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confusion.m
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classdef confusion
methods (Static)
function [c_matrix,Result,RefereceResult]= getMatrix(actual,predict,Display)
%confusion matrix for multiple class start
%Inputs-1.Actual Class Labels,2.Predict Class Labels and 3.Display if need
%Outputs
%1.C-matrix-Confution Matrix
%2.Result-Struct Over all output Which has follwing
%3.RefereceResult indidual output Which has follwing
%%%%%%%%1acuuracy
%%%%%%%%2.error
%%%%%%%%3.Sensitivity (Recall or True positive rate)
%%%%%%%%4.Specificity
%%%%%%%%5.Precision
%%%%%%%%6.FPR-False positive rate
%%%%%%%%7.F_score
%%%%%%%%8.MCC-Matthews correlation coefficient
%%%%%%%%9.kappa-Cohen's kappa
%%Developer Er.Abbas Manthiri S
%%Date 25-12-2016
%%Mail Id: abbasmanthiribe@gmail.com
%%http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/
%%https://en.wikipedia.org/wiki/Confusion_matrix
% clc
% clear all
% close all
% %%Multiclass
% n=100;m=2;
% actual=round(rand(1,n)*m);
% predict=round(rand(1,n)*m);
% [c_matrix,Result,RefereceResult]= confusionmat(actual,predict)
%
% %DIsplay off
% % [c_matrix,Result,RefereceResult]= confusionmat(actual,predict,0)
%
% %%
% %single Class
% n=100;m=1;
% actual=round(rand(1,n)*m);
% predict=round(rand(1,n)*m);
% [c_matrix,Result]= confusionmat(actual,predict)
%%
%Condition Check
if (nargin < 2)
error('Not enough input arguments. Need atleast two vectors as input');
elseif (nargin == 2)
Display=1;
elseif (nargin > 3)
error('Too many input arguments.');
end
actual=actual(:);
predict=predict(:);
if length(actual) ~= length(predict)
error('Input have different lengths')
end
un_actual=unique(actual);
un_predict=unique(predict);
condition=length(un_actual)==length(un_predict);
if ~condition
error('Class List is not same in given inputs')
end
condition=(sum(un_actual==un_predict)==length(un_actual));
if ~condition
error('Class List in given inputs are different')
end
%%
%Start process
%Build Confusion matrix
%Set variables
class_list=un_actual;
disp('Class List in given sample')
disp(class_list)
fprintf('\nTotal Instance = %d\n',length(actual));
n_class=length(un_actual);
c_matrix=zeros(n_class);
predict_class=cell(1,n_class);
class_ref=cell(n_class,1);
row_name=cell(1,n_class);
%Calculate conufsion for all classes
for i=1:n_class
class_ref{i,1}=strcat('class',num2str(i),'==>',num2str(class_list(i)));
for j=1:n_class
val=(actual==class_list(i)) & (predict==class_list(j));
c_matrix(i,j)=sum(val);
predict_class{i,j}=sum(val);
end
row_name{i}=strcat('Actual_class',num2str(i));
disp(class_ref{i})
end
c_matrix_table=cell2table(predict_class);
c_matrix_table.Properties.RowNames=row_name;
disp('Confusion Matrix')
disp(c_matrix_table)
[Result,RefereceResult]=confusion.getValues(c_matrix);
%Output Struct for individual Classes
RefereceResult.Class=class_ref;
%%
%Diplay
% Display=1;
if Display
if n_class>2
disp('Multi-Class Confusion Matrix Output')
TruePositive=RefereceResult.TruePositive;
FalsePositive=RefereceResult.FalsePositive;
FalseNegative=RefereceResult.FalseNegative;
TrueNegative=RefereceResult.TrueNegative;
TFPN=table(TruePositive,FalsePositive,FalseNegative,TrueNegative,...
'RowNames',row_name);
disp(TFPN);
Param=struct2table(RefereceResult);
disp(Param)
else
disp('Two-Class Confution Matrix')
param={'','TruePositive','FalsePositive';...
'FalseNegative',c_matrix(1,1),c_matrix(1,2);...
'TrueNegative=TN',c_matrix(2,1),c_matrix(2,2)};
disp(param)
end
disp('Over all valuses')
disp(Result)
end
end
function [Result,RefereceResult]= getValues(c_matrix)
%%
%Finding
%1.TP-True Positive
%2.FP-False Positive
%3.FN-False Negative
%4.TN-True Negative
if (nargin < 1)
error('Not enough input arguments. Need atleast two vectors as input');
elseif (nargin > 1)
error('Too many input arguments.');
end
[row,col]=size(c_matrix);
if row~=col
error('Confusion matrix dimention is wrong')
end
n_class=row;
switch n_class
case 2
TP=c_matrix(1,1);
FN=c_matrix(1,2);
FP=c_matrix(2,1);
TN=c_matrix(2,2);
otherwise
TP=zeros(1,n_class);
FN=zeros(1,n_class);
FP=zeros(1,n_class);
TN=zeros(1,n_class);
for i=1:n_class
TP(i)=c_matrix(i,i);
FN(i)=sum(c_matrix(i,:))-c_matrix(i,i);
FP(i)=sum(c_matrix(:,i))-c_matrix(i,i);
TN(i)=sum(c_matrix(:))-TP(i)-FP(i)-FN(i);
end
end
%%
%Calulations
%1.P-Positive
%2.N-Negative
%3.acuuracy
%4.error
%5.Sensitivity (Recall or True positive rate)
%6.Specificity
%7.Precision
%8.FPR-False positive rate
%9.F_score
%10.MCC-Matthews correlation coefficient
%11.kappa-Cohen's kappa
P=TP+FN;
N=FP+TN;
switch n_class
case 2
accuracy=(TP+TN)/(P+N);
Error=1-accuracy;
Result.Accuracy=(accuracy);
Result.Error=(Error);
otherwise
accuracy=(TP)./(P+N);
Error=(FP)./(P+N);
Result.Accuracy=sum(accuracy);
Result.Error=sum(Error);
end
RefereceResult.AccuracyOfSingle=(TP ./ P)';
RefereceResult.ErrorOfSingle=1-RefereceResult.AccuracyOfSingle;
Sensitivity=TP./P;
Specificity=TN./N;
Precision=TP./(TP+FP);
FPR=1-Specificity;
beta=1;
F1_score=( (1+(beta^2))*(Sensitivity.*Precision) ) ./ ( (beta^2)*(Precision+Sensitivity) );
MCC=[( TP.*TN - FP.*FN ) ./ ( ( (TP+FP).*P.*N.*(TN+FN) ).^(0.5) );...
( FP.*FN - TP.*TN ) ./ ( ( (TP+FP).*P.*N.*(TN+FN) ).^(0.5) )] ;
MCC=max(MCC);
%Kappa Calculation BY 2x2 Matrix Shape
pox=sum(accuracy);
Px=sum(P);TPx=sum(TP);FPx=sum(FP);TNx=sum(TN);FNx=sum(FN);Nx=sum(N);
pex=( (Px.*(TPx+FPx))+(Nx.*(FNx+TNx)) ) ./ ( (TPx+TNx+FPx+FNx).^2 );
kappa_overall=([( pox-pex ) ./ ( 1-pex );( pex-pox ) ./ ( 1-pox )]);
kappa_overall=max(kappa_overall);
%Kappa Calculation BY n_class x n_class Matrix Shape
po=accuracy;
pe=( (P.*(TP+FP))+(N.*(FN+TN)) ) ./ ( (TP+TN+FP+FN).^2 );
kappa=([( po-pe ) ./ ( 1-pe );( pe-po ) ./ ( 1-po )]);
kappa=max(kappa);
%%
%Output Struct for individual Classes
% RefereceResult.Class=class_ref;
RefereceResult.AccuracyInTotal=accuracy';
RefereceResult.ErrorInTotal=Error';
RefereceResult.Sensitivity=Sensitivity';
RefereceResult.Specificity=Specificity';
RefereceResult.Precision=Precision';
RefereceResult.FalsePositiveRate=FPR';
RefereceResult.F1_score=F1_score';
RefereceResult.MatthewsCorrelationCoefficient=MCC';
RefereceResult.Kappa=kappa';
RefereceResult.TruePositive=TP';
RefereceResult.FalsePositive=FP';
RefereceResult.FalseNegative=FN';
RefereceResult.TrueNegative=TN';
%Output Struct for over all class lists
Result.Sensitivity=mean(Sensitivity);
Result.Specificity=mean(Specificity);
Result.Precision=mean(Precision);
Result.FalsePositiveRate=mean(FPR);
Result.F1_score=mean(F1_score);
Result.MatthewsCorrelationCoefficient=mean(MCC);
Result.Kappa=kappa_overall;
end
end
end