phi-SquaredPhi-Squared 0.55
RCIRCI 0.35339
RRRR 4.0
Reference EntropyReference Entropy 1.48336
Response EntropyResponse Entropy 1.5
SOA1(Landis & Koch)SOA1(Landis & Koch) Fair
SOA2(Fleiss)SOA2(Fleiss) Poor
SOA3(Altman)SOA3(Altman) Fair
SOA4(Cicchetti)SOA4(Cicchetti) Poor
SOA5(Cramer)SOA5(Cramer) Relatively Strong
SOA6(Matthews)SOA6(Matthews) Weak
Scott PIScott PI 0.34426
Standard ErrorStandard Error 0.14232
TNR MacroTNR Macro 0.79048
TNR MicroTNR Micro 0.79167
TPR MacroTPR Macro 0.56667
TPR MicroTPR Micro 0.58333
Zero-one LossZero-one Loss 5
@@ -322,433 +322,433 @@

Class Statistics :

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Generated By PyCM Version 3.5

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Generated By PyCM Version 3.5

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0.6521739130434783, "L2": 0.45454545454545453, "L3": 0.5769230769230769}, "DP": {"L1": "None", "L2": 0.33193306999649924, "L3": 0.1659665349982495}, "IS": {"L1": 1.2630344058337937, "L2": 0.9999999999999998, "L3": 0.26303440583379367}, "FN": {"L1": 2, "L2": 1, "L3": 2}, "AGM": {"L1": 0.8576400016262, "L2": 0.708612108382005, "L3": 0.5803410802752335}, "POP": {"L1": 12, "L2": 12, "L3": 12}, "DOR": {"L1": "None", "L2": 4.000000000000001, "L3": 1.9999999999999998}, "BM": {"L1": 0.6000000000000001, "L2": 0.30000000000000004, "L3": 0.17142857142857126}, "TP": {"L1": 3, "L2": 1, "L3": 3}, "TON": {"L1": 9, "L2": 9, "L3": 6}, "FOR": {"L1": 0.2222222222222222, "L2": 0.11111111111111116, "L3": 0.33333333333333337}, "AUC": {"L1": 0.8, "L2": 0.65, "L3": 0.5857142857142856}, "AUPR": {"L1": 0.8, "L2": 0.41666666666666663, "L3": 0.55}, "PPV": {"L1": 1.0, "L2": 0.3333333333333333, "L3": 0.5}, "MCCI": {"L1": "Moderate", "L2": "Negligible", "L3": "Negligible"}, "NPV": {"L1": 0.7777777777777778, "L2": 0.8888888888888888, "L3": 0.6666666666666666}, "ICSI": {"L1": 0.6000000000000001, "L2": -0.16666666666666674, "L3": 0.10000000000000009}, "AUCI": {"L1": "Very Good", "L2": "Fair", "L3": "Poor"}, "MCEN": {"L1": 0.2643856189774724, "L2": 0.5, "L3": 0.6875}, "TNR": {"L1": 1.0, "L2": 0.8, "L3": 0.5714285714285714}, "AM": {"L1": -2, "L2": 1, "L3": 1}, "OOC": {"L1": 0.7745966692414834, "L2": 0.4082482904638631, "L3": 0.5477225575051661}, "P": {"L1": 5, "L2": 2, "L3": 5}, "dInd": {"L1": 0.4, "L2": 0.5385164807134504, "L3": 0.5862367008195198}, "ERR": {"L1": 0.16666666666666663, "L2": 0.25, "L3": 0.41666666666666663}, "FPR": {"L1": 0.0, "L2": 0.19999999999999996, "L3": 0.4285714285714286}, "CEN": {"L1": 0.25, "L2": 0.49657842846620864, "L3": 0.6044162769630221}}, "Prob-Vector": null, "Predict-Vector": null, "Overall-Stat": {"F1 Micro": 0.5833333333333334, "ARI": 0.09206349206349207, "Kappa": 0.35483870967741943, "Hamming Loss": 0.41666666666666663, "Mutual Information": 0.5242078379544428, "Krippendorff Alpha": 0.3715846994535519, "SOA4(Cicchetti)": "Poor", "KL Divergence": 0.09997757835164581, "FPR Micro": 0.20833333333333337, "Overall ACC": 0.5833333333333334, "SOA3(Altman)": "Fair", "Zero-one Loss": 5, "Joint Entropy": 2.4591479170272446, "Conditional Entropy": 0.9757921620455572, "AUNP": 0.6857142857142857, "Overall J": [1.225, 0.4083333333333334], "RR": 4.0, "FNR Macro": 0.43333333333333324, "Pearson C": 0.5956833971812706, "Overall MCC": 0.36666666666666664, "SOA5(Cramer)": "Relatively Strong", "ACC Macro": 0.7222222222222223, "Phi-Squared": 0.55, "TPR Macro": 0.5666666666666668, "Gwet AC1": 0.3893129770992367, "TPR Micro": 0.5833333333333334, "FPR Macro": 0.20952380952380956, "Response Entropy": 1.5, "Kappa No Prevalence": 0.16666666666666674, "Kappa 95% CI": [-0.07707577422109269, 0.7867531935759315], "F1 Macro": 0.5651515151515151, "PPV Macro": 0.611111111111111, "Overall MCEN": 0.5189369467580801, "CSI": 0.1777777777777778, "Cramer V": 0.5244044240850758, 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0], ["L3", 2]]], ["L2", [["L1", 0], ["L2", 1], ["L3", 1]]], ["L3", [["L1", 0], ["L2", 2], ["L3", 3]]]], "Imbalanced": false, "Actual-Vector": null, "Digit": 5, "Sample-Weight": null} \ No newline at end of file diff --git a/Document/Document_Files/cm1_summary.html b/Document/Document_Files/cm1_summary.html index ed86f25f..8b0ae223 100644 --- a/Document/Document_Files/cm1_summary.html +++ b/Document/Document_Files/cm1_summary.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,39 +60,39 @@

Confusion Matrix (Normalized):

Overall Statistics :

- + - + - + - + - + - + - + - + - +
ACC MacroACC Macro 0.72222
F1 MacroF1 Macro 0.56515
FPR MacroFPR Macro 0.20952
KappaKappa 0.35484
Overall ACCOverall ACC 0.58333
PPV MacroPPV Macro 0.61111
SOA1(Landis & Koch)SOA1(Landis & Koch) Fair
TPR MacroTPR Macro 0.56667
Zero-one LossZero-one Loss 5
@@ -106,118 +106,118 @@

Class Statistics :

Description -ACC +ACC 0.83333 0.75 0.58333 Accuracy -AUC +AUC 0.8 0.65 0.58571 Area under the ROC curve -AUCI +AUCI Very Good Fair Poor AUC value interpretation -F1 +F1 0.75 0.4 0.54545 F1 score - harmonic mean of precision and sensitivity -TPR +TPR 0.6 0.5 0.6 Sensitivity, recall, hit rate, or true positive rate -FPR +FPR 0.0 0.2 0.42857 Fall-out or false positive rate -PPV +PPV 1.0 0.33333 0.5 Precision or positive predictive value -TP +TP 3 1 3 True positive/hit -FP +FP 0 2 3 False positive/type 1 error/false alarm -FN +FN 2 1 2 False negative/miss/type 2 error -TN +TN 7 8 4 True negative/correct rejection -N +N 7 10 7 Condition negative -P +P 5 2 5 Condition positive or support -POP +POP 12 12 12 Population -TOP +TOP 3 3 6 Test outcome positive -TON +TON 9 9 6 Test outcome negative -

Generated By PyCM Version 3.5

+

Generated By PyCM Version 3.5

diff --git a/Document/Example1.ipynb b/Document/Example1.ipynb index 7670e299..9e257d33 100644 --- a/Document/Example1.ipynb +++ b/Document/Example1.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Please cite us if you use the software

" + "

Please cite us if you use the software

" ] }, { diff --git a/Document/Example1_Files/cm1.html b/Document/Example1_Files/cm1.html index 9482d9bf..ddf49f7b 100644 --- a/Document/Example1_Files/cm1.html +++ b/Document/Example1_Files/cm1.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,255 +60,255 @@

Confusion Matrix :

Overall Statistics :

- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
95% CI95% CI (0.72617,0.95804)
ACC MacroACC Macro 0.89474
ARIARI 0.63251
AUNPAUNP 0.89655
AUNUAUNU 0.90302
Bangdiwala BBangdiwala B 0.75431
Bennett SBennett S 0.76316
CBACBA 0.74167
CSICSI 0.74167
Chi-SquaredChi-Squared 52.25
Chi-Squared DFChi-Squared DF 4
Conditional EntropyConditional Entropy 0.40187
Cramer VCramer V 0.82916
Cross EntropyCross Entropy 1.65796
F1 MacroF1 Macro 0.83974
F1 MicroF1 Micro 0.84211
FNR MacroFNR Macro 0.125
FNR MicroFNR Micro 0.15789
FPR MacroFPR Macro 0.06897
FPR MicroFPR Micro 0.07895
Gwet AC1Gwet AC1 0.76324
Hamming LossHamming Loss 0.15789
Joint EntropyJoint Entropy 1.94887
KL DivergenceKL Divergence 0.11096
KappaKappa 0.76735
Kappa 95% CIKappa 95% CI (0.59651,0.93818)
Kappa No PrevalenceKappa No Prevalence 0.68421
Kappa Standard ErrorKappa Standard Error 0.08716
Kappa UnbiasedKappa Unbiased 0.76299
Krippendorff AlphaKrippendorff Alpha 0.76611
Lambda ALambda A 0.72727
Lambda BLambda B 0.73913
Mutual InformationMutual Information 1.16374
NIRNIR 0.42105
Overall ACCOverall ACC 0.84211
Overall CENOverall CEN 0.16245
Overall JOverall J (2.225,0.74167)
Overall MCCOverall MCC 0.79663
Overall MCENOverall MCEN 0.18661
Overall RACCOverall RACC 0.32133
Overall RACCUOverall RACCU 0.3338
P-ValueP-Value 0.0
PPV MacroPPV Macro 0.86667
PPV MicroPPV Micro 0.84211
Pearson CPearson C 0.76089
Phi-SquaredPhi-Squared 1.375
RCIRCI 0.75225
RRRR 12.66667
Reference EntropyReference Entropy 1.54701
Response EntropyResponse Entropy 1.5656
SOA1(Landis & Koch)SOA1(Landis & Koch) Substantial
SOA2(Fleiss)SOA2(Fleiss) Excellent
SOA3(Altman)SOA3(Altman) Good
SOA4(Cicchetti)SOA4(Cicchetti) Excellent
SOA5(Cramer)SOA5(Cramer) Very Strong
SOA6(Matthews)SOA6(Matthews) Strong
Scott PIScott PI 0.76299
Standard ErrorStandard Error 0.05915
TNR MacroTNR Macro 0.93103
TNR MicroTNR Micro 0.92105
TPR MacroTPR Macro 0.875
TPR MicroTPR Micro 0.84211
Zero-one LossZero-one Loss 6
@@ -322,433 +322,433 @@

Class Statistics :

Description -ACC +ACC 1.0 0.84211 0.84211 Accuracy -AGF +AGF 1.0 0.74475 0.91575 Adjusted F-score -AGM +AGM 1.0 0.86736 0.84838 Adjusted geometric mean -AM +AM 0 -6 6 Difference between automatic and manual classification -AUC +AUC 1.0 0.8125 0.89655 Area under the ROC curve -AUCI +AUCI Excellent Very Good Very Good AUC value interpretation -AUPR +AUPR 1.0 0.8125 0.8 Area under the PR curve -BCD +BCD 0.0 0.07895 0.07895 Bray-Curtis dissimilarity -BM +BM 1.0 0.625 0.7931 Informedness or bookmaker informedness -CEN +CEN 0 0.24409 0.25 Confusion entropy -DOR +DOR None None None Diagnostic odds ratio -DP +DP None None None Discriminant power -DPI +DPI None None None Discriminant power interpretation -ERR +ERR 0.0 0.15789 0.15789 Error rate -F0.5 +F0.5 1.0 0.89286 0.65217 F0.5 score -F1 +F1 1.0 0.76923 0.75 F1 score - harmonic mean of precision and sensitivity -F2 +F2 1.0 0.67568 0.88235 F2 score -FDR +FDR 0.0 0.0 0.4 False discovery rate -FN +FN 0 6 0 False negative/miss/type 2 error -FNR +FNR 0.0 0.375 0.0 Miss rate or false negative rate -FOR +FOR 0.0 0.21429 0.0 False omission rate -FP +FP 0 0 6 False positive/type 1 error/false alarm -FPR +FPR 0.0 0.0 0.2069 Fall-out or false positive rate -G +G 1.0 0.79057 0.7746 G-measure geometric mean of precision and sensitivity -GI +GI 1.0 0.625 0.7931 Gini index -GM +GM 1.0 0.79057 0.89056 G-mean geometric mean of specificity and sensitivity -IBA +IBA 1.0 0.39062 0.95719 Index of balanced accuracy -ICSI +ICSI 1.0 0.625 0.6 Individual classification success index -IS +IS 1.54749 1.24793 1.34104 Information score -J +J 1.0 0.625 0.6 Jaccard index -LS +LS 2.92308 2.375 2.53333 Lift score -MCC +MCC 1.0 0.70076 0.68983 Matthews correlation coefficient -MCCI +MCCI Very Strong Strong Moderate Matthews correlation coefficient interpretation -MCEN +MCEN 0 0.26532 0.26439 Modified confusion entropy -MK +MK 1.0 0.78571 0.6 Markedness -N +N 25 22 29 Condition negative -NLR +NLR 0.0 0.375 0.0 Negative likelihood ratio -NLRI +NLRI Good Poor Good Negative likelihood ratio interpretation -NPV +NPV 1.0 0.78571 1.0 Negative predictive value -OC +OC 1.0 1.0 1.0 Overlap coefficient -OOC +OOC 1.0 0.79057 0.7746 Otsuka-Ochiai coefficient -OP +OP 1.0 0.61134 0.72672 Optimized precision -P +P 13 16 9 Condition positive or support -PLR +PLR None None 4.83333 Positive likelihood ratio -PLRI +PLRI None None Poor Positive likelihood ratio interpretation -POP +POP 38 38 38 Population -PPV +PPV 1.0 1.0 0.6 Precision or positive predictive value -PRE +PRE 0.34211 0.42105 0.23684 Prevalence -Q +Q None None None Yule Q - coefficient of colligation -QI +QI None None None Yule Q interpretation -RACC +RACC 0.11704 0.1108 0.09349 Random accuracy -RACCU +RACCU 0.11704 0.11704 0.09972 Random accuracy unbiased -TN +TN 25 22 23 True negative/correct rejection -TNR +TNR 1.0 1.0 0.7931 Specificity or true negative rate -TON +TON 25 28 23 Test outcome negative -TOP +TOP 13 10 15 Test outcome positive -TP +TP 13 10 9 True positive/hit -TPR +TPR 1.0 0.625 1.0 Sensitivity, recall, hit rate, or true positive rate -Y +Y 1.0 0.625 0.7931 Youden index -dInd +dInd 0.0 0.375 0.2069 Distance index -sInd +sInd 1.0 0.73483 0.8537 Similarity index -

Generated By PyCM Version 3.5

+

Generated By PyCM Version 3.5

diff --git a/Document/Example1_Files/cm2.html b/Document/Example1_Files/cm2.html index 2d49e7d4..c5d06183 100644 --- a/Document/Example1_Files/cm2.html +++ b/Document/Example1_Files/cm2.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,255 +60,255 @@

Confusion Matrix :

Overall Statistics :

- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
95% CI95% CI (0.92279,1.02458)
ACC MacroACC Macro 0.98246
ARIARI 0.92263
AUNPAUNP 0.98276
AUNUAUNU 0.98384
Bangdiwala BBangdiwala B 0.9519
Bennett SBennett S 0.96053
CBACBA 0.94583
CSICSI 0.94583
Chi-SquaredChi-Squared 70.0625
Chi-Squared DFChi-Squared DF 4
Conditional EntropyConditional Entropy 0.14202
Cramer VCramer V 0.96014
Cross EntropyCross Entropy 1.55021
F1 MacroF1 Macro 0.9717
F1 MicroF1 Micro 0.97368
FNR MacroFNR Macro 0.02083
FNR MicroFNR Micro 0.02632
FPR MacroFPR Macro 0.01149
FPR MicroFPR Micro 0.01316
Gwet AC1Gwet AC1 0.9609
Hamming LossHamming Loss 0.02632
Joint EntropyJoint Entropy 1.68902
KL DivergenceKL Divergence 0.0032
KappaKappa 0.95979
Kappa 95% CIKappa 95% CI (0.88202,1.03756)
Kappa No PrevalenceKappa No Prevalence 0.94737
Kappa Standard ErrorKappa Standard Error 0.03968
Kappa UnbiasedKappa Unbiased 0.95977
Krippendorff AlphaKrippendorff Alpha 0.9603
Lambda ALambda A 0.95455
Lambda BLambda B 0.95652
Mutual InformationMutual Information 1.42359
NIRNIR 0.42105
Overall ACCOverall ACC 0.97368
Overall CENOverall CEN 0.06054
Overall JOverall J (2.8375,0.94583)
Overall MCCOverall MCC 0.96082
Overall MCENOverall MCEN 0.09387
Overall RACCOverall RACC 0.34557
Overall RACCUOverall RACCU 0.34591
P-ValueP-Value 0.0
PPV MacroPPV Macro 0.96667
PPV MicroPPV Micro 0.97368
Pearson CPearson C 0.8052
Phi-SquaredPhi-Squared 1.84375
RCIRCI 0.92022
RRRR 12.66667
Reference EntropyReference Entropy 1.54701
Response EntropyResponse Entropy 1.5656
SOA1(Landis & Koch)SOA1(Landis & Koch) Almost Perfect
SOA2(Fleiss)SOA2(Fleiss) Excellent
SOA3(Altman)SOA3(Altman) Very Good
SOA4(Cicchetti)SOA4(Cicchetti) Excellent
SOA5(Cramer)SOA5(Cramer) Very Strong
SOA6(Matthews)SOA6(Matthews) Very Strong
Scott PIScott PI 0.95977
Standard ErrorStandard Error 0.02597
TNR MacroTNR Macro 0.98851
TNR MicroTNR Micro 0.98684
TPR MacroTPR Macro 0.97917
TPR MicroTPR Micro 0.97368
Zero-one LossZero-one Loss 1
@@ -322,433 +322,433 @@

Class Statistics :

Description -ACC +ACC 1.0 0.97368 0.97368 Accuracy -AGF +AGF 1.0 0.95711 0.98556 Adjusted F-score -AGM +AGM 1.0 0.97989 0.97521 Adjusted geometric mean -AM +AM 0 -1 1 Difference between automatic and manual classification -AUC +AUC 1.0 0.96875 0.98276 Area under the ROC curve -AUCI +AUCI Excellent Excellent Excellent AUC value interpretation -AUPR +AUPR 1.0 0.96875 0.95 Area under the PR curve -BCD +BCD 0.0 0.01316 0.01316 Bray-Curtis dissimilarity -BM +BM 1.0 0.9375 0.96552 Informedness or bookmaker informedness -CEN +CEN 0 0.07991 0.11179 Confusion entropy -DOR +DOR None None None Diagnostic odds ratio -DP +DP None None None Discriminant power -DPI +DPI None None None Discriminant power interpretation -ERR +ERR 0.0 0.02632 0.02632 Error rate -F0.5 +F0.5 1.0 0.98684 0.91837 F0.5 score -F1 +F1 1.0 0.96774 0.94737 F1 score - harmonic mean of precision and sensitivity -F2 +F2 1.0 0.94937 0.97826 F2 score -FDR +FDR 0.0 0.0 0.1 False discovery rate -FN +FN 0 1 0 False negative/miss/type 2 error -FNR +FNR 0.0 0.0625 0.0 Miss rate or false negative rate -FOR +FOR 0.0 0.04348 0.0 False omission rate -FP +FP 0 0 1 False positive/type 1 error/false alarm -FPR +FPR 0.0 0.0 0.03448 Fall-out or false positive rate -G +G 1.0 0.96825 0.94868 G-measure geometric mean of precision and sensitivity -GI +GI 1.0 0.9375 0.96552 Gini index -GM +GM 1.0 0.96825 0.98261 G-mean geometric mean of specificity and sensitivity -IBA +IBA 1.0 0.87891 0.99881 Index of balanced accuracy -ICSI +ICSI 1.0 0.9375 0.9 Individual classification success index -IS +IS 1.54749 1.24793 1.926 Information score -J +J 1.0 0.9375 0.9 Jaccard index -LS +LS 2.92308 2.375 3.8 Lift score -MCC +MCC 1.0 0.94696 0.93218 Matthews correlation coefficient -MCCI +MCCI Very Strong Very Strong Very Strong Matthews correlation coefficient interpretation -MCEN +MCEN 0 0.125 0.1661 Modified confusion entropy -MK +MK 1.0 0.95652 0.9 Markedness -N +N 25 22 29 Condition negative -NLR +NLR 0.0 0.0625 0.0 Negative likelihood ratio -NLRI +NLRI Good Good Good Negative likelihood ratio interpretation -NPV +NPV 1.0 0.95652 1.0 Negative predictive value -OC +OC 1.0 1.0 1.0 Overlap coefficient -OOC +OOC 1.0 0.96825 0.94868 Otsuka-Ochiai coefficient -OP +OP 1.0 0.94143 0.95614 Optimized precision -P +P 13 16 9 Condition positive or support -PLR +PLR None None 29.0 Positive likelihood ratio -PLRI +PLRI None None Good Positive likelihood ratio interpretation -POP +POP 38 38 38 Population -PPV +PPV 1.0 1.0 0.9 Precision or positive predictive value -PRE +PRE 0.34211 0.42105 0.23684 Prevalence -Q +Q None None None Yule Q - coefficient of colligation -QI +QI None None None Yule Q interpretation -RACC +RACC 0.11704 0.1662 0.06233 Random accuracy -RACCU +RACCU 0.11704 0.16638 0.0625 Random accuracy unbiased -TN +TN 25 22 28 True negative/correct rejection -TNR +TNR 1.0 1.0 0.96552 Specificity or true negative rate -TON +TON 25 23 28 Test outcome negative -TOP +TOP 13 15 10 Test outcome positive -TP +TP 13 15 9 True positive/hit -TPR +TPR 1.0 0.9375 1.0 Sensitivity, recall, hit rate, or true positive rate -Y +Y 1.0 0.9375 0.96552 Youden index -dInd +dInd 0.0 0.0625 0.03448 Distance index -sInd +sInd 1.0 0.95581 0.97562 Similarity index -

Generated By PyCM Version 3.5

+

Generated By PyCM Version 3.5

diff --git a/Document/Example1_Files/cm3.html b/Document/Example1_Files/cm3.html index 47c3c95d..a1b1f326 100644 --- a/Document/Example1_Files/cm3.html +++ b/Document/Example1_Files/cm3.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,255 +60,255 @@

Confusion Matrix :

Overall Statistics :

- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
95% CI95% CI (0.79716,0.99231)
ACC MacroACC Macro 0.92982
ARIARI 0.73605
AUNPAUNP 0.91458
AUNUAUNU 0.90555
Bangdiwala BBangdiwala B 0.82692
Bennett SBennett S 0.84211
CBACBA 0.83333
CSICSI 0.76488
Chi-SquaredChi-Squared 53.85218
Chi-Squared DFChi-Squared DF 4
Conditional EntropyConditional Entropy 0.35951
Cramer VCramer V 0.84177
Cross EntropyCross Entropy 1.56133
F1 MacroF1 Macro 0.87745
F1 MicroF1 Micro 0.89474
FNR MacroFNR Macro 0.13194
FNR MicroFNR Micro 0.10526
FPR MacroFPR Macro 0.05695
FPR MicroFPR Micro 0.05263
Gwet AC1Gwet AC1 0.84537
Hamming LossHamming Loss 0.10526
Joint EntropyJoint Entropy 1.90651
KL DivergenceKL Divergence 0.01432
KappaKappa 0.8355
Kappa 95% CIKappa 95% CI (0.68301,0.98799)
Kappa No PrevalenceKappa No Prevalence 0.78947
Kappa Standard ErrorKappa Standard Error 0.0778
Kappa UnbiasedKappa Unbiased 0.83514
Krippendorff AlphaKrippendorff Alpha 0.83731
Lambda ALambda A 0.81818
Lambda BLambda B 0.8
Mutual InformationMutual Information 1.13011
NIRNIR 0.42105
Overall ACCOverall ACC 0.89474
Overall CENOverall CEN 0.17658
Overall JOverall J (2.38947,0.79649)
Overall MCCOverall MCC 0.83929
Overall MCENOverall MCEN 0.24726
Overall RACCOverall RACC 0.36011
Overall RACCUOverall RACCU 0.3615
P-ValueP-Value 0.0
PPV MacroPPV Macro 0.89683
PPV MicroPPV Micro 0.89474
Pearson CPearson C 0.7657
Phi-SquaredPhi-Squared 1.41716
RCIRCI 0.73051
RRRR 12.66667
Reference EntropyReference Entropy 1.54701
Response EntropyResponse Entropy 1.48962
SOA1(Landis & Koch)SOA1(Landis & Koch) Almost Perfect
SOA2(Fleiss)SOA2(Fleiss) Excellent
SOA3(Altman)SOA3(Altman) Very Good
SOA4(Cicchetti)SOA4(Cicchetti) Excellent
SOA5(Cramer)SOA5(Cramer) Very Strong
SOA6(Matthews)SOA6(Matthews) Strong
Scott PIScott PI 0.83514
Standard ErrorStandard Error 0.04978
TNR MacroTNR Macro 0.94305
TNR MicroTNR Micro 0.94737
TPR MacroTPR Macro 0.86806
TPR MicroTPR Micro 0.89474
Zero-one LossZero-one Loss 4
@@ -322,433 +322,433 @@

Class Statistics :

Description -ACC +ACC 1.0 0.89474 0.89474 Accuracy -AGF +AGF 1.0 0.92297 0.799 Adjusted F-score -AGM +AGM 1.0 0.88655 0.87294 Adjusted geometric mean -AM +AM 0 2 -2 Difference between automatic and manual classification -AUC +AUC 1.0 0.90057 0.81609 Area under the ROC curve -AUCI +AUCI Excellent Excellent Very Good AUC value interpretation -AUPR +AUPR 1.0 0.88542 0.7619 Area under the PR curve -BCD +BCD 0.0 0.02632 0.02632 Bray-Curtis dissimilarity -BM +BM 1.0 0.80114 0.63218 Informedness or bookmaker informedness -CEN +CEN 0 0.22934 0.35141 Confusion entropy -DOR +DOR None 95.0 56.0 Diagnostic odds ratio -DP +DP None 1.09038 0.96383 Discriminant power -DPI +DPI None Limited Poor Discriminant power interpretation -ERR +ERR 0.0 0.10526 0.10526 Error rate -F0.5 +F0.5 1.0 0.85227 0.81081 F0.5 score -F1 +F1 1.0 0.88235 0.75 F1 score - harmonic mean of precision and sensitivity -F2 +F2 1.0 0.91463 0.69767 F2 score -FDR +FDR 0.0 0.16667 0.14286 False discovery rate -FN +FN 0 1 3 False negative/miss/type 2 error -FNR +FNR 0.0 0.0625 0.33333 Miss rate or false negative rate -FOR +FOR 0.0 0.05 0.09677 False omission rate -FP +FP 0 3 1 False positive/type 1 error/false alarm -FPR +FPR 0.0 0.13636 0.03448 Fall-out or false positive rate -G +G 1.0 0.88388 0.75593 G-measure geometric mean of precision and sensitivity -GI +GI 1.0 0.80114 0.63218 Gini index -GM +GM 1.0 0.89981 0.8023 G-mean geometric mean of specificity and sensitivity -IBA +IBA 1.0 0.86946 0.45131 Index of balanced accuracy -ICSI +ICSI 1.0 0.77083 0.52381 Individual classification success index -IS +IS 1.54749 0.98489 1.85561 Information score -J +J 1.0 0.78947 0.6 Jaccard index -LS +LS 2.92308 1.97917 3.61905 Lift score -MCC +MCC 1.0 0.79218 0.69332 Matthews correlation coefficient -MCCI +MCCI Very Strong Strong Moderate Matthews correlation coefficient interpretation -MCEN +MCEN 0 0.32202 0.42664 Modified confusion entropy -MK +MK 1.0 0.78333 0.76037 Markedness -N +N 25 22 29 Condition negative -NLR +NLR 0.0 0.07237 0.34524 Negative likelihood ratio -NLRI +NLRI Good Good Poor Negative likelihood ratio interpretation -NPV +NPV 1.0 0.95 0.90323 Negative predictive value -OC +OC 1.0 0.9375 0.85714 Overlap coefficient -OOC +OOC 1.0 0.88388 0.75593 Otsuka-Ochiai coefficient -OP +OP 1.0 0.85373 0.71164 Optimized precision -P +P 13 16 9 Condition positive or support -PLR +PLR None 6.875 19.33333 Positive likelihood ratio -PLRI +PLRI None Fair Good Positive likelihood ratio interpretation -POP +POP 38 38 38 Population -PPV +PPV 1.0 0.83333 0.85714 Precision or positive predictive value -PRE +PRE 0.34211 0.42105 0.23684 Prevalence -Q +Q None 0.97917 0.96491 Yule Q - coefficient of colligation -QI +QI None Strong Strong Yule Q interpretation -RACC +RACC 0.11704 0.19945 0.04363 Random accuracy -RACCU +RACCU 0.11704 0.20014 0.04432 Random accuracy unbiased -TN +TN 25 19 28 True negative/correct rejection -TNR +TNR 1.0 0.86364 0.96552 Specificity or true negative rate -TON +TON 25 20 31 Test outcome negative -TOP +TOP 13 18 7 Test outcome positive -TP +TP 13 15 6 True positive/hit -TPR +TPR 1.0 0.9375 0.66667 Sensitivity, recall, hit rate, or true positive rate -Y +Y 1.0 0.80114 0.63218 Youden index -dInd +dInd 0.0 0.15 0.33511 Distance index -sInd +sInd 1.0 0.89393 0.76304 Similarity index -

Generated By PyCM Version 3.5

+

Generated By PyCM Version 3.5

diff --git a/Document/Example2.ipynb b/Document/Example2.ipynb index dc9043ac..d4ce458a 100644 --- a/Document/Example2.ipynb +++ b/Document/Example2.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Please cite us if you use the software

" + "

Please cite us if you use the software

" ] }, { diff --git a/Document/Example3.ipynb b/Document/Example3.ipynb index ef99cd74..2ce41a8b 100644 --- a/Document/Example3.ipynb +++ b/Document/Example3.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Please cite us if you use the software

" + "

Please cite us if you use the software

" ] }, { @@ -379,7 +379,7 @@ "Reference Entropy 1.58496\n", "Response Entropy 1.45915\n", "SOA1(Landis & Koch) Substantial\n", - "SOA2(Fleiss) Intermediate to Good\n", + "SOA2(Fleiss) Excellent\n", "SOA3(Altman) Good\n", "SOA4(Cicchetti) Excellent\n", "SOA5(Cramer) Very Strong\n", diff --git a/Document/Example4.ipynb b/Document/Example4.ipynb index 557341c9..f4ca5356 100644 --- a/Document/Example4.ipynb +++ b/Document/Example4.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Please cite us if you use the software

" + "

Please cite us if you use the software

" ] }, { @@ -542,7 +542,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"Sample-Weight\": null, \"Transpose\": false, \"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]]]], \"Imbalanced\": true, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Digit\": 5}\n" + "{\"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], \"Transpose\": false, \"Imbalanced\": true, \"Digit\": 5, \"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]]]], \"Prob-Vector\": null, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200]}\n" ] } ], @@ -559,7 +559,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{\"Overall-Stat\": {\"Overall RACC\": 0.29500000000000004, \"Overall MCC\": 0.1264200803632855, \"SOA3(Altman)\": \"Poor\", \"SOA2(Fleiss)\": \"Poor\", \"Phi-Squared\": \"None\", \"Kappa\": 0.07801418439716304, \"Overall MCEN\": 0.3746281299595305, \"TNR Micro\": 0.7833333333333333, \"RCI\": 0.11409066398451011, \"95% CI\": [0.14095885572452488, 0.559041144275475], \"Chi-Squared\": \"None\", \"Cross Entropy\": 1.709947752496911, \"FPR Macro\": 0.2147058823529412, \"Reference Entropy\": 0.8841837197791889, \"Kappa 95% CI\": [-0.21849807698648957, 0.3745264457808156], \"FNR Micro\": 0.65, \"Pearson C\": \"None\", \"CSI\": \"None\", \"Kappa Unbiased\": -0.12554112554112543, \"F1 Macro\": 0.23043478260869565, \"TPR Micro\": 0.35, \"SOA1(Landis & Koch)\": \"Slight\", \"ACC Macro\": 0.675, \"TPR Macro\": \"None\", \"Lambda A\": 0.0, \"FNR Macro\": \"None\", \"Joint Entropy\": 2.119973094021975, \"SOA4(Cicchetti)\": \"Poor\", \"FPR Micro\": 0.21666666666666667, \"P-Value\": 0.9999981549942787, \"TNR Macro\": 0.7852941176470588, \"Cramer V\": \"None\", \"Conditional Entropy\": 1.235789374242786, \"AUNP\": \"None\", \"Response Entropy\": 1.3366664819166876, \"ARI\": 0.02298247455136956, \"Hamming Loss\": 0.65, \"Bennett S\": 0.1333333333333333, \"Standard Error\": 0.1066536450385077, \"SOA6(Matthews)\": \"Negligible\", \"F1 Micro\": 0.35, \"PPV Micro\": 0.35, \"Kappa No Prevalence\": -0.30000000000000004, \"Zero-one Loss\": 13, \"Overall RACCU\": 0.42249999999999993, \"AUNU\": \"None\", \"Scott PI\": -0.12554112554112543, \"CBA\": 0.17708333333333331, \"PPV Macro\": \"None\", \"Overall ACC\": 0.35, \"SOA5(Cramer)\": \"None\", \"Bangdiwala B\": 0.3135593220338983, \"Lambda B\": 0.0, \"Chi-Squared DF\": 9, \"NIR\": 0.8, \"Kappa Standard Error\": 0.15128176601206766, \"Mutual Information\": 0.10087710767390168, \"Overall J\": [0.6029411764705883, 0.15073529411764708], \"Krippendorff Alpha\": -0.09740259740259723, \"KL Divergence\": \"None\", \"Overall CEN\": 0.3648028121279775, \"RR\": 5.0, \"Gwet AC1\": 0.19504643962848295}, \"Sample-Weight\": null, \"Transpose\": false, \"Class-Stat\": {\"DP\": {\"200\": 0.1407391082701595, \"500\": 0.49789960499474867, \"100\": \"None\", \"600\": \"None\"}, \"Y\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"F0.5\": {\"200\": 0.6818181818181818, \"500\": 0.45454545454545453, \"100\": 0.0, \"600\": 0.0}, \"MCEN\": {\"200\": 0.3739448088748241, \"500\": 0.5802792108518123, \"100\": 0.3349590631259315, \"600\": 0.0}, \"MCC\": {\"200\": 0.10482848367219183, \"500\": 0.32673201960653564, \"100\": \"None\", \"600\": \"None\"}, \"QI\": {\"200\": \"Weak\", \"500\": \"Strong\", \"100\": \"None\", \"600\": \"None\"}, \"dInd\": {\"200\": 0.673145600891813, \"500\": 0.6692567908186672, \"100\": \"None\", \"600\": 1.0}, \"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}, \"IS\": {\"200\": 0.09953567355091428, \"500\": 1.736965594166206, \"100\": \"None\", \"600\": \"None\"}, \"ICSI\": {\"200\": 0.2321428571428572, \"500\": -0.16666666666666674, \"100\": \"None\", \"600\": \"None\"}, \"AGM\": {\"200\": 0.5669417382415922, \"500\": 0.7351956938438939, \"100\": \"None\", \"600\": 0}, \"NPV\": {\"200\": 0.23076923076923078, \"500\": 0.8888888888888888, \"100\": 1.0, \"600\": 0.95}, \"F1\": {\"200\": 0.5217391304347826, \"500\": 0.4, \"100\": 0.0, \"600\": 0.0}, \"RACC\": {\"200\": 0.28, \"500\": 0.015, \"100\": 0.0, \"600\": 0.0}, \"RACCU\": {\"200\": 0.33062499999999995, \"500\": 0.015625, \"100\": 0.07562500000000001, \"600\": 0.0006250000000000001}, \"PLR\": {\"200\": 1.5, \"500\": 5.666666666666665, \"100\": \"None\", \"600\": \"None\"}, \"AUCI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"Poor\"}, \"G\": {\"200\": 0.5669467095138409, \"500\": 0.408248290463863, \"100\": \"None\", \"600\": \"None\"}, \"Q\": {\"200\": 0.28571428571428575, \"500\": 0.7777777777777778, \"100\": \"None\", \"600\": \"None\"}, \"BCD\": {\"200\": 0.225, \"500\": 0.025, \"100\": 0.275, \"600\": 0.025}, \"ACC\": {\"200\": 0.45, \"500\": 0.85, \"100\": 0.45, \"600\": 0.95}, \"AM\": {\"200\": -9, \"500\": -1, \"100\": 11, \"600\": -1}, \"FN\": {\"200\": 10, \"100\": 0, \"500\": 2, \"600\": 1}, \"IBA\": {\"200\": 0.17578125, \"500\": 0.1230296039984621, \"100\": \"None\", \"600\": 0.0}, \"PRE\": {\"200\": 0.8, \"500\": 0.15, \"100\": 0.0, \"600\": 0.05}, \"TNR\": {\"200\": 0.75, \"500\": 0.9411764705882353, \"100\": 0.45, \"600\": 1.0}, \"TOP\": {\"200\": 7, \"500\": 2, \"100\": 11, \"600\": 0}, \"F2\": {\"200\": 0.4225352112676056, \"500\": 0.35714285714285715, \"100\": 0.0, \"600\": 0.0}, \"AUPR\": {\"200\": 0.6160714285714286, \"500\": 0.41666666666666663, \"100\": \"None\", \"600\": \"None\"}, \"AUC\": {\"200\": 0.5625, \"500\": 0.6372549019607843, \"100\": \"None\", \"600\": 0.5}, \"NLR\": {\"200\": 0.8333333333333334, \"500\": 0.7083333333333334, \"100\": \"None\", \"600\": 1.0}, \"TON\": {\"200\": 13, \"500\": 18, \"100\": 9, \"600\": 20}, \"TP\": {\"200\": 6, \"100\": 0, \"500\": 1, \"600\": 0}, \"MCCI\": {\"200\": \"Negligible\", \"500\": \"Weak\", \"100\": \"None\", \"600\": \"None\"}, \"FPR\": {\"200\": 0.25, \"500\": 0.05882352941176472, \"100\": 0.55, \"600\": 0.0}, \"DPI\": {\"200\": \"Poor\", \"500\": \"Poor\", \"100\": \"None\", \"600\": \"None\"}, \"AGF\": {\"200\": 0.33642097801219245, \"500\": 0.5665926996700735, \"100\": 0.0, \"600\": 0.0}, \"FNR\": {\"200\": 0.625, \"500\": 0.6666666666666667, \"100\": \"None\", \"600\": 1.0}, \"GI\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}, \"POP\": {\"200\": 20, \"500\": 20, \"100\": 20, \"600\": 20}, \"TPR\": {\"200\": 0.375, \"500\": 0.3333333333333333, \"100\": \"None\", \"600\": 0.0}, \"PLRI\": {\"200\": \"Poor\", \"500\": \"Fair\", \"100\": \"None\", \"600\": \"None\"}, \"N\": {\"200\": 4, \"500\": 17, \"100\": 20, \"600\": 19}, \"sInd\": {\"200\": 0.5240141808835057, \"500\": 0.5267639848569737, \"100\": \"None\", \"600\": 0.29289321881345254}, \"TN\": {\"200\": 3, \"100\": 9, \"500\": 16, \"600\": 19}, \"FP\": {\"200\": 1, \"100\": 11, \"500\": 1, \"600\": 0}, \"PPV\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": 0.0, \"600\": \"None\"}, \"DOR\": {\"200\": 1.7999999999999998, \"500\": 7.999999999999997, \"100\": \"None\", \"600\": \"None\"}, \"MK\": {\"200\": 0.08791208791208782, \"500\": 0.38888888888888884, \"100\": 0.0, \"600\": \"None\"}, \"OP\": {\"200\": 0.1166666666666667, \"500\": 0.373076923076923, \"100\": \"None\", \"600\": -0.050000000000000044}, \"OC\": {\"200\": 0.8571428571428571, \"500\": 0.5, \"100\": \"None\", \"600\": \"None\"}, \"FDR\": {\"200\": 0.1428571428571429, \"500\": 0.5, \"100\": 1.0, \"600\": \"None\"}, \"J\": {\"200\": 0.35294117647058826, \"500\": 0.25, \"100\": 0.0, \"600\": 0.0}, \"CEN\": {\"200\": 0.3570795472009597, \"500\": 0.5389466410223563, \"100\": 0.3349590631259315, \"600\": 0.0}, \"FOR\": {\"200\": 0.7692307692307692, \"500\": 0.11111111111111116, \"100\": 0.0, \"600\": 0.050000000000000044}, \"LS\": {\"200\": 1.0714285714285714, \"500\": 3.3333333333333335, \"100\": \"None\", \"600\": \"None\"}, \"ERR\": {\"200\": 0.55, \"500\": 0.15000000000000002, \"100\": 0.55, \"600\": 0.050000000000000044}, \"NLRI\": {\"200\": \"Negligible\", \"500\": \"Negligible\", \"100\": \"None\", \"600\": \"Negligible\"}, \"BM\": {\"200\": 0.125, \"500\": 0.27450980392156854, \"100\": \"None\", \"600\": 0.0}}, \"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]]]], \"Imbalanced\": true, \"Predict-Vector\": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], \"Actual-Vector\": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], \"Digit\": 5}\n" + "{\"Overall-Stat\": {\"Conditional Entropy\": 1.235789374242786, \"NIR\": 0.8, \"FPR Micro\": 0.21666666666666667, \"TNR Micro\": 0.7833333333333333, \"Kappa Standard Error\": 0.15128176601206766, \"Cross Entropy\": 1.709947752496911, \"SOA6(Matthews)\": \"Negligible\", \"Overall RACCU\": 0.42249999999999993, \"AUNU\": \"None\", \"TPR Micro\": 0.35, \"Joint Entropy\": 2.119973094021975, \"Phi-Squared\": \"None\", \"Kappa Unbiased\": -0.12554112554112543, \"Zero-one Loss\": 13, 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"500": 0.38888888888888884, "100": 0.0, "600": "None"}, "OP": {"200": 0.1166666666666667, "500": 0.373076923076923, "100": "None", "600": -0.050000000000000044}, "OC": {"200": 0.8571428571428571, "500": 0.5, "100": "None", "600": "None"}, "FDR": {"200": 0.1428571428571429, "500": 0.5, "100": 1.0, "600": "None"}, "J": {"200": 0.35294117647058826, "500": 0.25, "100": 0.0, "600": 0.0}, "CEN": {"200": 0.3570795472009597, "500": 0.5389466410223563, "100": 0.3349590631259315, "600": 0.0}, "FOR": {"200": 0.7692307692307692, "500": 0.11111111111111116, "100": 0.0, "600": 0.050000000000000044}, "LS": {"200": 1.0714285714285714, "500": 3.3333333333333335, "100": "None", "600": "None"}, "ERR": {"200": 0.55, "500": 0.15000000000000002, "100": 0.55, "600": 0.050000000000000044}, "NLRI": {"200": "Negligible", "500": "Negligible", "100": "None", "600": "Negligible"}, "BM": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}}, "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]]]], "Imbalanced": true, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200], "Actual-Vector": [600, 200, 200, 200, 200, 200, 200, 200, 500, 500, 500, 200, 200, 200, 200, 200, 200, 200, 200, 200], "Digit": 5} \ No newline at end of file +{"Overall-Stat": {"Conditional Entropy": 1.235789374242786, "NIR": 0.8, "FPR Micro": 0.21666666666666667, "TNR Micro": 0.7833333333333333, "Kappa Standard Error": 0.15128176601206766, "Cross Entropy": 1.709947752496911, "SOA6(Matthews)": "Negligible", "Overall RACCU": 0.42249999999999993, "AUNU": "None", "TPR Micro": 0.35, "Joint Entropy": 2.119973094021975, "Phi-Squared": "None", "Kappa Unbiased": -0.12554112554112543, "Zero-one Loss": 13, "Response Entropy": 1.3366664819166876, "KL Divergence": "None", "PPV Micro": 0.35, "Reference Entropy": 0.8841837197791889, "Bangdiwala B": 0.3135593220338983, "ARI": 0.02298247455136956, "RCI": 0.11409066398451011, "AUNP": "None", "PPV Macro": "None", "P-Value": 0.9999981549942787, "Mutual Information": 0.10087710767390168, "Overall J": [0.6029411764705883, 0.15073529411764708], "Lambda A": 0.0, "Kappa": 0.07801418439716304, "Standard Error": 0.1066536450385077, "Kappa No Prevalence": -0.30000000000000004, "SOA2(Fleiss)": "Poor", "SOA5(Cramer)": "None", "SOA4(Cicchetti)": "Poor", "CSI": "None", "Gwet AC1": 0.19504643962848295, "FNR Macro": "None", "SOA3(Altman)": "Poor", "Pearson C": "None", "F1 Micro": 0.35, "TPR Macro": "None", "Scott PI": -0.12554112554112543, "Chi-Squared": "None", "Chi-Squared DF": 9, "95% CI": [0.14095885572452488, 0.559041144275475], "F1 Macro": 0.23043478260869565, "Krippendorff Alpha": -0.09740259740259723, "Overall RACC": 0.29500000000000004, "Kappa 95% CI": [-0.21849807698648957, 0.3745264457808156], "CBA": 0.17708333333333331, "FPR Macro": 0.2147058823529412, "Hamming Loss": 0.65, "SOA1(Landis & Koch)": "Slight", "Lambda B": 0.0, "Cramer V": "None", "Overall MCEN": 0.3746281299595305, "Overall ACC": 0.35, "RR": 5.0, "TNR Macro": 0.7852941176470588, "ACC Macro": 0.675, "Bennett S": 0.1333333333333333, "FNR Micro": 0.65, "Overall CEN": 0.3648028121279775, "Overall MCC": 0.1264200803632855}, "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], "Transpose": false, "Imbalanced": true, "Digit": 5, "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": {"PLR": {"200": 1.5, "500": 5.666666666666665, "100": "None", "600": "None"}, "FP": {"200": 1, "100": 11, "500": 1, "600": 0}, "AGF": {"200": 0.33642097801219245, "500": 0.5665926996700735, "100": 0.0, "600": 0.0}, "TP": {"200": 6, "100": 0, "500": 1, "600": 0}, "AUC": {"200": 0.5625, "500": 0.6372549019607843, "100": "None", "600": 0.5}, "F1": {"200": 0.5217391304347826, "500": 0.4, "100": 0.0, "600": 0.0}, "GM": {"200": 0.5303300858899106, "500": 0.5601120336112039, "100": "None", "600": 0.0}, "DP": {"200": 0.1407391082701595, "500": 0.49789960499474867, "100": "None", "600": "None"}, "TNR": {"200": 0.75, "500": 0.9411764705882353, "100": 0.45, "600": 1.0}, "RACC": {"200": 0.28, "500": 0.015, "100": 0.0, "600": 0.0}, "ICSI": {"200": 0.2321428571428572, "500": -0.16666666666666674, "100": "None", "600": "None"}, "NLRI": {"200": "Negligible", "500": "Negligible", "100": "None", "600": "Negligible"}, "Q": {"200": 0.28571428571428575, "500": 0.7777777777777778, "100": "None", "600": "None"}, "RACCU": {"200": 0.33062499999999995, "500": 0.015625, "100": 0.07562500000000001, "600": 0.0006250000000000001}, "dInd": {"200": 0.673145600891813, "500": 0.6692567908186672, "100": "None", "600": 1.0}, "MCC": {"200": 0.10482848367219183, "500": 0.32673201960653564, "100": "None", "600": "None"}, "BM": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "BCD": {"200": 0.225, "500": 0.025, "100": 0.275, "600": 0.025}, "MCCI": {"200": "Negligible", "500": "Weak", "100": "None", "600": "None"}, "TN": {"200": 3, "100": 9, "500": 16, "600": 19}, "FNR": {"200": 0.625, "500": 0.6666666666666667, "100": "None", "600": 1.0}, "OOC": {"200": 0.5669467095138409, "500": 0.4082482904638631, "100": "None", "600": "None"}, "LS": {"200": 1.0714285714285714, "500": 3.3333333333333335, "100": "None", "600": "None"}, "F2": {"200": 0.4225352112676056, "500": 0.35714285714285715, "100": 0.0, "600": 0.0}, "FPR": {"200": 0.25, "500": 0.05882352941176472, "100": 0.55, "600": 0.0}, "ACC": {"200": 0.45, "500": 0.85, "100": 0.45, "600": 0.95}, "FN": {"200": 10, "100": 0, "500": 2, "600": 1}, "J": {"200": 0.35294117647058826, "500": 0.25, "100": 0.0, "600": 0.0}, "sInd": {"200": 0.5240141808835057, "500": 0.5267639848569737, "100": "None", "600": 0.29289321881345254}, "IBA": {"200": 0.17578125, "500": 0.1230296039984621, "100": "None", "600": 0.0}, "P": {"200": 16, "500": 3, "100": 0, "600": 1}, "TON": {"200": 13, "500": 18, "100": 9, "600": 20}, "AUCI": {"200": "Poor", "500": "Fair", "100": "None", "600": "Poor"}, "PLRI": {"200": "Poor", "500": "Fair", "100": "None", "600": "None"}, "TOP": {"200": 7, "500": 2, "100": 11, "600": 0}, "MK": {"200": 0.08791208791208782, "500": 0.38888888888888884, "100": 0.0, "600": "None"}, "ERR": {"200": 0.55, "500": 0.15000000000000002, "100": 0.55, "600": 0.050000000000000044}, "QI": {"200": "Weak", "500": "Strong", "100": "None", "600": "None"}, "OC": {"200": 0.8571428571428571, "500": 0.5, "100": "None", "600": "None"}, "PPV": {"200": 0.8571428571428571, "500": 0.5, "100": 0.0, "600": "None"}, "Y": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "IS": {"200": 0.09953567355091428, "500": 1.736965594166206, "100": "None", "600": "None"}, "G": {"200": 0.5669467095138409, "500": 0.408248290463863, "100": "None", "600": "None"}, "AM": {"200": -9, "500": -1, "100": 11, "600": -1}, "AUPR": {"200": 0.6160714285714286, "500": 0.41666666666666663, "100": "None", "600": "None"}, "DOR": {"200": 1.7999999999999998, "500": 7.999999999999997, "100": "None", "600": "None"}, "AGM": {"200": 0.5669417382415922, "500": 0.7351956938438939, "100": "None", "600": 0}, "POP": {"200": 20, "500": 20, "100": 20, "600": 20}, "FDR": {"200": 0.1428571428571429, "500": 0.5, "100": 1.0, "600": "None"}, "NPV": {"200": 0.23076923076923078, "500": 0.8888888888888888, "100": 1.0, "600": 0.95}, "N": {"200": 4, "500": 17, "100": 20, "600": 19}, "FOR": {"200": 0.7692307692307692, "500": 0.11111111111111116, "100": 0.0, "600": 0.050000000000000044}, "MCEN": {"200": 0.3739448088748241, "500": 0.5802792108518123, "100": 0.3349590631259315, "600": 0.0}, "NLR": {"200": 0.8333333333333334, "500": 0.7083333333333334, "100": "None", "600": 1.0}, "OP": {"200": 0.1166666666666667, "500": 0.373076923076923, "100": "None", "600": -0.050000000000000044}, "CEN": {"200": 0.3570795472009597, "500": 0.5389466410223563, "100": 0.3349590631259315, "600": 0.0}, "GI": {"200": 0.125, "500": 0.27450980392156854, "100": "None", "600": 0.0}, "TPR": {"200": 0.375, "500": 0.3333333333333333, "100": "None", "600": 0.0}, "F0.5": {"200": 0.6818181818181818, "500": 0.45454545454545453, "100": 0.0, "600": 0.0}, "DPI": {"200": "Poor", "500": "Poor", "100": "None", "600": "None"}, "PRE": {"200": 0.8, "500": 0.15, "100": 0.0, "600": 0.05}}, "Prob-Vector": null, "Predict-Vector": [100, 200, 200, 100, 100, 200, 200, 200, 100, 200, 500, 100, 100, 100, 100, 100, 100, 100, 500, 200]} \ No newline at end of file diff --git a/Document/Example5.ipynb b/Document/Example5.ipynb index 2a720a1a..834769c3 100644 --- a/Document/Example5.ipynb +++ b/Document/Example5.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Please cite us if you use the software

" + "

Please cite us if you use the software

" ] }, { diff --git a/Document/Example6.ipynb b/Document/Example6.ipynb index f614493d..2e0e3ca9 100644 --- a/Document/Example6.ipynb +++ b/Document/Example6.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Please cite us if you use the software

" + "

Please cite us if you use the software

" ] }, { @@ -261,11 +261,11 @@ "Class4 0.0 0.0 2e-05 0.99998 \n", "\n", "\n", - "ACC: {'Class4': 0.9999500199920032, 'Class3': 0.9999250299880048, 'Class2': 0.9999500199920032, 'Class1': 0.9999750099960016}\n", - "MCC: {'Class4': 0.9333083339583177, 'Class3': 0.7302602381427055, 'Class2': 0.7999750068731099, 'Class1': 0.8944160139432883}\n", - "CEN: {'Class4': 0.0001575200922489127, 'Class3': 0.3649884090288471, 'Class2': 0.25701944178769376, 'Class1': 0.13625493172565745}\n", - "MCEN: {'Class4': 0.00029569133318617423, 'Class3': 0.4654427710721536, 'Class2': 0.3333333333333333, 'Class1': 0.17964888034078544}\n", - "DP: {'Class4': 3.1691421556058055, 'Class3': 2.7032690544190636, 'Class2': 2.869241573973406, 'Class1': 'None'}\n", + "ACC: {'Class3': 0.9999250299880048, 'Class4': 0.9999500199920032, 'Class2': 0.9999500199920032, 'Class1': 0.9999750099960016}\n", + "MCC: {'Class3': 0.7302602381427055, 'Class4': 0.9333083339583177, 'Class2': 0.7999750068731099, 'Class1': 0.8944160139432883}\n", + "CEN: {'Class3': 0.3649884090288471, 'Class4': 0.0001575200922489127, 'Class2': 0.25701944178769376, 'Class1': 0.13625493172565745}\n", + "MCEN: {'Class3': 0.4654427710721536, 'Class4': 0.00029569133318617423, 'Class2': 0.3333333333333333, 'Class1': 0.17964888034078544}\n", + "DP: {'Class3': 2.7032690544190636, 'Class4': 3.1691421556058055, 'Class2': 2.869241573973406, 'Class1': 'None'}\n", "Kappa: 0.8666333383326446\n", "RCI: 0.8711441699127427\n", "SOA1: Almost Perfect\n" @@ -320,11 +320,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class4': 0.625, 'Class3': 0.625, 'Class2': 0.625, 'Class1': 0.625}\n", - "MCC: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", - "CEN: {'Class4': 0.8704188162777186, 'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.8704188162777186}\n", - "MCEN: {'Class4': 0.9308855421443073, 'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.9308855421443073}\n", - "DP: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "ACC: {'Class3': 0.625, 'Class4': 0.625, 'Class2': 0.625, 'Class1': 0.625}\n", + "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.8704188162777186}\n", + "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.9308855421443073}\n", + "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", "Kappa: 0.0\n", "RCI: 0.0\n", "SOA1: Slight\n" @@ -379,11 +379,11 @@ "Class4 0.76923 0.07692 0.07692 0.07692 \n", "\n", "\n", - "ACC: {'Class4': 0.4, 'Class3': 0.76, 'Class2': 0.76, 'Class1': 0.4}\n", - "MCC: {'Class4': -0.2358640882624316, 'Class3': 0.10714285714285714, 'Class2': 0.10714285714285714, 'Class1': -0.2358640882624316}\n", - "CEN: {'Class4': 0.6392779429225796, 'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.6392779429225794}\n", - "MCEN: {'Class4': 0.647512271542988, 'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.647512271542988}\n", - "DP: {'Class4': -0.3319330699964992, 'Class3': 0.16596653499824943, 'Class2': 0.16596653499824943, 'Class1': -0.33193306999649924}\n", + "ACC: {'Class3': 0.76, 'Class4': 0.4, 'Class2': 0.76, 'Class1': 0.4}\n", + "MCC: {'Class3': 0.10714285714285714, 'Class4': -0.2358640882624316, 'Class2': 0.10714285714285714, 'Class1': -0.2358640882624316}\n", + "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.6392779429225796, 'Class2': 0.8704188162777186, 'Class1': 0.6392779429225794}\n", + "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.647512271542988, 'Class2': 0.9308855421443073, 'Class1': 0.647512271542988}\n", + "DP: {'Class3': 0.16596653499824943, 'Class4': -0.3319330699964992, 'Class2': 0.16596653499824943, 'Class1': -0.33193306999649924}\n", "Kappa: -0.07361963190184047\n", "RCI: 0.11603030564493627\n", "SOA1: Poor\n" @@ -438,11 +438,11 @@ "Class4 0.76923 0.07692 0.07692 0.07692 \n", "\n", "\n", - "ACC: {'Class4': 0.000998502246630055, 'Class3': 0.999400898652022, 'Class2': 0.999400898652022, 'Class1': 0.000998502246630055}\n", - "MCC: {'Class4': -0.43266656861311537, 'Class3': 0.24970032963739885, 'Class2': 0.24970032963739885, 'Class1': -0.43266656861311537}\n", - "CEN: {'Class4': 0.0029588592520426657, 'Class3': 0.8704188162777186, 'Class2': 0.8704188162777186, 'Class1': 0.0029588592520426657}\n", - "MCEN: {'Class4': 0.002903385725603509, 'Class3': 0.9308855421443073, 'Class2': 0.9308855421443073, 'Class1': 0.002903385725603509}\n", - "DP: {'Class4': -1.9423127303715728, 'Class3': 1.6794055876913858, 'Class2': 1.6794055876913858, 'Class1': -1.9423127303715728}\n", + "ACC: {'Class3': 0.999400898652022, 'Class4': 0.000998502246630055, 'Class2': 0.999400898652022, 'Class1': 0.000998502246630055}\n", + "MCC: {'Class3': 0.24970032963739885, 'Class4': -0.43266656861311537, 'Class2': 0.24970032963739885, 'Class1': -0.43266656861311537}\n", + "CEN: {'Class3': 0.8704188162777186, 'Class4': 0.0029588592520426657, 'Class2': 0.8704188162777186, 'Class1': 0.0029588592520426657}\n", + "MCEN: {'Class3': 0.9308855421443073, 'Class4': 0.002903385725603509, 'Class2': 0.9308855421443073, 'Class1': 0.002903385725603509}\n", + "DP: {'Class3': 1.6794055876913858, 'Class4': -1.9423127303715728, 'Class2': 1.6794055876913858, 'Class1': -1.9423127303715728}\n", "Kappa: -0.0003990813465900262\n", "RCI: 0.5536610475678804\n", "SOA1: Poor\n" @@ -497,11 +497,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class4': 0.36538461538461536, 'Class3': 0.7115384615384616, 'Class2': 0.7115384615384616, 'Class1': 0.7115384615384616}\n", - "MCC: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", - "CEN: {'Class4': 0.6522742127953861, 'Class3': 0.6392779429225794, 'Class2': 0.6392779429225794, 'Class1': 0.6392779429225794}\n", - "MCEN: {'Class4': 0.7144082229288313, 'Class3': 0.647512271542988, 'Class2': 0.647512271542988, 'Class1': 0.647512271542988}\n", - "DP: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "ACC: {'Class3': 0.7115384615384616, 'Class4': 0.36538461538461536, 'Class2': 0.7115384615384616, 'Class1': 0.7115384615384616}\n", + "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class3': 0.6392779429225794, 'Class4': 0.6522742127953861, 'Class2': 0.6392779429225794, 'Class1': 0.6392779429225794}\n", + "MCEN: {'Class3': 0.647512271542988, 'Class4': 0.7144082229288313, 'Class2': 0.647512271542988, 'Class1': 0.647512271542988}\n", + "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", "Kappa: 0.0\n", "RCI: 0.0\n", "SOA1: Slight\n" @@ -556,11 +556,11 @@ "Class4 0.25 0.25 0.25 0.25 \n", "\n", "\n", - "ACC: {'Class4': 0.25014995501349596, 'Class3': 0.7499500149955014, 'Class2': 0.7499500149955014, 'Class1': 0.7499500149955014}\n", - "MCC: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", - "CEN: {'Class4': 0.539296694603886, 'Class3': 0.0029588592520426657, 'Class2': 0.0029588592520426657, 'Class1': 0.0029588592520426657}\n", - "MCEN: {'Class4': 0.580710610324597, 'Class3': 0.002903385725603509, 'Class2': 0.002903385725603509, 'Class1': 0.002903385725603509}\n", - "DP: {'Class4': 0.0, 'Class3': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "ACC: {'Class3': 0.7499500149955014, 'Class4': 0.25014995501349596, 'Class2': 0.7499500149955014, 'Class1': 0.7499500149955014}\n", + "MCC: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", + "CEN: {'Class3': 0.0029588592520426657, 'Class4': 0.539296694603886, 'Class2': 0.0029588592520426657, 'Class1': 0.0029588592520426657}\n", + "MCEN: {'Class3': 0.002903385725603509, 'Class4': 0.580710610324597, 'Class2': 0.002903385725603509, 'Class1': 0.002903385725603509}\n", + "DP: {'Class3': 0.0, 'Class4': 0.0, 'Class2': 0.0, 'Class1': 0.0}\n", "Kappa: 0.0\n", "RCI: 0.0\n", "SOA1: Slight\n" diff --git a/Document/Example7.ipynb b/Document/Example7.ipynb index 67078cb7..8a75588e 100644 --- a/Document/Example7.ipynb +++ b/Document/Example7.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Please cite us if you use the software

" + "

Please cite us if you use the software

" ] }, { diff --git a/Document/Example8.ipynb b/Document/Example8.ipynb index 71ba4447..af3f7bc6 100644 --- a/Document/Example8.ipynb +++ b/Document/Example8.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "

Please cite us if you use the software

" + "

Please cite us if you use the software

" ] }, { diff --git a/Document/README.md b/Document/README.md index e2b077ec..c91b74b9 100644 --- a/Document/README.md +++ b/Document/README.md @@ -8,50 +8,50 @@ ## Document - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Document.ipynb) -- [HTML](http://www.pycm.ir/doc/) +- [HTML](http://www.pycm.io/doc/) ## Example-1 (Comparison of three different classifiers) - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example1.ipynb) -- [HTML](http://www.pycm.ir/doc/Example1.html) +- [HTML](http://www.pycm.io/doc/Example1.html) ## Example-2 (How to plot via matplotlib) - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example2.ipynb) -- [HTML](http://www.pycm.ir/doc/Example2.html) +- [HTML](http://www.pycm.io/doc/Example2.html) ## Example-3 (Activation threshold) - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example3.ipynb) -- [HTML](http://www.pycm.ir/doc/Example3.html) +- [HTML](http://www.pycm.io/doc/Example3.html) ## Example-4 (File) - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example4.ipynb) -- [HTML](http://www.pycm.ir/doc/Example4.html) +- [HTML](http://www.pycm.io/doc/Example4.html) ## Example-5 (Sample weights) - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example5.ipynb) -- [HTML](http://www.pycm.ir/doc/Example5.html) +- [HTML](http://www.pycm.io/doc/Example5.html) ## Example-6 (Unbalanced data) - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example6.ipynb) -- [HTML](http://www.pycm.ir/doc/Example6.html) +- [HTML](http://www.pycm.io/doc/Example6.html) ## Example-7 (How to plot via seaborn+pandas) - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example7.ipynb) -- [HTML](http://www.pycm.ir/doc/Example7.html) +- [HTML](http://www.pycm.io/doc/Example7.html) ## Example-8 (Confidence interval) - [Jupyter Notebook](https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Example8.ipynb) -- [HTML](http://www.pycm.ir/doc/Example8.html) +- [HTML](http://www.pycm.io/doc/Example8.html) ## Try PyCM in your browser interactively! diff --git a/Otherfiles/meta.yaml b/Otherfiles/meta.yaml index 24f0ee28..0a6f06b6 100644 --- a/Otherfiles/meta.yaml +++ b/Otherfiles/meta.yaml @@ -28,7 +28,7 @@ about: description: | PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and accurate evaluation of a large variety of classifiers. - Website: https://www.pycm.ir + Website: https://www.pycm.io Repo: https://github.com/sepandhaghighi/pycm extra: diff --git a/Otherfiles/test.html b/Otherfiles/test.html index af8f9502..7bfc5ea1 100644 --- a/Otherfiles/test.html +++ b/Otherfiles/test.html @@ -3,15 +3,15 @@ PyCM Report - + - - - - + + + + - +

PyCM Report

@@ -60,255 +60,255 @@

Confusion Matrix :

Overall Statistics :

- + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - + - +
95% CI95% CI (0.30439,0.86228)
ACC MacroACC Macro 0.72222
ARIARI 0.09206
AUNPAUNP 0.68571
AUNUAUNU 0.67857
Bangdiwala BBangdiwala B 0.37255
Bennett SBennett S 0.375
CBACBA 0.47778
CSICSI 0.17778
Chi-SquaredChi-Squared 6.6
Chi-Squared DFChi-Squared DF 4
Conditional EntropyConditional Entropy 0.97579
Cramer VCramer V 0.5244
Cross EntropyCross Entropy 1.58333
F1 MacroF1 Macro 0.56515
F1 MicroF1 Micro 0.58333
FNR MacroFNR Macro 0.43333
FNR MicroFNR Micro 0.41667
FPR MacroFPR Macro 0.20952
FPR MicroFPR Micro 0.20833
Gwet AC1Gwet AC1 0.38931
Hamming LossHamming Loss 0.41667
Joint EntropyJoint Entropy 2.45915
KL DivergenceKL Divergence 0.09998
KappaKappa 0.35484
Kappa 95% CIKappa 95% CI (-0.07708,0.78675)
Kappa No PrevalenceKappa No Prevalence 0.16667
Kappa Standard ErrorKappa Standard Error 0.22036
Kappa UnbiasedKappa Unbiased 0.34426
Krippendorff AlphaKrippendorff Alpha 0.37158
Lambda ALambda A 0.42857
Lambda BLambda B 0.16667
Mutual InformationMutual Information 0.52421
NIRNIR 0.41667
Overall ACCOverall ACC 0.58333
Overall CENOverall CEN 0.46381
Overall JOverall J (1.225,0.40833)
Overall MCCOverall MCC 0.36667
Overall MCENOverall MCEN 0.51894
Overall RACCOverall RACC 0.35417
Overall RACCUOverall RACCU 0.36458
P-ValueP-Value 0.18926
PPV MacroPPV Macro 0.61111
PPV MicroPPV Micro 0.58333
Pearson CPearson C 0.59568
Phi-SquaredPhi-Squared 0.55
RCIRCI 0.35339
RRRR 4.0
Reference EntropyReference Entropy 1.48336
Response EntropyResponse Entropy 1.5
SOA1(Landis & Koch)SOA1(Landis & Koch) Fair
SOA2(Fleiss)SOA2(Fleiss) Poor
SOA3(Altman)SOA3(Altman) Fair
SOA4(Cicchetti)SOA4(Cicchetti) Poor
SOA5(Cramer)SOA5(Cramer) Relatively Strong
SOA6(Matthews)SOA6(Matthews) Weak
Scott PIScott PI 0.34426
Standard ErrorStandard Error 0.14232
TNR MacroTNR Macro 0.79048
TNR MicroTNR Micro 0.79167
TPR MacroTPR Macro 0.56667
TPR MicroTPR Micro 0.58333
Zero-one LossZero-one Loss 5
@@ -322,433 +322,433 @@

Class Statistics :

Description -ACC +ACC 0.83333 0.75 0.58333 Accuracy -AGF +AGF 0.72859 0.62869 0.61009 Adjusted F-score -AGM +AGM 0.85764 0.70861 0.58034 Adjusted geometric mean -AM +AM -2 1 1 Difference between automatic and manual classification -AUC +AUC 0.8 0.65 0.58571 Area under the ROC curve -AUCI +AUCI Very Good Fair Poor AUC value interpretation -AUPR +AUPR 0.8 0.41667 0.55 Area under the PR curve -BCD +BCD 0.08333 0.04167 0.04167 Bray-Curtis dissimilarity -BM +BM 0.6 0.3 0.17143 Informedness or bookmaker informedness -CEN +CEN 0.25 0.49658 0.60442 Confusion entropy -DOR +DOR None 4.0 2.0 Diagnostic odds ratio -DP +DP None 0.33193 0.16597 Discriminant power -DPI +DPI None Poor Poor Discriminant power interpretation -ERR +ERR 0.16667 0.25 0.41667 Error rate -F0.5 +F0.5 0.88235 0.35714 0.51724 F0.5 score -F1 +F1 0.75 0.4 0.54545 F1 score - harmonic mean of precision and sensitivity -F2 +F2 0.65217 0.45455 0.57692 F2 score -FDR +FDR 0.0 0.66667 0.5 False discovery rate -FN +FN 2 1 2 False negative/miss/type 2 error -FNR +FNR 0.4 0.5 0.4 Miss rate or false negative rate -FOR +FOR 0.22222 0.11111 0.33333 False omission rate -FP +FP 0 2 3 False positive/type 1 error/false alarm -FPR +FPR 0.0 0.2 0.42857 Fall-out or false positive rate -G +G 0.7746 0.40825 0.54772 G-measure geometric mean of precision and sensitivity -GI +GI 0.6 0.3 0.17143 Gini index -GM +GM 0.7746 0.63246 0.58554 G-mean geometric mean of specificity and sensitivity -IBA +IBA 0.36 0.28 0.35265 Index of balanced accuracy -ICSI +ICSI 0.6 -0.16667 0.1 Individual classification success index -IS +IS 1.26303 1.0 0.26303 Information score -J +J 0.6 0.25 0.375 Jaccard index -LS +LS 2.4 2.0 1.2 Lift score -MCC +MCC 0.68313 0.2582 0.16903 Matthews correlation coefficient -MCCI +MCCI Moderate Negligible Negligible Matthews correlation coefficient interpretation -MCEN +MCEN 0.26439 0.5 0.6875 Modified confusion entropy -MK +MK 0.77778 0.22222 0.16667 Markedness -N +N 7 10 7 Condition negative -NLR +NLR 0.4 0.625 0.7 Negative likelihood ratio -NLRI +NLRI Poor Negligible Negligible Negative likelihood ratio interpretation -NPV +NPV 0.77778 0.88889 0.66667 Negative predictive value -OC +OC 1.0 0.5 0.6 Overlap coefficient -OOC +OOC 0.7746 0.40825 0.54772 Otsuka-Ochiai coefficient -OP +OP 0.58333 0.51923 0.55894 Optimized precision -P +P 5 2 5 Condition positive or support -PLR +PLR None 2.5 1.4 Positive likelihood ratio -PLRI +PLRI None Poor Poor Positive likelihood ratio interpretation -POP +POP 12 12 12 Population -PPV +PPV 1.0 0.33333 0.5 Precision or positive predictive value -PRE +PRE 0.41667 0.16667 0.41667 Prevalence -Q +Q None 0.6 0.33333 Yule Q - coefficient of colligation -QI +QI None Moderate Weak Yule Q interpretation -RACC +RACC 0.10417 0.04167 0.20833 Random accuracy -RACCU +RACCU 0.11111 0.0434 0.21007 Random accuracy unbiased -TN +TN 7 8 4 True negative/correct rejection -TNR +TNR 1.0 0.8 0.57143 Specificity or true negative rate -TON +TON 9 9 6 Test outcome negative -TOP +TOP 3 3 6 Test outcome positive -TP +TP 3 1 3 True positive/hit -TPR +TPR 0.6 0.5 0.6 Sensitivity, recall, hit rate, or true positive rate -Y +Y 0.6 0.3 0.17143 Youden index -dInd +dInd 0.4 0.53852 0.58624 Distance index -sInd +sInd 0.71716 0.61921 0.58547 Similarity index -

Generated By PyCM Version 3.5

+

Generated By PyCM Version 3.5

diff --git a/README.md b/README.md index d8cba6a5..5778f255 100644 --- a/README.md +++ b/README.md @@ -533,17 +533,17 @@ Class2 0 5 ### Activation threshold `threshold` is added in `version 0.9` for real value prediction. -For more information visit [Example3](http://www.pycm.ir/doc/Example3.html "Example3") +For more information visit [Example3](http://www.pycm.io/doc/Example3.html "Example3") ### Load from file `file` is added in `version 0.9.5` in order to load saved confusion matrix with `.obj` format generated by `save_obj` method. -For more information visit [Example4](http://www.pycm.ir/doc/Example4.html "Example4") +For more information visit [Example4](http://www.pycm.io/doc/Example4.html "Example4") ### Sample weights `sample_weight` is added in `version 1.2` -For more information visit [Example5](http://www.pycm.ir/doc/Example5.html "Example5") +For more information visit [Example5](http://www.pycm.io/doc/Example5.html "Example5") ### Transpose `transpose` is added in `version 1.2` in order to transpose input matrix (only in `Direct CM` mode) @@ -740,14 +740,14 @@ PyCM can be used online in interactive Jupyter Notebooks via the Binder or Colab 1. Fill an issue and describe it. We'll check it ASAP! - Please complete the issue template 2. Discord : [https://discord.com/invite/zqpU2b3J3f](https://discord.com/invite/zqpU2b3J3f) -3. Website : [https://www.pycm.ir](https://www.pycm.ir) +3. Website : [https://www.pycm.io](https://www.pycm.io) 4. Mailing List : [https://mail.python.org/mailman3/lists/pycm.python.org/](https://mail.python.org/mailman3/lists/pycm.python.org/) -5. Email : [info@pycm.ir](mailto:info@pycm.ir "info@pycm.ir") +5. Email : [info@pycm.io](mailto:info@pycm.io "info@pycm.io") ## Outputs -1. [HTML](http://www.pycm.ir/test.html) +1. [HTML](http://www.pycm.io/test.html) 2. [CSV](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.csv) 3. [PyCM](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.pycm) 4. [OBJ](https://github.com/sepandhaghighi/pycm/blob/master/Otherfiles/test.obj) @@ -955,7 +955,7 @@ Haghighi, S., Jasemi, M., Hessabi, S. and Zolanvari, A. (2018). PyCM: Multiclass -Download [PyCM.bib](http://www.pycm.ir/PYCM.bib) +Download [PyCM.bib](http://www.pycm.io/PYCM.bib) @@ -984,6 +984,6 @@ Give a ⭐️ if this project helped you! If you do like our project and we hope that you do, can you please support us? Our project is not and is never going to be working for profit. We need the money just so we can continue doing what we do ;-) . -PyCM Donation +PyCM Donation diff --git a/Test/function_test.py b/Test/function_test.py index 3084231c..bd06a77a 100644 --- a/Test/function_test.py +++ b/Test/function_test.py @@ -38,7 +38,7 @@ Repo : https://github.com/sepandhaghighi/pycm -Webpage : https://www.pycm.ir +Webpage : https://www.pycm.io >>> online_help(param=None) Please choose one parameter : diff --git a/pycm/pycm_output.py b/pycm/pycm_output.py index 9fca33ff..206bde86 100644 --- a/pycm/pycm_output.py +++ b/pycm/pycm_output.py @@ -282,7 +282,7 @@ def pycm_help(): """ print(OVERVIEW) print("Repo : https://github.com/sepandhaghighi/pycm") - print("Webpage : https://www.pycm.ir") + print("Webpage : https://www.pycm.io") def table_print(classes, table): diff --git a/pycm/pycm_param.py b/pycm/pycm_param.py index f867e323..0be58814 100644 --- a/pycm/pycm_param.py +++ b/pycm/pycm_param.py @@ -18,9 +18,9 @@ ''' -OG_IMAGE_URL = "http://www.pycm.ir/images/logo-og.png" +OG_IMAGE_URL = "http://www.pycm.io/images/logo-og.png" -OG_DESCRIPTION = "PyCM is a multi-class confusion matrix library written in Python. http://www.pycm.ir" +OG_DESCRIPTION = "PyCM is a multi-class confusion matrix library written in Python. http://www.pycm.io" HTML_INIT_TEMPLATE = ''' @@ -30,7 +30,7 @@ - + @@ -41,7 +41,7 @@

PyCM Report

''' -HTML_END_TEMPLATE = '''

Generated By PyCM Version {0}

+HTML_END_TEMPLATE = '''

Generated By PyCM Version {0}

''' @@ -386,7 +386,7 @@ RECOMMEND_HTML_MESSAGE2 = 'Note 2 : {0}'.format( RECOMMEND_WARNING) -DOCUMENT_ADR = "http://www.pycm.ir/doc/index.html#" +DOCUMENT_ADR = "http://www.pycm.io/doc/index.html#" DOCUMENT_ADR_ALT = "https://nbviewer.jupyter.org/github/sepandhaghighi/pycm/blob/master/Document/Document.ipynb#" PARAMS_DESCRIPTION = { diff --git a/setup.py b/setup.py index a9b07041..df849e62 100644 --- a/setup.py +++ b/setup.py @@ -40,13 +40,13 @@ def read_description(): description='Multi-class confusion matrix library in Python', long_description=read_description(), long_description_content_type='text/markdown', - author='Sepand Haghighi', - author_email='info@pycm.ir', + author='PyCM Development Team', + author_email='info@pycm.io', url='https://github.com/sepandhaghighi/pycm', download_url='https://github.com/sepandhaghighi/pycm/tarball/v3.5', keywords="confusion-matrix python3 python machine_learning ML", project_urls={ - 'Webpage': 'https://www.pycm.ir', + 'Webpage': 'https://www.pycm.io', 'Source': 'https://github.com/sepandhaghighi/pycm', 'Discord': 'https://discord.com/invite/zqpU2b3J3f', },