add base64 model format

src/learner/learner-inl.hpp

@@ -12,6 +12,8 @@
 #include <limits>
 // rabit library for synchronization
 #include <rabit.h>
+#include "../utils/io.h"
+#include "../utils/base64.h"
 #include "./objective.h"
 #include "./evaluation.h"
 #include "../gbm/gbm.h"
@@ -36,6 +38,7 @@ class BoostLearner : public rabit::ISerializable {
     pred_buffer_size = 0;
     seed_per_iteration = 0;
     seed = 0;
+    save_base64 = 0;
   }
   virtual ~BoostLearner(void) {
     if (obj_ != NULL) delete obj_;
@@ -68,9 +71,6 @@ class BoostLearner : public rabit::ISerializable {
     utils::SPrintf(str_temp, sizeof(str_temp), "%lu", 
                    static_cast<unsigned long>(buffer_size));
     this->SetParam("num_pbuffer", str_temp);
-    if (!silent) {
-      utils::Printf("buffer_size=%ld\n", static_cast<long>(buffer_size));
-    }
     this->pred_buffer_size = buffer_size;
   }
   /*!
@@ -108,6 +108,7 @@ class BoostLearner : public rabit::ISerializable {
       this->seed = seed; random::Seed(atoi(val));
     }
     if (!strcmp("seed_per_iter", name)) seed_per_iteration = atoi(val);
+    if (!strcmp("save_base64", name)) save_base64 = atoi(val);
     if (!strcmp(name, "num_class")) this->SetParam("num_output_group", val);
     if (!strcmp(name, "nthread")) {
       omp_set_num_threads(atoi(val));
@@ -191,9 +192,29 @@ class BoostLearner : public rabit::ISerializable {
    * \param fname file name
    */
   inline void LoadModel(const char *fname) {
-    utils::FileStream fi(utils::FopenCheck(fname, "rb"));
+    FILE *fp = utils::FopenCheck(fname, "rb");
+    std::string header; header.resize(4);
+    utils::FileStream fi(fp);
+    // check header for different binary encode
+    // can be base64 or binary
+    if (fi.Read(&header[0], 4) != 0) {
+      // base64 format
+      if (header == "bs64") {
+        utils::Base64InStream bsin(fp);
+        bsin.InitPosition();
+        this->LoadModel(bsin);
+        fclose(fp);
+        return;
+      }
+      if (header == "binf") {
+        this->LoadModel(fi);
+        fclose(fp);
+        return;
+      }
+    }
+    fi.Seek(0);
     this->LoadModel(fi);
-    fi.Close();
+    fclose(fp);
   }
   inline void SaveModel(utils::IStream &fo, bool with_pbuffer = true) const {
     fo.Write(&mparam, sizeof(ModelParam));
@@ -206,9 +227,24 @@ class BoostLearner : public rabit::ISerializable {
    * \param fname file name
    */
   inline void SaveModel(const char *fname) const {
-    utils::FileStream fo(utils::FopenCheck(fname, "wb"));
-    this->SaveModel(fo);
-    fo.Close();
+    FILE *fp;
+    if (!strcmp(fname, "stdout")) {
+      fp = stdout;
+    } else {
+      fp = utils::FopenCheck(fname, "wb");      
+    }
+    utils::FileStream fo(fp);
+    std::string header;
+    if (save_base64 != 0|| fp == stdout) {
+      fo.Write("bs64\t", 5);
+      utils::Base64OutStream bout(fp);
+      this->SaveModel(bout);
+      bout.Finish('\n');
+    } else {
+      fo.Write("binf", 4);
+      this->SaveModel(fo);      
+    }
+    if (fp != stdout) fclose(fp);
   }
   /*!
    * \brief check if data matrix is ready to be used by training,
@@ -383,6 +419,8 @@ class BoostLearner : public rabit::ISerializable {
   // this is important for restart from existing iterations
   // default set to no, but will auto switch on in distributed mode
   int seed_per_iteration;
+  // save model in base64 encoding
+  int save_base64;
   // silent during training
   int silent;
   // distributed learning mode, if any, 0:none, 1:col, 2:row