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/*
* tsuruoka_maxent.h
*
*/
#ifndef TSURUOKA_MAXENT_H_
#define TSURUOKA_MAXENT_H_
#include "utility.h"
#include "stringlib.h"
#include "maxent-3.0/maxent.h"
#include <assert.h>
#include <vector>
#include <string>
#include <string.h>
#include <tr1/unordered_map>
using namespace std;
typedef std::tr1::unordered_map<std::string, int> Map;
typedef std::tr1::unordered_map<std::string, int>::iterator Iterator;
struct Tsuruoka_Maxent{
Tsuruoka_Maxent(const char* pszModelFName) {
if (pszModelFName != NULL) {
m_pModel = new ME_Model();
m_pModel->load_from_file(pszModelFName);
} else
m_pModel = NULL;
}
~Tsuruoka_Maxent() {
if (m_pModel != NULL)
delete m_pModel;
}
void fnTrain(const char* pszInstanceFName, const char* pszAlgorithm, const char* pszModelFName, int iNumIteration) {
assert(strcmp(pszAlgorithm, "l1") == 0
|| strcmp(pszAlgorithm, "l2") == 0
|| strcmp(pszAlgorithm, "sgd") == 0
|| strcmp(pszAlgorithm, "SGD") == 0);
FILE *fpIn = fopen(pszInstanceFName, "r");
ME_Model *pModel = new ME_Model();
char *pszLine = new char[100001];
int iNumInstances = 0;
int iLen;
while (!feof(fpIn)) {
pszLine[0] = '\0';
fgets(pszLine, 20000, fpIn);
if (strlen(pszLine) == 0) {
continue;
}
iLen = strlen(pszLine);
while (iLen > 0 && pszLine[iLen - 1] > 0 && pszLine[iLen -1] < 33) {
pszLine[ iLen - 1 ] = '\0';
iLen--;
}
iNumInstances++;
ME_Sample *pmes = new ME_Sample();
char *p = strrchr(pszLine, ' ');
assert(p != NULL);
p[0] = '\0';
p++;
vector<string> vecContext;
SplitOnWhitespace(string(pszLine), &vecContext);
pmes->label = string(p);
for (size_t i = 0; i < vecContext.size(); i++)
pmes->add_feature(vecContext[i]);
pModel->add_training_sample((*pmes));
if (iNumInstances % 100000 == 0)
fprintf(stdout, "......Reading #Instances: %1d\n", iNumInstances);
delete pmes;
}
fprintf(stdout, "......Reading #Instances: %1d\n", iNumInstances);
fclose(fpIn);
if (strcmp(pszAlgorithm, "l1") == 0)
pModel->use_l1_regularizer(1.0);
else if (strcmp(pszAlgorithm, "l2") == 0)
pModel->use_l2_regularizer(1.0);
else
pModel->use_SGD();
pModel->train();
pModel->save_to_file(pszModelFName);
delete pModel;
fprintf(stdout, "......Finished Training\n");
fprintf(stdout, "......Model saved as %s\n", pszModelFName);
delete [] pszLine;
}
double fnEval(const char* pszContext, const char* pszOutcome) const {
vector<string> vecContext;
ME_Sample *pmes = new ME_Sample();
SplitOnWhitespace(string(pszContext), &vecContext);
for (size_t i = 0; i < vecContext.size(); i++)
pmes->add_feature(vecContext[i]);
vector<double> vecProb = m_pModel->classify(*pmes);
delete pmes;
int iLableID = m_pModel->get_class_id(pszOutcome);
return vecProb[iLableID];
}
void fnEval(const char* pszContext, vector<pair<string, double> >& vecOutput) const {
vector<string> vecContext;
ME_Sample *pmes = new ME_Sample();
SplitOnWhitespace(string(pszContext), &vecContext);
vecOutput.clear();
for (size_t i = 0; i < vecContext.size(); i++)
pmes->add_feature(vecContext[i]);
vector<double> vecProb = m_pModel->classify(*pmes);
for (size_t i = 0; i < vecProb.size(); i++) {
string label = m_pModel->get_class_label(i);
vecOutput.push_back(make_pair(label, vecProb[i]));
}
delete pmes;
}
void fnEval(const char* pszContext, vector<double>& vecOutput) const{
vector<string> vecContext;
ME_Sample *pmes = new ME_Sample();
SplitOnWhitespace(string(pszContext), &vecContext);
vecOutput.clear();
for (size_t i = 0; i < vecContext.size(); i++)
pmes->add_feature(vecContext[i]);
vector<double> vecProb = m_pModel->classify(*pmes);
for (size_t i = 0; i < vecProb.size(); i++) {
string label = m_pModel->get_class_label(i);
vecOutput.push_back(vecProb[i]);
}
delete pmes;
}
int fnGetClassId(const string& strLabel) const {
return m_pModel->get_class_id(strLabel);
}
private:
ME_Model *m_pModel;
};
#endif /* TSURUOKA_MAXENT_H_ */
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