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/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();