Move training routine out of ff_const_reorder_common.h

6 files changed, 91 insertions, 101 deletions

diff --git a/decoder/ff_const_reorder_common.h b/decoder/ff_const_reorder_common.h
index 7c111de3..b124ce47 100644
--- a/decoder/ff_const_reorder_common.h
+++ b/decoder/ff_const_reorder_common.h
@@ -1091,99 +1091,6 @@ struct 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++;
-      std::vector<std::string> vecContext;
-      SplitOnWhitespace(std::string(pszLine), &vecContext);
-
-      pmes->label = std::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 {
-    std::vector<std::string> vecContext;
-    ME_Sample* pmes = new ME_Sample();
-    SplitOnWhitespace(std::string(pszContext), &vecContext);
-
-    for (size_t i = 0; i < vecContext.size(); i++)
-      pmes->add_feature(vecContext[i]);
-    std::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,
-              std::vector<std::pair<std::string, double> >& vecOutput) const {
-    std::vector<std::string> vecContext;
-    ME_Sample* pmes = new ME_Sample();
-    SplitOnWhitespace(std::string(pszContext), &vecContext);
-
-    vecOutput.clear();
-
-    for (size_t i = 0; i < vecContext.size(); i++)
-      pmes->add_feature(vecContext[i]);
-    std::vector<double> vecProb = m_pModel->classify(*pmes);
-
-    for (size_t i = 0; i < vecProb.size(); i++) {
-      std::string label = m_pModel->get_class_label(i);
-      vecOutput.push_back(make_pair(label, vecProb[i]));
-    }
-    delete pmes;
-  }
   void fnEval(const char* pszContext, std::vector<double>& vecOutput) const {
     std::vector<std::string> vecContext;
     ME_Sample* pmes = new ME_Sample();
diff --git a/training/const_reorder/Makefile.am b/training/const_reorder/Makefile.am
index 2e81e588..367ac904 100644
--- a/training/const_reorder/Makefile.am
+++ b/training/const_reorder/Makefile.am
@@ -1,8 +1,12 @@
+noinst_LIBRARIES = libtrainer.a
+
+libtrainer_a_SOURCES = trainer.h trainer.cc
+
 bin_PROGRAMS = const_reorder_model_trainer argument_reorder_model_trainer
 
 AM_CPPFLAGS = -I$(top_srcdir) -I$(top_srcdir)/utils -I$(top_srcdir)/decoder
 
 const_reorder_model_trainer_SOURCES = constituent_reorder_model.cc
-const_reorder_model_trainer_LDADD = ../../utils/libutils.a
+const_reorder_model_trainer_LDADD = ../../utils/libutils.a libtrainer.a
 argument_reorder_model_trainer_SOURCES = argument_reorder_model.cc
-argument_reorder_model_trainer_LDADD = ../../utils/libutils.a
+argument_reorder_model_trainer_LDADD = ../../utils/libutils.a libtrainer.a
diff --git a/training/const_reorder/argument_reorder_model.cc b/training/const_reorder/argument_reorder_model.cc
index 54402436..87f2ce2f 100644
--- a/training/const_reorder/argument_reorder_model.cc
+++ b/training/const_reorder/argument_reorder_model.cc
@@ -14,7 +14,7 @@
 
 #include "utils/filelib.h"
 
-#include "decoder/ff_const_reorder_common.h"
+#include "trainer.h"
 
 using namespace std;
 using namespace const_reorder;
@@ -93,8 +93,8 @@ struct SArgumentReorderTrainer {
       strcpy(pszNewInstanceFName, pszInstanceFname);
     }
 
-    Tsuruoka_Maxent* pMaxent = new Tsuruoka_Maxent(NULL);
-    pMaxent->fnTrain(pszNewInstanceFName, "l1", pszModelFname, 300);
+    Tsuruoka_Maxent_Trainer* pMaxent = new Tsuruoka_Maxent_Trainer;
+    pMaxent->fnTrain(pszNewInstanceFName, "l1", pszModelFname);
     delete pMaxent;
 
     if (strcmp(pszNewInstanceFName, pszInstanceFname) != 0) {
diff --git a/training/const_reorder/constituent_reorder_model.cc b/training/const_reorder/constituent_reorder_model.cc
index 6bec3f0b..d3ad0f2b 100644
--- a/training/const_reorder/constituent_reorder_model.cc
+++ b/training/const_reorder/constituent_reorder_model.cc
@@ -12,7 +12,7 @@
 
 #include "utils/filelib.h"
 
-#include "decoder/ff_const_reorder_common.h"
+#include "trainer.h"
 
 using namespace std;
 using namespace const_reorder;
@@ -104,8 +104,8 @@ struct SConstReorderTrainer {
 pZhangleMaxent->fnTrain(pszInstanceFname, "lbfgs", pszModelFname, 100, 2.0);
 delete pZhangleMaxent;*/
 
-    Tsuruoka_Maxent* pMaxent = new Tsuruoka_Maxent(NULL);
-    pMaxent->fnTrain(pszNewInstanceFName, "l1", pszModelFname, 300);
+    Tsuruoka_Maxent_Trainer* pMaxent = new Tsuruoka_Maxent_Trainer;
+    pMaxent->fnTrain(pszNewInstanceFName, "l1", pszModelFname);
     delete pMaxent;
 
     if (strcmp(pszNewInstanceFName, pszInstanceFname) != 0) {
diff --git a/training/const_reorder/trainer.cc b/training/const_reorder/trainer.cc
new file mode 100644
index 00000000..e22a8a66
--- /dev/null
+++ b/training/const_reorder/trainer.cc
@@ -0,0 +1,67 @@
+#include "trainer.h"
+
+Tsuruoka_Maxent_Trainer::Tsuruoka_Maxent_Trainer()
+    : const_reorder::Tsuruoka_Maxent(NULL) {}
+
+void Tsuruoka_Maxent_Trainer::fnTrain(const char* pszInstanceFName,
+                                      const char* pszAlgorithm,
+                                      const char* pszModelFName) {
+  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++;
+    std::vector<std::string> vecContext;
+    SplitOnWhitespace(std::string(pszLine), &vecContext);
+
+    pmes->label = std::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;
+}
diff --git a/training/const_reorder/trainer.h b/training/const_reorder/trainer.h
new file mode 100644
index 00000000..e574a536
--- /dev/null
+++ b/training/const_reorder/trainer.h
@@ -0,0 +1,12 @@
+#ifndef TRAINING_CONST_REORDER_TRAINER_H_
+#define TRAINING_CONST_REORDER_TRAINER_H_
+
+#include "decoder/ff_const_reorder_common.h"
+
+struct Tsuruoka_Maxent_Trainer : const_reorder::Tsuruoka_Maxent {
+  Tsuruoka_Maxent_Trainer();
+  void fnTrain(const char* pszInstanceFName, const char* pszAlgorithm,
+               const char* pszModelFName);
+};
+
+#endif  // TRAINING_CONST_REORDER_TRAINER_H_