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.

Fig1. ConfusionMatrix Block Diagram

Installation¶

⚠️ PyCM 2.4 is the last version to support Python 2.7 & Python 3.4

⚠️ Plotting capability requires Matplotlib (>= 3.0.0) or Seaborn (>= 0.9.1)

Source code¶

Download Version 3.1 or Latest Source
Run pip install -r requirements.txt or pip3 install -r requirements.txt (Need root access)
Run python3 setup.py install or python setup.py install (Need root access)

PyPI¶

Check Python Packaging User Guide
Run pip install pycm==3.1 or pip3 install pycm==3.1 (Need root access)

Conda¶

Check Conda Managing Package
conda install -c sepandhaghighi pycm (Need root access)

Easy install¶

Run easy_install --upgrade pycm (Need root access)

Docker¶

Run docker pull sepandhaghighi/pycm (Need root access)
Configuration :
- Ubuntu 16.04
- Python 3.6

MATLAB¶

Download and install MATLAB (>=8.5, 64/32 bit)
Download and install Python3.x (>=3.5, 64/32 bit)
- Select Add to PATH option
- Select Install pip option
Run pip install pycm or pip3 install pycm (Need root access)

Configure Python interpreter

>> pyversion PYTHON_EXECUTABLE_FULL_PATH

Visit MATLAB Examples

Usage¶

From vector¶

from pycm import *

y_actu = [2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
y_pred = [0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]

cm = ConfusionMatrix(y_actu, y_pred,digit=5)

Notice : `digit` (the number of digits to the right of the decimal point in a number) is new in version 0.6 (default value : 5)
Only for print and save

cm

pycm.ConfusionMatrix(classes: [0, 1, 2])

cm.actual_vector

[2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]

cm.predict_vector

[0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]

cm.classes

[0, 1, 2]

cm.class_stat

{'ACC': {0: 0.8333333333333334, 1: 0.75, 2: 0.5833333333333334},
 'AGF': {0: 0.9135962935560564, 1: 0.5399492471560389, 2: 0.5515973485146916},
 'AGM': {0: 0.837285964012303, 1: 0.6919986974962765, 2: 0.6071224016819726},
 'AM': {0: 2, 1: -1, 2: -1},
 'AUC': {0: 0.8888888888888888, 1: 0.611111111111111, 2: 0.5833333333333333},
 'AUCI': {0: 'Very Good', 1: 'Fair', 2: 'Poor'},
 'AUPR': {0: 0.8, 1: 0.41666666666666663, 2: 0.55},
 'BCD': {0: 0.08333333333333333,
  1: 0.041666666666666664,
  2: 0.041666666666666664},
 'BM': {0: 0.7777777777777777, 1: 0.2222222222222221, 2: 0.16666666666666652},
 'CEN': {0: 0.25, 1: 0.49657842846620864, 2: 0.6044162769630221},
 'DOR': {0: 'None', 1: 3.999999999999998, 2: 1.9999999999999998},
 'DP': {0: 'None', 1: 0.331933069996499, 2: 0.16596653499824957},
 'DPI': {0: 'None', 1: 'Poor', 2: 'Poor'},
 'ERR': {0: 0.16666666666666663, 1: 0.25, 2: 0.41666666666666663},
 'F0.5': {0: 0.6521739130434783,
  1: 0.45454545454545453,
  2: 0.5769230769230769},
 'F1': {0: 0.75, 1: 0.4, 2: 0.5454545454545454},
 'F2': {0: 0.8823529411764706, 1: 0.35714285714285715, 2: 0.5172413793103449},
 'FDR': {0: 0.4, 1: 0.5, 2: 0.4},
 'FN': {0: 0, 1: 2, 2: 3},
 'FNR': {0: 0.0, 1: 0.6666666666666667, 2: 0.5},
 'FOR': {0: 0.0, 1: 0.19999999999999996, 2: 0.4285714285714286},
 'FP': {0: 2, 1: 1, 2: 2},
 'FPR': {0: 0.2222222222222222,
  1: 0.11111111111111116,
  2: 0.33333333333333337},
 'G': {0: 0.7745966692414834, 1: 0.408248290463863, 2: 0.5477225575051661},
 'GI': {0: 0.7777777777777777, 1: 0.2222222222222221, 2: 0.16666666666666652},
 'GM': {0: 0.8819171036881969, 1: 0.5443310539518174, 2: 0.5773502691896257},
 'IBA': {0: 0.9506172839506174, 1: 0.1316872427983539, 2: 0.2777777777777778},
 'ICSI': {0: 0.6000000000000001,
  1: -0.16666666666666674,
  2: 0.10000000000000009},
 'IS': {0: 1.263034405833794, 1: 1.0, 2: 0.2630344058337938},
 'J': {0: 0.6, 1: 0.25, 2: 0.375},
 'LS': {0: 2.4, 1: 2.0, 2: 1.2},
 'MCC': {0: 0.6831300510639732, 1: 0.25819888974716115, 2: 0.1690308509457033},
 'MCCI': {0: 'Moderate', 1: 'Negligible', 2: 'Negligible'},
 'MCEN': {0: 0.2643856189774724, 1: 0.5, 2: 0.6875},
 'MK': {0: 0.6000000000000001, 1: 0.30000000000000004, 2: 0.17142857142857126},
 'N': {0: 9, 1: 9, 2: 6},
 'NLR': {0: 0.0, 1: 0.7500000000000001, 2: 0.75},
 'NLRI': {0: 'Good', 1: 'Negligible', 2: 'Negligible'},
 'NPV': {0: 1.0, 1: 0.8, 2: 0.5714285714285714},
 'OC': {0: 1.0, 1: 0.5, 2: 0.6},
 'OOC': {0: 0.7745966692414834, 1: 0.4082482904638631, 2: 0.5477225575051661},
 'OP': {0: 0.7083333333333334, 1: 0.2954545454545454, 2: 0.4404761904761905},
 'P': {0: 3, 1: 3, 2: 6},
 'PLR': {0: 4.5, 1: 2.9999999999999987, 2: 1.4999999999999998},
 'PLRI': {0: 'Poor', 1: 'Poor', 2: 'Poor'},
 'POP': {0: 12, 1: 12, 2: 12},
 'PPV': {0: 0.6, 1: 0.5, 2: 0.6},
 'PRE': {0: 0.25, 1: 0.25, 2: 0.5},
 'Q': {0: 'None', 1: 0.6, 2: 0.3333333333333333},
 'QI': {0: 'None', 1: 'Moderate', 2: 'Weak'},
 'RACC': {0: 0.10416666666666667,
  1: 0.041666666666666664,
  2: 0.20833333333333334},
 'RACCU': {0: 0.1111111111111111,
  1: 0.04340277777777778,
  2: 0.21006944444444442},
 'TN': {0: 7, 1: 8, 2: 4},
 'TNR': {0: 0.7777777777777778, 1: 0.8888888888888888, 2: 0.6666666666666666},
 'TON': {0: 7, 1: 10, 2: 7},
 'TOP': {0: 5, 1: 2, 2: 5},
 'TP': {0: 3, 1: 1, 2: 3},
 'TPR': {0: 1.0, 1: 0.3333333333333333, 2: 0.5},
 'Y': {0: 0.7777777777777777, 1: 0.2222222222222221, 2: 0.16666666666666652},
 'dInd': {0: 0.2222222222222222, 1: 0.6758625033664689, 2: 0.6009252125773316},
 'sInd': {0: 0.8428651597363228, 1: 0.5220930407198541, 2: 0.5750817072006014}}

Notice : `cm.statistic_result` prev versions (0.2 >)

cm.overall_stat

{'95% CI': (0.30438856248221097, 0.8622781041844558),
 'ACC Macro': 0.7222222222222223,
 'ARI': 0.09206349206349207,
 'AUNP': 0.6666666666666666,
 'AUNU': 0.6944444444444443,
 'Bangdiwala B': 0.37254901960784315,
 'Bennett S': 0.37500000000000006,
 'CBA': 0.4777777777777778,
 'CSI': 0.1777777777777778,
 'Chi-Squared': 6.6,
 'Chi-Squared DF': 4,
 'Conditional Entropy': 0.9591479170272448,
 'Cramer V': 0.5244044240850757,
 'Cross Entropy': 1.5935164295556343,
 'F1 Macro': 0.5651515151515151,
 'F1 Micro': 0.5833333333333334,
 'FNR Macro': 0.38888888888888895,
 'FNR Micro': 0.41666666666666663,
 'FPR Macro': 0.22222222222222232,
 'FPR Micro': 0.20833333333333337,
 'Gwet AC1': 0.3893129770992367,
 'Hamming Loss': 0.41666666666666663,
 'Joint Entropy': 2.4591479170272446,
 'KL Divergence': 0.09351642955563438,
 'Kappa': 0.35483870967741943,
 'Kappa 95% CI': (-0.07707577422109269, 0.7867531935759315),
 'Kappa No Prevalence': 0.16666666666666674,
 'Kappa Standard Error': 0.2203645326012817,
 'Kappa Unbiased': 0.34426229508196726,
 'Krippendorff Alpha': 0.3715846994535519,
 'Lambda A': 0.16666666666666666,
 'Lambda B': 0.42857142857142855,
 'Mutual Information': 0.5242078379544426,
 'NIR': 0.5,
 'Overall ACC': 0.5833333333333334,
 'Overall CEN': 0.4638112995385119,
 'Overall J': (1.225, 0.4083333333333334),
 'Overall MCC': 0.36666666666666664,
 'Overall MCEN': 0.5189369467580801,
 'Overall RACC': 0.3541666666666667,
 'Overall RACCU': 0.3645833333333333,
 'P-Value': 0.38720703125,
 'PPV Macro': 0.5666666666666668,
 'PPV Micro': 0.5833333333333334,
 'Pearson C': 0.5956833971812705,
 'Phi-Squared': 0.5499999999999999,
 'RCI': 0.3494718919696284,
 'RR': 4.0,
 'Reference Entropy': 1.5,
 'Response Entropy': 1.4833557549816874,
 'SOA1(Landis & Koch)': 'Fair',
 'SOA2(Fleiss)': 'Poor',
 'SOA3(Altman)': 'Fair',
 'SOA4(Cicchetti)': 'Poor',
 'SOA5(Cramer)': 'Relatively Strong',
 'SOA6(Matthews)': 'Weak',
 'Scott PI': 0.34426229508196726,
 'Standard Error': 0.14231876063832777,
 'TNR Macro': 0.7777777777777777,
 'TNR Micro': 0.7916666666666666,
 'TPR Macro': 0.611111111111111,
 'TPR Micro': 0.5833333333333334,
 'Zero-one Loss': 5}

Notice : new in version 0.3

Notice : `_` removed from overall statistics names in version 1.6

cm.table

{0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}}

cm.matrix

{0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}}

cm.normalized_matrix

{0: {0: 1.0, 1: 0.0, 2: 0.0},
 1: {0: 0.0, 1: 0.33333, 2: 0.66667},
 2: {0: 0.33333, 1: 0.16667, 2: 0.5}}

cm.normalized_table

{0: {0: 1.0, 1: 0.0, 2: 0.0},
 1: {0: 0.0, 1: 0.33333, 2: 0.66667},
 2: {0: 0.33333, 1: 0.16667, 2: 0.5}}

Notice : `matrix`, `normalized_matrix` & `normalized_table` added in version 1.5 (changed from print style)

import numpy

y_actu = numpy.array([2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2])
y_pred = numpy.array([0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2])

cm = ConfusionMatrix(y_actu, y_pred,digit=5)

cm

pycm.ConfusionMatrix(classes: [0, 1, 2])

Notice : `numpy.array` support in versions > 0.7

Direct CM¶

cm2 = ConfusionMatrix(matrix={0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}},digit=5)

cm2

pycm.ConfusionMatrix(classes: [0, 1, 2])

cm2.actual_vector

cm2.predict_vector

cm2.classes

[0, 1, 2]

cm2.class_stat

{'ACC': {0: 0.8333333333333334, 1: 0.75, 2: 0.5833333333333334},
 'AGF': {0: 0.9135962935560564, 1: 0.5399492471560389, 2: 0.5515973485146916},
 'AGM': {0: 0.837285964012303, 1: 0.6919986974962765, 2: 0.6071224016819726},
 'AM': {0: 2, 1: -1, 2: -1},
 'AUC': {0: 0.8888888888888888, 1: 0.611111111111111, 2: 0.5833333333333333},
 'AUCI': {0: 'Very Good', 1: 'Fair', 2: 'Poor'},
 'AUPR': {0: 0.8, 1: 0.41666666666666663, 2: 0.55},
 'BCD': {0: 0.08333333333333333,
  1: 0.041666666666666664,
  2: 0.041666666666666664},
 'BM': {0: 0.7777777777777777, 1: 0.2222222222222221, 2: 0.16666666666666652},
 'CEN': {0: 0.25, 1: 0.49657842846620864, 2: 0.6044162769630221},
 'DOR': {0: 'None', 1: 3.999999999999998, 2: 1.9999999999999998},
 'DP': {0: 'None', 1: 0.331933069996499, 2: 0.16596653499824957},
 'DPI': {0: 'None', 1: 'Poor', 2: 'Poor'},
 'ERR': {0: 0.16666666666666663, 1: 0.25, 2: 0.41666666666666663},
 'F0.5': {0: 0.6521739130434783,
  1: 0.45454545454545453,
  2: 0.5769230769230769},
 'F1': {0: 0.75, 1: 0.4, 2: 0.5454545454545454},
 'F2': {0: 0.8823529411764706, 1: 0.35714285714285715, 2: 0.5172413793103449},
 'FDR': {0: 0.4, 1: 0.5, 2: 0.4},
 'FN': {0: 0, 1: 2, 2: 3},
 'FNR': {0: 0.0, 1: 0.6666666666666667, 2: 0.5},
 'FOR': {0: 0.0, 1: 0.19999999999999996, 2: 0.4285714285714286},
 'FP': {0: 2, 1: 1, 2: 2},
 'FPR': {0: 0.2222222222222222,
  1: 0.11111111111111116,
  2: 0.33333333333333337},
 'G': {0: 0.7745966692414834, 1: 0.408248290463863, 2: 0.5477225575051661},
 'GI': {0: 0.7777777777777777, 1: 0.2222222222222221, 2: 0.16666666666666652},
 'GM': {0: 0.8819171036881969, 1: 0.5443310539518174, 2: 0.5773502691896257},
 'IBA': {0: 0.9506172839506174, 1: 0.1316872427983539, 2: 0.2777777777777778},
 'ICSI': {0: 0.6000000000000001,
  1: -0.16666666666666674,
  2: 0.10000000000000009},
 'IS': {0: 1.263034405833794, 1: 1.0, 2: 0.2630344058337938},
 'J': {0: 0.6, 1: 0.25, 2: 0.375},
 'LS': {0: 2.4, 1: 2.0, 2: 1.2},
 'MCC': {0: 0.6831300510639732, 1: 0.25819888974716115, 2: 0.1690308509457033},
 'MCCI': {0: 'Moderate', 1: 'Negligible', 2: 'Negligible'},
 'MCEN': {0: 0.2643856189774724, 1: 0.5, 2: 0.6875},
 'MK': {0: 0.6000000000000001, 1: 0.30000000000000004, 2: 0.17142857142857126},
 'N': {0: 9, 1: 9, 2: 6},
 'NLR': {0: 0.0, 1: 0.7500000000000001, 2: 0.75},
 'NLRI': {0: 'Good', 1: 'Negligible', 2: 'Negligible'},
 'NPV': {0: 1.0, 1: 0.8, 2: 0.5714285714285714},
 'OC': {0: 1.0, 1: 0.5, 2: 0.6},
 'OOC': {0: 0.7745966692414834, 1: 0.4082482904638631, 2: 0.5477225575051661},
 'OP': {0: 0.7083333333333334, 1: 0.2954545454545454, 2: 0.4404761904761905},
 'P': {0: 3, 1: 3, 2: 6},
 'PLR': {0: 4.5, 1: 2.9999999999999987, 2: 1.4999999999999998},
 'PLRI': {0: 'Poor', 1: 'Poor', 2: 'Poor'},
 'POP': {0: 12, 1: 12, 2: 12},
 'PPV': {0: 0.6, 1: 0.5, 2: 0.6},
 'PRE': {0: 0.25, 1: 0.25, 2: 0.5},
 'Q': {0: 'None', 1: 0.6, 2: 0.3333333333333333},
 'QI': {0: 'None', 1: 'Moderate', 2: 'Weak'},
 'RACC': {0: 0.10416666666666667,
  1: 0.041666666666666664,
  2: 0.20833333333333334},
 'RACCU': {0: 0.1111111111111111,
  1: 0.04340277777777778,
  2: 0.21006944444444442},
 'TN': {0: 7, 1: 8, 2: 4},
 'TNR': {0: 0.7777777777777778, 1: 0.8888888888888888, 2: 0.6666666666666666},
 'TON': {0: 7, 1: 10, 2: 7},
 'TOP': {0: 5, 1: 2, 2: 5},
 'TP': {0: 3, 1: 1, 2: 3},
 'TPR': {0: 1.0, 1: 0.3333333333333333, 2: 0.5},
 'Y': {0: 0.7777777777777777, 1: 0.2222222222222221, 2: 0.16666666666666652},
 'dInd': {0: 0.2222222222222222, 1: 0.6758625033664689, 2: 0.6009252125773316},
 'sInd': {0: 0.8428651597363228, 1: 0.5220930407198541, 2: 0.5750817072006014}}

cm2.overall_stat

{'95% CI': (0.30438856248221097, 0.8622781041844558),
 'ACC Macro': 0.7222222222222223,
 'ARI': 0.09206349206349207,
 'AUNP': 0.6666666666666666,
 'AUNU': 0.6944444444444443,
 'Bangdiwala B': 0.37254901960784315,
 'Bennett S': 0.37500000000000006,
 'CBA': 0.4777777777777778,
 'CSI': 0.1777777777777778,
 'Chi-Squared': 6.6,
 'Chi-Squared DF': 4,
 'Conditional Entropy': 0.9591479170272448,
 'Cramer V': 0.5244044240850757,
 'Cross Entropy': 1.5935164295556343,
 'F1 Macro': 0.5651515151515151,
 'F1 Micro': 0.5833333333333334,
 'FNR Macro': 0.38888888888888895,
 'FNR Micro': 0.41666666666666663,
 'FPR Macro': 0.22222222222222232,
 'FPR Micro': 0.20833333333333337,
 'Gwet AC1': 0.3893129770992367,
 'Hamming Loss': 0.41666666666666663,
 'Joint Entropy': 2.4591479170272446,
 'KL Divergence': 0.09351642955563438,
 'Kappa': 0.35483870967741943,
 'Kappa 95% CI': (-0.07707577422109269, 0.7867531935759315),
 'Kappa No Prevalence': 0.16666666666666674,
 'Kappa Standard Error': 0.2203645326012817,
 'Kappa Unbiased': 0.34426229508196726,
 'Krippendorff Alpha': 0.3715846994535519,
 'Lambda A': 0.16666666666666666,
 'Lambda B': 0.42857142857142855,
 'Mutual Information': 0.5242078379544426,
 'NIR': 0.5,
 'Overall ACC': 0.5833333333333334,
 'Overall CEN': 0.4638112995385119,
 'Overall J': (1.225, 0.4083333333333334),
 'Overall MCC': 0.36666666666666664,
 'Overall MCEN': 0.5189369467580801,
 'Overall RACC': 0.3541666666666667,
 'Overall RACCU': 0.3645833333333333,
 'P-Value': 0.38720703125,
 'PPV Macro': 0.5666666666666668,
 'PPV Micro': 0.5833333333333334,
 'Pearson C': 0.5956833971812705,
 'Phi-Squared': 0.5499999999999999,
 'RCI': 0.3494718919696284,
 'RR': 4.0,
 'Reference Entropy': 1.5,
 'Response Entropy': 1.4833557549816874,
 'SOA1(Landis & Koch)': 'Fair',
 'SOA2(Fleiss)': 'Poor',
 'SOA3(Altman)': 'Fair',
 'SOA4(Cicchetti)': 'Poor',
 'SOA5(Cramer)': 'Relatively Strong',
 'SOA6(Matthews)': 'Weak',
 'Scott PI': 0.34426229508196726,
 'Standard Error': 0.14231876063832777,
 'TNR Macro': 0.7777777777777777,
 'TNR Micro': 0.7916666666666666,
 'TPR Macro': 0.611111111111111,
 'TPR Micro': 0.5833333333333334,
 'Zero-one Loss': 5}

Notice : new in version 0.8.1
In direct matrix mode `actual_vector` and `predict_vector` are empty

Activation threshold¶

threshold is added in version 0.9 for real value prediction.

For more information visit Example 3

Notice : new in version 0.9

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 Example 4

Notice : new in version 0.9.5

Sample weights¶

sample_weight is added in version 1.2

For more information visit Example 5

Notice : new in version 1.2

Transpose¶

transpose is added in version 1.2 in order to transpose input matrix (only in Direct CM mode)

cm = ConfusionMatrix(matrix={0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}},digit=5,transpose=True)

cm.print_matrix()

Predict 0       1       2       
Actual
0       3       0       2       

1       0       1       1       

2       0       2       3

Notice : new in version 1.2

Relabel¶

relabel method is added in version 1.5 in order to change ConfusionMatrix class names.

cm.relabel(mapping={0:"L1",1:"L2",2:"L3"})

cm

pycm.ConfusionMatrix(classes: ['L1', 'L2', 'L3'])

Parameters¶

mapping : mapping dictionary (type : dict)

Notice : new in version 1.5

Position¶

position method is added in version 2.8 in order to find the indexes of observations in predict_vector which made TP, TN, FP, FN.

cm3 = ConfusionMatrix(y_actu, y_pred,digit=5)
cm3.position()

{0: {'FN': [], 'FP': [0, 7], 'TN': [2, 3, 5, 6, 8, 10, 11], 'TP': [1, 4, 9]},
 1: {'FN': [5, 10], 'FP': [3], 'TN': [0, 1, 2, 4, 7, 8, 9, 11], 'TP': [6]},
 2: {'FN': [0, 3, 7], 'FP': [5, 10], 'TN': [1, 4, 6, 9], 'TP': [2, 8, 11]}}

Notice : new in version 2.8

Notice : only works in vector mode

To array¶

to_array method is added in version 2.9 in order to returns the confusion matrix in the form of a NumPy array. This can be helpful to apply different operations over the confusion matrix for different purposes such as aggregation, normalization, and combination.

cm.to_array()

array([[3, 0, 2],
       [0, 1, 1],
       [0, 2, 3]])

cm.to_array(normalized=True)

array([[0.6, 0. , 0.4],
       [0. , 0.5, 0.5],
       [0. , 0.4, 0.6]])

cm.to_array(normalized=True,one_vs_all=True, class_name="L1")

array([[0.6, 0.4],
       [0. , 1. ]])

Parameters¶

normalized : a flag for getting normalized confusion matrix (type : bool, default : False)
one_vs_all : One-Vs-All mode flag (type : bool, default : False)
class_name : target class name for One-Vs-All mode (type : any valid type, default : None)

Output¶

Confusion Matrix in NumPy array format

Notice : new in version 2.9

Combine¶

combine method is added in version 3.0 in order to merge two confusion matrices. This option will be useful in mini-batch learning.

cm_combined = cm2.combine(cm3)
cm_combined.print_matrix()

Predict 0       1       2       
Actual
0       6       0       0       

1       0       2       4       

2       4       2       6

Parameters¶

other : the other matrix that is going to be combined (type : ConfusionMatrix)

Output¶

New ConfusionMatrix

Notice : new in version 3.0

Plot¶

plot method is added in version 3.0 in order to plot a confusion matrix using Matplotlib or Seaborn.

import sys
!{sys.executable}  -m pip -q -q install matplotlib;
!{sys.executable}  -m pip -q -q install seaborn;
import matplotlib.pyplot as plt

cm.plot()

<matplotlib.axes._subplots.AxesSubplot at 0xf60e5d0>

cm.plot(cmap=plt.cm.Greens,number_label=True,normalized=True)

<matplotlib.axes._subplots.AxesSubplot at 0x42011d0>

cm.plot(plot_lib = "seaborn",number_label=True)

<matplotlib.axes._subplots.AxesSubplot at 0x42676d0>

cm.plot(cmap=plt.cm.Blues,number_label=True,one_vs_all=True,class_name="L1")

<matplotlib.axes._subplots.AxesSubplot at 0x1b893a70>

cm.plot(cmap=plt.cm.Reds,number_label=True,normalized=True,one_vs_all=True,class_name="L3")

<matplotlib.axes._subplots.AxesSubplot at 0x1b8bfb70>

Parameters¶

normalized :normalized flag for matrix (type : bool, default : False)
one_vs_all : one_vs_all flag for matrix (type : bool, default : False)
class_name : class name of one_vs_all action (type : any valid type, default : None)
title : plot title (type : str, default : Confusion Matrix)
number_label : number label flag (type : bool, default : False)
cmap : color map (type : matplotlib.colors.ListedColormap, default : None)
plot_lib : plotting library (type : str, default : matplotlib)

Output¶

Plot axes

Notice : new in version 3.0

Online help¶

online_help function is added in version 1.1 in order to open each statistics definition in web browser.

>>> from pycm import online_help
>>> online_help("J")
>>> online_help("J", alt_link=True)
>>> online_help("SOA1(Landis & Koch)")
>>> online_help(2)

List of items are available by calling online_help() (without argument)

If PyCM website is not available, set alt_link = True

online_help()

Please choose one parameter : 

Example : online_help("J") or online_help(2)

1-95% CI
2-ACC
3-ACC Macro
4-AGF
5-AGM
6-AM
7-ARI
8-AUC
9-AUCI
10-AUNP
11-AUNU
12-AUPR
13-BCD
14-BM
15-Bangdiwala B
16-Bennett S
17-CBA
18-CEN
19-CSI
20-Chi-Squared
21-Chi-Squared DF
22-Conditional Entropy
23-Cramer V
24-Cross Entropy
25-DOR
26-DP
27-DPI
28-ERR
29-F0.5
30-F1
31-F1 Macro
32-F1 Micro
33-F2
34-FDR
35-FN
36-FNR
37-FNR Macro
38-FNR Micro
39-FOR
40-FP
41-FPR
42-FPR Macro
43-FPR Micro
44-G
45-GI
46-GM
47-Gwet AC1
48-Hamming Loss
49-IBA
50-ICSI
51-IS
52-J
53-Joint Entropy
54-KL Divergence
55-Kappa
56-Kappa 95% CI
57-Kappa No Prevalence
58-Kappa Standard Error
59-Kappa Unbiased
60-Krippendorff Alpha
61-LS
62-Lambda A
63-Lambda B
64-MCC
65-MCCI
66-MCEN
67-MK
68-Mutual Information
69-N
70-NIR
71-NLR
72-NLRI
73-NPV
74-OC
75-OOC
76-OP
77-Overall ACC
78-Overall CEN
79-Overall J
80-Overall MCC
81-Overall MCEN
82-Overall RACC
83-Overall RACCU
84-P
85-P-Value
86-PLR
87-PLRI
88-POP
89-PPV
90-PPV Macro
91-PPV Micro
92-PRE
93-Pearson C
94-Phi-Squared
95-Q
96-QI
97-RACC
98-RACCU
99-RCI
100-RR
101-Reference Entropy
102-Response Entropy
103-SOA1(Landis & Koch)
104-SOA2(Fleiss)
105-SOA3(Altman)
106-SOA4(Cicchetti)
107-SOA5(Cramer)
108-SOA6(Matthews)
109-Scott PI
110-Standard Error
111-TN
112-TNR
113-TNR Macro
114-TNR Micro
115-TON
116-TOP
117-TP
118-TPR
119-TPR Macro
120-TPR Micro
121-Y
122-Zero-one Loss
123-dInd
124-sInd

Parameters¶

param : input parameter (type : int or str, default : None)
alt_link : alternative link for document flag (type : bool, default : False)

Notice : `alt_link` , new in version 2.4

Parameter recommender¶

This option has been added in version 1.9 to recommend the most related parameters considering the characteristics of the input dataset. The suggested parameters are selected according to some characteristics of the input such as being balance/imbalance and binary/multi-class. All suggestions can be categorized into three main groups: imbalanced dataset, binary classification for a balanced dataset, and multi-class classification for a balanced dataset. The recommendation lists have been gathered according to the respective paper of each parameter and the capabilities which had been claimed by the paper.

Fig2. Parameter Recommender Block Diagram

cm.imbalance

False

cm.binary

False

cm.recommended_list

['ERR',
 'TPR Micro',
 'TPR Macro',
 'F1 Macro',
 'PPV Macro',
 'ACC',
 'Overall ACC',
 'MCC',
 'MCCI',
 'Overall MCC',
 'SOA6(Matthews)',
 'BCD',
 'Hamming Loss',
 'Zero-one Loss']

Notice : also available in HTML report

Notice : The recommender system assumes that the input is the result of classification over the whole data rather than just a part of it. If the confusion matrix is the result of test data classification, the recommendation is not valid.

Comapre¶

In version 2.0, a method for comparing several confusion matrices is introduced. This option is a combination of several overall and class-based benchmarks. Each of the benchmarks evaluates the performance of the classification algorithm from good to poor and give them a numeric score. The score of good and poor performances are 1 and 0, respectively.

After that, two scores are calculated for each confusion matrices, overall and class-based. The overall score is the average of the score of six overall benchmarks which are Landis & Koch, Fleiss, Altman, Cicchetti, Cramer, and Matthews. In the same manner, the class-based score is the average of the score of six class-based benchmarks which are Positive Likelihood Ratio Interpretation, Negative Likelihood Ratio Interpretation, Discriminant Power Interpretation, AUC value Interpretation, Matthews Correlation Coefficient Interpretation and Yule's Q Interpretation. It should be noticed that if one of the benchmarks returns none for one of the classes, that benchmarks will be eliminated in total averaging. If the user sets weights for the classes, the averaging over the value of class-based benchmark scores will transform to a weighted average.

If the user sets the value of by_class boolean input True, the best confusion matrix is the one with the maximum class-based score. Otherwise, if a confusion matrix obtains the maximum of both overall and class-based scores, that will be reported as the best confusion matrix, but in any other case, the compared object doesn’t select the best confusion matrix.

Fig3. Compare Block Diagram

cm2 = ConfusionMatrix(matrix={0:{0:2,1:50,2:6},1:{0:5,1:50,2:3},2:{0:1,1:7,2:50}})
cm3 = ConfusionMatrix(matrix={0:{0:50,1:2,2:6},1:{0:50,1:5,2:3},2:{0:1,1:55,2:2}})

cp = Compare({"cm2":cm2,"cm3":cm3})

print(cp)

Best : cm2

Rank  Name   Class-Score    Overall-Score
1     cm2    9.05           2.55
2     cm3    6.05           1.98333

cp.scores

{'cm2': {'class': 9.05, 'overall': 2.55},
 'cm3': {'class': 6.05, 'overall': 1.98333}}

cp.sorted

['cm2', 'cm3']

cp.best

pycm.ConfusionMatrix(classes: [0, 1, 2])

cp.best_name

'cm2'

cp2 = Compare({"cm2":cm2,"cm3":cm3},by_class=True,weight={0:5,1:1,2:1})

print(cp2)

Best : cm3

Rank  Name   Class-Score     Overall-Score
1     cm3    19.05           1.98333
2     cm2    14.65           2.55

Acceptable data types¶

ConfusionMatrix¶

actual_vector : python list or numpy array of any stringable objects
predict_vector : python list or numpy array of any stringable objects
matrix : dict
digit: int
threshold : FunctionType (function or lambda)
file : File object
sample_weight : python list or numpy array of numbers
transpose : bool

run help(ConfusionMatrix) for more information

Compare¶

cm_dict : python dict of ConfusionMatrix object (str : ConfusionMatrix)
by_class : bool
weight : python dict of class weights (class_name : float)
digit: int

run help(Compare) for more information

Basic parameters¶

TP (True positive)¶

A true positive test result is one that detects the condition when the condition is present (correctly identified) [3].

cm.TP

{'L1': 3, 'L2': 1, 'L3': 3}

TN (True negative)¶

A true negative test result is one that does not detect the condition when the condition is absent (correctly rejected) [3].

cm.TN

{'L1': 7, 'L2': 8, 'L3': 4}

FP (False positive)¶

A false positive test result is one that detects the condition when the condition is absent (incorrectly identified) [3].

cm.FP

{'L1': 0, 'L2': 2, 'L3': 3}

FN (False negative)¶

A false negative test result is one that does not detect the condition when the condition is present (incorrectly rejected) [3].

cm.FN

{'L1': 2, 'L2': 1, 'L3': 2}

P (Condition positive)¶

Number of positive samples. Also known as support (the number of occurrences of each class in y_true) [3].

$$P=TP+FN$$

cm.P

{'L1': 5, 'L2': 2, 'L3': 5}

N (Condition negative)¶

Number of negative samples [3].

$$N=TN+FP$$

cm.N

{'L1': 7, 'L2': 10, 'L3': 7}

TOP (Test outcome positive)¶

Number of positive outcomes [3].

$$TOP=TP+FP$$

cm.TOP

{'L1': 3, 'L2': 3, 'L3': 6}

TON (Test outcome negative)¶

Number of negative outcomes [3].

$$TON=TN+FN$$

cm.TON

{'L1': 9, 'L2': 9, 'L3': 6}

POP (Population)¶

Total sample size [3].

$$POP=TP+TN+FN+FP$$

cm.POP

{'L1': 12, 'L2': 12, 'L3': 12}

Wikipedia page

Class statistics¶

TPR (True positive rate)¶

Sensitivity (also called the true positive rate, the recall, or probability of detection in some fields) measures the proportion of positives that are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having the condition) [3].

Wikipedia page

$$TPR=\frac{TP}{P}=\frac{TP}{TP+FN}$$

cm.TPR

{'L1': 0.6, 'L2': 0.5, 'L3': 0.6}

TNR (True negative rate)¶

Specificity (also called the true negative rate) measures the proportion of negatives that are correctly identified as such (e.g. the percentage of healthy people who are correctly identified as not having the condition) [3].

Wikipedia page

$$TNR=\frac{TN}{N}=\frac{TN}{TN+FP}$$

cm.TNR

{'L1': 1.0, 'L2': 0.8, 'L3': 0.5714285714285714}

PPV (Positive predictive value)¶

Positive predictive value (PPV) is the proportion of positives that correspond to the presence of the condition [3].

Wikipedia page

$$PPV=\frac{TP}{TP+FP}$$

cm.PPV

{'L1': 1.0, 'L2': 0.3333333333333333, 'L3': 0.5}

NPV (Negative predictive value)¶

Negative predictive value (NPV) is the proportion of negatives that correspond to the absence of the condition [3].

Wikipedia page

$$NPV=\frac{TN}{TN+FN}$$

cm.NPV

{'L1': 0.7777777777777778, 'L2': 0.8888888888888888, 'L3': 0.6666666666666666}

FNR (False negative rate)¶

The false negative rate is the proportion of positives which yield negative test outcomes with the test, i.e., the conditional probability of a negative test result given that the condition being looked for is present [3].

Wikipedia page

$$FNR=\frac{FN}{P}=\frac{FN}{FN+TP}=1-TPR$$

cm.FNR

{'L1': 0.4, 'L2': 0.5, 'L3': 0.4}

FPR (False positive rate)¶

The false positive rate is the proportion of all negatives that still yield positive test outcomes, i.e., the conditional probability of a positive test result given an event that was not present [3].

The false positive rate is equal to the significance level. The specificity of the test is equal to $ 1 $ minus the false positive rate.

Wikipedia page

$$FPR=\frac{FP}{N}=\frac{FP}{FP+TN}=1-TNR$$

cm.FPR

{'L1': 0.0, 'L2': 0.19999999999999996, 'L3': 0.4285714285714286}

FDR (False discovery rate)¶

The false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the expected proportion of "discoveries" (rejected null hypotheses) that are false (incorrect rejections) [3].

Wikipedia page

$$FDR=\frac{FP}{FP+TP}=1-PPV$$

cm.FDR

{'L1': 0.0, 'L2': 0.6666666666666667, 'L3': 0.5}

FOR (False omission rate)¶

False omission rate (FOR) is a statistical method used in multiple hypothesis testing to correct for multiple comparisons and it is the complement of the negative predictive value. It measures the proportion of false negatives which are incorrectly rejected [3].

Wikipedia page

$$FOR=\frac{FN}{FN+TN}=1-NPV$$

cm.FOR

{'L1': 0.2222222222222222,
 'L2': 0.11111111111111116,
 'L3': 0.33333333333333337}

ACC (Accuracy)¶

The accuracy is the number of correct predictions from all predictions made [3].

Wikipedia page

$$ACC=\frac{TP+TN}{P+N}=\frac{TP+TN}{TP+TN+FP+FN}$$

cm.ACC

{'L1': 0.8333333333333334, 'L2': 0.75, 'L3': 0.5833333333333334}

ERR (Error rate)¶

The error rate is the number of incorrect predictions from all predictions made [3].

$$ERR=\frac{FP+FN}{P+N}=\frac{FP+FN}{TP+TN+FP+FN}=1-ACC$$

cm.ERR

{'L1': 0.16666666666666663, 'L2': 0.25, 'L3': 0.41666666666666663}

Notice : new in version 0.4

FBeta-Score¶

In statistical analysis of classification, the F1 score (also F-score or F-measure) is a measure of a test's accuracy. It considers both the precision $ p $ and the recall $ r $ of the test to compute the score. The F1 score is the harmonic average of the precision and recall, where F1 score reaches its best value at $ 1 $ (perfect precision and recall) and worst at $ 0 $ [3].

Wikipedia page

$$F_{\beta}=(1+\beta^2)\times \frac{PPV\times TPR}{(\beta^2 \times PPV)+TPR}=\frac{(1+\beta^2) \times TP}{(1+\beta^2)\times TP+FP+\beta^2 \times FN}$$

cm.F1

{'L1': 0.75, 'L2': 0.4, 'L3': 0.5454545454545454}

cm.F05

{'L1': 0.8823529411764706, 'L2': 0.35714285714285715, 'L3': 0.5172413793103449}

cm.F2

{'L1': 0.6521739130434783, 'L2': 0.45454545454545453, 'L3': 0.5769230769230769}

cm.F_beta(beta=4)

{'L1': 0.6144578313253012, 'L2': 0.4857142857142857, 'L3': 0.5930232558139535}

Parameters¶

beta : beta parameter (type : float)

Output¶

{class1: FBeta-Score1, class2: FBeta-Score2, ...}

Notice : new in version 0.4

MCC (Matthews correlation coefficient)¶

The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications, introduced by biochemist Brian W. Matthews in 1975. It takes into account true and false positives and negatives and is generally regarded as a balanced measure that can be used even if the classes are of very different sizes. The MCC is, in essence, a correlation coefficient between the observed and predicted binary classifications; it returns a value between $ −1 $ and $ +1 $. A coefficient of $ +1 $ represents a perfect prediction, $ 0 $ no better than random prediction and $ −1 $ indicates total disagreement between prediction and observation [27].

Interpretation

Wikipedia page

$$MCC=\frac{TP \times TN-FP \times FN}{\sqrt{(TP+FP)\times (TP+FN)\times (TN+FP)\times (TN+FN)}}$$

cm.MCC

{'L1': 0.6831300510639732, 'L2': 0.25819888974716115, 'L3': 0.1690308509457033}

BM (Bookmaker informedness)¶

The informedness of a prediction method as captured by a contingency matrix is defined as the probability that the prediction method will make a correct decision as opposed to guessing and is calculated using the bookmaker algorithm [2].

Equals to Youden Index

$$BM=TPR+TNR-1$$

cm.BM

{'L1': 0.6000000000000001,
 'L2': 0.30000000000000004,
 'L3': 0.17142857142857126}

MK (Markedness)¶

In statistics and psychology, the social science concept of markedness is quantified as a measure of how much one variable is marked as a predictor or possible cause of another and is also known as $ \triangle P $ in simple two-choice cases [2].

$$MK=PPV+NPV-1$$

cm.MK

{'L1': 0.7777777777777777, 'L2': 0.2222222222222221, 'L3': 0.16666666666666652}

PLR (Positive likelihood ratio)¶

Likelihood ratios are used for assessing the value of performing a diagnostic test. They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954 [28].

Interpretation

Wikipedia page

$$LR_+=PLR=\frac{TPR}{FPR}$$

cm.PLR

{'L1': 'None', 'L2': 2.5000000000000004, 'L3': 1.4}

Notice : `LR+` renamed to `PLR` in version 1.5

NLR (Negative likelihood ratio)¶

Likelihood ratios are used for assessing the value of performing a diagnostic test. They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954 [28].

Interpretation

Wikipedia page

$$LR_-=NLR=\frac{FNR}{TNR}$$

cm.NLR

{'L1': 0.4, 'L2': 0.625, 'L3': 0.7000000000000001}

Notice : `LR-` renamed to `NLR` in version 1.5

DOR (Diagnostic odds ratio)¶

The diagnostic odds ratio is a measure of the effectiveness of a diagnostic test. It is defined as the ratio of the odds of the test being positive if the subject has a disease relative to the odds of the test being positive if the subject does not have the disease [28].

Wikipedia page

$$DOR=\frac{LR_+}{LR_-}$$

cm.DOR

{'L1': 'None', 'L2': 4.000000000000001, 'L3': 1.9999999999999998}

PRE (Prevalence)¶

Prevalence is a statistical concept referring to the number of cases of a disease that are present in a particular population at a given time (Reference Likelihood) [14].

Wikipedia page

$$Prevalence=\frac{P}{POP}$$

cm.PRE

{'L1': 0.4166666666666667, 'L2': 0.16666666666666666, 'L3': 0.4166666666666667}

G (G-measure)¶

The geometric mean of precision and sensitivity, also known as Fowlkes–Mallows index [3].

Wikipedia page

$$G=\sqrt{PPV\times TPR}$$

cm.G

{'L1': 0.7745966692414834, 'L2': 0.408248290463863, 'L3': 0.5477225575051661}

RACC (Random accuracy)¶

The expected accuracy from a strategy of randomly guessing categories according to reference and response distributions [24].

$$RACC=\frac{TOP \times P}{POP^2}$$

cm.RACC

{'L1': 0.10416666666666667,
 'L2': 0.041666666666666664,
 'L3': 0.20833333333333334}

Notice : new in version 0.3

RACCU (Random accuracy unbiased)¶

The expected accuracy from a strategy of randomly guessing categories according to the average of the reference and response distributions [25].

$$RACCU=(\frac{TOP+P}{2 \times POP})^2$$

cm.RACCU

{'L1': 0.1111111111111111,
 'L2': 0.04340277777777778,
 'L3': 0.21006944444444442}

Notice : new in version 0.8.1

J (Jaccard index)¶

The Jaccard index, also known as Intersection over Union and the Jaccard similarity coefficient (originally coined coefficient de communauté by Paul Jaccard), is a statistic used for comparing the similarity and diversity of sample sets [29].

Wikipedia page

$$J=\frac{TP}{TOP+P-TP}$$

cm.J

{'L1': 0.6, 'L2': 0.25, 'L3': 0.375}

Notice : new in version 0.9

IS (Information score)¶

The amount of information needed to correctly classify an example into class C, whose prior probability is $ p(C) $, is defined as $ -\log_2(p(C)) $ [18] [39].

$$IS=-log_2(\frac{TP+FN}{POP})+log_2(\frac{TP}{TP+FP})$$

cm.IS

{'L1': 1.2630344058337937, 'L2': 0.9999999999999998, 'L3': 0.26303440583379367}

Notice : new in version 1.3

CEN (Confusion entropy)¶

CEN based upon the concept of entropy for evaluating classifier performances. By exploiting the misclassification information of confusion matrices, the measure evaluates the confusion level of the class distribution of misclassified samples. Both theoretical analysis and statistical results show that the proposed measure is more discriminating than accuracy and RCI while it remains relatively consistent with the two measures. Moreover, it is more capable of measuring how the samples of different classes have been separated from each other. Hence the proposed measure is more precise than the two measures and can substitute for them to evaluate classifiers in classification applications [17].

$$P_{i,j}^{j}=\frac{Matrix(i,j)}{\sum_{k=1}^{|C|}\Big(Matrix(j,k)+Matrix(k,j)\Big)}$$

$$P_{i,j}^{i}=\frac{Matrix(i,j)}{\sum_{k=1}^{|C|}\Big(Matrix(i,k)+Matrix(k,i)\Big)}$$

$$CEN_j=-\sum_{k=1,k\neq j}^{|C|}\Bigg(P_{j,k}^jlog_{2(|C|-1)}\Big(P_{j,k}^j\Big)+P_{k,j}^jlog_{2(|C|-1)}\Big(P_{k,j}^j\Big)\Bigg)$$

cm.CEN

{'L1': 0.25, 'L2': 0.49657842846620864, 'L3': 0.6044162769630221}

Notice : $ |C| $ is the number of classes

Notice : new in version 1.3

MCEN (Modified confusion entropy)¶

Modified version of CEN [19].

$$P_{i,j}^{j}=\frac{Matrix(i,j)}{\sum_{k=1}^{|C|}\Big(Matrix(j,k)+Matrix(k,j)\Big)-Matrix(j,j)}$$

$$P_{i,j}^{i}=\frac{Matrix(i,j)}{\sum_{k=1}^{|C|}\Big(Matrix(i,k)+Matrix(k,i)\Big)-Matrix(i,i)}$$

$$MCEN_j=-\sum_{k=1,k\neq j}^{|C|}\Bigg(P_{j,k}^jlog_{2(|C|-1)}\Big(P_{j,k}^j\Big)+P_{k,j}^jlog_{2(|C|-1)}\Big(P_{k,j}^j\Big)\Bigg)$$

cm.MCEN

{'L1': 0.2643856189774724, 'L2': 0.5, 'L3': 0.6875}

Notice : new in version 1.3

AUC (Area under the ROC curve)¶

The area under the curve (often referred to as simply the AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one (assuming 'positive' ranks higher than 'negative'). Thus, AUC corresponds to the arithmetic mean of sensitivity and specificity values of each class [23].

Interpretation

$$AUC=\frac{TNR+TPR}{2}$$

cm.AUC

{'L1': 0.8, 'L2': 0.65, 'L3': 0.5857142857142856}

Notice : new in version 1.4
Notice : this is an approximate calculation of AUC

dInd (Distance index)¶

Euclidean distance of a ROC point from the top left corner of the ROC space, which can take values between 0 (perfect classification) and $ \sqrt{2} $ [23].

$$dInd=\sqrt{(1-TNR)^2+(1-TPR)^2}$$

cm.dInd

{'L1': 0.4, 'L2': 0.5385164807134504, 'L3': 0.5862367008195198}

Notice : new in version 1.4

sInd (Similarity index)¶

sInd is comprised between $ 0 $ (no correct classifications) and $ 1 $ (perfect classification) [23].

$$sInd = 1 - \sqrt{\frac{(1-TNR)^2+(1-TPR)^2}{2}}$$

cm.sInd

{'L1': 0.717157287525381, 'L2': 0.6192113447068046, 'L3': 0.5854680534700882}

Notice : new in version 1.4

DP (Discriminant power)¶

Discriminant power (DP) is a measure that summarizes sensitivity and specificity. The DP has been used mainly in feature selection over imbalanced data [33].

Interpretation

$$X=\frac{TPR}{1-TPR}$$

$$Y=\frac{TNR}{1-TNR}$$

$$DP=\frac{\sqrt{3}}{\pi}(log_{10}X+log_{10}Y)$$

cm.DP

{'L1': 'None', 'L2': 0.33193306999649924, 'L3': 0.1659665349982495}

Notice : new in version 1.5

Y (Youden index)¶

Youden’s index evaluates the algorithm’s ability to avoid failure; it’s derived from sensitivity and specificity and denotes a linear correspondence balanced accuracy. As Youden’s index is a linear transformation of the mean sensitivity and specificity, its values are difficult to interpret, we retain that a higher value of Y indicates better ability to avoid failure. Youden’s index has been conventionally used to evaluate tests diagnostic, improve the efficiency of Telemedical prevention [33] [34].

Wikipedia page

Equals to Bookmaker Informedness

$$Y=BM=TPR+TNR-1$$

cm.Y

{'L1': 0.6000000000000001,
 'L2': 0.30000000000000004,
 'L3': 0.17142857142857126}

Notice : new in version 1.5

PLRI (Positive likelihood ratio interpretation)¶

For more information visit [33].

PLR	Model contribution
1 >	Negligible
1 - 5	Poor
5 - 10	Fair
> 10	Good

cm.PLRI

{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}

Notice : new in version 1.5

NLRI (Negative likelihood ratio interpretation)¶

For more information visit [48].

NLR	Model contribution
0.5 - 1	Negligible
0.2 - 0.5	Poor
0.1 - 0.2	Fair
0.1 >	Good

cm.NLRI

{'L1': 'Poor', 'L2': 'Negligible', 'L3': 'Negligible'}

Notice : new in version 2.2

DPI (Discriminant power interpretation)¶

For more information visit [33].

DP	Model contribution
1 >	Poor
1 - 2	Limited
2 - 3	Fair
> 3	Good

cm.DPI

{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}

Notice : new in version 1.5

AUCI (AUC value interpretation)¶

For more information visit [33].

AUC	Model performance
0.5 - 0.6	Poor
0.6 - 0.7	Fair
0.7 - 0.8	Good
0.8 - 0.9	Very Good
0.9 - 1.0	Excellent

cm.AUCI

{'L1': 'Very Good', 'L2': 'Fair', 'L3': 'Poor'}

Notice : new in version 1.6

MCCI (Matthews correlation coefficient interpretation)¶

MCC is a confusion matrix method of calculating the Pearson product-moment correlation coefficient (not to be confused with Pearson's C). Therefore, it has the same interpretation [2].

For more information visit [49].

MCC	Interpretation
0.3 >	Negligible
0.3 - 0.5	Weak
0.5 - 0.7	Moderate
0.7 - 0.9	Strong
0.9 - 1.0	Very Strong

cm.MCCI

{'L1': 'Moderate', 'L2': 'Negligible', 'L3': 'Negligible'}

Notice : new in version 2.2

Notice : only positive values are considered

QI (Yule's Q interpretation)¶

For more information visit [67].

Q	Interpretation
0.25 >	Negligible
0.25 - 0.5	Weak
0.5 - 0.75	Moderate
> 0.75	Strong

cm.QI

{'L1': 'None', 'L2': 'Moderate', 'L3': 'Weak'}

Notice : new in version 2.6

GI (Gini index)¶

A chance-standardized variant of the AUC is given by Gini coefficient, taking values between $ 0 $ (no difference between the score distributions of the two classes) and $ 1 $ (complete separation between the two distributions). Gini coefficient is widespread use metric in imbalanced data learning [33].

Wikipedia page

$$GI=2\times AUC-1$$

cm.GI

{'L1': 0.6000000000000001,
 'L2': 0.30000000000000004,
 'L3': 0.17142857142857126}

Notice : new in version 1.7

LS (Lift score)¶

In the context of classification, lift compares model predictions to randomly generated predictions. Lift is often used in marketing research combined with gain and lift charts as a visual aid [35] [36].

$$LS=\frac{PPV}{PRE}$$

cm.LS

{'L1': 2.4, 'L2': 2.0, 'L3': 1.2}

Notice : new in version 1.8

AM (Automatic/Manual)¶

Difference between automatic and manual classification i.e., the difference between positive outcomes and of positive samples.

$$AM=TOP-P=(TP+FP)-(TP+FN)$$

cm.AM

{'L1': -2, 'L2': 1, 'L3': 1}

Notice : new in version 1.9

BCD (Bray-Curtis dissimilarity)¶

In ecology and biology, the Bray–Curtis dissimilarity, named after J. Roger Bray and John T. Curtis, is a statistic used to quantify the compositional dissimilarity between two different sites, based on counts at each site [37].

Wikipedia page

$$BCD=\frac{|AM|}{\sum_{i=1}^{|C|}\Big(TOP_i+P_i\Big)}$$

cm.BCD

{'L1': 0.08333333333333333,
 'L2': 0.041666666666666664,
 'L3': 0.041666666666666664}

Notice : new in version 1.9

OP (Optimized precision)¶

Optimized precision is a type of hybrid threshold metric and has been proposed as a discriminator for building an optimized heuristic classifier. This metric is a combination of accuracy, sensitivity and specificity metrics. The sensitivity and specificity metrics were used for stabilizing and optimizing the accuracy performance when dealing with an imbalanced class of two-class problems [40] [42].

$$OP = ACC - \frac{|TNR-TPR|}{|TNR+TPR|}$$

cm.OP

{'L1': 0.5833333333333334, 'L2': 0.5192307692307692, 'L3': 0.5589430894308943}

Notice : new in version 2.0

IBA (Index of balanced accuracy)¶

The method combines an unbiased index of its overall accuracy and a measure about how dominant is the class with the highest individual accuracy rate [41] [42].

$$IBA_{\alpha}=(1+\alpha \times(TPR-TNR))\times TNR \times TPR$$

cm.IBA

{'L1': 0.36, 'L2': 0.27999999999999997, 'L3': 0.35265306122448975}

cm.IBA_alpha(0.5)

{'L1': 0.48, 'L2': 0.34, 'L3': 0.3477551020408163}

cm.IBA_alpha(0.1)

{'L1': 0.576, 'L2': 0.388, 'L3': 0.34383673469387754}

Parameters¶

alpha : alpha parameter (type : float)

Output¶

{class1: IBA1, class2: IBA2, ...}

Notice : new in version 2.0

GM (G-mean)¶

Geometric mean of specificity and sensitivity [3] [41] [42].

$$GM=\sqrt{TPR \times TNR}$$

cm.GM

{'L1': 0.7745966692414834, 'L2': 0.6324555320336759, 'L3': 0.5855400437691198}

Notice : new in version 2.0

Q (Yule's Q)¶

In statistics, Yule's Q, also known as the coefficient of colligation, is a measure of association between two binary variables [45].

Interpretation

Wikipedia page

$$OR = \frac{TP\times TN}{FP\times FN}$$

$$Q = \frac{OR-1}{OR+1}$$

cm.Q

{'L1': 'None', 'L2': 0.6, 'L3': 0.3333333333333333}

Notice : new in version 2.1

AGM (Adjusted G-mean)¶

An adjusted version of the geometric mean of specificity and sensitivity [46].

$$N_n=\frac{N}{POP}$$

$$AGM=\frac{GM+TNR\times N_n}{1+N_n};TPR>0$$

$$AGM=0;TPR=0$$

cm.AGM

{'L1': 0.8576400016262, 'L2': 0.708612108382005, 'L3': 0.5803410802752335}

Notice : new in version 2.1

AGF (Adjusted F-score)¶

The F-measures used only three of the four elements of the confusion matrix and hence two classifiers with different TNR values may have the same F-score. Therefore, the AGF metric is introduced to use all elements of the confusion matrix and provide more weights to samples which are correctly classified in the minority class [50].

$$AGF=\sqrt{F_2 \times InvF_{0.5}}$$

$$F_{2}=5\times \frac{PPV\times TPR}{(4 \times PPV)+TPR}$$

$$InvF_{0.5}=(1+0.5^2)\times \frac{NPV\times TNR}{(0.5^2 \times NPV)+TNR}$$

cm.AGF

{'L1': 0.7285871475307653, 'L2': 0.6286946134619315, 'L3': 0.610088876086563}

Notice : new in version 2.3

OC (Overlap coefficient)¶

The overlap coefficient, or Szymkiewicz–Simpson coefficient, is a similarity measure that measures the overlap between two finite sets. It is defined as the size of the intersection divided by the smaller of the size of the two sets [52].

Wikipedia page

$$OC=\frac{TP}{min(TOP,P)}=max(PPV,TPR)$$

cm.OC

{'L1': 1.0, 'L2': 0.5, 'L3': 0.6}

Notice : new in version 2.3

OOC (Otsuka-Ochiai coefficient)¶

In biology, there is a similarity index, known as the Otsuka-Ochiai coefficient named after Yanosuke Otsuka and Akira Ochiai, also known as the Ochiai-Barkman or Ochiai coefficient. If sets are represented as bit vectors, the Otsuka-Ochiai coefficient can be seen to be the same as the cosine similarity [53].

Wikipedia page

$$OOC=\frac{TP}{\sqrt{TOP\times P}}$$

cm.OOC

{'L1': 0.7745966692414834, 'L2': 0.4082482904638631, 'L3': 0.5477225575051661}

Notice : new in version 2.3

TI (Tversky index)¶

The Tversky index, named after Amos Tversky, is an asymmetric similarity measure on sets that compares a variant to a prototype. The Tversky index can be seen as a generalization of Dice's coefficient and Tanimoto coefficient [54].

Wikipedia page

$$TI(\alpha,\beta)=\frac{TP}{TP+\alpha FN+\beta FP}$$

cm.TI(2,3)

{'L1': 0.42857142857142855, 'L2': 0.1111111111111111, 'L3': 0.1875}

Parameters¶

alpha : alpha coefficient (type : float)
beta : beta coefficient (type : float)

Output¶

{class1: TI1, class2: TI2, ...}

Notice : new in version 2.4

AUPR (Area under the PR curve)¶

A PR curve is plotting precision against recall. The precision recall area under curve (AUPR) is just the area under the PR curve. The higher it is, the better the model is [55] [56].

$$AUPR=\frac{TPR+PPV}{2}$$

cm.AUPR

{'L1': 0.8, 'L2': 0.41666666666666663, 'L3': 0.55}

Notice : new in version 2.4
Notice : this is an approximate calculation of AUPR

ICSI (Individual classification success index)¶

The Individual Classification Success Index (ICSI), is a class-specific symmetric measure defined for classification assessment purpose. ICSI is hence $ 1 $ minus the sum of type I and type II errors. It ranges from $ -1 $ (both errors are maximal, i.e. $ 1 $) to $ 1 $ (both errors are minimal, i.e. $ 0 $), but the value $ 0 $ does not have any clear meaning. The measure is symmetric, and linearly related to the arithmetic mean of TPR and PPV [58].

$$ICSI=PPV+TPR-1$$

cm.ICSI

{'L1': 0.6000000000000001,
 'L2': -0.16666666666666674,
 'L3': 0.10000000000000009}

Notice : new in version 2.5

CI (Confidence interval)¶

In statistics, a confidence interval (CI) is a type of interval estimate (of a population parameter) that is computed from the observed data. The confidence level is the frequency (i.e., the proportion) of possible confidence intervals that contain the true value of their corresponding parameter. In other words, if confidence intervals are constructed using a given confidence level in an infinite number of independent experiments, the proportion of those intervals that contain the true value of the parameter will match the confidence level [31].

Supported statistics : ACC,AUC,PRE,Overall ACC,Kappa,TPR,TNR,PPV,NPV,PLR,NLR

Supported alpha values (two-sided) : 0.001, 0.002, 0.01, 0.02, 0.05, 0.1, 0.2

Supported alpha values (one-sided) : 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1

Confidence intervals for TPR,TNR,PPV,NPV,ACC,PRE and Overall ACC are calculated using the normal approximation to the binomial distribution [59], Wilson score [62] and Agresti-Coull method [63]:

Normal approximation:¶

$$SE=\sqrt{\frac{\hat{p}(1-\hat{p})}{n}}$$

$$CI=\hat{p}\pm z\times SE$$

$$n=\begin{cases}P & \hat{p} == TPR/FNR\\N & \hat{p} == TNR/FPR\\TOP & \hat{p} == PPV\\TON & \hat{p} ==NPV \\POP& \hat{p} == ACC/ACC_{Overall}\end{cases}$$

Wilson score:¶

$$CI=\frac{\hat{p}+\frac{z^2}{2n}}{1+\frac{z^2}{n}}\pm\frac{z}{1+\frac{z^2}{n}}\sqrt{\frac{\hat{p}(1-\hat{p})}{n}+\frac{z^2}{4n^2}}$$

Agresti-Coull:¶

$$\hat{p}=\frac{x}{n}$$

$$\tilde{p}=\frac{x+\frac{z^2}{2}}{n+z^2}$$

$$CI =\tilde{p}\pm\sqrt{\frac{\tilde{p}(1-\tilde{p})}{n+z^2}}$$

Confidence interval for Kappa are calculated using Fleiss formula [24] [38] :

$$SE_{Kappa}=\sqrt{\frac{ACC_{Overall}\times (1-RACC_{Overall})}{(1-RACC_{Overall})^2}}$$

$$CI_{Kappa}=Kappa\pm z\times SE_{Kappa}$$

Confidence intervals for NLR and PLR are calculated using the log method [60] :

$$SE_{LR}=\sqrt{\frac{1}{a}-\frac{1}{b}+\frac{1}{c}-\frac{1}{d}}$$

$$CI_{LR}=e^{ln(LR)\pm z\times SE_{LR}}$$

$$PLR:\begin{cases}a=TP\\b=P\\c=FP\\d=N\end{cases}$$

$$NLR:\begin{cases}a=FN\\b=P\\c=TN\\d=N\end{cases}$$

Confidence interval for AUC is calculated using Hanley and McNeil formula [61] :

$$SE_{AUC}=\sqrt{\frac{q_0+(N-1)q_1+(P-1)q_2}{N\times P}}$$

$$q_0=AUC(1-AUC)$$

$$q_1=\frac{AUC}{2-AUC}-AUC^2$$

$$q_2=\frac{2AUC^2}{1+AUC}-AUC^2$$

$$CI_{AUC}=AUC\pm z\times SE_{AUC}$$

cm.CI("TPR")

{'L1': [0.21908902300206645, (0.17058551491594975, 1.0294144850840503)],
 'L2': [0.3535533905932738, (-0.19296464556281656, 1.1929646455628165)],
 'L3': [0.21908902300206645, (0.17058551491594975, 1.0294144850840503)]}

cm.CI("FNR",alpha=0.001,one_sided=True)

{'L1': [0.21908902300206645, (-0.2769850810763853, 1.0769850810763852)],
 'L2': [0.3535533905932738, (-0.5924799769332159, 1.5924799769332159)],
 'L3': [0.21908902300206645, (-0.2769850810763853, 1.0769850810763852)]}

cm.CI("PRE",alpha=0.05,binom_method="wilson")

{'L1': [0.14231876063832774, (0.19325746190524654, 0.6804926643446272)],
 'L2': [0.10758287072798381, (0.04696414761482223, 0.44803635738467273)],
 'L3': [0.14231876063832774, (0.19325746190524654, 0.6804926643446272)]}

cm.CI("Overall ACC",alpha=0.02,binom_method="agresti-coull")

[0.14231876063832777, (0.2805568916340536, 0.8343177950165198)]

cm.CI("Overall ACC",alpha=0.05)

[0.14231876063832777, (0.30438856248221097, 0.8622781041844558)]

Parameters¶

param : input parameter (type : str)
alpha : type I error (type : float, default : 0.05)
one_sided : one-sided mode (type : bool, default : False)
binom_method : binomial confidence intervals method (type : str, default : normal-approx)

Output¶

Two-sided : {class1: [SE1, (Lower CI, Upper CI)], ...}
One-sided : {class1: [SE1, (Lower one-sided CI, Upper one-sided CI)], ...}

Notice : For more information visit Example 8

Notice : new in version 2.5

NB (Net benefit)¶

NB is a weighted sum of true positive classifications with compensation for false positive classifications by giving these a weight $ w $ [64] [65].

$$NB=\frac{TP-w\times FP}{POP}$$

Vickers and Elkin (2006) suggested considering a range of thresholds and calculating the NB across these thresholds. The results can be plotted in a decision curve [66].

$$p_t=threshold$$ $$w=\frac{p_t}{1-p_t}$$

cm.NB(w=0.059)

{'L1': 0.25, 'L2': 0.0735, 'L3': 0.23525}

Parameters¶

w : weight

Output¶

{class1: NB1, class2: NB2, ...}

Notice : new in version 2.6

Average¶

Here "average" refers to the arithmetic mean, the sum of the numbers divided by how many numbers are being averaged.

Wikipedia page

cm.average("PPV")

0.6111111111111112

cm.average("F1")

0.5651515151515151

cm.average("DOR",none_omit=True)

3.0000000000000004

Parameters¶

param : input parameter (type : str)
none_omit : none items omitting flag (type : bool, default : False)

Output¶

Average

Notice : new in version 2.7

Weighted average¶

The weighted average is similar to an ordinary average, except that instead of each of the data points contributing equally to the final average, some data points contribute more than others.

Default weight is condition positive (number of positive samples).

Wikipedia page

cm.weighted_average("PPV")

0.6805555555555555

cm.weighted_average("F1")

0.6064393939393938

cm.weighted_average("DOR",none_omit=True)

2.5714285714285716

cm.weighted_average("F1",weight={"L1":23,"L2":2,"L3":1})

0.7152097902097903

Parameters¶

param : input parameter (type : str)
weight : explicitly passes weights (type : dict, default : None)
none_omit : none items omitting flag (type : bool, default : False)

Output¶

Weighted average

Notice : new in version 2.7

Sensitivity index¶

The sensitivity index or d′ is a statistic used in signal detection theory. It provides the separation between the means of the signal and the noise distributions, compared against the standard deviation of the signal or noise distribution. d′ can be estimated from the observed hit rate and false-alarm rate, as follows [76]:

$$d^{\prime}=Z(TPR) - Z(FPR)$$

Function Z(p), p ∈ [0,1], is the inverse of the cumulative distribution function of the Gaussian distribution.

Wikipedia page

cm.sensitivity_index()

{'L1': 'None', 'L2': 0.8416212335729143, 'L3': 0.4333594729285047}

Output¶

{class1: SI1, class2: SI2, ...}

Notice : new in version 3.1

Overall statistics¶

Kappa¶

Kappa is a statistic that measures inter-rater agreement for qualitative (categorical) items. It is generally thought to be a more robust measure than simple percent agreement calculation, as kappa takes into account the possibility of the agreement occurring by chance [24].

Benchmark1 Benchmark2 Benchmark3 Benchmark4

Wikipedia page

$$Kappa=\frac{ACC_{Overall}-RACC_{Overall}}{1-RACC_{Overall}}$$

cm.Kappa

0.35483870967741943

Notice : new in version 0.3

Kappa unbiased¶

The unbiased kappa value is defined in terms of total accuracy and a slightly different computation of expected likelihood that averages the reference and response probabilities [25].

Equals to Scott's Pi

$$Kappa_{Unbiased}=\frac{ACC_{Overall}-RACCU_{Overall}}{1-RACCU_{Overall}}$$

cm.KappaUnbiased

0.34426229508196726

Notice : new in version 0.8.1

Kappa no prevalence¶

The kappa statistic adjusted for prevalence [14].

$$Kappa_{NoPrevalence}=2 \times ACC_{Overall}-1$$

cm.KappaNoPrevalence

0.16666666666666674

Notice : new in version 0.8.1

Weighted kappa¶

The weighted kappa allows disagreements to be weighted differently and is especially useful when codes are ordered. Three matrices are involved, the matrix of observed scores, the matrix of expected scores based on chance agreement, and the weight matrix [70] [71].

$$v_{ij}=1-\frac{w_{ij}}{max(w)}$$

$$P_e=\sum_{i,j=1}^{|C|}\frac{TOP_i \times P_j}{POP^2}\times v_{ij}$$

$$P_a=\sum_{i,j=1}^{|C|}\frac{Matrix(i,j)}{POP}\times v_{ij}$$

$$Kappa_{Weighted}=\frac{P_a-P_e}{1-P_e}$$

cm.weighted_kappa(weight={"L1":{"L1":0,"L2":1,"L3":2},"L2":{"L1":1,"L2":0,"L3":1},"L3":{"L1":2,"L2":1,"L3":0}})

0.39130434782608675

cm.weighted_kappa()

C:\Users\Sepkjaer\AppData\Local\Programs\Python\Python35-32\lib\site-packages\pycm-3.1-py3.5.egg\pycm\pycm_obj.py:764: RuntimeWarning: The weight format is wrong, the result is for unweighted kappa.

0.35483870967741943

Parameters¶

weight : weight matrix (type : dict, default : None)

Output¶

Weighted kappa

Notice : new in version 2.7

Kappa standard error¶

The standard error(s) of the Kappa coefficient was obtained by Fleiss (1969) [24] [38].

$$SE_{Kappa}=\sqrt{\frac{ACC_{Overall}\times (1-RACC_{Overall})}{(1-RACC_{Overall})^2}}$$

cm.Kappa_SE

0.2203645326012817

Notice : new in version 0.7

Kappa 95% CI¶

Kappa 95% Confidence Interval [24] [38].

$$CI_{Kappa}=Kappa \pm 1.96\times SE_{Kappa}$$

cm.Kappa_CI

(-0.07707577422109269, 0.7867531935759315)

Notice : new in version 0.7

Chi-squared¶

Pearson's chi-squared test is a statistical test applied to sets of categorical data to evaluate how likely it is that any observed difference between the sets arose by chance. It is suitable for unpaired data from large samples [10].

Wikipedia page

$$\chi^2=\sum_{i=1}^{|C|}\sum_{j=1}^{|C|}\frac{\Big(Matrix(i,j)-E(i,j)\Big)^2}{E(i,j)}$$

$$E(i,j)=\frac{TOP_j\times P_i}{POP}$$

cm.Chi_Squared

6.6000000000000005

Notice : new in version 0.7

Chi-squared DF¶

Number of degrees of freedom of this confusion matrix for the chi-squared statistic [10].

$$DF=(|C|-1)^2$$

cm.DF

4

Notice : new in version 0.7

Phi-squared¶

In statistics, the phi coefficient (or mean square contingency coefficient) is a measure of association for two binary variables. Introduced by Karl Pearson, this measure is similar to the Pearson correlation coefficient in its interpretation. In fact, a Pearson correlation coefficient estimated for two binary variables will return the phi coefficient [10].

Wikipedia page

$$\phi^2=\frac{\chi^2}{POP}$$

cm.Phi_Squared

0.55

Notice : new in version 0.7

Cramer's V¶

In statistics, Cramér's V (sometimes referred to as Cramér's phi) is a measure of association between two nominal variables, giving a value between $ 0 $ and $ +1 $ (inclusive). It is based on Pearson's chi-squared statistic and was published by Harald Cramér in 1946 [26].

Benchmark

Wikipedia page

$$V=\sqrt{\frac{\phi^2}{|C|-1}}$$

cm.V

0.5244044240850758

Notice : new in version 0.7

Standard error¶

The standard error (SE) of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution or an estimate of that standard deviation [31].

Wikipedia page

$$SE_{ACC}=\sqrt{\frac{ACC\times (1-ACC)}{POP}}$$

cm.SE

0.14231876063832777

Notice : new in version 0.7

95% CI¶

In statistics, a confidence interval (CI) is a type of interval estimate (of a population parameter) that is computed from the observed data. The confidence level is the frequency (i.e., the proportion) of possible confidence intervals that contain the true value of their corresponding parameter. In other words, if confidence intervals are constructed using a given confidence level in an infinite number of independent experiments, the proportion of those intervals that contain the true value of the parameter will match the confidence level [31].

Wikipedia page

$$CI=ACC \pm 1.96\times SE_{ACC}$$

cm.CI95

(0.30438856248221097, 0.8622781041844558)

Notice : new in version 0.7

Notice : `CI` renamed to `CI95` in version 2.5

Bennett's S¶

Bennett, Alpert & Goldstein’s S is a statistical measure of inter-rater agreement. It was created by Bennett et al. in 1954. Bennett et al. suggested adjusting inter-rater reliability to accommodate the percentage of rater agreement that might be expected by chance was a better measure than a simple agreement between raters [8].

Wikipedia Page

$$p_c=\frac{1}{|C|}$$

$$S=\frac{ACC_{Overall}-p_c}{1-p_c}$$

cm.S

0.37500000000000006

Notice : new in version 0.5

Scott's Pi¶

Scott's pi (named after William A. Scott) is a statistic for measuring inter-rater reliability for nominal data in communication studies. Textual entities are annotated with categories by different annotators, and various measures are used to assess the extent of agreement between the annotators, one of which is Scott's pi. Since automatically annotating text is a popular problem in natural language processing, and the goal is to get the computer program that is being developed to agree with the humans in the annotations it creates, assessing the extent to which humans agree with each other is important for establishing a reasonable upper limit on computer performance [7].

Wikipedia page

Equals to Kappa Unbiased

$$p_c=\sum_{i=1}^{|C|}(\frac{TOP_i + P_i}{2\times POP})^2$$

$$\pi=\frac{ACC_{Overall}-p_c}{1-p_c}$$

cm.PI

0.34426229508196726

Notice : new in version 0.5

Gwet's AC1¶

AC1 was originally introduced by Gwet in 2001 (Gwet, 2001). The interpretation of AC1 is similar to generalized kappa (Fleiss, 1971), which is used to assess inter-rater reliability when there are multiple raters. Gwet (2002) demonstrated that AC1 can overcome the limitations that kappa is sensitive to trait prevalence and rater's classification probabilities (i.e., marginal probabilities), whereas AC1 provides more robust measure of inter-rater reliability [6].

$$\pi_i=\frac{TOP_i + P_i}{2\times POP}$$

$$p_c=\frac{1}{|C|-1}\sum_{i=1}^{|C|}\Big(\pi_i\times (1-\pi_i)\Big)$$

$$AC_1=\frac{ACC_{Overall}-p_c}{1-p_c}$$

cm.AC1

0.3893129770992367

Notice : new in version 0.5

Reference entropy¶

The entropy of the decision problem itself as defined by the counts for the reference. The entropy of a distribution is the average negative log probability of outcomes [30].

$$Likelihood_{Reference}=\frac{P_i}{POP}$$

$$Entropy_{Reference}=-\sum_{i=1}^{|C|}Likelihood_{Reference}(i)\times\log_{2}{Likelihood_{Reference}(i)}$$

$$0\times\log_{2}{0}\equiv0$$

cm.ReferenceEntropy

1.4833557549816874

Notice : new in version 0.8.1

Response entropy¶

The entropy of the response distribution. The entropy of a distribution is the average negative log probability of outcomes [30].

$$Likelihood_{Response}=\frac{TOP_i}{POP}$$

$$Entropy_{Response}=-\sum_{i=1}^{|C|}Likelihood_{Response}(i)\times\log_{2}{Likelihood_{Response}(i)}$$

$$0\times\log_{2}{0}\equiv0$$

cm.ResponseEntropy

1.5

Notice : new in version 0.8.1

Cross entropy¶

The cross-entropy of the response distribution against the reference distribution. The cross-entropy is defined by the negative log probabilities of the response distribution weighted by the reference distribution [30].

Wikipedia page

$$Likelihood_{Reference}=\frac{P_i}{POP}$$

$$Likelihood_{Response}=\frac{TOP_i}{POP}$$

$$Entropy_{Cross}=-\sum_{i=1}^{|C|}Likelihood_{Reference}(i)\times\log_{2}{Likelihood_{Response}(i)}$$

$$0\times\log_{2}{0}\equiv0$$

cm.CrossEntropy

1.5833333333333335

Notice : new in version 0.8.1

Joint entropy¶

The entropy of the joint reference and response distribution as defined by the underlying matrix [30].

$$P^{'}(i,j)=\frac{Matrix(i,j)}{POP}$$

$$Entropy_{Joint}=-\sum_{i=1}^{|C|}\sum_{j=1}^{|C|}P^{'}(i,j)\times\log_{2}{P^{'}(i,j)}$$

$$0\times\log_{2}{0}\equiv0$$

cm.JointEntropy

2.4591479170272446

Notice : new in version 0.8.1

Conditional entropy¶

The entropy of the distribution of categories in the response given that the reference category was as specified [30].

Wikipedia page

$$P^{'}(j|i)=\frac{Matrix(j,i)}{P_i}$$

$$Entropy_{Conditional}=\sum_{i=1}^{|C|}\Bigg(Likelihood_{Reference}(i)\times\Big(-\sum_{j=1}^{|C|}P^{'}(j|i)\times\log_{2}{P^{'}(j|i)}\Big)\Bigg)$$

$$0\times\log_{2}{0}\equiv0$$

cm.ConditionalEntropy

0.9757921620455572

Notice : new in version 0.8.1

Kullback-Leibler divergence¶

In mathematical statistics, the Kullback–Leibler divergence (also called relative entropy) is a measure of how one probability distribution diverges from a second, expected probability distribution [11] [30].

Wikipedia Page

$$Likelihood_{Response}=\frac{TOP_i}{POP}$$

$$Likelihood_{Reference}=\frac{P_i}{POP}$$

$$Divergence=-\sum_{i=1}^{|C|}Likelihood_{Reference}\times\log_{2}{\frac{Likelihood_{Reference}}{Likelihood_{Response}}}$$

cm.KL

0.09997757835164581

Notice : new in version 0.8.1

Mutual information¶

Mutual information is defined as Kullback-Leibler divergence, between the product of the individual distributions and the joint distribution. Mutual information is symmetric. We could also subtract the conditional entropy of the reference given the response from the reference entropy to get the same result [11] [30].

Wikipedia Page

$$P^{'}(i,j)=\frac{Matrix(i,j)}{POP}$$

$$Likelihood_{Reference}=\frac{P_i}{POP}$$

$$Likelihood_{Response}=\frac{TOP_i}{POP}$$

$$MI=-\sum_{i=1}^{|C|}\sum_{j=1}^{|C|}P^{'}(i,j)\times\log_{2}\Big({\frac{P^{'}(i,j)}{Likelihood_{Reference}(i)\times Likelihood_{Response}(i) }\Big)}$$

$$MI=Entropy_{Response}-Entropy_{Conditional}$$

cm.MutualInformation

0.5242078379544428

Notice : new in version 0.8.1

Goodman & Kruskal's lambda A¶

In probability theory and statistics, Goodman & Kruskal's lambda is a measure of proportional reduction in error in cross tabulation analysis [12].

Wikipedia page

$$\lambda_A=\frac{\sum_{j=1}^{|C|}Max\Big(Matrix(-,j)\Big)-Max(P)}{POP-Max(P)}$$

cm.LambdaA

0.42857142857142855

Notice : new in version 0.8.1

Goodman & Kruskal's lambda B¶

In probability theory and statistics, Goodman & Kruskal's lambda is a measure of proportional reduction in error in cross tabulation analysis [13].

Wikipedia Page

$$\lambda_B=\frac{\sum_{i=1}^{|C|}Max\Big(Matrix(i,-)\Big)-Max(TOP)}{POP-Max(TOP)}$$

cm.LambdaB

0.16666666666666666

Notice : new in version 0.8.1

SOA1 (Landis & Koch's benchmark)¶

For more information visit [1].

Kappa	Strength of Agreement
0 >	Poor
0 - 0.2	Slight
0.2 – 0.4	Fair
0.4 – 0.6	Moderate
0.6 – 0.8	Substantial
0.8 – 1.0	Almost perfect

cm.SOA1

'Fair'

Notice : new in version 0.3

SOA2 (Fleiss' benchmark)¶

For more information visit [4].

Kappa	Strength of Agreement
0.40 >	Poor
0.40 - 0.75	Intermediate to Good
More than 0.75	Excellent

cm.SOA2

'Poor'

Notice : new in version 0.4

SOA3 (Altman's benchmark)¶

For more information visit [5].

Kappa	Strength of Agreement
0.2 >	Poor
0.2 – 0.4	Fair
0.4 – 0.6	Moderate
0.6 – 0.8	Good
0.8 – 1.0	Very Good

cm.SOA3

'Fair'

Notice : new in version 0.4

SOA4 (Cicchetti's benchmark)¶

For more information visit [9].

Kappa	Strength of Agreement
0.40 >	Poor
0.40 – 0.59	Fair
0.59 – 0.74	Good
0.74 – 1.00	Excellent

cm.SOA4

'Poor'

Notice : new in version 0.7

SOA5 (Cramer's benchmark)¶

For more information visit [47].

Cramer's V	Strength of Association
0.1 >	Negligible
0.1 – 0.2	Weak
0.2 – 0.4	Moderate
0.4 – 0.6	Relatively Strong
0.6 – 0.8	Strong
0.8 – 1.0	Very Strong

cm.SOA5

'Relatively Strong'

Notice : new in version 2.2

SOA6 (Matthews's benchmark)¶

MCC is a confusion matrix method of calculating the Pearson product-moment correlation coefficient (not to be confused with Pearson's C). Therefore, it has the same interpretation [2].

For more information visit [49].

Overall MCC	Strength of Association
0.3 >	Negligible
0.3 - 0.5	Weak
0.5 - 0.7	Moderate
0.7 - 0.9	Strong
0.9 - 1.0	Very Strong

cm.SOA6

'Weak'

Notice : new in version 2.2

Notice : only positive values are considered

Overall_ACC¶

For more information visit [3].

$$ACC_{Overall}=\frac{\sum_{i=1}^{|C|}TP_i}{POP}$$

Equals to TPR Micro, F1 Micro and PPV Micro

cm.Overall_ACC

0.5833333333333334

Notice : new in version 0.4

Overall_RACC¶

For more information visit [24].

$$RACC_{Overall}=\sum_{i=1}^{|C|}RACC_i$$

cm.Overall_RACC

0.3541666666666667

Notice : new in version 0.4

Overall_RACCU¶

For more information visit [25].

$$RACCU_{Overall}=\sum_{i=1}^{|C|}RACCU_i$$

cm.Overall_RACCU

0.3645833333333333

Notice : new in version 0.8.1

PPV_Micro¶

For more information visit [3].

$$PPV_{Micro}=\frac{\sum_{i=1}^{|C|}TP_i}{\sum_{i=1}^{|C|}TP_i+FP_i}$$

Equals to TPR Micro, F1 Micro and Overall ACC

cm.PPV_Micro

0.5833333333333334

Notice : new in version 0.4

TPR_Micro¶

For more information visit [3].

$$TPR_{Micro}=\frac{\sum_{i=1}^{|C|}TP_i}{\sum_{i=1}^{|C|}TP_i+FN_i}$$

Equals to PPV Micro, F1 Micro and Overall ACC

cm.TPR_Micro

0.5833333333333334

Notice : new in version 0.4

TNR_Micro¶

For more information visit [3].

$$TNR_{Micro}=\frac{\sum_{i=1}^{|C|}TN_i}{\sum_{i=1}^{|C|}TN_i+FP_i}$$

cm.TNR_Micro

0.7916666666666666

Notice : new in version 2.6

FPR_Micro¶

For more information visit [3].

$$FPR_{Micro}=\frac{\sum_{i=1}^{|C|}FP_i}{\sum_{i=1}^{|C|}TN_i+FP_i}$$

cm.FPR_Micro

0.20833333333333337

Notice : new in version 2.6

FNR_Micro¶

For more information visit [3].

$$FNR_{Micro}=\frac{\sum_{i=1}^{|C|}FN_i}{\sum_{i=1}^{|C|}TP_i+FN_i}$$

cm.FNR_Micro

0.41666666666666663

Notice : new in version 2.6

F1_Micro¶

For more information visit [3].

$$F_{1_{Micro}}=2\frac{\sum_{i=1}^{|C|}TPR_i\times PPV_i}{\sum_{i=1}^{|C|}TPR_i+PPV_i}$$

Equals to PPV Micro, TPR Micro and Overall ACC

cm.F1_Micro

0.5833333333333334

Notice : new in version 2.2

PPV_Macro¶

For more information visit [3].

$$PPV_{Macro}=\frac{1}{|C|}\sum_{i=1}^{|C|}\frac{TP_i}{TP_i+FP_i}$$

cm.PPV_Macro

0.611111111111111

Notice : new in version 0.4

TPR_Macro¶

For more information visit [3].

$$TPR_{Macro}=\frac{1}{|C|}\sum_{i=1}^{|C|}\frac{TP_i}{TP_i+FN_i}$$

cm.TPR_Macro

0.5666666666666668

Notice : new in version 0.4

TNR_Macro¶

For more information visit [3].

$$TNR_{Macro}=\frac{1}{|C|}\sum_{i=1}^{|C|}\frac{TN_i}{TN_i+FP_i}$$

cm.TNR_Macro

0.7904761904761904

Notice : new in version 2.6

FPR_Macro¶

For more information visit [3].

$$FPR_{Macro}=\frac{1}{|C|}\sum_{i=1}^{|C|}\frac{FP_i}{TN_i+FP_i}$$

cm.FPR_Macro

0.20952380952380956

Notice : new in version 2.6

FNR_Macro¶

For more information visit [3].

$$FNR_{Macro}=\frac{1}{|C|}\sum_{i=1}^{|C|}\frac{FN_i}{TP_i+FN_i}$$

cm.FNR_Macro

0.43333333333333324

Notice : new in version 2.6

F1_Macro¶

For more information visit [3].

$$F_{1_{Macro}}=\frac{2}{|C|}\sum_{i=1}^{|C|}\frac{TPR_i\times PPV_i}{TPR_i+PPV_i}$$

cm.F1_Macro

0.5651515151515151

Notice : new in version 2.2

ACC_Macro¶

For more information visit [3].

$$ACC_{Macro}=\frac{1}{|C|}\sum_{i=1}^{|C|}{ACC_i}$$

cm.ACC_Macro

0.7222222222222223

Notice : new in version 2.2

Overall_J¶

For more information visit [29].

$$J_{Mean}=\frac{1}{|C|}\sum_{i=1}^{|C|}J_i$$

$$J_{Sum}=\sum_{i=1}^{|C|}J_i$$

$$J_{Overall}=(J_{Sum},J_{Mean})$$

cm.Overall_J

(1.225, 0.4083333333333334)

Notice : new in version 0.9

Hamming loss¶

The average Hamming loss or Hamming distance between two sets of samples [31].

$$L_{Hamming}=\frac{1}{POP}\sum_{i=1}^{POP}1(y_i \neq \widehat{y}_i)$$

cm.HammingLoss

0.41666666666666663

Notice : new in version 1.0

Zero-one loss¶

Zero-one loss is a common loss function used with classification learning. It assigns $ 0 $ to loss for a correct classification and $ 1 $ for an incorrect classification [31].

$$L_{0-1}=\sum_{i=1}^{POP}1(y_i \neq \widehat{y}_i)$$

cm.ZeroOneLoss

5

Notice : new in version 1.1

NIR (No information rate)¶

Largest class percentage in the data [57].

$$NIR=\frac{1}{POP}Max(P)$$

cm.NIR

0.4166666666666667

Notice : new in version 1.2

P-Value¶

In statistical hypothesis testing, the p-value or probability value is, for a given statistical model, the probability that, when the null hypothesis is true, the statistical summary (such as the absolute value of the sample mean difference between two compared groups) would be greater than or equal to the actual observed results [31] .
Here a one-sided binomial test to see if the accuracy is better than the no information rate [57].

Wikipedia Page

$$x=\sum_{i=1}^{|C|}TP_{i}$$

$$p=NIR$$

$$n=POP$$

$$P-Value_{(ACC > NIR)}=1-\sum_{i=1}^{x}\left(\begin{array}{c}n\\ i\end{array}\right)p^{i}(1-p)^{n-i}$$

cm.PValue

0.18926430237560654

Notice : new in version 1.2

Overall_CEN¶

For more information visit [17].

$$P_j=\frac{\sum_{k=1}^{|C|}\Big(Matrix(j,k)+Matrix(k,j)\Big)}{2\sum_{k,l=1}^{|C|}Matrix(k,l)}$$

$$CEN_{Overall}=\sum_{j=1}^{|C|}P_jCEN_j$$

cm.Overall_CEN

0.4638112995385119

Notice : new in version 1.3

Overall_MCEN¶

For more information visit [19].

$$\alpha=\begin{cases}1 & |C| > 2\\0 & |C| = 2\end{cases}$$

$$P_j=\frac{\sum_{k=1}^{|C|}\Big(Matrix(j,k)+Matrix(k,j)\Big)-Matrix(j,j)}{2\sum_{k,l=1}^{|C|}Matrix(k,l)-\alpha \sum_{k=1}^{|C|}Matrix(k,k)}$$

$$MCEN_{Overall}=\sum_{j=1}^{|C|}P_jMCEN_j$$

cm.Overall_MCEN

0.5189369467580801

Notice : new in version 1.3

Overall_MCC¶

For more information visit [20] [27].

Benchmark

$$MCC_{Overall}=\frac{cov(X,Y)}{\sqrt{cov(X,X)\times cov(Y,Y)}}$$

$$cov(X,Y)=\sum_{i,j,k=1}^{|C|}\Big(Matrix(i,i)Matrix(k,j)-Matrix(j,i)Matrix(i,k)\Big)$$

$$cov(X,X) = \sum_{i=1}^{|C|}\Bigg[\Big(\sum_{j=1}^{|C|}Matrix(j,i)\Big)\Big(\sum_{k,l=1,k\neq i}^{|C|}Matrix(l,k)\Big)\Bigg]$$

$$cov(Y,Y) = \sum_{i=1}^{|C|}\Bigg[\Big(\sum_{j=1}^{|C|}Matrix(i,j)\Big)\Big(\sum_{k,l=1,k\neq i}^{|C|}Matrix(k,l)\Big)\Bigg]$$

cm.Overall_MCC

0.36666666666666664

Notice : new in version 1.4

RR (Global performance index)¶

For more information visit [21].

$$RR=\frac{1}{|C|}\sum_{i,j=1}^{|C|}Matrix(i,j)$$

cm.RR

4.0

Notice : new in version 1.4

CBA (Class balance accuracy)¶

As an evaluation tool, CBA creates an overall assessment of model predictive power by scrutinizing measures simultaneously across each class in a conservative manner that guarantees that a model’s ability to recall observations from each class and its ability to do so efficiently won’t fall below the bound [22] [51].

$$CBA=\frac{\sum_{i=1}^{|C|}\frac{Matrix(i,i)}{Max(TOP_i,P_i)}}{|C|}$$

cm.CBA

0.4777777777777778

Notice : new in version 1.4

AUNU¶

When dealing with multiclass problems, a global measure of classification performances based on the ROC approach (AUNU) has been proposed as the average of single-class measures [23].

$$AUNU=\frac{\sum_{i=1}^{|C|}AUC_i}{|C|}$$

cm.AUNU

0.6785714285714285

Notice : new in version 1.4

AUNP¶

Another option (AUNP) is that of averaging the $ AUC_i $ values with weights proportional to the number of samples experimentally belonging to each class, that is, the a priori class distribution [23].

$$AUNP=\sum_{i=1}^{|C|}\frac{P_i}{POP}AUC_i$$

cm.AUNP

0.6857142857142857

Notice : new in version 1.4

RCI (Relative classifier information)¶

Performance of different classifiers on the same domain can be measured by comparing relative classifier information while classifier information (mutual information) can be used for comparison across different decision problems [32] [22].

$$H_d=-\sum_{i=1}^{|C|}\Big(\frac{\sum_{l=1}^{|C|}Matrix(i,l)}{\sum_{h,k=1}^{|C|}Matrix(h,k)}log_2\frac{\sum_{l=1}^{|C|}Matrix(i,l)}{\sum_{h,k=1}^{|C|}Matrix(h,k)}\Big)=Entropy_{Reference}$$

$$H_o=\sum_{j=1}^{|C|}\Big(\frac{\sum_{k=1}^{|C|}Matrix(k,j)}{\sum_{h,l=0}^{|C|}Matrix(h,l)}H_{oj}\Big)=Entropy_{Conditional}$$

$$H_{oj}=-\sum_{i=1}^{|C|}\Big(\frac{Matrix(i,j)}{\sum_{k=1}^{|C|}Matrix(k,j)}log_2\frac{Matrix(i,j)}{\sum_{k=1}^{|C|}Matrix(k,j)}\Big)$$

$$RCI=\frac{H_d-H_o}{H_d}=\frac{MI}{Entropy_{Reference}}$$

cm.RCI

0.3533932006492363

Notice : new in version 1.5

Pearson's C¶

The contingency coefficient is a coefficient of association that tells whether two variables or data sets are independent or dependent of/on each other. It is also known as Pearson’s coefficient (not to be confused with Pearson’s coefficient of skewness) [43] [44].

$$C=\sqrt{\frac{\chi^2}{\chi^2+POP}}$$

cm.C

0.5956833971812706

Notice : new in version 2.0

CSI (Classification success index)¶

The Classification Success Index (CSI) is an overall measure defined by averaging ICSI over all classes [58].

$$CSI=\frac{1}{|C|}\sum_{i=1}^{|C|}{ICSI_i}$$

cm.CSI

0.1777777777777778

Notice : new in version 2.5

ARI (Adjusted Rand index)¶

The Rand index or Rand measure (named after William M. Rand) in statistics, and in particular in data clustering, is a measure of the similarity between two data clusterings. A form of the Rand index may be defined that is adjusted for the chance grouping of elements, this is the adjusted Rand index. From a mathematical standpoint, Rand index is related to the accuracy, but is applicable even when class labels are not used [68].

The Adjusted Rand Index (ARI) is frequently used in cluster validation since it is a measure of agreement between two partitions: one given by the clustering process and the other defined by external criteria, but it can also be used in supervised learning [69].

Wikipedia Page

$$X=\frac{\sum_{i}C_{2}^{P_i}\times \sum_{j}C_{2}^{TOP_j}}{C_2^{POP}}$$

$$ARI=\frac{\sum_{i,j}C_{2}^{Matrix(i,j)}-X}{\frac{1}{2}[\sum_{i}C_{2}^{P_i} + \sum_{j}C_{2}^{TOP_j}]-X}$$

cm.ARI

0.09206349206349207

Notice : $ C_{r}^{n} $ is the number of combinations of $ n $ objects taken $ r $

Notice : new in version 2.6

Bangdiwala's B¶

Bangdiwala's B statistic was created by Shrikant Bangdiwala in 1985 and is a measure of inter-rater agreement. While not as commonly used as the kappa statistic the B test has been used by various workers [72] [73].

Wikipedia Page

$$B=\frac{\sum_{i=1}^{|C|}TP_i^2}{\sum_{i=1}^{|C|}TOP_i\times P_i}$$

cm.B

0.37254901960784315

Notice : new in version 2.7

Krippendorff's alpha¶

Krippendorff's alpha coefficient, named after academic Klaus Krippendorff, is a statistical measure of the agreement achieved when coding a set of units of analysis in terms of the values of a variable. Krippendorff's alpha generalizes several known statistics, often called measures of inter-coder agreement, inter-rater reliability, reliability of coding given sets of units (as distinct from unitizing) but it also distinguishes itself from statistics that are called reliability coefficients but are unsuitable to the particulars of coding data generated for subsequent analysis [74].

Wikipedia Page

$$\epsilon = \frac{1}{2\times POP}$$

$$P_a=(1-\epsilon)\times ACC_{Overall}+\epsilon$$

$$P_e=RACCU_{Overall}$$

$$\alpha=\frac{P_a-P_e}{1-P_e}$$

cm.Alpha

0.3715846994535519

Notice : new in version 2.8

Weighted alpha¶

Weighted Krippendorff's alpha coefficient [74].

$$\epsilon = \frac{1}{2\times POP}$$

$$v_{ij}=1-\frac{w_{ij}}{max(w)}$$

$$P_e=\sum_{i,j=1}^{|C|}(\frac{TOP_i \times P_j}{2 \times POP})^2\times v_{ij}$$

$$P_a^*=\sum_{i,j=1}^{|C|}\frac{Matrix(i,j)}{POP}\times v_{ij}$$

$$P_a=(1-\epsilon)\times P_a^*+\epsilon$$

$$\alpha_{Weighted}=\frac{P_a-P_e}{1-P_e}$$

cm.weighted_alpha(weight={"L1":{"L1":0,"L2":1,"L3":2},"L2":{"L1":1,"L2":0,"L3":1},"L3":{"L1":2,"L2":1,"L3":0}})

0.374757281553398

cm.weighted_alpha()

C:\Users\Sepkjaer\AppData\Local\Programs\Python\Python35-32\lib\site-packages\pycm-3.1-py3.5.egg\pycm\pycm_obj.py:786: RuntimeWarning: The weight format is wrong, the result is for unweighted alpha.

0.3715846994535519

Parameters¶

weight : weight matrix (type : dict, default : None)

Output¶

Weighted alpha

Notice : new in version 2.8

Aickin's alpha¶

Aickin's alpha coefficient [75].

$$\alpha^{(t+1)}=\frac{p_a-p_e^{(t)}}{1-p_e^{(t)}}$$

$$p_e^{(t)}=\sum_{k=1}^{|C|}p_{k|H}^{A(t)}\times p_{k|H}^{B(t)}$$

$$p_a=ACC_{Overall}$$

$$p_{k|H}^{A(t+1)}=\frac{TOP_k}{(1-\alpha^{(t)})+\alpha^{(t)}\times \frac{p_{k|H}^{B(t)}}{p_e^{(t)}}\times POP}$$

$$p_{k|H}^{B(t+1)}=\frac{P_k}{(1-\alpha^{(t)})+\alpha^{(t)}\times \frac{p_{k|H}^{A(t)}}{p_e^{(t)}}\times POP}$$

$$Stop:|\alpha^{(t+1)}-\alpha^{(t)}|<\epsilon$$

cm.aickin_alpha()

0.38540577344968524

cm.aickin_alpha(max_iter=2000,epsilon=0.00003)

0.38545857383594895

Parameters¶

epsilon : difference threshold (type : float, default : 0.0001)
max_iter : maximum iteration (type : int, default : 200)

Output¶

Aickin's alpha

Notice : new in version 2.8

Print¶

Full¶

print(cm)

Predict  L1       L2       L3       
Actual
L1       3        0        2        

L2       0        1        1        

L3       0        2        3        


Overall Statistics : 

95% CI                                                            (0.30439,0.86228)
ACC Macro                                                         0.72222
ARI                                                               0.09206
AUNP                                                              0.68571
AUNU                                                              0.67857
Bangdiwala B                                                      0.37255
Bennett S                                                         0.375
CBA                                                               0.47778
CSI                                                               0.17778
Chi-Squared                                                       6.6
Chi-Squared DF                                                    4
Conditional Entropy                                               0.97579
Cramer V                                                          0.5244
Cross Entropy                                                     1.58333
F1 Macro                                                          0.56515
F1 Micro                                                          0.58333
FNR Macro                                                         0.43333
FNR Micro                                                         0.41667
FPR Macro                                                         0.20952
FPR Micro                                                         0.20833
Gwet AC1                                                          0.38931
Hamming Loss                                                      0.41667
Joint Entropy                                                     2.45915
KL Divergence                                                     0.09998
Kappa                                                             0.35484
Kappa 95% CI                                                      (-0.07708,0.78675)
Kappa No Prevalence                                               0.16667
Kappa Standard Error                                              0.22036
Kappa Unbiased                                                    0.34426
Krippendorff Alpha                                                0.37158
Lambda A                                                          0.42857
Lambda B                                                          0.16667
Mutual Information                                                0.52421
NIR                                                               0.41667
Overall ACC                                                       0.58333
Overall CEN                                                       0.46381
Overall J                                                         (1.225,0.40833)
Overall MCC                                                       0.36667
Overall MCEN                                                      0.51894
Overall RACC                                                      0.35417
Overall RACCU                                                     0.36458
P-Value                                                           0.18926
PPV Macro                                                         0.61111
PPV Micro                                                         0.58333
Pearson C                                                         0.59568
Phi-Squared                                                       0.55
RCI                                                               0.35339
RR                                                                4.0
Reference Entropy                                                 1.48336
Response Entropy                                                  1.5
SOA1(Landis & Koch)                                               Fair
SOA2(Fleiss)                                                      Poor
SOA3(Altman)                                                      Fair
SOA4(Cicchetti)                                                   Poor
SOA5(Cramer)                                                      Relatively Strong
SOA6(Matthews)                                                    Weak
Scott PI                                                          0.34426
Standard Error                                                    0.14232
TNR Macro                                                         0.79048
TNR Micro                                                         0.79167
TPR Macro                                                         0.56667
TPR Micro                                                         0.58333
Zero-one Loss                                                     5

Class Statistics :

Classes                                                           L1            L2            L3            
ACC(Accuracy)                                                     0.83333       0.75          0.58333       
AGF(Adjusted F-score)                                             0.72859       0.62869       0.61009       
AGM(Adjusted geometric mean)                                      0.85764       0.70861       0.58034       
AM(Difference between automatic and manual classification)        -2            1             1             
AUC(Area under the ROC curve)                                     0.8           0.65          0.58571       
AUCI(AUC value interpretation)                                    Very Good     Fair          Poor          
AUPR(Area under the PR curve)                                     0.8           0.41667       0.55          
BCD(Bray-Curtis dissimilarity)                                    0.08333       0.04167       0.04167       
BM(Informedness or bookmaker informedness)                        0.6           0.3           0.17143       
CEN(Confusion entropy)                                            0.25          0.49658       0.60442       
DOR(Diagnostic odds ratio)                                        None          4.0           2.0           
DP(Discriminant power)                                            None          0.33193       0.16597       
DPI(Discriminant power interpretation)                            None          Poor          Poor          
ERR(Error rate)                                                   0.16667       0.25          0.41667       
F0.5(F0.5 score)                                                  0.88235       0.35714       0.51724       
F1(F1 score - harmonic mean of precision and sensitivity)         0.75          0.4           0.54545       
F2(F2 score)                                                      0.65217       0.45455       0.57692       
FDR(False discovery rate)                                         0.0           0.66667       0.5           
FN(False negative/miss/type 2 error)                              2             1             2             
FNR(Miss rate or false negative rate)                             0.4           0.5           0.4           
FOR(False omission rate)                                          0.22222       0.11111       0.33333       
FP(False positive/type 1 error/false alarm)                       0             2             3             
FPR(Fall-out or false positive rate)                              0.0           0.2           0.42857       
G(G-measure geometric mean of precision and sensitivity)          0.7746        0.40825       0.54772       
GI(Gini index)                                                    0.6           0.3           0.17143       
GM(G-mean geometric mean of specificity and sensitivity)          0.7746        0.63246       0.58554       
IBA(Index of balanced accuracy)                                   0.36          0.28          0.35265       
ICSI(Individual classification success index)                     0.6           -0.16667      0.1           
IS(Information score)                                             1.26303       1.0           0.26303       
J(Jaccard index)                                                  0.6           0.25          0.375         
LS(Lift score)                                                    2.4           2.0           1.2           
MCC(Matthews correlation coefficient)                             0.68313       0.2582        0.16903       
MCCI(Matthews correlation coefficient interpretation)             Moderate      Negligible    Negligible    
MCEN(Modified confusion entropy)                                  0.26439       0.5           0.6875        
MK(Markedness)                                                    0.77778       0.22222       0.16667       
N(Condition negative)                                             7             10            7             
NLR(Negative likelihood ratio)                                    0.4           0.625         0.7           
NLRI(Negative likelihood ratio interpretation)                    Poor          Negligible    Negligible    
NPV(Negative predictive value)                                    0.77778       0.88889       0.66667       
OC(Overlap coefficient)                                           1.0           0.5           0.6           
OOC(Otsuka-Ochiai coefficient)                                    0.7746        0.40825       0.54772       
OP(Optimized precision)                                           0.58333       0.51923       0.55894       
P(Condition positive or support)                                  5             2             5             
PLR(Positive likelihood ratio)                                    None          2.5           1.4           
PLRI(Positive likelihood ratio interpretation)                    None          Poor          Poor          
POP(Population)                                                   12            12            12            
PPV(Precision or positive predictive value)                       1.0           0.33333       0.5           
PRE(Prevalence)                                                   0.41667       0.16667       0.41667       
Q(Yule Q - coefficient of colligation)                            None          0.6           0.33333       
QI(Yule Q interpretation)                                         None          Moderate      Weak          
RACC(Random accuracy)                                             0.10417       0.04167       0.20833       
RACCU(Random accuracy unbiased)                                   0.11111       0.0434        0.21007       
TN(True negative/correct rejection)                               7             8             4             
TNR(Specificity or true negative rate)                            1.0           0.8           0.57143       
TON(Test outcome negative)                                        9             9             6             
TOP(Test outcome positive)                                        3             3             6             
TP(True positive/hit)                                             3             1             3             
TPR(Sensitivity, recall, hit rate, or true positive rate)         0.6           0.5           0.6           
Y(Youden index)                                                   0.6           0.3           0.17143       
dInd(Distance index)                                              0.4           0.53852       0.58624       
sInd(Similarity index)                                            0.71716       0.61921       0.58547

Matrix¶

cm.print_matrix()

Predict  L1       L2       L3       
Actual
L1       3        0        2        

L2       0        1        1        

L3       0        2        3

cm.matrix

{'L1': {'L1': 3, 'L2': 0, 'L3': 2},
 'L2': {'L1': 0, 'L2': 1, 'L3': 1},
 'L3': {'L1': 0, 'L2': 2, 'L3': 3}}

cm.print_matrix(one_vs_all=True,class_name = "L1")

Predict  L1       ~        
Actual
L1       3        2        

~        0        7

sparse_cm = ConfusionMatrix(matrix={1:{1:0,2:2},2:{1:0,2:18}})

sparse_cm.print_matrix(sparse=True)

Predict  2        
Actual
1        2        

2        18

Parameters¶

one_vs_all : One-Vs-All mode flag (type : bool, default : False)
class_name : target class name for One-Vs-All mode (type : any valid type, default : None)
sparse : sparse mode printing flag (type : bool, default : False)

Notice : `one_vs_all` option, new in version 1.4

Notice : `matrix()` renamed to `print_matrix()` and `matrix` return confusion matrix as `dict` in version 1.5

Notice : `sparse` option, new in version 2.6

Normalized matrix¶

cm.print_normalized_matrix()

Predict   L1        L2        L3        
Actual
L1        0.6       0.0       0.4       

L2        0.0       0.5       0.5       

L3        0.0       0.4       0.6

cm.normalized_matrix

{'L1': {'L1': 0.6, 'L2': 0.0, 'L3': 0.4},
 'L2': {'L1': 0.0, 'L2': 0.5, 'L3': 0.5},
 'L3': {'L1': 0.0, 'L2': 0.4, 'L3': 0.6}}

cm.print_normalized_matrix(one_vs_all=True,class_name = "L1")

Predict   L1        ~         
Actual
L1        0.6       0.4       

~         0.0       1.0

sparse_cm.print_normalized_matrix(sparse=True)

Predict   2         
Actual
1         1.0       

2         1.0

Parameters¶

one_vs_all : One-Vs-All mode flag (type : bool, default : False)
class_name : target class name for One-Vs-All mode (type : any valid type, default : None)
sparse : sparse mode printing flag (type : bool, default : False)

Notice : `one_vs_all` option, new in version 1.4

Notice : `normalized_matrix()` renamed to `print_normalized_matrix()` and `normalized_matrix` return normalized confusion matrix as `dict` in version 1.5

Notice : `sparse` option, new in version 2.6

Stat¶

cm.stat()

Overall Statistics : 

95% CI                                                            (0.30439,0.86228)
ACC Macro                                                         0.72222
ARI                                                               0.09206
AUNP                                                              0.68571
AUNU                                                              0.67857
Bangdiwala B                                                      0.37255
Bennett S                                                         0.375
CBA                                                               0.47778
CSI                                                               0.17778
Chi-Squared                                                       6.6
Chi-Squared DF                                                    4
Conditional Entropy                                               0.97579
Cramer V                                                          0.5244
Cross Entropy                                                     1.58333
F1 Macro                                                          0.56515
F1 Micro                                                          0.58333
FNR Macro                                                         0.43333
FNR Micro                                                         0.41667
FPR Macro                                                         0.20952
FPR Micro                                                         0.20833
Gwet AC1                                                          0.38931
Hamming Loss                                                      0.41667
Joint Entropy                                                     2.45915
KL Divergence                                                     0.09998
Kappa                                                             0.35484
Kappa 95% CI                                                      (-0.07708,0.78675)
Kappa No Prevalence                                               0.16667
Kappa Standard Error                                              0.22036
Kappa Unbiased                                                    0.34426
Krippendorff Alpha                                                0.37158
Lambda A                                                          0.42857
Lambda B                                                          0.16667
Mutual Information                                                0.52421
NIR                                                               0.41667
Overall ACC                                                       0.58333
Overall CEN                                                       0.46381
Overall J                                                         (1.225,0.40833)
Overall MCC                                                       0.36667
Overall MCEN                                                      0.51894
Overall RACC                                                      0.35417
Overall RACCU                                                     0.36458
P-Value                                                           0.18926
PPV Macro                                                         0.61111
PPV Micro                                                         0.58333
Pearson C                                                         0.59568
Phi-Squared                                                       0.55
RCI                                                               0.35339
RR                                                                4.0
Reference Entropy                                                 1.48336
Response Entropy                                                  1.5
SOA1(Landis & Koch)                                               Fair
SOA2(Fleiss)                                                      Poor
SOA3(Altman)                                                      Fair
SOA4(Cicchetti)                                                   Poor
SOA5(Cramer)                                                      Relatively Strong
SOA6(Matthews)                                                    Weak
Scott PI                                                          0.34426
Standard Error                                                    0.14232
TNR Macro                                                         0.79048
TNR Micro                                                         0.79167
TPR Macro                                                         0.56667
TPR Micro                                                         0.58333
Zero-one Loss                                                     5

Class Statistics :

Classes                                                           L1            L2            L3            
ACC(Accuracy)                                                     0.83333       0.75          0.58333       
AGF(Adjusted F-score)                                             0.72859       0.62869       0.61009       
AGM(Adjusted geometric mean)                                      0.85764       0.70861       0.58034       
AM(Difference between automatic and manual classification)        -2            1             1             
AUC(Area under the ROC curve)                                     0.8           0.65          0.58571       
AUCI(AUC value interpretation)                                    Very Good     Fair          Poor          
AUPR(Area under the PR curve)                                     0.8           0.41667       0.55          
BCD(Bray-Curtis dissimilarity)                                    0.08333       0.04167       0.04167       
BM(Informedness or bookmaker informedness)                        0.6           0.3           0.17143       
CEN(Confusion entropy)                                            0.25          0.49658       0.60442       
DOR(Diagnostic odds ratio)                                        None          4.0           2.0           
DP(Discriminant power)                                            None          0.33193       0.16597       
DPI(Discriminant power interpretation)                            None          Poor          Poor          
ERR(Error rate)                                                   0.16667       0.25          0.41667       
F0.5(F0.5 score)                                                  0.88235       0.35714       0.51724       
F1(F1 score - harmonic mean of precision and sensitivity)         0.75          0.4           0.54545       
F2(F2 score)                                                      0.65217       0.45455       0.57692       
FDR(False discovery rate)                                         0.0           0.66667       0.5           
FN(False negative/miss/type 2 error)                              2             1             2             
FNR(Miss rate or false negative rate)                             0.4           0.5           0.4           
FOR(False omission rate)                                          0.22222       0.11111       0.33333       
FP(False positive/type 1 error/false alarm)                       0             2             3             
FPR(Fall-out or false positive rate)                              0.0           0.2           0.42857       
G(G-measure geometric mean of precision and sensitivity)          0.7746        0.40825       0.54772       
GI(Gini index)                                                    0.6           0.3           0.17143       
GM(G-mean geometric mean of specificity and sensitivity)          0.7746        0.63246       0.58554       
IBA(Index of balanced accuracy)                                   0.36          0.28          0.35265       
ICSI(Individual classification success index)                     0.6           -0.16667      0.1           
IS(Information score)                                             1.26303       1.0           0.26303       
J(Jaccard index)                                                  0.6           0.25          0.375         
LS(Lift score)                                                    2.4           2.0           1.2           
MCC(Matthews correlation coefficient)                             0.68313       0.2582        0.16903       
MCCI(Matthews correlation coefficient interpretation)             Moderate      Negligible    Negligible    
MCEN(Modified confusion entropy)                                  0.26439       0.5           0.6875        
MK(Markedness)                                                    0.77778       0.22222       0.16667       
N(Condition negative)                                             7             10            7             
NLR(Negative likelihood ratio)                                    0.4           0.625         0.7           
NLRI(Negative likelihood ratio interpretation)                    Poor          Negligible    Negligible    
NPV(Negative predictive value)                                    0.77778       0.88889       0.66667       
OC(Overlap coefficient)                                           1.0           0.5           0.6           
OOC(Otsuka-Ochiai coefficient)                                    0.7746        0.40825       0.54772       
OP(Optimized precision)                                           0.58333       0.51923       0.55894       
P(Condition positive or support)                                  5             2             5             
PLR(Positive likelihood ratio)                                    None          2.5           1.4           
PLRI(Positive likelihood ratio interpretation)                    None          Poor          Poor          
POP(Population)                                                   12            12            12            
PPV(Precision or positive predictive value)                       1.0           0.33333       0.5           
PRE(Prevalence)                                                   0.41667       0.16667       0.41667       
Q(Yule Q - coefficient of colligation)                            None          0.6           0.33333       
QI(Yule Q interpretation)                                         None          Moderate      Weak          
RACC(Random accuracy)                                             0.10417       0.04167       0.20833       
RACCU(Random accuracy unbiased)                                   0.11111       0.0434        0.21007       
TN(True negative/correct rejection)                               7             8             4             
TNR(Specificity or true negative rate)                            1.0           0.8           0.57143       
TON(Test outcome negative)                                        9             9             6             
TOP(Test outcome positive)                                        3             3             6             
TP(True positive/hit)                                             3             1             3             
TPR(Sensitivity, recall, hit rate, or true positive rate)         0.6           0.5           0.6           
Y(Youden index)                                                   0.6           0.3           0.17143       
dInd(Distance index)                                              0.4           0.53852       0.58624       
sInd(Similarity index)                                            0.71716       0.61921       0.58547

cm.stat(overall_param=["Kappa"],class_param=["ACC","AUC","TPR"])

Overall Statistics : 

Kappa                                                             0.35484

Class Statistics :

Classes                                                           L1            L2            L3            
ACC(Accuracy)                                                     0.83333       0.75          0.58333       
AUC(Area under the ROC curve)                                     0.8           0.65          0.58571       
TPR(Sensitivity, recall, hit rate, or true positive rate)         0.6           0.5           0.6

cm.stat(overall_param=["Kappa"],class_param=["ACC","AUC","TPR"],class_name=["L1","L3"])

Overall Statistics : 

Kappa                                                             0.35484

Class Statistics :

Classes                                                           L1            L3            
ACC(Accuracy)                                                     0.83333       0.58333       
AUC(Area under the ROC curve)                                     0.8           0.58571       
TPR(Sensitivity, recall, hit rate, or true positive rate)         0.6           0.6

cm.stat(summary=True)

Overall Statistics : 

ACC Macro                                                         0.72222
F1 Macro                                                          0.56515
FPR Macro                                                         0.20952
Kappa                                                             0.35484
Overall ACC                                                       0.58333
PPV Macro                                                         0.61111
SOA1(Landis & Koch)                                               Fair
TPR Macro                                                         0.56667
Zero-one Loss                                                     5

Class Statistics :

Classes                                                           L1            L2            L3            
ACC(Accuracy)                                                     0.83333       0.75          0.58333       
AUC(Area under the ROC curve)                                     0.8           0.65          0.58571       
AUCI(AUC value interpretation)                                    Very Good     Fair          Poor          
F1(F1 score - harmonic mean of precision and sensitivity)         0.75          0.4           0.54545       
FN(False negative/miss/type 2 error)                              2             1             2             
FP(False positive/type 1 error/false alarm)                       0             2             3             
FPR(Fall-out or false positive rate)                              0.0           0.2           0.42857       
N(Condition negative)                                             7             10            7             
P(Condition positive or support)                                  5             2             5             
POP(Population)                                                   12            12            12            
PPV(Precision or positive predictive value)                       1.0           0.33333       0.5           
TN(True negative/correct rejection)                               7             8             4             
TON(Test outcome negative)                                        9             9             6             
TOP(Test outcome positive)                                        3             3             6             
TP(True positive/hit)                                             3             1             3             
TPR(Sensitivity, recall, hit rate, or true positive rate)         0.6           0.5           0.6

Parameters¶

overall_param : overall statistics names for print (type : list, default : None)
class_param : class statistics names for print (type : list, default : None)
class_name : class names for print (sub set of classes) (type : list, default : None)
summary : summary mode flag (type : bool, default : False)

Notice : `cm.params()` in prev versions (0.2 >)

Notice : `overall_param` & `class_param` , new in version 1.6

Notice : `class_name` , new in version 1.7

Notice : `summary` , new in version 2.4

Compare report¶

cp.print_report()

Best : cm2

Rank  Name   Class-Score    Overall-Score
1     cm2    9.05           2.55
2     cm3    6.05           1.98333

print(cp)

Best : cm2

Rank  Name   Class-Score    Overall-Score
1     cm2    9.05           2.55
2     cm3    6.05           1.98333

Save¶

import os
if "Document_Files" not in os.listdir():
    os.mkdir("Document_Files")

.pycm file¶

cm.save_stat(os.path.join("Document_Files","cm1"))

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1.pycm',
 'Status': True}

Open File

cm.save_stat(os.path.join("Document_Files","cm1_filtered"),overall_param=["Kappa"],class_param=["ACC","AUC","TPR"])

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_filtered.pycm',
 'Status': True}

Open File

cm.save_stat(os.path.join("Document_Files","cm1_filtered2"),overall_param=["Kappa"],class_param=["ACC","AUC","TPR"],class_name=["L1"])

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_filtered2.pycm',
 'Status': True}

Open File

cm.save_stat(os.path.join("Document_Files","cm1_summary"),summary=True)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_summary.pycm',
 'Status': True}

Open File

sparse_cm.save_stat(os.path.join("Document_Files","sparse_cm"),summary=True,sparse=True)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\sparse_cm.pycm',
 'Status': True}

Open File

cm.save_stat("cm1asdasd/")

{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.pycm'",
 'Status': False}

Parameters¶

name : output file name (type : str)
address : flag for address return (type : bool, default : True)
overall_param : overall statistics names for save (type : list, default : None)
class_param : class statistics names for save (type : list, default : None)
class_name : class names for print (sub set of classes) (type : list, default : None)
summary : summary mode flag (type : bool, default : False)
sparse : sparse mode printing flag (type : bool, default : False)

Notice : new in version 0.4

Notice : `overall_param` & `class_param` , new in version 1.6

Notice : `class_name` , new in version 1.7

Notice : `summary` , new in version 2.4

Notice : `sparse`, new in version 2.6

HTML¶

cm.save_html(os.path.join("Document_Files","cm1"))

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1.html',
 'Status': True}

Open File

cm.save_html(os.path.join("Document_Files","cm1_filtered"),overall_param=["Kappa"],class_param=["ACC","AUC","TPR"])

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_filtered.html',
 'Status': True}

Open File

cm.save_html(os.path.join("Document_Files","cm1_filtered2"),overall_param=["Kappa"],class_param=["ACC","AUC","TPR"],class_name=["L1"])

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_filtered2.html',
 'Status': True}

Open File

cm.save_html(os.path.join("Document_Files","cm1_colored"),color=(255, 204, 255))

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_colored.html',
 'Status': True}

Open File

cm.save_html(os.path.join("Document_Files","cm1_colored2"),color="Crimson")

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_colored2.html',
 'Status': True}

Open File

cm.save_html(os.path.join("Document_Files","cm1_normalized"),color="Crimson",normalize=True)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_normalized.html',
 'Status': True}

Open File

cm.save_html(os.path.join("Document_Files","cm1_summary"),summary=True,normalize=True)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_summary.html',
 'Status': True}

Open File

cm.save_html("cm1asdasd/")

{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.html'",
 'Status': False}

Parameters¶

name : output file name (type : str)
address : flag for address return (type : bool, default : True)
overall_param : overall statistics names for save (type : list, default : None)
class_param : class statistics names for save (type : list, default : None)
class_name : class names for print (sub set of classes) (type : list, default : None)
color : matrix color (R,G,B) (type : tuple/str, default : (0,0,0)), support X11 color names
normalize : save normalize matrix flag (type : bool, default : False)
summary : summary mode flag (type : bool, default : False)
alt_link : alternative link for document flag (type : bool, default : False)

Notice : new in version 0.5

Notice : `overall_param` & `class_param` , new in version 1.6

Notice : `class_name` , new in version 1.7

Notice : `color`, new in version 1.8

Notice : `normalize`, new in version 2.0

Notice : `summary` and `alt_link` , new in version 2.4

Notice : If PyCM website is not available, set `alt_link = True`

CSV¶

cm.save_csv(os.path.join("Document_Files","cm1"))

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1.csv',
 'Status': True}

Open Stat File
Open Matrix File

cm.save_csv(os.path.join("Document_Files","cm1_filtered"),class_param=["ACC","AUC","TPR"])

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_filtered.csv',
 'Status': True}

Open Stat File
Open Matrix File

cm.save_csv(os.path.join("Document_Files","cm1_filtered2"),class_param=["ACC","AUC","TPR"],normalize=True)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_filtered2.csv',
 'Status': True}

Open Stat File
Open Matrix File

cm.save_csv(os.path.join("Document_Files","cm1_filtered3"),class_param=["ACC","AUC","TPR"],class_name=["L1"])

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_filtered3.csv',
 'Status': True}

Open Stat File
Open Matrix File

cm.save_csv(os.path.join("Document_Files","cm1_header"),header=True)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_header.csv',
 'Status': True}

Open Stat File
Open Matrix File

cm.save_csv(os.path.join("Document_Files","cm1_summary"),summary=True,matrix_save=False)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_summary.csv',
 'Status': True}

Open Stat File

cm.save_csv("cm1asdasd/")

{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.csv'",
 'Status': False}

Parameters¶

name : output file name (type : str)
address : flag for address return (type : bool, default : True)
class_param : class statistics names for save (type : list, default : None)
class_name : class names for print (sub set of classes) (type : list, default : None)
matrix_save : flag for saving matrix in seperate CSV file (type : bool, default : True)
normalize : flag for saving normalized matrix instead of matrix (type : bool, default : False)
summary : summary mode flag (type : bool, default : False)
header : flag for adding header to matrix CSV file (type : bool, default : False)

Notice : new in version 0.6

Notice : `class_param` , new in version 1.6

Notice : `class_name` , new in version 1.7

Notice : `matrix_save` and `normalize`, new in version 1.9

Notice : `summary` , new in version 2.4

Notice : `header` , new in version 2.6

OBJ¶

cm.save_obj(os.path.join("Document_Files","cm1"))

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1.obj',
 'Status': True}

Open File

cm.save_obj(os.path.join("Document_Files","cm1_stat"),save_stat=True)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_stat.obj',
 'Status': True}

Open File

cm.save_obj(os.path.join("Document_Files","cm1_no_vectors"),save_vector=False)

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cm1_no_vectors.obj',
 'Status': True}

Open File

cm.save_obj("cm1asdasd/")

{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.obj'",
 'Status': False}

Parameters¶

name : output file name (type : str)
address : flag for address return (type : bool, default : True)
save_stat : save statistics flag (type : bool, default : False)
save_vector : save vectors flag (type : bool, default : True)

Notice : new in version 0.9.5

Notice : `save_vector` and `save_stat`, new in version 2.3

Notice : For more information visit Example 4

comp¶

cp.save_report(os.path.join("Document_Files","cp"))

{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\Document_Files\\cp.comp',
 'Status': True}

Open File

cp.save_report("cm1asdasd/")

{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.comp'",
 'Status': False}

Parameters¶

name : output file name (type : str)
address : flag for address return (type : bool, default : True)

Notice : new in version 2.0

Input errors¶

try:
    cm2=ConfusionMatrix(y_actu, 2)
except pycmVectorError as e:
    print(str(e))

The type of input vectors is assumed to be a list or a NumPy array

try:
    cm3=ConfusionMatrix(y_actu, [1,2,3])
except pycmVectorError as e:
    print(str(e))

Input vectors must have same length

try:
    cm_4 = ConfusionMatrix([], [])
except pycmVectorError as e:
    print(str(e))

Input vectors are empty

try:
    cm_5 = ConfusionMatrix([1,1,1,], [1,1,1,1])
except pycmVectorError as e:
    print(str(e))

Input vectors must have same length

try:
    cm3=ConfusionMatrix(matrix={})
except pycmMatrixError as e:
    print(str(e))

Input confusion matrix format error

try:
    cm_4=ConfusionMatrix(matrix={1:{1:2,"1":2},"1":{1:2,"1":3}})
except pycmMatrixError as e:
    print(str(e))

Type of the input matrix classes is assumed  be the same

try:
    cm_5=ConfusionMatrix(matrix={1:{1:2}})
except pycmMatrixError as e:
    print(str(e))

Number of the classes is lower than 2

try:
    cp=Compare([cm2,cm3])
except pycmCompareError as e:
    print(str(e))

The input type is supposed to be dictionary but it's not!

try:
    cp=Compare({"cm1":cm,"cm2":cm2})
except pycmCompareError as e:
    print(str(e))

The domain of all ConfusionMatrix objects must be same! The sample size or the number of classes are different.

try:
    cp=Compare({"cm1":[],"cm2":cm2})
except pycmCompareError as e:
    print(str(e))

The input is supposed to consist of pycm.ConfusionMatrix object but it's not!

try:
    cp=Compare({"cm2":cm2})
except pycmCompareError as e:
    print(str(e))

Lower than two confusion matrices is given for comparing. The minimum number of confusion matrix for comparing is 2.

try:
    cp=Compare({"cm1":cm2,"cm2":cm3},by_class=True,weight={1:2,2:0})
except pycmCompareError as e:
    print(str(e))

The weight type must be dictionary and also must be specified for all of the classes.

try:
    cm.CI("MCC")
except pycmCIError as e:
    print(str(e))

CI calculation for this parameter is not supported on this version of pycm.
Supported parameters : TPR,TNR,PPV,NPV,ACC,PLR,NLR,FPR,FNR,AUC,PRE,Kappa,Overall ACC

try:
    cm.CI(2)
except pycmCIError as e:
    print(str(e))

The input type is supposed to be string but it's not!

try:
    cm.average("AXY")
except pycmAverageError as e:
    print(str(e))

Invalid parameter!

try:
    cm.weighted_average("AXY")
except pycmAverageError as e:
    print(str(e))

Invalid parameter!

try:
    cm.weighted_average("AUC",weight={1:22})
except pycmAverageError as e:
    print(str(e))

The weight type must be dictionary and also must be specified for all of the classes.

try:
    cm.position()
except pycmVectorError as e:
    print(str(e))

This option only works in vector mode

try:
    cm.combine(2)
except pycmMatrixError as e:
    print(str(e))

The input type is supposed to be pycm.ConfusionMatrix object but it's not!

Notice : updated in version 3.0

Examples¶

Example-1 (Comparison of three different classifiers)¶

Example-2 (How to plot via matplotlib)¶

Example-3 (Activation threshold)¶

Example-4 (File)¶

Example-5 (Sample weights)¶

Example-6 (Unbalanced data)¶

Example-7 (How to plot via seaborn+pandas)¶

Example-8 (Confidence interval)¶

Cite¶

If you use PyCM in your research, we would appreciate citations to the following paper :

Haghighi, S., Jasemi, M., Hessabi, S. and Zolanvari, A. (2018). PyCM: Multiclass confusion matrix library in Python.
Journal of Open Source Software, 3(25), p.729.

@article{Haghighi2018,
  doi = {10.21105/joss.00729},
  url = {https://doi.org/10.21105/joss.00729},
  year  = {2018},
  month = {may},
  publisher = {The Open Journal},
  volume = {3},
  number = {25},
  pages = {729},
  author = {Sepand Haghighi and Masoomeh Jasemi and Shaahin Hessabi and Alireza Zolanvari},
  title = {{PyCM}: Multiclass confusion matrix library in Python},
  journal = {Journal of Open Source Software}
}

Download PyCM.bib

References¶

1- J. R. Landis and G. G. Koch, "The measurement of observer agreement for categorical data," biometrics, pp. 159-174, 1977.

2- D. M. Powers, "Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation," arXiv preprint arXiv:2010.16061, 2020.

3- C. Sammut and G. I. Webb, Encyclopedia of machine learning. Springer Science & Business Media, 2011.

4- J. L. Fleiss, "Measuring nominal scale agreement among many raters," Psychological bulletin, vol. 76, no. 5, p. 378, 1971.

5- D. G. Altman, Practical statistics for medical research. CRC press, 1990.

6- K. L. Gwet, "Computing inter-rater reliability and its variance in the presence of high agreement," British Journal of Mathematical and Statistical Psychology, vol. 61, no. 1, pp. 29-48, 2008.

7- W. A. Scott, "Reliability of content analysis: The case of nominal scale coding," Public opinion quarterly, pp. 321-325, 1955.

8- E. M. Bennett, R. Alpert, and A. Goldstein, "Communications through limited-response questioning," Public Opinion Quarterly, vol. 18, no. 3, pp. 303-308, 1954.

9- D. V. Cicchetti, "Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology," Psychological assessment, vol. 6, no. 4, p. 284, 1994.

10- R. B. Davies, "Algorithm AS 155: The distribution of a linear combination of χ2 random variables," Applied Statistics, pp. 323-333, 1980.

11- S. Kullback and R. A. Leibler, "On information and sufficiency," The annals of mathematical statistics, vol. 22, no. 1, pp. 79-86, 1951.

12- L. A. Goodman and W. H. Kruskal, "Measures of association for cross classifications, IV: Simplification of asymptotic variances," Journal of the American Statistical Association, vol. 67, no. 338, pp. 415-421, 1972.

13- L. A. Goodman and W. H. Kruskal, "Measures of association for cross classifications III: Approximate sampling theory," Journal of the American Statistical Association, vol. 58, no. 302, pp. 310-364, 1963.

14- T. Byrt, J. Bishop, and J. B. Carlin, "Bias, prevalence and kappa," Journal of clinical epidemiology, vol. 46, no. 5, pp. 423-429, 1993.

15- M. Shepperd, D. Bowes, and T. Hall, "Researcher bias: The use of machine learning in software defect prediction," IEEE Transactions on Software Engineering, vol. 40, no. 6, pp. 603-616, 2014.

16- X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, "An improved method to construct basic probability assignment based on the confusion matrix for classification problem," Information Sciences, vol. 340, pp. 250-261, 2016.

17- J.-M. Wei, X.-J. Yuan, Q.-H. Hu, and S.-Q. Wang, "A novel measure for evaluating classifiers," Expert Systems with Applications, vol. 37, no. 5, pp. 3799-3809, 2010.

18- I. Kononenko and I. Bratko, "Information-based evaluation criterion for classifier's performance," Machine learning, vol. 6, no. 1, pp. 67-80, 1991.

19- R. Delgado and J. D. Núnez-González, "Enhancing confusion entropy as measure for evaluating classifiers," in The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications, 2018: Springer, pp. 79-89.

20- J. Gorodkin, "Comparing two K-category assignments by a K-category correlation coefficient," Computational biology and chemistry, vol.28, no. 5-6, pp. 367-374, 2004.

21- C. O. Freitas, J. M. De Carvalho, J. Oliveira, S. B. Aires, and R. Sabourin, "Confusion matrix disagreement for multiple classifiers," in Iberoamerican Congress on Pattern Recognition, 2007: Springer, pp. 387-396.

22- P. Branco, L. Torgo, and R. P. Ribeiro, "Relevance-based evaluation metrics for multi-class imbalanced domains," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2017: Springer, pp. 698-710.

23- D. Ballabio, F. Grisoni, and R. Todeschini, "Multivariate comparison of classification performance measures," Chemometrics and Intelligent Laboratory Systems, vol. 174, pp. 33-44, 2018.

24- J. Cohen, "A coefficient of agreement for nominal scales," Educational and psychological measurement, vol. 20, no. 1, pp. 37-46, 1960.

25- S. Siegel, "Nonparametric statistics for the behavioral sciences," 1956.

26- H. Cramér, Mathematical methods of statistics. Princeton university press, 1999.

27- B. W. Matthews, "Comparison of the predicted and observed secondary structure of T4 phage lysozyme," Biochimica et Biophysica Acta (BBA)-Protein Structure, vol. 405, no. 2, pp. 442-451, 1975.

28- J. A. Swets, "The relative operating characteristic in psychology: a technique for isolating effects of response bias finds wide use in the study of perception and cognition," Science, vol. 182, no. 4116, pp. 990-1000, 1973.

29- P. Jaccard, "Étude comparative de la distribution florale dans une portion des Alpes et des Jura," Bull Soc Vaudoise Sci Nat, vol. 37, pp. 547-579, 1901.

30- T. M. Cover and J. A. Thomas, Elements of Information Theory. John Wiley & Sons, 2012.

31- E. S. Keeping, Introduction to statistical inference. Courier Corporation, 1995.

32- V. Sindhwani, P. Bhattacharya, and S. Rakshit, "Information theoretic feature crediting in multiclass support vector machines," in Proceedings of the 2001 SIAM International Conference on Data Mining, 2001: SIAM, pp. 1-18.

33- M. Bekkar, H. K. Djemaa, and T. A. Alitouche, "Evaluation measures for models assessment over imbalanced data sets," J Inf Eng Appl, vol. 3, no. 10, 2013.

34- W. J. Youden, "Index for rating diagnostic tests," Cancer, vol. 3, no. 1, pp. 32-35, 1950.

35- S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, "Dynamic itemset counting and implication rules for market basket data," in Proceedings of the 1997 ACM SIGMOD international conference on Management of data, 1997, pp. 255-264.

36- S. Raschka, "MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack," Journal of open source software, vol. 3, no. 24, p. 638, 2018.

37- J. R. Bray and J. T. Curtis, "An ordination of the upland forest communities of southern Wisconsin," Ecological monographs, vol. 27, no. 4, pp. 325-349, 1957.

38- J. L. Fleiss, J. Cohen, and B. S. Everitt, "Large sample standard errors of kappa and weighted kappa," Psychological bulletin, vol. 72, no. 5, p. 323, 1969.

39- M. Felkin, "Comparing classification results between n-ary and binary problems," in Quality Measures in Data Mining: Springer, 2007, pp. 277-301.

40- R. Ranawana and V. Palade, "Optimized precision-a new measure for classifier performance evaluation," in 2006 IEEE International Conference on Evolutionary Computation, 2006: IEEE, pp. 2254-2261.

41- V. García, R. A. Mollineda, and J. S. Sánchez, "Index of balanced accuracy: A performance measure for skewed class distributions," in Iberian conference on pattern recognition and image analysis, 2009: Springer, pp. 441-448.

42- P. Branco, L. Torgo, and R. P. Ribeiro, "A survey of predictive modeling on imbalanced domains," ACM Computing Surveys (CSUR), vol. 49, no. 2, pp. 1-50, 2016.

43- K. Pearson, "Notes on Regression and Inheritance in the Case of Two Parents," in Proceedings of the Royal Society of London, p. 240-242, 1895.

44- W. J. Conover, Practical nonparametric statistics. John Wiley & Sons, 1998.

45- G. U. Yule, "On the methods of measuring association between two attributes," Journal of the Royal Statistical Society, vol. 75, no. 6, pp. 579-652, 1912.

46- R. Batuwita and V. Palade, "A new performance measure for class imbalance learning. application to bioinformatics problems," in 2009 International Conference on Machine Learning and Applications, 2009: IEEE, pp. 545-550.

47- D. K. Lee, "Alternatives to P value: confidence interval and effect size," Korean journal of anesthesiology, vol. 69, no. 6, p. 555, 2016.

48- M. A. Raslich, R. J. Markert, and S. A. Stutes, "Selecting and interpreting diagnostic tests," Biochemia Medica, vol. 17, no. 2, pp. 151-161, 2007.

49- D. E. Hinkle, W. Wiersma, and S. G. Jurs, Applied statistics for the behavioral sciences. Houghton Mifflin College Division, 2003.

50- A. Maratea, A. Petrosino, and M. Manzo, "Adjusted F-measure and kernel scaling for imbalanced data learning," Information Sciences, vol. 257, pp. 331-341, 2014.

51- L. Mosley, "A balanced approach to the multi-class imbalance problem," 2013.

52- M. Vijaymeena and K. Kavitha, "A survey on similarity measures in text mining," Machine Learning and Applications: An International Journal, vol. 3, no. 2, pp. 19-28, 2016.

53- Y. Otsuka, "The faunal character of the Japanese Pleistocene marine Mollusca, as evidence of climate having become colder during the Pleistocene in Japan," Biogeograph Soc Japan, vol. 6, no. 16, pp. 165-170, 1936.

54- A. Tversky, "Features of similarity," Psychological review, vol. 84, no. 4, p. 327, 1977.

55- K. Boyd, K. H. Eng, and C. D. Page, "Area under the precision-recall curve: point estimates and confidence intervals," in Joint European conference on machine learning and knowledge discovery in databases, 2013: Springer, pp. 451-466.

56- J. Davis and M. Goadrich, "The relationship between Precision-Recall and ROC curves," in Proceedings of the 23rd international conference on Machine learning, 2006, pp. 233-240.

57- M. Kuhn, "Building predictive models in R using the caret package," J Stat Softw, vol. 28, no. 5, pp. 1-26, 2008.

58- V. Labatut and H. Cherifi, "Accuracy measures for the comparison of classifiers," arXiv preprint arXiv:1207.3790, 2012.

59- S. Wallis, "Binomial confidence intervals and contingency tests: mathematical fundamentals and the evaluation of alternative methods," Journal of Quantitative Linguistics, vol. 20, no. 3, pp. 178-208, 2013.

60- D. Altman, D. Machin, T. Bryant, and M. Gardner, Statistics with confidence: confidence intervals and statistical guidelines. John Wiley & Sons, 2013.

61- J. A. Hanley and B. J. McNeil, "The meaning and use of the area under a receiver operating characteristic (ROC) curve," Radiology, vol. 143, no. 1, pp. 29-36, 1982.

62- E. B. Wilson, "Probable inference, the law of succession, and statistical inference," Journal of the American Statistical Association, vol. 22, no. 158, pp. 209-212, 1927.

63- A. Agresti and B. A. Coull, "Approximate is better than “exact” for interval estimation of binomial proportions," The American Statistician, vol. 52, no. 2, pp. 119-126, 1998.

64- C. S. Peirce, "The numerical measure of the success of predictions," Science, no. 93, pp. 453-454, 1884.

65- E. W. Steyerberg, B. Van Calster, and M. J. Pencina, "Performance measures for prediction models and markers: evaluation of predictions and classifications," Revista Española de Cardiología (English Edition), vol. 64, no. 9, pp. 788-794, 2011.

66- A. J. Vickers and E. B. Elkin, "Decision curve analysis: a novel method for evaluating prediction models," Medical Decision Making, vol. 26, no. 6, pp. 565-574, 2006.

67- G. W. Bohrnstedt and D. Knoke,"Statistics for social data analysis," 1982.

68- W. M. Rand, "Objective criteria for the evaluation of clustering methods," Journal of the American Statistical association, vol. 66, no. 336, pp. 846-850, 1971.

69- J. M. Santos and M. Embrechts, "On the use of the adjusted rand index as a metric for evaluating supervised classification," in International conference on artificial neural networks, 2009: Springer, pp. 175-184.

70- J. Cohen, "Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit," Psychological bulletin, vol. 70, no. 4, p. 213, 1968.

71- R. Bakeman and J. M. Gottman, Observing interaction: An introduction to sequential analysis. Cambridge university press, 1997.

72- S. Bangdiwala, "A graphical test for observer agreement," in 45th International Statistical Institute Meeting, 1985, vol. 1985, p. 307.

73- K. Bangdiwala and H. Bryan, "Using SAS software graphical procedures for the observer agreement chart," in Proceedings of the SAS Users Group International Conference, 1987, vol. 12, pp. 1083-1088.

74- A. F. Hayes and K. Krippendorff, "Answering the call for a standard reliability measure for coding data," Communication methods and measures, vol. 1, no. 1, pp. 77-89, 2007.

75- M. Aickin, "Maximum likelihood estimation of agreement in the constant predictive probability model, and its relation to Cohen's kappa," Biometrics, pp. 293-302, 1990.

76- N. A. Macmillan and C. D. Creelman, Detectiontheory: A user's guide. Psychology press, 2004.