remake the wrapper

src/learner/dmatrix.h

@@ -6,6 +6,7 @@
  *        used for regression/classification/ranking
  * \author Tianqi Chen
  */
+#include <vector>
 #include "../data.h"
 
 namespace xgboost {
@@ -43,7 +44,7 @@ struct MetaInfo {
   }
   /*! \brief get weight of each instances */
   inline float GetWeight(size_t i) const {
-    if(weights.size() != 0) {
+    if (weights.size() != 0) {
       return weights[i];
     } else {
       return 1.0f;
@@ -51,7 +52,7 @@ struct MetaInfo {
   }
   /*! \brief get root index of i-th instance */
   inline float GetRoot(size_t i) const {
-    if(root_index.size() != 0) {
+    if (root_index.size() != 0) {
       return static_cast<float>(root_index[i]);
     } else {
       return 0;
@@ -76,7 +77,7 @@ struct MetaInfo {
   // try to load group information from file, if exists
   inline bool TryLoadGroup(const char* fname, bool silent = false) {
     FILE *fi = fopen64(fname, "r");
-    if (fi == NULL) return false;       
+    if (fi == NULL) return false;
     group_ptr.push_back(0);
     unsigned nline;
     while (fscanf(fi, "%u", &nline) == 1) {
@@ -110,6 +111,11 @@ struct MetaInfo {
  */
 template<typename FMatrix>
 struct DMatrix {
+  /*! 
+   * \brief magic number associated with this object 
+   *    used to check if it is specific instance
+   */
+  const int magic;
   /*! \brief meta information about the dataset */
   MetaInfo info;
   /*! \brief feature matrix about data content */
@@ -120,7 +126,7 @@ struct DMatrix {
    */
   void *cache_learner_ptr_;
   /*! \brief default constructor */
-  DMatrix(void) : cache_learner_ptr_(NULL) {}
+  explicit DMatrix(int magic) : magic(magic), cache_learner_ptr_(NULL) {}
   // virtual destructor
   virtual ~DMatrix(void){}
 };

src/learner/evaluation.h

@@ -39,7 +39,7 @@ inline IEvaluator* CreateEvaluator(const char *name) {
   if (!strcmp(name, "merror")) return new EvalMatchError();
   if (!strcmp(name, "logloss")) return new EvalLogLoss();
   if (!strcmp(name, "auc")) return new EvalAuc();
-  if (!strncmp(name, "ams@",4)) return new EvalAMS(name);
+  if (!strncmp(name, "ams@", 4)) return new EvalAMS(name);
   if (!strncmp(name, "pre@", 4)) return new EvalPrecision(name);
   if (!strncmp(name, "map", 3)) return new EvalMAP(name);
   if (!strncmp(name, "ndcg", 3)) return new EvalNDCG(name);

src/learner/learner-inl.hpp

@@ -78,6 +78,7 @@ class BoostLearner {
   inline void SetParam(const char *name, const char *val) {
     if (!strcmp(name, "silent")) silent = atoi(val);
     if (!strcmp(name, "eval_metric")) evaluator_.AddEval(val);
+    if (!strcmp("seed", name)) random::Seed(atoi(val));
     if (gbm_ == NULL) {
       if (!strcmp(name, "objective")) name_obj_ = val;
       if (!strcmp(name, "booster")) name_gbm_ = val;
@@ -132,16 +133,24 @@ class BoostLearner {
     utils::FileStream fo(utils::FopenCheck(fname, "wb"));
     this->SaveModel(fo);
     fo.Close();
-  }  
+  }
+  /*!
+   * \brief check if data matrix is ready to be used by training,
+   *  if not intialize it
+   * \param p_train pointer to the matrix used by training
+   */
+  inline void CheckInit(DMatrix<FMatrix> *p_train) const {
+    p_train->fmat.InitColAccess();
+  }
   /*!
    * \brief update the model for one iteration
    * \param iter current iteration number
    * \param p_train pointer to the data matrix
    */
-  inline void UpdateOneIter(int iter, DMatrix<FMatrix> *p_train) {
-    this->PredictRaw(*p_train, &preds_);
-    obj_->GetGradient(preds_, p_train->info, iter, &gpair_);
-    gbm_->DoBoost(gpair_, p_train->fmat, p_train->info.root_index);
+  inline void UpdateOneIter(int iter, const DMatrix<FMatrix> &train) {
+    this->PredictRaw(train, &preds_);
+    obj_->GetGradient(preds_, train.info, iter, &gpair_);
+    gbm_->DoBoost(gpair_, train.fmat, train.info.root_index);
   }
   /*!
    * \brief evaluate the model for specific iteration