# PyCM Document¶

## Overview¶

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 an accurate evaluation of large variety of classifiers.

Fig1. PyCM Block Diagram

## Installation¶

### Source code¶

• 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)

### Easy install¶

• Run easy_install --upgrade pycm (Need root access)

## Usage¶

### From vector¶

In [1]:
from pycm import *

In [2]:
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]

In [3]:
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
In [4]:
cm

Out[4]:
pycm.ConfusionMatrix(classes: [0, 1, 2])
In [5]:
cm.actual_vector

Out[5]:
[2, 0, 2, 2, 0, 1, 1, 2, 2, 0, 1, 2]
In [6]:
cm.predict_vector

Out[6]:
[0, 0, 2, 1, 0, 2, 1, 0, 2, 0, 2, 2]
In [7]:
cm.classes

Out[7]:
[0, 1, 2]
In [8]:
cm.class_stat

Out[8]:
{'ACC': {0: 0.8333333333333334, 1: 0.75, 2: 0.5833333333333334},
'AUC': {0: 0.8888888888888888, 1: 0.611111111111111, 2: 0.5833333333333333},
'AUCI': {0: 'Very Good', 1: 'Fair', 2: 'Poor'},
'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},
'IS': {0: 1.263034405833794, 1: 1.0, 2: 0.2630344058337938},
'J': {0: 0.6, 1: 0.25, 2: 0.375},
'MCC': {0: 0.6831300510639732, 1: 0.25819888974716115, 2: 0.1690308509457033},
'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},
'NPV': {0: 1.0, 1: 0.8, 2: 0.5714285714285714},
'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},
'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 >)
In [9]:
cm.overall_stat

Out[9]:
{'95% CI': (0.30438856248221097, 0.8622781041844558),
'AUNP': 0.6666666666666666,
'AUNU': 0.6944444444444443,
'Bennett S': 0.37500000000000006,
'CBA': 0.4777777777777778,
'Chi-Squared': 6.6,
'Chi-Squared DF': 4,
'Conditional Entropy': 0.9591479170272448,
'Cramer V': 0.5244044240850757,
'Cross Entropy': 1.5935164295556343,
'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,
'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,
'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',
'Scott PI': 0.34426229508196726,
'Standard Error': 0.14231876063832777,
'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
In [10]:
cm.table

Out[10]:
{0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}}
In [11]:
cm.matrix

Out[11]:
{0: {0: 3, 1: 0, 2: 0}, 1: {0: 0, 1: 1, 2: 2}, 2: {0: 2, 1: 1, 2: 3}}
In [12]:
cm.normalized_matrix

Out[12]:
{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}}
In [13]:
cm.normalized_table

Out[13]:
{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)
In [14]:
import numpy

In [15]:
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])

In [16]:
cm = ConfusionMatrix(y_actu, y_pred,digit=5)

In [17]:
cm

Out[17]:
pycm.ConfusionMatrix(classes: [0, 1, 2])
• Notice : numpy.array support in versions > 0.7

### Direct CM¶

In [18]:
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)

In [19]:
cm2

Out[19]:
pycm.ConfusionMatrix(classes: [0, 1, 2])
In [20]:
cm2.actual_vector

In [21]:
cm2.predict_vector

In [22]:
cm2.classes

Out[22]:
[0, 1, 2]
In [23]:
cm2.class_stat

Out[23]:
{'ACC': {0: 0.8333333333333334, 1: 0.75, 2: 0.5833333333333334},
'AUC': {0: 0.8888888888888888, 1: 0.611111111111111, 2: 0.5833333333333333},
'AUCI': {0: 'Very Good', 1: 'Fair', 2: 'Poor'},
'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},
'IS': {0: 1.263034405833794, 1: 1.0, 2: 0.2630344058337938},
'J': {0: 0.6, 1: 0.25, 2: 0.375},
'MCC': {0: 0.6831300510639732, 1: 0.25819888974716115, 2: 0.1690308509457033},
'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},
'NPV': {0: 1.0, 1: 0.8, 2: 0.5714285714285714},
'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},
'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}}
In [24]:
cm.overall_stat

Out[24]:
{'95% CI': (0.30438856248221097, 0.8622781041844558),
'AUNP': 0.6666666666666666,
'AUNU': 0.6944444444444443,
'Bennett S': 0.37500000000000006,
'CBA': 0.4777777777777778,
'Chi-Squared': 6.6,
'Chi-Squared DF': 4,
'Conditional Entropy': 0.9591479170272448,
'Cramer V': 0.5244044240850757,
'Cross Entropy': 1.5935164295556343,
'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,
'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,
'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',
'Scott PI': 0.34426229508196726,
'Standard Error': 0.14231876063832777,
'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.

• Notice : new in version 0.9

file is added in version 0.9.5 in order to load saved confusion matrix with .obj format generated by save_obj method.

• Notice : new in version 0.9.5

### Sample weights¶

sample_weight is added in version 1.2

• 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)

In [25]:
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)

In [26]:
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 classnames.

In [27]:
cm.relabel(mapping={0:"L1",1:"L2",2:"L3"})

In [28]:
cm

Out[28]:
pycm.ConfusionMatrix(classes: ['L1', 'L2', 'L3'])
• Notice : new in version 1.5

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("SOA1(Landis & Koch)")
>>> online_help(2)

• List of items are available by calling online_help() (without argument)
In [29]:
online_help()

Please choose one parameter :

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

1-95% CI
2-ACC
3-AUC
4-AUCI
5-AUNP
6-AUNU
7-BM
8-Bennett S
9-CBA
10-CEN
11-Chi-Squared
12-Chi-Squared DF
13-Conditional Entropy
14-Cramer V
15-Cross Entropy
16-DOR
17-DP
18-DPI
19-ERR
20-F0.5
21-F1
22-F2
23-FDR
24-FN
25-FNR
26-FOR
27-FP
28-FPR
29-G
30-Gwet AC1
31-Hamming Loss
32-IS
33-J
34-Joint Entropy
35-KL Divergence
36-Kappa
37-Kappa 95% CI
38-Kappa No Prevalence
39-Kappa Standard Error
40-Kappa Unbiased
41-Lambda A
42-Lambda B
43-MCC
44-MCEN
45-MK
46-Mutual Information
47-N
48-NIR
49-NLR
50-NPV
51-Overall ACC
52-Overall CEN
53-Overall J
54-Overall MCC
55-Overall MCEN
56-Overall RACC
57-Overall RACCU
58-P
59-P-Value
60-PLR
61-PLRI
62-POP
63-PPV
64-PPV Macro
65-PPV Micro
66-PRE
67-Phi-Squared
68-RACC
69-RACCU
70-RCI
71-RR
72-Reference Entropy
73-Response Entropy
74-SOA1(Landis & Koch)
75-SOA2(Fleiss)
76-SOA3(Altman)
77-SOA4(Cicchetti)
78-Scott PI
79-Standard Error
80-TN
81-TNR
82-TON
83-TOP
84-TP
85-TPR
86-TPR Macro
87-TPR Micro
88-Y
89-Zero-one Loss
90-dInd
91-sInd


### Acceptable data types¶

1. actual_vector : python list or numpy array of any stringable objects
2. predict_vector : python list or numpy array of any stringable objects
3. matrix : dict
4. digit: int
5. threshold : FunctionType (function or lambda)
6. file : File object
7. sample_weight : python list or numpy array of any stringable objects
8. transpose : bool
• run help(ConfusionMatrix) 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].

In [30]:
cm.TP

Out[30]:
{'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].

In [31]:
cm.TN

Out[31]:
{'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].

In [32]:
cm.FP

Out[32]:
{'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].

In [33]:
cm.FN

Out[33]:
{'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].

In [34]:
cm.P

Out[34]:
{'L1': 5, 'L2': 2, 'L3': 5}

### N (Condition negative)¶

Number of negative samples [3].

In [35]:
cm.N

Out[35]:
{'L1': 7, 'L2': 10, 'L3': 7}

### TOP (Test outcome positive)¶

Number of positive outcomes [3].

In [36]:
cm.TOP

Out[36]:
{'L1': 3, 'L2': 3, 'L3': 6}

### TON (Test outcome negative)¶

Number of negative outcomes [3].

In [37]:
cm.TON

Out[37]:
{'L1': 9, 'L2': 9, 'L3': 6}

### POP (Population)¶

In [38]:
cm.POP

Out[38]:
{'L1': 12, 'L2': 12, 'L3': 12}

## 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].

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

In [39]:
cm.TPR

Out[39]:
{'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].

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

In [40]:
cm.TNR

Out[40]:
{'L1': 1.0, 'L2': 0.8, 'L3': 0.5714285714285714}

### PPV (Positive predictive value)¶

Predictive value positive is the proportion of positives that correspond to the presence of the condition [3].

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

In [41]:
cm.PPV

Out[41]:
{'L1': 1.0, 'L2': 0.3333333333333333, 'L3': 0.5}

### NPV (Negative predictive value)¶

Predictive value negative is the proportion of negatives that correspond to the absence of the condition [3].

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

In [42]:
cm.NPV

Out[42]:
{'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].

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

In [43]:
cm.FNR

Out[43]:
{'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.

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

In [44]:
cm.FPR

Out[44]:
{'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].

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

In [45]:
cm.FDR

Out[45]:
{'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].

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

In [46]:
cm.FOR

Out[46]:
{'L1': 0.2222222222222222,
'L2': 0.11111111111111116,
'L3': 0.33333333333333337}

### ACC (Accuracy)¶

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

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

In [47]:
cm.ACC

Out[47]:
{'L1': 0.8333333333333334, 'L2': 0.75, 'L3': 0.5833333333333334}

### ERR (Error rate)¶

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

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

In [48]:
cm.ERR

Out[48]:
{'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].

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

In [49]:
cm.F1

Out[49]:
{'L1': 0.75, 'L2': 0.4, 'L3': 0.5454545454545454}
In [50]:
cm.F05

Out[50]:
{'L1': 0.8823529411764706, 'L2': 0.35714285714285715, 'L3': 0.5172413793103449}
In [51]:
cm.F2

Out[51]:
{'L1': 0.6521739130434783, 'L2': 0.45454545454545453, 'L3': 0.5769230769230769}
In [52]:
cm.F_beta(Beta=4)

Out[52]:
{'L1': 0.6144578313253012, 'L2': 0.4857142857142857, 'L3': 0.5930232558139535}
• 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 which 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].

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

In [53]:
cm.MCC

Out[53]:
{'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].

$$BM=TPR+TNR-1$$

In [54]:
cm.BM

Out[54]:
{'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 Δp (deltaP) in simple two-choice cases [2].

$$MK=PPV+NPV-1$$

In [55]:
cm.MK

Out[55]:
{'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].

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

In [56]:
cm.PLR

Out[56]:
{'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].

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

In [57]:
cm.NLR

Out[57]:
{'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].

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

In [58]:
cm.DOR

Out[58]:
{'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].

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

In [59]:
cm.PRE

Out[59]:
{'L1': 0.4166666666666667, 'L2': 0.16666666666666666, 'L3': 0.4166666666666667}

### G (G-measure)¶

Geometric mean of precision and sensitivity [3].

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

In [60]:
cm.G

Out[60]:
{'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].

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

In [61]:
cm.RACC

Out[61]:
{'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].

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

In [62]:
cm.RACCU

Out[62]:
{'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].

$$A=Vector_{Actual}$$ $$B=Vector_{Predict}$$

$$J(A,B)=\frac{|A\cap B|}{|A\cup B|}=\frac{|A\cap B|}{|A|+|B|-|A\cap B|}$$

In [63]:
cm.J

Out[63]:
{'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 -log2(p(C)) [18].

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

In [64]:
cm.IS

Out[64]:
{'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].

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

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

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

In [65]:
cm.CEN

Out[65]:
{'L1': 0.25, 'L2': 0.49657842846620864, 'L3': 0.6044162769630221}
• Notice : new in version 1.3

### MCEN (Modified confusion entropy)¶

Modified version of CEN [19].

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

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

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

In [66]:
cm.MCEN

Out[66]:
{'L1': 0.2643856189774724, 'L2': 0.5, 'L3': 0.6875}
• Notice : new in version 1.3

### AUC (Area under the ROC curve)¶

Thus, AUC corresponds to the arithmetic mean of sensitivity and specificity values of each class [23].

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

In [67]:
cm.AUC

Out[67]:
{'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].

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

In [68]:
cm.dInd

Out[68]:
{'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].

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

In [69]:
cm.sInd

Out[69]:
{'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].

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

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

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

In [70]:
cm.DP

Out[70]:
{'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 efficiency of Telemedical prevention [33] [34].

$$\gamma=BM=TPR+TNR-1$$

In [71]:
cm.Y

Out[71]:
{'L1': 0.6000000000000001,
'L2': 0.30000000000000004,
'L3': 0.17142857142857126}
• Notice : new in version 1.5

### PLRI (Positive likelihood ratio interpretation)¶

 PLR Model contribution 1 > Negligible 1 - 5 Poor 5 - 10 Fair > 10 Good
In [72]:
cm.PLRI

Out[72]:
{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}
• Notice : new in version 1.5

### DPI (Discriminant power interpretation)¶

 DP Model contribution 1 > Poor 1 - 2 Limited 2 - 3 Fair > 3 Good
In [73]:
cm.DPI

Out[73]:
{'L1': 'None', 'L2': 'Poor', 'L3': 'Poor'}
• Notice : new in version 1.5

### AUCI (AUC value interpretation)¶

 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
In [74]:
cm.AUCI

Out[74]:
{'L1': 'Very Good', 'L2': 'Fair', 'L3': 'Poor'}
• Notice : new in version 1.6

## Overall statistics¶

### Kappa¶

Kappa is a statistic which 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].

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

In [75]:
cm.Kappa

Out[75]:
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].

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

In [76]:
cm.KappaUnbiased

Out[76]:
0.34426229508196726
• Notice : new in version 0.8.1

### Kappa no prevalence¶

The kappa statistic adjusted for prevalence [14].

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

In [77]:
cm.KappaNoPrevalence

Out[77]:
0.16666666666666674
• Notice : new in version 0.8.1

### Kappa 95% CI¶

Kappa 95% Confidence Interval [24].

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

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

In [78]:
cm.Kappa_SE

Out[78]:
0.2203645326012817
In [79]:
cm.Kappa_CI

Out[79]:
(-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].

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

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

In [80]:
cm.Chi_Squared

Out[80]:
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].

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

In [81]:
cm.DF

Out[81]:
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].

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

In [82]:
cm.Phi_Squared

Out[82]:
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].

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

In [83]:
cm.V

Out[83]:
0.5244044240850758
• 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].

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

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

In [84]:
cm.CI

Out[84]:
(0.30438856248221097, 0.8622781041844558)
In [85]:
cm.SE

Out[85]:
0.14231876063832777
• Notice : new in version 0.7

### 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 simple agreement between raters [8].

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

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

In [86]:
cm.S

Out[86]:
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 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].

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

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

In [87]:
cm.PI

Out[87]:
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 interrater reliability of 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 interrater reliability [6].

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

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

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

In [88]:
cm.AC1

Out[88]:
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].

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

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

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

In [89]:
cm.ReferenceEntropy

Out[89]:
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].

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

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

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

In [90]:
cm.ResponseEntropy

Out[90]:
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].

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

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

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

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

In [91]:
cm.CrossEntropy

Out[91]:
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].

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

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

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

In [92]:
cm.JointEntropy

Out[92]:
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].

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

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

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

In [93]:
cm.ConditionalEntropy

Out[93]:
0.9757921620455572
• Notice : new in version 0.8.1

### Kullback-Liebler 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].

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

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

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

In [94]:
cm.KL

Out[94]:
0.09997757835164581
• Notice : new in version 0.8.1

### Mutual information¶

Mutual information is defined Kullback-Lieblier 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].

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

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

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

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

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

In [95]:
cm.MutualInformation

Out[95]:
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].

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

In [96]:
cm.LambdaA

Out[96]:
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].

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

In [97]:
cm.LambdaB

Out[97]:
0.16666666666666666
• Notice : new in version 0.8.1

### SOA1 (Landis & Koch’s benchmark)¶

 Kappa Strength of Agreement 0 > Poor 0 - 0.20 Slight 0.21 – 0.40 Fair 0.41 – 0.60 Moderate 0.61 – 0.80 Substantial 0.81 – 1.00 Almost perfect
In [98]:
cm.SOA1

Out[98]:
'Fair'
• Notice : new in version 0.3

### SOA2 (Fleiss’ benchmark)¶

 Kappa Strength of Agreement 0.40 > Poor 0.4 - 0.75 Intermediate to Good More than 0.75 Excellent
In [99]:
cm.SOA2

Out[99]:
'Poor'
• Notice : new in version 0.4

### SOA3 (Altman’s benchmark)¶

 Kappa Strength of Agreement 0.2 > Poor 0.21 – 0.40 Fair 0.41 – 0.60 Moderate 0.61 – 0.80 Good 0.81 – 1.00 Very Good
In [100]:
cm.SOA3

Out[100]:
'Fair'
• Notice : new in version 0.4

### SOA4 (Cicchetti’s benchmark)¶

 Kappa Strength of Agreement 0.4 > Poor 0.4 – 0.59 Fair 0.6 – 0.74 Good 0.74 – 1.00 Excellent
In [101]:
cm.SOA4

Out[101]:
'Poor'
• Notice : new in version 0.7

### Overall_ACC¶

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

In [102]:
cm.Overall_ACC

Out[102]:
0.5833333333333334
• Notice : new in version 0.4

### Overall_RACC¶

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

In [103]:
cm.Overall_RACC

Out[103]:
0.3541666666666667
• Notice : new in version 0.4

### Overall_RACCU¶

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

In [104]:
cm.Overall_RACCU

Out[104]:
0.3645833333333333
• Notice : new in version 0.8.1

### PPV_Micro¶

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

In [105]:
cm.PPV_Micro

Out[105]:
0.5833333333333334
• Notice : new in version 0.4

### TPR_Micro¶

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

In [106]:
cm.TPR_Micro

Out[106]:
0.5833333333333334
• Notice : new in version 0.4

### PPV_Macro¶

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

In [107]:
cm.PPV_Macro

Out[107]:
0.611111111111111
• Notice : new in version 0.4

### TPR_Macro¶

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

In [108]:
cm.TPR_Macro

Out[108]:
0.5666666666666668
• Notice : new in version 0.4

### Overall_J¶

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

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

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

In [109]:
cm.Overall_J

Out[109]:
(1.225, 0.4083333333333334)
• Notice : new in version 0.9

### Hamming loss¶

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

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

In [110]:
cm.HammingLoss

Out[110]:
0.41666666666666663
• Notice : new in version 1.0

### Zero-one loss¶

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

In [111]:
cm.ZeroOneLoss

Out[111]:
5
• Notice : new in version 1.1

### NIR (No information rate)¶

The no information error rate is the error rate when the input and output are independent.

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

In [112]:
cm.NIR

Out[112]:
0.4166666666666667
• Notice : new in version 1.2

### P-Value¶

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

$$p=NIR$$

$$n=POP$$

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

In [113]:
cm.PValue

Out[113]:
0.18926430237560654
• Notice : new in version 1.2

### Overall_CEN¶

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

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

In [114]:
cm.Overall_CEN

Out[114]:
0.4638112995385119
• Notice : new in version 1.3

### Overall_MCEN¶

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

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

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

In [115]:
cm.Overall_MCEN

Out[115]:
0.5189369467580801
• Notice : new in version 1.3

### Overall_MCC¶

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

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

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

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

In [116]:
cm.Overall_MCC

Out[116]:
0.36666666666666664
• Notice : new in version 1.4

### RR (Global performance index)¶

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

In [117]:
cm.RR

Out[117]:
4.0
• Notice : new in version 1.4

### CBA (Class balance accuracy)¶

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

In [118]:
cm.CBA

Out[118]:
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].

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

In [119]:
cm.AUNU

Out[119]:
0.6785714285714285
• Notice : new in version 1.4

### AUNP¶

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

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

In [120]:
cm.AUNP

Out[120]:
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].

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

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

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

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

In [121]:
cm.RCI

Out[121]:
0.3533932006492363
• Notice : new in version 1.5

## Print¶

### Full¶

In [122]:
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)
AUNP                                                             0.68571
AUNU                                                             0.67857
Bennett S                                                        0.375
CBA                                                              0.47778
Chi-Squared                                                      6.6
Chi-Squared DF                                                   4
Conditional Entropy                                              0.97579
Cramer V                                                         0.5244
Cross Entropy                                                    1.58333
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
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
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
Scott PI                                                         0.34426
Standard Error                                                   0.14232
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
AUC(Area under the roc curve)                                    0.8                     0.65                    0.58571
AUCI(Auc value interpretation)                                   Very Good               Fair                    Poor
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
IS(Information score)                                            1.26303                 1.0                     0.26303
J(Jaccard index)                                                 0.6                     0.25                    0.375
MCC(Matthews correlation coefficient)                            0.68313                 0.2582                  0.16903
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
NPV(Negative predictive value)                                   0.77778                 0.88889                 0.66667
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
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¶

In [123]:
cm.print_matrix()

Predict          L1    L2    L3
Actual
L1               3     0     2

L2               0     1     1

L3               0     2     3


In [124]:
cm.matrix

Out[124]:
{0: {0: 3, 1: 0, 2: 2},
1: {0: 0, 1: 1, 2: 1},
2: {0: 0, 1: 2, 2: 3},
'L1': {'L1': 3, 'L2': 0, 'L3': 2},
'L2': {'L1': 0, 'L2': 1, 'L3': 1},
'L3': {'L1': 0, 'L2': 2, 'L3': 3}}
In [125]:
cm.print_matrix(one_vs_all=True,class_name = 1)

Predict          L1    L2    L3
Actual
L1               3     0     2

L2               0     1     1

L3               0     2     3


• 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

### Normalized matrix¶

In [126]:
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


In [127]:
cm.normalized_matrix

Out[127]:
{0: {0: 0.6, 1: 0.0, 2: 0.4},
1: {0: 0.0, 1: 0.5, 2: 0.5},
2: {0: 0.0, 1: 0.4, 2: 0.6},
'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}}
In [128]:
cm.print_normalized_matrix(one_vs_all=True,class_name = 1)

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


• 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

### Stat¶

In [129]:
cm.stat()

Overall Statistics :

95% CI                                                           (0.30439,0.86228)
AUNP                                                             0.68571
AUNU                                                             0.67857
Bennett S                                                        0.375
CBA                                                              0.47778
Chi-Squared                                                      6.6
Chi-Squared DF                                                   4
Conditional Entropy                                              0.97579
Cramer V                                                         0.5244
Cross Entropy                                                    1.58333
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
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
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
Scott PI                                                         0.34426
Standard Error                                                   0.14232
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
AUC(Area under the roc curve)                                    0.8                     0.65                    0.58571
AUCI(Auc value interpretation)                                   Very Good               Fair                    Poor
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
IS(Information score)                                            1.26303                 1.0                     0.26303
J(Jaccard index)                                                 0.6                     0.25                    0.375
MCC(Matthews correlation coefficient)                            0.68313                 0.2582                  0.16903
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
NPV(Negative predictive value)                                   0.77778                 0.88889                 0.66667
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
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


In [130]:
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


• Notice : overall_param & class_param , new in version 1.6
• Notice : cm.params() in prev versions (0.2 >)

## Save¶

### .pycm file¶

In [131]:
cm.save_stat("cm1")

Out[131]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\cm1.pycm',
'Status': True}
In [132]:
cm.save_stat("cm1_filtered",overall_param=["Kappa"],class_param=["ACC","AUC","TPR"])

Out[132]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\cm1_filtered.pycm',
'Status': True}
In [133]:
cm.save_stat("cm1asdasd/")

Out[133]:
{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.pycm'",
'Status': False}
• Notice : overall_param & class_param , new in version 1.6
• Notice : new in version 0.4

### HTML¶

In [134]:
cm.save_html("cm1")

Out[134]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\cm1.html',
'Status': True}
In [135]:
cm.save_html("cm1_filtered",overall_param=["Kappa"],class_param=["ACC","AUC","TPR"])

Out[135]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\cm1_filtered.html',
'Status': True}
In [136]:
cm.save_html("cm1asdasd/")

Out[136]:
{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.html'",
'Status': False}
• Notice : overall_param & class_param , new in version 1.6
• Notice : new in version 0.5

### CSV¶

In [137]:
cm.save_csv("cm1")

Out[137]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\cm1.csv',
'Status': True}
In [138]:
cm.save_csv("cm1_filtered",class_param=["ACC","AUC","TPR"])

Out[138]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\cm1_filtered.csv',
'Status': True}
In [139]:
cm.save_csv("cm1asdasd/")

Out[139]:
{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.csv'",
'Status': False}
• Notice : class_param , new in version 1.6
• Notice : new in version 0.6

### OBJ¶

In [140]:
cm.save_obj("cm1")

Out[140]:
{'Message': 'D:\\For Asus Laptop\\projects\\pycm\\Document\\cm1.obj',
'Status': True}
In [141]:
cm.save_obj("cm1asdasd/")

Out[141]:
{'Message': "[Errno 2] No such file or directory: 'cm1asdasd/.obj'",
'Status': False}
• Notice : new in version 0.9.5

## Input errors¶

In [142]:
try:
cm2=ConfusionMatrix(y_actu, 2)
except pycmVectorError as e:
print(str(e))

Input Vectors Must Be List

In [143]:
try:
cm3=ConfusionMatrix(y_actu, [1,2,3])
except pycmVectorError as e:
print(str(e))

Input Vectors Must Be The Same Length

In [144]:
try:
cm_4 = ConfusionMatrix([], [])
except pycmVectorError as e:
print(str(e))

Input Vectors Are Empty

In [145]:
try:
cm_5 = ConfusionMatrix([1,1,1,], [1,1,1,1])
except pycmVectorError as e:
print(str(e))

Input Vectors Must Be The Same Length

In [146]:
try:
cm3=ConfusionMatrix(matrix={})
except pycmMatrixError as e:
print(str(e))

Input Confusion Matrix Format Error

In [147]:
try:
cm_4=ConfusionMatrix(matrix={1:{1:2,"1":2},"1":{1:2,"1":3}})
except pycmMatrixError as e:
print(str(e))

Input Matrix Classes Must Be Same Type

In [148]:
try:
cm_5=ConfusionMatrix(matrix={1:{1:2}})
except pycmVectorError as e:
print(str(e))

Number Of Classes < 2

• Notice : updated in version 0.8

## References¶

1- J. R. Landis, G. G. Koch, “The measurement of observer agreement for categorical data. Biometrics,” in International Biometric Society, pp. 159–174, 1977.
2- D. M. W. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness & correlation,” in Journal of Machine Learning Technologies, pp.37-63, 2011.
3- C. Sammut, G. Webb, “Encyclopedia of Machine Learning” in Springer, 2011.
4- J. L. Fleiss, “Measuring nominal scale agreement among many raters,” in Psychological Bulletin, pp. 378-382.
5- D.G. Altman, “Practical Statistics for Medical Research,” in Chapman and Hall, 1990.
6- K. L. Gwet, “Computing inter-rater reliability and its variance in the presence of high agreement,” in The British Journal of Mathematical and Statistical Psychology, pp. 29–48, 2008.”
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