/
POM.m
121 lines (112 loc) · 5.87 KB
/
POM.m
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
classdef POM < Algorithm
%POM Proportional Odd Model for Ordinal Regression
%
% POM methods:
% fitpredict - runs the corresponding algorithm,
% fitting the model and testing it in a dataset.
% fit - Fits a model from training data
% predict - Performs label prediction
%
% POM properties:
% linkFunction - Link function, default set to logit
% Available options are 'logit',
% 'probit', 'comploglog' or 'loglog'.
% Octave only supports 'logit'
% References:
% [1] P. McCullagh, Regression models for ordinal data, Journal of
% the Royal Statistical Society. Series B (Methodological), vol. 42,
% no. 2, pp. 109–142, 1980.
% [2] P.A. Gutiérrez, M. Pérez-Ortiz, J. Sánchez-Monedero,
% F. Fernández-Navarro and C. Hervás-Martínez
% Ordinal regression methods: survey and experimental study
% IEEE Transactions on Knowledge and Data Engineering, Vol. 28. Issue 1
% 2016
% http://dx.doi.org/10.1109/TKDE.2015.2457911
%
% This file is part of ORCA: https://github.com/ayrna/orca
% Original authors: Pedro Antonio Gutiérrez, María Pérez Ortiz, Javier Sánchez Monedero
% Citation: If you use this code, please cite the associated paper http://www.uco.es/grupos/ayrna/orreview
% Copyright:
% This software is released under the The GNU General Public License v3.0 licence
% available at http://www.gnu.org/licenses/gpl-3.0.html
% POM Proportional Odd Model for Ordinal Regression
% This class derives from the Algorithm Class and implements the
% POM method.
% Further details in: * P.A. Gutiérrez, M. Pérez-Ortiz, J. Sánchez-Monedero,
% F. Fernández-Navarro and C. Hervás-Martínez (2015),
% "Ordinal regression methods: survey and
% experimental study",
% IEEE Transactions on Knowledge and Data
% Engineering. Vol. Accepted
% * P. McCullagh, "Regression models for ordinal
% data," Journal of the Royal Statistical
% Society. Series B (Methodological), vol. 42,
% no. 2, pp. 109–142, 1980.
properties
linkFunction = 'logit';
description = 'Linear Proportional Odds Model for Ordinal Regression';
parameters = [];
end
methods
function obj = POM(varargin)
%POM constructs an object of the class POM. This method does not
%have any parameters
obj.parseArgs(varargin);
end
function obj = set.linkFunction(obj, value)
%SET.LINKFUNCTION verifies if the value for the variable
%linkFunction is correct. Returns class object wiht correct
%value for the variable |linkFunction|.
if exist ('OCTAVE_VERSION', 'builtin') > 0 && ...
~strcmpi(value,'logit')
error('Invalid link function. Octave only supports logit link function');
elseif ~(strcmpi(value,'logit') || strcmpi(value,'probit') || ...
strcmpi(value,'comploglog') || strcmpi(value,'loglog'))
error('Invalid link function. Supported MATLAB link functions: logit, probit, comploglog, loglog');
else
obj.linkFunction = value;
end
end
function [projectedTrain, predictedTrain]= privfit( obj,train,parameters)
%PRIVFIT trains the model for the POM method with TRAIN data and
%vector of parameters PARAMETERS.
nOfClasses = numel(unique(train.targets));
% TODO: Debug size octave
if exist ('OCTAVE_VERSION', 'builtin') > 0
pkg load statistics;
[model.thresholds, model.projection] = logistic_regression(train.targets, train.patterns);
pkg unload statistics;
else
% Obtain coefficients of the ordinal regression model
betaHatOrd = mnrfit(train.patterns,train.targets,'model',...
'ordinal','interactions','off','Link',obj.linkFunction);
model.thresholds = -betaHatOrd(1:nOfClasses-1);
model.projection = betaHatOrd(nOfClasses:end);
end
model.thresholds = model.thresholds';
obj.model = model;
[projectedTrain, predictedTrain] = obj.predict(train.patterns);
end
function [projected, predicted ] = privpredict( obj, testPatterns)
%PREDICT predict labels of TEST patterns labels using fitted model
model = obj.model;
if exist ('OCTAVE_VERSION', 'builtin') > 0
numClasses = size(model.thresholds,2)+1;
m = size(testPatterns,1);
projected = model.projection' * testPatterns';
z3=repmat(model.thresholds,m,1)-repmat(projected',1,numClasses-1);
a3T = 1.0 ./ (1.0 + exp(-z3));
a3 = [a3T ones(m,1)];
a3(:,2:end) = a3(:,2:end) - a3(:,1:(end-1));
[M,predicted] = max(a3,[],2);
else
prob = mnrval([-model.thresholds'; model.projection],...
testPatterns,'model','ordinal','interactions','off',...
'Link',obj.linkFunction);
[aux,predicted] = max(prob,[],2);
projected = model.projection' * testPatterns';
projected = projected';
end
end
end
end