check in io module

src/learner/dmatrix.h

@@ -10,10 +10,14 @@
 
 namespace xgboost {
 namespace learner {
-/*! 
+/*!
  * \brief meta information needed in training, including label, weight
  */
 struct MetaInfo {
+  /*! \brief number of rows in the data */
+  size_t num_row;
+  /*! \brief number of columns in the data */
+  size_t num_col;
   /*! \brief label of each instance */
   std::vector<float> labels;
   /*!
@@ -28,6 +32,15 @@ struct MetaInfo {
    *  can be used for multi task setting
    */
   std::vector<unsigned> root_index;
+  MetaInfo(void) : num_row(0), num_col(0) {}
+  /*! \brief clear all the information */
+  inline void Clear(void) {
+    labels.clear();
+    group_ptr.clear();
+    weights.clear();
+    root_index.clear();
+    num_row = num_col = 0;
+  }
   /*! \brief get weight of each instances */
   inline float GetWeight(size_t i) const {
     if(weights.size() != 0) {
@@ -45,20 +58,53 @@ struct MetaInfo {
     }
   }
   inline void SaveBinary(utils::IStream &fo) {
+    fo.Write(&num_row, sizeof(num_row));
+    fo.Write(&num_col, sizeof(num_col));
     fo.Write(labels);
     fo.Write(group_ptr);
     fo.Write(weights);
     fo.Write(root_index);
   }
   inline void LoadBinary(utils::IStream &fi) {
+    utils::Check(fi.Read(&num_row, sizeof(num_row)), "MetaInfo: invalid format");
+    utils::Check(fi.Read(&num_col, sizeof(num_col)), "MetaInfo: invalid format");
     utils::Check(fi.Read(&labels), "MetaInfo: invalid format");
     utils::Check(fi.Read(&group_ptr), "MetaInfo: invalid format");
     utils::Check(fi.Read(&weights), "MetaInfo: invalid format");
     utils::Check(fi.Read(&root_index), "MetaInfo: invalid format");
   }
+  // 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;       
+    group_ptr.push_back(0);
+    unsigned nline;
+    while (fscanf(fi, "%u", &nline) == 1) {
+      group_ptr.push_back(group_ptr.back()+nline);
+    }
+    if (!silent) {
+      printf("%lu groups are loaded from %s\n", group_ptr.size()-1, fname);
+    }
+    fclose(fi);
+    return true;
+  }
+  // try to load weight information from file, if exists
+  inline bool TryLoadWeight(const char* fname, bool silent = false) {
+    FILE *fi = fopen64(fname, "r");
+    if (fi == NULL) return false;
+    float wt;
+    while (fscanf(fi, "%f", &wt) == 1) {
+      weights.push_back(wt);
+    }
+    if (!silent) {
+      printf("loading weight from %s\n", fname);
+    }
+    fclose(fi);
+    return true;
+  }
 };
 
-/*! 
+/*!
  * \brief data object used for learning,
  * \tparam FMatrix type of feature data source
  */
@@ -66,8 +112,6 @@ template<typename FMatrix>
 struct DMatrix {
   /*! \brief meta information about the dataset */
   MetaInfo info;
-  /*! \brief number of rows in the DMatrix */
-  size_t num_row;
   /*! \brief feature matrix about data content */
   FMatrix fmat;
   /*! 
@@ -77,6 +121,8 @@ struct DMatrix {
   void *cache_learner_ptr_;
   /*! \brief default constructor */
   DMatrix(void) : cache_learner_ptr_(NULL) {}
+  // virtual destructor
+  virtual ~DMatrix(void){}
 };
 
 }  // namespace learner

src/learner/learner-inl.hpp

@@ -55,9 +55,9 @@ class BoostLearner {
       if (dupilicate) continue;
       // set mats[i]'s cache learner pointer to this
       mats[i]->cache_learner_ptr_ = this;
-      cache_.push_back(CacheEntry(mats[i], buffer_size, mats[i]->num_row));
-      buffer_size += mats[i]->num_row;
-      num_feature = std::max(num_feature, static_cast<unsigned>(mats[i]->num_col));
+      cache_.push_back(CacheEntry(mats[i], buffer_size, mats[i]->info.num_row));
+      buffer_size += mats[i]->info.num_row;
+      num_feature = std::max(num_feature, static_cast<unsigned>(mats[i]->info.num_col));
     }
     char str_temp[25];
     if (num_feature > mparam.num_feature) {