Split up the configuration.

src/learner.cc

@@ -202,9 +202,11 @@ linalg::TensorView<float const, 1> LearnerModelParam::BaseScore(int32_t device)
   // multi-class is not supported yet.
   CHECK_EQ(base_score_.Size(), 1);
   if (device == Context::kCpuId) {
+    // Make sure that we won't run it race condition.
     CHECK(base_score_.Data()->HostCanRead());
     return base_score_.HostView();
   }
+  // Make sure that we won't run it race condition.
   CHECK(base_score_.Data()->DeviceCanRead());
   auto v = base_score_.View(device);
   CHECK(base_score_.Data()->HostCanRead());  // make sure read access is not removed.
@@ -358,8 +360,10 @@ class LearnerConfiguration : public Learner {
 
   /**
    * \brief Calculate the `base_score` based on input data.
+   *
+   * \param p_fmat The training DMatrix used to estimate the base score.
    */
-  void ConfigureLearnerParam(DMatrix const* p_fmat) {
+  void InitBaseScore(DMatrix const* p_fmat) {
     linalg::Tensor<float, 1> base_score;
     // Before 1.0.0, we save `base_score` into binary as a transformed value by objective.
     // After 1.0.0 we save the value provided by user and keep it immutable instead.  To
@@ -387,21 +391,31 @@ class LearnerConfiguration : public Learner {
       base_score(0) = ObjFunction::DefaultBaseScore();
     }
 
-    auto task = obj_->Task();
     mparam_.base_score = base_score(0);
+    CHECK(!std::isnan(mparam_.base_score));
+    // Update the shared model parameter
+    this->ConfigureModelParam();
+  }
 
-    // transform to margin
-    linalg::Tensor<float, 1> copy(base_score.Shape(), ctx_.gpu_id);
-    auto in = base_score.HostView();
+  // Convert mparam to learner_model_param
+  void ConfigureModelParam() {
+    CHECK(obj_);
+    auto task = obj_->Task();
+    linalg::Tensor<float, 1> copy({1}, ctx_.gpu_id);
     auto out = copy.HostView();
-    std::transform(linalg::cbegin(in), linalg::cend(in), linalg::begin(out),
-                   [&](float v) { return obj_->ProbToMargin(v); });
 
-    // move it to model param, which is shared with all other components.
-    learner_model_param_ = LearnerModelParam(&ctx_, mparam_, std::move(copy), task);
-    CHECK(learner_model_param_.Initialized());
-    CHECK_NE(learner_model_param_.BaseScore(&ctx_).Size(), 0);
-    CHECK(!std::isnan(mparam_.base_score));
+    if (!std::isnan(mparam_.base_score)) {
+      // transform to margin
+      out(0) = obj_->ProbToMargin(mparam_.base_score);
+      // move it to model param, which is shared with all other components.
+      learner_model_param_ = LearnerModelParam(&ctx_, mparam_, std::move(copy), task);
+      CHECK(learner_model_param_.Initialized());
+      CHECK_NE(learner_model_param_.BaseScore(&ctx_).Size(), 0);
+    } else {
+      // Model is not yet fitted, use default base score.
+      out(0) = ObjFunction::DefaultBaseScore();
+      learner_model_param_ = LearnerModelParam(&ctx_, mparam_, std::move(copy), task);
+    }
   }
 
  public:
@@ -465,6 +479,7 @@ class LearnerConfiguration : public Learner {
     learner_model_param_.task = obj_->Task();  // required by gbm configuration.
     this->ConfigureGBM(old_tparam, args);
     ctx_.ConfigureGpuId(this->gbm_->UseGPU());
+    this->ConfigureModelParam();
 
     this->ConfigureMetrics(args);
 
@@ -479,6 +494,7 @@ class LearnerConfiguration : public Learner {
 
   void CheckModelInitialized() const {
     CHECK(learner_model_param_.Initialized()) << "Model not fit.";
+    CHECK_NE(learner_model_param_.BaseScore(this->Ctx()).Size(), 0);
   }
 
   virtual PredictionContainer* GetPredictionCache() const {
@@ -1255,7 +1271,7 @@ class LearnerImpl : public LearnerIO {
     monitor_.Start("UpdateOneIter");
     TrainingObserver::Instance().Update(iter);
     this->Configure();
-    this->ConfigureLearnerParam(train.get());
+    this->InitBaseScore(train.get());
 
     if (ctx_.seed_per_iteration) {
       common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);
@@ -1285,7 +1301,7 @@ class LearnerImpl : public LearnerIO {
                     HostDeviceVector<GradientPair>* in_gpair) override {
     monitor_.Start("BoostOneIter");
     this->Configure();
-    this->ConfigureLearnerParam(train.get());
+    this->InitBaseScore(train.get());
 
     if (ctx_.seed_per_iteration) {
       common::GlobalRandom().seed(ctx_.seed * kRandSeedMagic + iter);