[PYTHON-DIST] Distributed xgboost python training API.

src/c_api/c_api.cc

@@ -91,7 +91,7 @@ int XGDMatrixCreateFromFile(const char *fname,
                  << "will split data among workers";
   }
   *out = DMatrix::Load(
-      fname, silent != 0, false);
+      fname, false, true);
   API_END();
 }
 
@@ -533,18 +533,44 @@ int XGBoosterDumpModelWithFeatures(BoosterHandle handle,
   API_END();
 }
 
+int XGBoosterGetAttr(BoosterHandle handle,
+                     const char* key,
+                     const char** out,
+                     int* success) {
+  Booster* bst = static_cast<Booster*>(handle);
+  std::string& ret_str = XGBAPIThreadLocalStore::Get()->ret_str;
+  API_BEGIN();
+  if (bst->learner()->GetAttr(key, &ret_str)) {
+    *out = ret_str.c_str();
+    *success = 1;
+  } else {
+    *out = nullptr;
+    *success = 0;
+  }
+  API_END();
+}
+
+int XGBoosterSetAttr(BoosterHandle handle,
+                     const char* key,
+                     const char* value) {
+  Booster* bst = static_cast<Booster*>(handle);
+  API_BEGIN();
+  bst->learner()->SetAttr(key, value);
+  API_END();
+}
+
 int XGBoosterLoadRabitCheckpoint(BoosterHandle handle,
                                  int* version) {
   API_BEGIN();
   Booster* bst = static_cast<Booster*>(handle);
   *version = rabit::LoadCheckPoint(bst->learner());
-  if (version != 0) {
+  if (*version != 0) {
     bst->initialized_ = true;
   }
   API_END();
 }
 
-int XGBoosterSaveRabitCheckPoint(BoosterHandle handle) {
+int XGBoosterSaveRabitCheckpoint(BoosterHandle handle) {
   API_BEGIN();
   Booster* bst = static_cast<Booster*>(handle);
   if (bst->learner()->AllowLazyCheckPoint()) {

src/data/data.cc

@@ -184,7 +184,7 @@ DMatrix* DMatrix::Load(const std::string& uri,
                  << " of " << npart << " parts";
   }
   // legacy handling of binary data loading
-  if (file_format == "auto" && !load_row_split) {
+  if (file_format == "auto" && npart == 1) {
     int magic;
     std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r", true));
     if (fi.get() != nullptr) {

src/learner.cc

@@ -5,6 +5,7 @@
  * \author Tianqi Chen
  */
 #include <xgboost/learner.h>
+#include <dmlc/io.h>
 #include <algorithm>
 #include <vector>
 #include <utility>
@@ -43,8 +44,10 @@ struct LearnerModelParam
   unsigned num_feature;
   /* \brief number of classes, if it is multi-class classification  */
   int num_class;
+  /*! \brief Model contain additional properties */
+  int contain_extra_attrs;
   /*! \brief reserved field */
-  int reserved[31];
+  int reserved[30];
   /*! \brief constructor */
   LearnerModelParam() {
     std::memset(this, 0, sizeof(LearnerModelParam));
@@ -243,6 +246,12 @@ class LearnerImpl : public Learner {
     obj_.reset(ObjFunction::Create(name_obj_));
     gbm_.reset(GradientBooster::Create(name_gbm_));
     gbm_->Load(fi);
+    if (mparam.contain_extra_attrs != 0) {
+      std::vector<std::pair<std::string, std::string> > attr;
+      fi->Read(&attr);
+      attributes_ = std::map<std::string, std::string>(
+          attr.begin(), attr.end());
+    }
 
     if (metrics_.size() == 0) {
       metrics_.emplace_back(Metric::Create(obj_->DefaultEvalMetric()));
@@ -259,6 +268,11 @@ class LearnerImpl : public Learner {
     fo->Write(name_obj_);
     fo->Write(name_gbm_);
     gbm_->Save(fo);
+    if (mparam.contain_extra_attrs != 0) {
+      std::vector<std::pair<std::string, std::string> > attr(
+          attributes_.begin(), attributes_.end());
+      fo->Write(attr);
+    }
   }
 
   void UpdateOneIter(int iter, DMatrix* train) override {
@@ -300,6 +314,18 @@ class LearnerImpl : public Learner {
     return os.str();
   }
 
+  void SetAttr(const std::string& key, const std::string& value) override {
+    attributes_[key] = value;
+    mparam.contain_extra_attrs = 1;
+  }
+
+  bool GetAttr(const std::string& key, std::string* out) const override {
+    auto it = attributes_.find(key);
+    if (it == attributes_.end()) return false;
+    *out = it->second;
+    return true;
+  }
+
   std::pair<std::string, float> Evaluate(DMatrix* data, std::string metric) {
     if (metric == "auto") metric = obj_->DefaultEvalMetric();
     std::unique_ptr<Metric> ev(Metric::Create(metric.c_str()));
@@ -427,6 +453,8 @@ class LearnerImpl : public Learner {
   LearnerTrainParam tparam;
   // configurations
   std::map<std::string, std::string> cfg_;
+  // attributes
+  std::map<std::string, std::string> attributes_;
   // name of gbm
   std::string name_gbm_;
   // name of objective functon

src/tree/updater_basemaker-inl.h

@@ -56,6 +56,9 @@ class BaseMaker: public TreeUpdater {
           }
         }
       }
+    }
+    /*! \brief synchronize the information */
+    inline void SyncInfo() {
       rabit::Allreduce<rabit::op::Max>(dmlc::BeginPtr(fminmax), fminmax.size());
     }
     // get feature type, 0:empty 1:binary 2:real

src/tree/updater_histmaker.cc

@@ -313,6 +313,7 @@ class CQHistMaker: public HistMaker<TStats> {
       feat_helper.InitByCol(p_fmat, tree);
       cache_dmatrix_ = p_fmat;
     }
+    feat_helper.SyncInfo();
     feat_helper.SampleCol(this->param.colsample_bytree, p_fset);
   }
   // code to create histogram