Pass infomation about objective to tree methods. (#7385)

* Define the `ObjInfo` and pass it down to every tree updater.

src/objective/regression_loss.h

@@ -7,6 +7,8 @@
 #include <dmlc/omp.h>
 #include <xgboost/logging.h>
 #include <algorithm>
+
+#include "xgboost/task.h"
 #include "../common/math.h"
 
 namespace xgboost {
@@ -36,6 +38,7 @@ struct LinearSquareLoss {
   static const char* DefaultEvalMetric() { return "rmse"; }
 
   static const char* Name() { return "reg:squarederror"; }
+  static ObjInfo Info() { return {ObjInfo::kRegression, true}; }
 };
 
 struct SquaredLogError {
@@ -61,6 +64,8 @@ struct SquaredLogError {
   static const char* DefaultEvalMetric() { return "rmsle"; }
 
   static const char* Name() { return "reg:squaredlogerror"; }
+
+  static ObjInfo Info() { return {ObjInfo::kRegression, false}; }
 };
 
 // logistic loss for probability regression task
@@ -96,6 +101,8 @@ struct LogisticRegression {
   static const char* DefaultEvalMetric() { return "rmse"; }
 
   static const char* Name() { return "reg:logistic"; }
+
+  static ObjInfo Info() { return {ObjInfo::kRegression, false}; }
 };
 
 struct PseudoHuberError {
@@ -127,12 +134,14 @@ struct PseudoHuberError {
   static const char* Name() {
     return "reg:pseudohubererror";
   }
+  static ObjInfo Info() { return {ObjInfo::kRegression, false}; }
 };
 
 // logistic loss for binary classification task
 struct LogisticClassification : public LogisticRegression {
   static const char* DefaultEvalMetric() { return "logloss"; }
   static const char* Name() { return "binary:logistic"; }
+  static ObjInfo Info() { return {ObjInfo::kBinary, false}; }
 };
 
 // logistic loss, but predict un-transformed margin
@@ -168,6 +177,8 @@ struct LogisticRaw : public LogisticRegression {
   static const char* DefaultEvalMetric() { return "logloss"; }
 
   static const char* Name() { return "binary:logitraw"; }
+
+  static ObjInfo Info() { return {ObjInfo::kRegression, false}; }
 };
 
 }  // namespace obj