Extract fit intercept. (#8793)

src/objective/init_estimation.cc

@@ -0,0 +1,39 @@
+#include "init_estimation.h"
+
+#include "../common/stats.h"             // Mean
+#include "../tree/fit_stump.h"           // FitStump
+#include "xgboost/base.h"                // GradientPair
+#include "xgboost/data.h"                // MetaInfo
+#include "xgboost/host_device_vector.h"  // HostDeviceVector
+#include "xgboost/json.h"                // Json
+#include "xgboost/linalg.h"              // Tensor,Vector
+#include "xgboost/task.h"                // ObjInfo
+
+namespace xgboost {
+namespace obj {
+void FitIntercept::InitEstimation(MetaInfo const& info, linalg::Vector<float>* base_score) const {
+  if (this->Task().task == ObjInfo::kRegression) {
+    CheckInitInputs(info);
+  }
+  // Avoid altering any state in child objective.
+  HostDeviceVector<float> dummy_predt(info.labels.Size(), 0.0f, this->ctx_->gpu_id);
+  HostDeviceVector<GradientPair> gpair(info.labels.Size(), GradientPair{}, this->ctx_->gpu_id);
+
+  Json config{Object{}};
+  this->SaveConfig(&config);
+
+  std::unique_ptr<ObjFunction> new_obj{
+      ObjFunction::Create(get<String const>(config["name"]), this->ctx_)};
+  new_obj->LoadConfig(config);
+  new_obj->GetGradient(dummy_predt, info, 0, &gpair);
+  bst_target_t n_targets = this->Targets(info);
+  linalg::Vector<float> leaf_weight;
+  tree::FitStump(this->ctx_, gpair, n_targets, &leaf_weight);
+
+  // workaround, we don't support multi-target due to binary model serialization for
+  // base margin.
+  common::Mean(this->ctx_, leaf_weight, base_score);
+  this->PredTransform(base_score->Data());
+}
+}  // namespace obj
+}  // namespace xgboost