Refactor configuration [Part II]. (#4577)

* Refactor configuration [Part II].

* General changes:
** Remove `Init` methods to avoid ambiguity.
** Remove `Configure(std::map<>)` to avoid redundant copying and prepare for
   parameter validation. (`std::vector` is returned from `InitAllowUnknown`).
** Add name to tree updaters for easier debugging.

* Learner changes:
** Make `LearnerImpl` the only source of configuration.

    All configurations are stored and carried out by `LearnerImpl::Configure()`.

** Remove booster in C API.

    Originally kept for "compatibility reason", but did not state why.  So here
    we just remove it.

** Add a `metric_names_` field in `LearnerImpl`.
** Remove `LazyInit`.  Configuration will always be lazy.
** Run `Configure` before every iteration.

* Predictor changes:
** Allocate both cpu and gpu predictor.
** Remove cpu_predictor from gpu_predictor.

    `GBTree` is now used to dispatch the predictor.

** Remove some GPU Predictor tests.

* IO

No IO changes.  The binary model format stability is tested by comparing
hashing value of save models between two commits

tests/cpp/common/test_common.cu

@@ -46,14 +46,14 @@ TEST(GPUSet, Verbose) {
     args["verbosity"] = "3";  // LOG INFO
 
     testing::internal::CaptureStderr();
-    ConsoleLogger::Configure(args.cbegin(), args.cend());
+    ConsoleLogger::Configure({args.cbegin(), args.cend()});
     GPUSet::All(0, 1);
     std::string output = testing::internal::GetCapturedStderr();
     ASSERT_NE(output.find("GPU ID: 0"), std::string::npos);
     ASSERT_NE(output.find("GPUs: 1"), std::string::npos);
 
     args["verbosity"] = "1";  // restore
-    ConsoleLogger::Configure(args.cbegin(), args.cend());
+    ConsoleLogger::Configure({args.cbegin(), args.cend()});
   }
 }
 

tests/cpp/common/test_monitor.cc

@@ -14,8 +14,8 @@ TEST(Monitor, Logging) {
         monitor_.Stop("basic");
       };
 
-  std::map<std::string, std::string> args = {std::make_pair("verbosity", "3")};
-  ConsoleLogger::Configure(args.cbegin(), args.cend());
+  Args args = {std::make_pair("verbosity", "3")};
+  ConsoleLogger::Configure(args);
   testing::internal::CaptureStderr();
   run_monitor();
   std::string output = testing::internal::GetCapturedStderr();
@@ -23,7 +23,7 @@ TEST(Monitor, Logging) {
 
   // Monitor only prints messages when set to DEBUG.
   args = {std::make_pair("verbosity", "2")};
-  ConsoleLogger::Configure(args.cbegin(), args.cend());
+  ConsoleLogger::Configure(args);
   testing::internal::CaptureStderr();
   run_monitor();
   output = testing::internal::GetCapturedStderr();

tests/cpp/gbm/test_gbtree.cc

@@ -8,42 +8,47 @@ TEST(GBTree, SelectTreeMethod) {
   using Arg = std::pair<std::string, std::string>;
   size_t constexpr kRows = 10;
   size_t constexpr kCols = 10;
-  auto mat_ptr = CreateDMatrix(kRows, kCols, 0);
-  std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
+  auto p_shared_ptr_dmat = CreateDMatrix(kRows, kCols, 0);
+  auto p_dmat {(*p_shared_ptr_dmat).get()};
 
-  LearnerTrainParam learner_param;
-  learner_param.InitAllowUnknown(std::vector<Arg>{Arg("n_gpus", "0")});
+  GenericParameter generic_param;
+  generic_param.InitAllowUnknown(std::vector<Arg>{Arg("n_gpus", "0")});
   std::unique_ptr<GradientBooster> p_gbm{
-    GradientBooster::Create("gbtree", &learner_param, {}, 0)};
+    GradientBooster::Create("gbtree", &generic_param, {}, 0)};
   auto& gbtree = dynamic_cast<gbm::GBTree&> (*p_gbm);
 
   // Test if `tree_method` can be set
   std::string n_feat = std::to_string(kCols);
-  gbtree.Configure({Arg{"tree_method", "approx"}, Arg{"num_feature", n_feat}});
+  std::map<std::string, std::string> args {Arg{"tree_method", "approx"}, Arg{"num_feature", n_feat}};
+  gbtree.Configure({args.cbegin(), args.cend()});
+
+  gbtree.ConfigureWithKnownData(args, p_dmat);
   auto const& tparam = gbtree.GetTrainParam();
+  gbtree.ConfigureWithKnownData({Arg{"tree_method", "approx"}, Arg{"num_feature", n_feat}}, p_dmat);
   ASSERT_EQ(tparam.updater_seq, "grow_histmaker,prune");
-  gbtree.Configure({Arg("tree_method", "exact"), Arg("num_feature", n_feat)});
+  gbtree.ConfigureWithKnownData({Arg("tree_method", "exact"), Arg("num_feature", n_feat)}, p_dmat);
   ASSERT_EQ(tparam.updater_seq, "grow_colmaker,prune");
-  gbtree.Configure({Arg("tree_method", "hist"), Arg("num_feature", n_feat)});
+  gbtree.ConfigureWithKnownData({Arg("tree_method", "hist"), Arg("num_feature", n_feat)}, p_dmat);
   ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
   ASSERT_EQ(tparam.predictor, "cpu_predictor");
-  gbtree.Configure({Arg{"booster", "dart"}, Arg{"tree_method", "hist"},
-                    Arg{"num_feature", n_feat}});
+  gbtree.ConfigureWithKnownData({Arg{"booster", "dart"}, Arg{"tree_method", "hist"},
+                                 Arg{"num_feature", n_feat}}, p_dmat);
   ASSERT_EQ(tparam.updater_seq, "grow_quantile_histmaker");
 #ifdef XGBOOST_USE_CUDA
-  learner_param.InitAllowUnknown(std::vector<Arg>{Arg{"n_gpus", "1"}});
-  gbtree.Configure({Arg("tree_method", "gpu_exact"),
-                    Arg("num_feature", n_feat)});
+  generic_param.InitAllowUnknown(std::vector<Arg>{Arg{"n_gpus", "1"}});
+  gbtree.ConfigureWithKnownData({Arg("tree_method", "gpu_exact"),
+                                 Arg("num_feature", n_feat)}, p_dmat);
   ASSERT_EQ(tparam.updater_seq, "grow_gpu,prune");
   ASSERT_EQ(tparam.predictor, "gpu_predictor");
-  gbtree.Configure({Arg("tree_method", "gpu_hist"), Arg("num_feature", n_feat)});
+  gbtree.ConfigureWithKnownData({Arg("tree_method", "gpu_hist"), Arg("num_feature", n_feat)},
+                                p_dmat);
   ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
   ASSERT_EQ(tparam.predictor, "gpu_predictor");
-  gbtree.Configure({Arg{"booster", "dart"}, Arg{"tree_method", "gpu_hist"},
-                    Arg{"num_feature", n_feat}});
+  gbtree.ConfigureWithKnownData({Arg{"booster", "dart"}, Arg{"tree_method", "gpu_hist"},
+                                 Arg{"num_feature", n_feat}}, p_dmat);
   ASSERT_EQ(tparam.updater_seq, "grow_gpu_hist");
 #endif
 
-  delete mat_ptr;
+  delete p_shared_ptr_dmat;
 }
 }  // namespace xgboost

tests/cpp/helpers.h

@@ -189,8 +189,8 @@ std::unique_ptr<DMatrix> CreateSparsePageDMatrixWithRC(size_t n_rows, size_t n_c
 
 gbm::GBTreeModel CreateTestModel();
 
-inline LearnerTrainParam CreateEmptyGenericParam(int gpu_id, int n_gpus) {
-  xgboost::LearnerTrainParam tparam;
+inline GenericParameter CreateEmptyGenericParam(int gpu_id, int n_gpus) {
+  xgboost::GenericParameter tparam;
   std::vector<std::pair<std::string, std::string>> args {
     {"gpu_id", std::to_string(gpu_id)},
     {"n_gpus", std::to_string(n_gpus)}};

tests/cpp/linear/test_linear.cc

@@ -1,5 +1,5 @@
 /*!
- * Copyright 2018 by Contributors
+ * Copyright 2018-2019 by Contributors
  */
 #include <xgboost/linear_updater.h>
 #include "../helpers.h"
@@ -11,7 +11,7 @@ TEST(Linear, shotgun) {
   {
     auto updater = std::unique_ptr<xgboost::LinearUpdater>(
         xgboost::LinearUpdater::Create("shotgun", &lparam));
-    updater->Init({{"eta", "1."}});
+    updater->Configure({{"eta", "1."}});
     xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
         (*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
     xgboost::gbm::GBLinearModel model;
@@ -26,7 +26,7 @@ TEST(Linear, shotgun) {
   {
     auto updater = std::unique_ptr<xgboost::LinearUpdater>(
         xgboost::LinearUpdater::Create("shotgun", &lparam));
-    EXPECT_ANY_THROW(updater->Init({{"feature_selector", "random"}}));
+    EXPECT_ANY_THROW(updater->Configure({{"feature_selector", "random"}}));
   }
   delete mat;
 }
@@ -36,7 +36,7 @@ TEST(Linear, coordinate) {
   auto lparam = xgboost::CreateEmptyGenericParam(0, 0);
   auto updater = std::unique_ptr<xgboost::LinearUpdater>(
       xgboost::LinearUpdater::Create("coord_descent", &lparam));
-  updater->Init({{"eta", "1."}});
+  updater->Configure({{"eta", "1."}});
   xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
       (*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
   xgboost::gbm::GBLinearModel model;

tests/cpp/linear/test_linear.cu

@@ -11,7 +11,7 @@ TEST(Linear, GPUCoordinate) {
   lparam.n_gpus = 1;
   auto updater = std::unique_ptr<xgboost::LinearUpdater>(
       xgboost::LinearUpdater::Create("gpu_coord_descent", &lparam));
-  updater->Init({{"eta", "1."}});
+  updater->Configure({{"eta", "1."}});
   xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
       (*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
   xgboost::gbm::GBLinearModel model;
@@ -33,7 +33,7 @@ TEST(Linear, MGPU_GPUCoordinate) {
     lparam.n_gpus = -1;
     auto updater = std::unique_ptr<xgboost::LinearUpdater>(
         xgboost::LinearUpdater::Create("gpu_coord_descent", &lparam));
-    updater->Init({{"eta", "1."}});
+    updater->Configure({{"eta", "1."}});
     xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
         (*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
     xgboost::gbm::GBLinearModel model;
@@ -52,7 +52,7 @@ TEST(Linear, MGPU_GPUCoordinate) {
     auto mat = xgboost::CreateDMatrix(10, 10, 0);
     auto updater = std::unique_ptr<xgboost::LinearUpdater>(
         xgboost::LinearUpdater::Create("gpu_coord_descent", &lparam));
-    updater->Init({{"eta", "1."}});
+    updater->Configure({{"eta", "1."}});
     xgboost::HostDeviceVector<xgboost::GradientPair> gpair(
         (*mat)->Info().num_row_, xgboost::GradientPair(-5, 1.0));
     xgboost::gbm::GBLinearModel model;

tests/cpp/objective/test_hinge.cc

@@ -6,7 +6,7 @@
 #include "../helpers.h"
 
 TEST(Objective, DeclareUnifiedTest(HingeObj)) {
-  xgboost::LearnerTrainParam tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("binary:hinge", &tparam);
 
   xgboost::bst_float eps = std::numeric_limits<xgboost::bst_float>::min();

tests/cpp/objective/test_multiclass_obj.cc

@@ -7,7 +7,7 @@
 #include "../helpers.h"
 
 TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassObjGPair)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args {{"num_class", "3"}};
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("multi:softmax", &lparam);
 
@@ -47,7 +47,7 @@ TEST(Objective, DeclareUnifiedTest(SoftmaxMultiClassBasic)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(SoftprobMultiClassBasic)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args {
     std::pair<std::string, std::string>("num_class", "3")};
 

tests/cpp/objective/test_objective.cc

@@ -7,7 +7,7 @@
 
 TEST(Objective, UnknownFunction) {
   xgboost::ObjFunction* obj = nullptr;
-  xgboost::LearnerTrainParam tparam;
+  xgboost::GenericParameter tparam;
   std::vector<std::pair<std::string, std::string>> args;
   tparam.InitAllowUnknown(args);
 

tests/cpp/objective/test_ranking_obj.cc

@@ -4,7 +4,7 @@
 #include "../helpers.h"
 
 TEST(Objective, PairwiseRankingGPair) {
-  xgboost::LearnerTrainParam tparam;
+  xgboost::GenericParameter tparam;
   std::vector<std::pair<std::string, std::string>> args;
   tparam.InitAllowUnknown(args);
 

tests/cpp/objective/test_regression_obj.cc

@@ -7,7 +7,7 @@
 #include "../helpers.h"
 
 TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) {
-  xgboost::LearnerTrainParam tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
 
   xgboost::ObjFunction * obj =
@@ -32,7 +32,7 @@ TEST(Objective, DeclareUnifiedTest(LinearRegressionGPair)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(SquaredLog)) {
-  xgboost::LearnerTrainParam tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
 
   xgboost::ObjFunction * obj =
@@ -56,7 +56,7 @@ TEST(Objective, DeclareUnifiedTest(SquaredLog)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(LogisticRegressionGPair)) {
-  xgboost::LearnerTrainParam tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter tparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:logistic", &tparam);
 
@@ -72,7 +72,7 @@ TEST(Objective, DeclareUnifiedTest(LogisticRegressionGPair)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:logistic", &lparam);
 
@@ -102,7 +102,7 @@ TEST(Objective, DeclareUnifiedTest(LogisticRegressionBasic)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("binary:logitraw", &lparam);
 
@@ -118,7 +118,7 @@ TEST(Objective, DeclareUnifiedTest(LogisticRawGPair)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(PoissonRegressionGPair)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("count:poisson", &lparam);
 
@@ -140,7 +140,7 @@ TEST(Objective, DeclareUnifiedTest(PoissonRegressionGPair)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(PoissonRegressionBasic)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("count:poisson", &lparam);
 
@@ -168,7 +168,7 @@ TEST(Objective, DeclareUnifiedTest(PoissonRegressionBasic)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(GammaRegressionGPair)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:gamma", &lparam);
 
@@ -189,7 +189,7 @@ TEST(Objective, DeclareUnifiedTest(GammaRegressionGPair)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(GammaRegressionBasic)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:gamma", &lparam);
 
@@ -217,7 +217,7 @@ TEST(Objective, DeclareUnifiedTest(GammaRegressionBasic)) {
 }
 
 TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:tweedie", &lparam);
 
@@ -241,7 +241,7 @@ TEST(Objective, DeclareUnifiedTest(TweedieRegressionGPair)) {
 
 #if defined(__CUDACC__)
 TEST(Objective, CPU_vs_CUDA) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, 1);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, 1);
 
   xgboost::ObjFunction * obj =
       xgboost::ObjFunction::Create("reg:squarederror", &lparam);
@@ -294,7 +294,7 @@ TEST(Objective, CPU_vs_CUDA) {
 #endif
 
 TEST(Objective, DeclareUnifiedTest(TweedieRegressionBasic)) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, NGPUS);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj = xgboost::ObjFunction::Create("reg:tweedie", &lparam);
 
@@ -325,7 +325,7 @@ TEST(Objective, DeclareUnifiedTest(TweedieRegressionBasic)) {
 // CoxRegression not implemented in GPU code, no need for testing.
 #if !defined(__CUDACC__)
 TEST(Objective, CoxRegressionGPair) {
-  xgboost::LearnerTrainParam lparam = xgboost::CreateEmptyGenericParam(0, 0);
+  xgboost::GenericParameter lparam = xgboost::CreateEmptyGenericParam(0, 0);
   std::vector<std::pair<std::string, std::string>> args;
   xgboost::ObjFunction * obj =
       xgboost::ObjFunction::Create("survival:cox", &lparam);

tests/cpp/predictor/test_gpu_predictor.cu

@@ -2,10 +2,12 @@
 /*!
  * Copyright 2017-2019 XGBoost contributors
  */
-#include <dmlc/logging.h>
 #include <dmlc/filesystem.h>
 #include <xgboost/c_api.h>
 #include <xgboost/predictor.h>
+#include <xgboost/logging.h>
+#include <xgboost/learner.h>
+
 #include <string>
 #include "gtest/gtest.h"
 #include "../helpers.h"
@@ -20,8 +22,14 @@ inline void CheckCAPICall(int ret) {
 }  // namespace anonymous
 #endif
 
-extern const std::map<std::string, std::string>&
-QueryBoosterConfigurationArguments(BoosterHandle handle);
+const std::map<std::string, std::string>&
+QueryBoosterConfigurationArguments(BoosterHandle handle) {
+  CHECK_NE(handle, static_cast<void*>(nullptr));
+  auto* bst = static_cast<xgboost::Learner*>(handle);
+  bst->Configure();
+  return bst->GetConfigurationArguments();
+}
+
 
 namespace xgboost {
 namespace predictor {
@@ -35,8 +43,8 @@ TEST(gpu_predictor, Test) {
   std::unique_ptr<Predictor> cpu_predictor =
       std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &cpu_lparam));
 
-  gpu_predictor->Init({}, {});
-  cpu_predictor->Init({}, {});
+  gpu_predictor->Configure({}, {});
+  cpu_predictor->Configure({}, {});
 
   int n_row = 5;
   int n_col = 5;
@@ -56,35 +64,6 @@ TEST(gpu_predictor, Test) {
   for (int i = 0; i < gpu_out_predictions.Size(); i++) {
     ASSERT_NEAR(gpu_out_predictions_h[i], cpu_out_predictions_h[i], abs_tolerance);
   }
-  // Test predict instance
-  const auto &batch = *(*dmat)->GetRowBatches().begin();
-  for (int i = 0; i < batch.Size(); i++) {
-    std::vector<float> gpu_instance_out_predictions;
-    std::vector<float> cpu_instance_out_predictions;
-    cpu_predictor->PredictInstance(batch[i], &cpu_instance_out_predictions,
-                                   model);
-    gpu_predictor->PredictInstance(batch[i], &gpu_instance_out_predictions,
-                                   model);
-    ASSERT_EQ(gpu_instance_out_predictions[0], cpu_instance_out_predictions[0]);
-  }
-
-  // Test predict leaf
-  std::vector<float> gpu_leaf_out_predictions;
-  std::vector<float> cpu_leaf_out_predictions;
-  cpu_predictor->PredictLeaf((*dmat).get(), &cpu_leaf_out_predictions, model);
-  gpu_predictor->PredictLeaf((*dmat).get(), &gpu_leaf_out_predictions, model);
-  for (int i = 0; i < gpu_leaf_out_predictions.size(); i++) {
-    ASSERT_EQ(gpu_leaf_out_predictions[i], cpu_leaf_out_predictions[i]);
-  }
-
-  // Test predict contribution
-  std::vector<float> gpu_out_contribution;
-  std::vector<float> cpu_out_contribution;
-  cpu_predictor->PredictContribution((*dmat).get(), &cpu_out_contribution, model);
-  gpu_predictor->PredictContribution((*dmat).get(), &gpu_out_contribution, model);
-  for (int i = 0; i < gpu_out_contribution.size(); i++) {
-    ASSERT_EQ(gpu_out_contribution[i], cpu_out_contribution[i]);
-  }
 
   delete dmat;
 }
@@ -93,7 +72,7 @@ TEST(gpu_predictor, ExternalMemoryTest) {
   auto lparam = CreateEmptyGenericParam(0, 1);
   std::unique_ptr<Predictor> gpu_predictor =
       std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &lparam));
-  gpu_predictor->Init({}, {});
+  gpu_predictor->Configure({}, {});
   gbm::GBTreeModel model = CreateTestModel();
   int n_col = 3;
   model.param.num_feature = n_col;
@@ -108,38 +87,6 @@ TEST(gpu_predictor, ExternalMemoryTest) {
   for (const auto& v : out_predictions.HostVector()) {
     ASSERT_EQ(v, 1.5);
   }
-
-  // Test predict leaf
-  std::vector<float> leaf_out_predictions;
-  gpu_predictor->PredictLeaf(dmat.get(), &leaf_out_predictions, model);
-  EXPECT_EQ(leaf_out_predictions.size(), dmat->Info().num_row_);
-  for (const auto& v : leaf_out_predictions) {
-    ASSERT_EQ(v, 0);
-  }
-
-  // Test predict contribution
-  std::vector<float> out_contribution;
-  gpu_predictor->PredictContribution(dmat.get(), &out_contribution, model);
-  EXPECT_EQ(out_contribution.size(), dmat->Info().num_row_ * (n_col + 1));
-  for (int i = 0; i < out_contribution.size(); i++) {
-    if (i % (n_col + 1) == n_col) {
-      ASSERT_EQ(out_contribution[i], 1.5);
-    } else {
-      ASSERT_EQ(out_contribution[i], 0);
-    }
-  }
-
-  // Test predict contribution (approximate method)
-  std::vector<float> out_contribution_approximate;
-  gpu_predictor->PredictContribution(dmat.get(), &out_contribution_approximate, model, true);
-  EXPECT_EQ(out_contribution.size(), dmat->Info().num_row_ * (n_col + 1));
-  for (int i = 0; i < out_contribution.size(); i++) {
-    if (i % (n_col + 1) == n_col) {
-      ASSERT_EQ(out_contribution[i], 1.5);
-    } else {
-      ASSERT_EQ(out_contribution[i], 0);
-    }
-  }
 }
 
 #if defined(XGBOOST_USE_NCCL)
@@ -231,7 +178,7 @@ TEST(gpu_predictor, MGPU_Test) {
   std::unique_ptr<Predictor> cpu_predictor =
       std::unique_ptr<Predictor>(Predictor::Create("cpu_predictor", &cpu_lparam));
 
-  cpu_predictor->Init({}, {});
+  cpu_predictor->Configure({}, {});
 
   for (size_t i = 1; i < 33; i *= 2) {
     int n_row = i, n_col = i;
@@ -263,7 +210,7 @@ TEST(gpu_predictor, MGPU_ExternalMemoryTest) {
 
   std::unique_ptr<Predictor> gpu_predictor =
       std::unique_ptr<Predictor>(Predictor::Create("gpu_predictor", &gpu_lparam));
-  gpu_predictor->Init({}, {});
+  gpu_predictor->Configure({}, {});
 
   gbm::GBTreeModel model = CreateTestModel();
   model.param.num_feature = 3;

tests/cpp/test_learner.cc

@@ -14,7 +14,7 @@ TEST(Learner, Basic) {
   auto mat_ptr = CreateDMatrix(10, 10, 0);
   std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {*mat_ptr};
   auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
-  learner->Configure(args);
+  learner->SetParams(args);
 
   delete mat_ptr;
 }
@@ -46,9 +46,7 @@ TEST(Learner, CheckGroup) {
 
   std::vector<std::shared_ptr<xgboost::DMatrix>> mat = {p_mat};
   auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
-  learner->Configure({Arg{"objective", "rank:pairwise"}});
-  learner->InitModel();
-
+  learner->SetParams({Arg{"objective", "rank:pairwise"}});
   EXPECT_NO_THROW(learner->UpdateOneIter(0, p_mat.get()));
 
   group.resize(kNumGroups+1);
@@ -77,11 +75,34 @@ TEST(Learner, SLOW_CheckMultiBatch) {
   dmat->Info().SetInfo("label", labels.data(), DataType::kFloat32, num_row);
   std::vector<std::shared_ptr<DMatrix>> mat{dmat};
   auto learner = std::unique_ptr<Learner>(Learner::Create(mat));
-  learner->Configure({Arg{"objective", "binary:logistic"}});
-  learner->InitModel();
+  learner->SetParams({Arg{"objective", "binary:logistic"}, Arg{"verbosity", "3"}});
   learner->UpdateOneIter(0, dmat.get());
 }
 
+TEST(Learner, Configuration) {
+  std::string const emetric = "eval_metric";
+  {
+    std::unique_ptr<Learner> learner { Learner::Create({nullptr}) };
+    learner->SetParam(emetric, "auc");
+    learner->SetParam(emetric, "rmsle");
+    learner->SetParam("foo", "bar");
+
+    // eval_metric is not part of configuration
+    auto attr_names = learner->GetConfigurationArguments();
+    ASSERT_EQ(attr_names.size(), 1);
+    ASSERT_EQ(attr_names.find(emetric), attr_names.cend());
+    ASSERT_EQ(attr_names.at("foo"), "bar");
+  }
+
+  {
+    std::unique_ptr<Learner> learner { Learner::Create({nullptr}) };
+    learner->SetParams({{"foo", "bar"}, {emetric, "auc"}, {emetric, "entropy"}, {emetric, "KL"}});
+    auto attr_names = learner->GetConfigurationArguments();
+    ASSERT_EQ(attr_names.size(), 1);
+    ASSERT_EQ(attr_names.at("foo"), "bar");
+  }
+}
+
 #if defined(XGBOOST_USE_CUDA)
 
 TEST(Learner, IO) {
@@ -98,13 +119,12 @@ TEST(Learner, IO) {
   std::vector<std::shared_ptr<DMatrix>> mat {p_dmat};
 
   std::unique_ptr<Learner> learner {Learner::Create(mat)};
-  learner->Configure({Arg{"tree_method", "auto"},
+  learner->SetParams({Arg{"tree_method", "auto"},
                       Arg{"predictor", "gpu_predictor"},
                       Arg{"n_gpus", "-1"}});
-  learner->InitModel();
   learner->UpdateOneIter(0, p_dmat.get());
-  ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
-  ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, -1);
+  ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
+  ASSERT_EQ(learner->GetGenericParameter().n_gpus, -1);
 
   dmlc::TemporaryDirectory tempdir;
   const std::string fname = tempdir.path + "/model.bst";
@@ -117,8 +137,8 @@ TEST(Learner, IO) {
 
   std::unique_ptr<dmlc::Stream> fi(dmlc::Stream::Create(fname.c_str(), "r"));
   learner->Load(fi.get());
-  ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
-  ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 0);
+  ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
+  ASSERT_EQ(learner->GetGenericParameter().n_gpus, 0);
 
   delete pp_dmat;
 }
@@ -137,59 +157,53 @@ TEST(Learner, GPUConfiguration) {
   p_dmat->Info().labels_.HostVector() = labels;
   {
     std::unique_ptr<Learner> learner {Learner::Create(mat)};
-    learner->Configure({Arg{"booster", "gblinear"},
+    learner->SetParams({Arg{"booster", "gblinear"},
                         Arg{"updater", "gpu_coord_descent"}});
-    learner->InitModel();
     learner->UpdateOneIter(0, p_dmat.get());
-    ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
-    ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
+    ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
+    ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
   }
   {
     std::unique_ptr<Learner> learner {Learner::Create(mat)};
-    learner->Configure({Arg{"tree_method", "gpu_exact"}});
-    learner->InitModel();
+    learner->SetParams({Arg{"tree_method", "gpu_exact"}});
     learner->UpdateOneIter(0, p_dmat.get());
-    ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
-    ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
+    ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
+    ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
   }
   {
     std::unique_ptr<Learner> learner {Learner::Create(mat)};
-    learner->Configure({Arg{"tree_method", "gpu_hist"}});
-    learner->InitModel();
+    learner->SetParams({Arg{"tree_method", "gpu_hist"}});
     learner->UpdateOneIter(0, p_dmat.get());
-    ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
-    ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
+    ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
+    ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
   }
   {
     // with CPU algorithm
     std::unique_ptr<Learner> learner {Learner::Create(mat)};
-    learner->Configure({Arg{"tree_method", "hist"}});
-    learner->InitModel();
+    learner->SetParams({Arg{"tree_method", "hist"}});
     learner->UpdateOneIter(0, p_dmat.get());
-    ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
-    ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 0);
+    ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
+    ASSERT_EQ(learner->GetGenericParameter().n_gpus, 0);
   }
   {
     // with CPU algorithm, but `n_gpus` takes priority
     std::unique_ptr<Learner> learner {Learner::Create(mat)};
-    learner->Configure({Arg{"tree_method", "hist"},
+    learner->SetParams({Arg{"tree_method", "hist"},
                         Arg{"n_gpus", "1"}});
-    learner->InitModel();
     learner->UpdateOneIter(0, p_dmat.get());
-    ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
-    ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
+    ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
+    ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
   }
   {
     // With CPU algorithm but GPU Predictor, this is to simulate when
     // XGBoost is only used for prediction, so tree method is not
     // specified.
     std::unique_ptr<Learner> learner {Learner::Create(mat)};
-    learner->Configure({Arg{"tree_method", "hist"},
+    learner->SetParams({Arg{"tree_method", "hist"},
                         Arg{"predictor", "gpu_predictor"}});
-    learner->InitModel();
     learner->UpdateOneIter(0, p_dmat.get());
-    ASSERT_EQ(learner->GetLearnerTrainParameter().gpu_id, 0);
-    ASSERT_EQ(learner->GetLearnerTrainParameter().n_gpus, 1);
+    ASSERT_EQ(learner->GetGenericParameter().gpu_id, 0);
+    ASSERT_EQ(learner->GetGenericParameter().n_gpus, 1);
   }
 
   delete pp_dmat;

tests/cpp/test_logging.cc

@@ -10,14 +10,14 @@ TEST(Logging, Basic) {
   std::string output;
 
   args["verbosity"] = "0";  // silent
-  ConsoleLogger::Configure(args.cbegin(), args.cend());
+  ConsoleLogger::Configure({args.cbegin(), args.cend()});
   testing::internal::CaptureStderr();
   LOG(DEBUG) << "Test silent.";
   output = testing::internal::GetCapturedStderr();
   ASSERT_EQ(output.length(), 0);
 
   args["verbosity"] = "3";  // debug
-  ConsoleLogger::Configure(args.cbegin(), args.cend());
+  ConsoleLogger::Configure({args.cbegin(), args.cend()});
 
   testing::internal::CaptureStderr();
   LOG(WARNING) << "Test Log Warning.";
@@ -35,14 +35,14 @@ TEST(Logging, Basic) {
   ASSERT_NE(output.find("DEBUG"), std::string::npos);
 
   args["verbosity"] = "1";  // warning
-  ConsoleLogger::Configure(args.cbegin(), args.cend());
+  ConsoleLogger::Configure({args.cbegin(), args.cend()});
   testing::internal::CaptureStderr();
   LOG(INFO) << "INFO should not be displayed when set to warning.";
   output = testing::internal::GetCapturedStderr();
   ASSERT_EQ(output.size(), 0);
 
   args["silent"] = "True";
-  ConsoleLogger::Configure(args.cbegin(), args.cend());
+  ConsoleLogger::Configure({args.cbegin(), args.cend()});
   testing::internal::CaptureStderr();
   LOG(INFO) << "Test silent parameter.";
   output = testing::internal::GetCapturedStderr();
@@ -54,7 +54,7 @@ TEST(Logging, Basic) {
   ASSERT_NE(output.find("Test Log Console"), std::string::npos);
 
   args["verbosity"] = "1";  // restore
-  ConsoleLogger::Configure(args.cbegin(), args.cend());
+  ConsoleLogger::Configure({args.cbegin(), args.cend()});
 }
 
 }  // namespace xgboost

tests/cpp/test_main.cc

@@ -1,12 +1,13 @@
 // Copyright by Contributors
 #include <gtest/gtest.h>
+#include <xgboost/base.h>
 #include <xgboost/logging.h>
 #include <string>
 #include <vector>
 
 int main(int argc, char ** argv) {
-  std::vector<std::pair<std::string, std::string>> args {{"verbosity", "2"}};
-  xgboost::ConsoleLogger::Configure(args.begin(), args.end());
+  xgboost::Args args {{"verbosity", "2"}};
+  xgboost::ConsoleLogger::Configure(args);
   testing::InitGoogleTest(&argc, argv);
   testing::FLAGS_gtest_death_test_style = "threadsafe";
   return RUN_ALL_TESTS();

tests/cpp/tree/test_gpu_exact.cu

@@ -16,7 +16,7 @@ TEST(GPUExact, Update) {
   std::vector<Arg> args{{"max_depth", "1"}};
 
   auto* p_gpuexact_maker = TreeUpdater::Create("grow_gpu", &lparam);
-  p_gpuexact_maker->Init(args);
+  p_gpuexact_maker->Configure(args);
 
   size_t constexpr kNRows = 4;
   size_t constexpr kNCols = 8;

tests/cpp/tree/test_gpu_hist.cu

@@ -384,10 +384,11 @@ void TestHistogramIndexImpl(int n_gpus) {
     {"max_leaves", "0"}
   };
 
-  LearnerTrainParam learner_param(CreateEmptyGenericParam(0, n_gpus));
-  hist_maker.Init(training_params, &learner_param);
+  GenericParameter generic_param(CreateEmptyGenericParam(0, n_gpus));
+  hist_maker.Configure(training_params, &generic_param);
+
   hist_maker.InitDataOnce(hist_maker_dmat.get());
-  hist_maker_ext.Init(training_params, &learner_param);
+  hist_maker_ext.Configure(training_params, &generic_param);
   hist_maker_ext.InitDataOnce(hist_maker_ext_dmat.get());
 
   ASSERT_EQ(hist_maker.shards_.size(), hist_maker_ext.shards_.size());

tests/cpp/tree/test_prune.cc

@@ -37,7 +37,7 @@ TEST(Updater, Prune) {
   std::vector<RegTree*> trees {&tree};
   // prepare pruner
   std::unique_ptr<TreeUpdater> pruner(TreeUpdater::Create("prune", &lparam));
-  pruner->Init(cfg);
+  pruner->Configure(cfg);
 
   // loss_chg < min_split_loss;
   tree.ExpandNode(0, 0, 0, true, 0.0f, 0.3f, 0.4f, 0.0f, 0.0f);

tests/cpp/tree/test_quantile_hist.cc

@@ -236,7 +236,7 @@ class QuantileHistMock : public QuantileHistMaker {
   explicit QuantileHistMock(
       const std::vector<std::pair<std::string, std::string> >& args) :
       cfg_{args} {
-    QuantileHistMaker::Init(args);
+    QuantileHistMaker::Configure(args);
     builder_.reset(
         new BuilderMock(
             param_,

tests/cpp/tree/test_refresh.cc

@@ -37,7 +37,7 @@ TEST(Updater, Refresh) {
   tree.Stat(cleft).base_weight = 1.2;
   tree.Stat(cright).base_weight = 1.3;
 
-  refresher->Init(cfg);
+  refresher->Configure(cfg);
   refresher->Update(&gpair, dmat->get(), trees);
 
   bst_float constexpr kEps = 1e-6;

tests/cpp/tree/test_split_evaluator.cc

@@ -32,7 +32,7 @@ TEST(SplitEvaluator, Interaction) {
     }
     std::vector<int32_t> solutions{4, 5};
     ASSERT_EQ(accepted_features.size(), solutions.size());
-    for (int32_t f = 0; f < accepted_features.size(); ++f) {
+    for (size_t f = 0; f < accepted_features.size(); ++f) {
       ASSERT_EQ(accepted_features[f], solutions[f]);
     }
   }

tests/python/test_basic_models.py

@@ -154,7 +154,7 @@ class TestModels(unittest.TestCase):
 
     def test_multi_eval_metric(self):
         watchlist = [(dtest, 'eval'), (dtrain, 'train')]
-        param = {'max_depth': 2, 'eta': 0.2, 'verbosity': 0,
+        param = {'max_depth': 2, 'eta': 0.2, 'verbosity': 1,
                  'objective': 'binary:logistic'}
         param['eval_metric'] = ["auc", "logloss", 'error']
         evals_result = {}