fix base score, and print message

src/learner/learner-inl.hpp

@@ -85,15 +85,22 @@ class BoostLearner {
       if (!strcmp(name, "booster")) name_gbm_ = val;
       mparam.SetParam(name, val);
     }
+    if (gbm_ != NULL) gbm_->SetParam(name, val);
+    if (obj_ != NULL) obj_->SetParam(name, val);
     cfg_.push_back(std::make_pair(std::string(name), std::string(val)));
   }
   /*!
    * \brief initialize the model
    */
   inline void InitModel(void) {
+    // initialize model
     this->InitObjGBM();
-    // adapt the base score
+    // reset the base score
     mparam.base_score = obj_->ProbToMargin(mparam.base_score);
+    char tmp[32];
+    snprintf(tmp, sizeof(tmp), "%g", mparam.base_score);
+    this->SetParam("base_score", tmp);
+    // initialize GBM model
     gbm_->InitModel();
   }
   /*!

src/learner/objective-inl.hpp

@@ -124,7 +124,7 @@ class RegLossObj : public IObjFunction{
                            loss.SecondOrderGradient(p, info.labels[j]) * w);
     }
   }
-  virtual const char* DefaultEvalMetric(void) {
+  virtual const char* DefaultEvalMetric(void) const {
     return loss.DefaultEvalMetric();
   }
   virtual void PredTransform(std::vector<float> *io_preds) {
@@ -135,6 +135,9 @@ class RegLossObj : public IObjFunction{
       preds[j] = loss.PredTransform(preds[j]);
     }
   }
+  virtual float ProbToMargin(float base_score) const {
+    return loss.ProbToMargin(base_score);
+  }
 
  protected:
   float scale_pos_weight;
@@ -192,7 +195,7 @@ class SoftmaxMultiClassObj : public IObjFunction {
   virtual void EvalTransform(std::vector<float> *io_preds) {
     this->Transform(io_preds, 0);
   }
-  virtual const char* DefaultEvalMetric(void) {
+  virtual const char* DefaultEvalMetric(void) const {
     return "merror";
   }
 
@@ -320,7 +323,7 @@ class LambdaRankObj : public IObjFunction {
       }
     }
   }
-  virtual const char* DefaultEvalMetric(void) {
+  virtual const char* DefaultEvalMetric(void) const {
     return "map";
   }
 

src/learner/objective.h

@@ -32,7 +32,7 @@ class IObjFunction{
                            int iter,
                            std::vector<bst_gpair> *out_gpair) = 0;
   /*! \return the default evaluation metric for the objective */
-  virtual const char* DefaultEvalMetric(void) = 0;
+  virtual const char* DefaultEvalMetric(void) const = 0;
   // the following functions are optional, most of time default implementation is good enough
   /*!
    * \brief transform prediction values, this is only called when Prediction is called
@@ -53,7 +53,7 @@ class IObjFunction{
    * used by gradient boosting
    * \return transformed value
    */
-  virtual float ProbToMargin(float base_score) {
+  virtual float ProbToMargin(float base_score) const {
     return base_score;
   }
 };