| pktools 2.6.7 Processing Kernel for geospatial data

CostFactorySVM.cc
1/**********************************************************************
2 CostFactorySVM.cc: select features, typical use: feature selection for classification
3 Copyright (C) 2008-2014 Pieter Kempeneers
4
5 This file is part of pktools
6
7 pktools is free software: you can redistribute it and/or modify
8 it under the terms of the GNU General Public License as published by
9 the Free Software Foundation, either version 3 of the License, or
10(at your option) any later version.
11
12 pktools is distributed in the hope that it will be useful,
13 but WITHOUT ANY WARRANTY; without even the implied warranty of
14 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
15 GNU General Public License for more details.
16
17 You should have received a copy of the GNU General Public License
18 along with pktools. If not, see <http://www.gnu.org/licenses/>.
19***********************************************************************/
20#include "CostFactorySVM.h"
21#include "svm.h"
22
23#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
24
25CostFactorySVM::CostFactorySVM()
26 : CostFactory(2,0), m_svm_type("C_SVC"), m_kernel_type("radial"), m_kernel_degree(3), m_gamma(1.0), m_coef0(0), m_ccost(1000), m_nu(0.5), m_epsilon_loss(100), m_cache(100), m_epsilon_tol(0.001), m_shrinking(false), m_prob_est(true){
27}
28
29CostFactorySVM::~CostFactorySVM(){
30}
31
32CostFactorySVM::CostFactorySVM(std::string svm_type, std::string kernel_type, unsigned short kernel_degree, float gamma, float coef0, float ccost, float nu, float epsilon_loss, int cache, float epsilon_tol, bool shrinking, bool prob_est, unsigned short cv, short verbose)
33 : CostFactory(cv,verbose), m_svm_type(svm_type), m_kernel_type(kernel_type), m_kernel_degree(kernel_degree), m_gamma(gamma), m_coef0(coef0), m_ccost(ccost), m_nu(nu), m_epsilon_loss(epsilon_loss), m_cache(cache), m_epsilon_tol(epsilon_tol), m_shrinking(shrinking), m_prob_est(prob_est){};
34
35 double CostFactorySVM::getCost(const std::vector<Vector2d<float> > &trainingFeatures){
36 std::map<std::string, svm::SVM_TYPE> svmMap;
37
38 svmMap["C_SVC"]=svm::C_SVC;
39 svmMap["nu_SVC"]=svm::nu_SVC;
40 svmMap["one_class"]=svm::one_class;
41 svmMap["epsilon_SVR"]=svm::epsilon_SVR;
42 svmMap["nu_SVR"]=svm::nu_SVR;
43
44 std::map<std::string, svm::KERNEL_TYPE> kernelMap;
45
46 kernelMap["linear"]=svm::linear;
47 kernelMap["polynomial"]=svm::polynomial;
48 kernelMap["radial"]=svm::radial;
49 kernelMap["sigmoid;"]=svm::sigmoid;
50
51 unsigned short nclass=trainingFeatures.size();
52 unsigned int ntraining=0;
53 unsigned int ntest=0;
54 for(int iclass=0;iclass<nclass;++iclass){
55 ntraining+=m_nctraining[iclass];
56 ntest+=m_nctest[iclass];
57 }
58 if(ntest)
59 assert(!m_cv);
60 if(!m_cv)
61 assert(ntest);
62 unsigned short nFeatures=trainingFeatures[0][0].size();
63
64 struct svm_parameter param;
65 param.svm_type = svmMap[m_svm_type];
66 param.kernel_type = kernelMap[m_kernel_type];
67 param.degree = m_kernel_degree;
68 param.gamma = (m_gamma>0)? m_gamma : 1.0/nFeatures;
69 param.coef0 = m_coef0;
70 param.nu = m_nu;
71 param.cache_size = m_cache;
72 param.C = m_ccost;
73 param.eps = m_epsilon_tol;
74 param.p = m_epsilon_loss;
75 param.shrinking = (m_shrinking)? 1 : 0;
76 param.probability = (m_prob_est)? 1 : 0;
77 param.nr_weight = 0;//not used: I use priors and balancing
78 param.weight_label = NULL;
79 param.weight = NULL;
80 param.verbose=(m_verbose>1)? true:false;
81 struct svm_model* svm;
82 struct svm_problem prob;
83 struct svm_node* x_space;
84
85 prob.l=ntraining;
86 prob.y = Malloc(double,prob.l);
87 prob.x = Malloc(struct svm_node *,prob.l);
88 x_space = Malloc(struct svm_node,(nFeatures+1)*ntraining);
89 unsigned long int spaceIndex=0;
90 int lIndex=0;
91 for(int iclass=0;iclass<nclass;++iclass){
92 // for(int isample=0;isample<trainingFeatures[iclass].size();++isample){
93 for(int isample=0;isample<m_nctraining[iclass];++isample){
94 prob.x[lIndex]=&(x_space[spaceIndex]);
95 for(int ifeature=0;ifeature<nFeatures;++ifeature){
96 x_space[spaceIndex].index=ifeature+1;
97 x_space[spaceIndex].value=trainingFeatures[iclass][isample][ifeature];
98 ++spaceIndex;
99 }
100 x_space[spaceIndex++].index=-1;
101 prob.y[lIndex]=iclass;
102 ++lIndex;
103 }
104 }
105
106 assert(lIndex==prob.l);
107 if(m_verbose>2)
108 std::cout << "checking parameters" << std::endl;
109 svm_check_parameter(&prob,&param);
110 if(m_verbose>2)
111 std::cout << "parameters ok, training" << std::endl;
112 svm=svm_train(&prob,&param);
113 if(m_verbose>2)
114 std::cout << "SVM is now trained" << std::endl;
115
116 m_cm.clearResults();
117 if(m_cv>1){
118 double *target = Malloc(double,prob.l);
119 svm_cross_validation(&prob,&param,m_cv,target);
120 assert(param.svm_type != EPSILON_SVR&&param.svm_type != NU_SVR);//only for regression
121 for(int i=0;i<prob.l;i++){
122 std::string refClassName=m_nameVector[prob.y[i]];
123 std::string className=m_nameVector[target[i]];
124 if(m_classValueMap.size())
125 m_cm.incrementResult(type2string<short>(m_classValueMap[refClassName]),type2string<short>(m_classValueMap[className]),1.0);
126 else
127 m_cm.incrementResult(m_cm.getClass(prob.y[i]),m_cm.getClass(target[i]),1.0);
128 }
129 free(target);
130 }
131 else{
132 struct svm_node *x_test;
133 std::vector<double> result(nclass);
134 x_test = Malloc(struct svm_node,(nFeatures+1));
135 for(int iclass=0;iclass<nclass;++iclass){
136 for(int isample=0;isample<m_nctest[iclass];++isample){
137 for(int ifeature=0;ifeature<nFeatures;++ifeature){
138 x_test[ifeature].index=ifeature+1;
139 x_test[ifeature].value=trainingFeatures[iclass][m_nctraining[iclass]+isample][ifeature];
140 }
141 x_test[nFeatures].index=-1;
142 double predict_label=0;
143 assert(svm_check_probability_model(svm));
144 predict_label = svm_predict_probability(svm,x_test,&(result[0]));
145 // predict_label = svm_predict(svm,x_test);
146 std::string refClassName=m_nameVector[iclass];
147 std::string className=m_nameVector[static_cast<short>(predict_label)];
148 if(m_classValueMap.size())
149 m_cm.incrementResult(type2string<short>(m_classValueMap[refClassName]),type2string<short>(m_classValueMap[className]),1.0);
150 else
151 m_cm.incrementResult(refClassName,className,1.0);
152 }
153 }
154 free(x_test);
155 }
156 if(m_verbose>1)
157 std::cout << m_cm << std::endl;
158 assert(m_cm.nReference());
159 // if(m_verbose)
160
161 // std::cout << m_cm << std::endl;
162 // std::cout << "Kappa: " << m_cm.kappa() << std::endl;
163 // double se95_oa=0;
164 // double doa=0;
165 // doa=m_cm.oa_pct(&se95_oa);
166 // std::cout << "Overall Accuracy: " << doa << " (" << se95_oa << ")" << std::endl;
167
168 // *NOTE* Because svm_model contains pointers to svm_problem, you can
169 // not free the memory used by svm_problem if you are still using the
170 // svm_model produced by svm_train().
171 // however, we will re-train the svm later on after the feature selection
172 free(prob.y);
173 free(prob.x);
174 free(x_space);
175 svm_free_and_destroy_model(&(svm));
176
177 return(m_cm.kappa());
178}
CostFactory
Definition: CostFactory.h:30
Vector2d
Definition: Vector2d.h:32
svm_model
Definition: svm.h:54
svm_node
Definition: svm.h:13
svm_parameter
Definition: svm.h:29
svm_problem
Definition: svm.h:19