* Use `UpdateAllowUnknown' for non-model related parameter.
Model parameter can not pack an additional boolean value due to binary IO
format. This commit deals only with non-model related parameter configuration.
* Add tidy command line arg for use-dmlc-gtest.
* 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
* Remove GHistRow, GHistEntry, GHistIndexRow.
* Remove kSimpleStats.
* Remove CheckInfo, SetLeafVec in GradStats and in SKStats.
* Clean up the GradStats.
* Cleanup calcgain.
* Move LossChangeMissing out of common.
* Remove [] operator from GHistIndexBlock.
* DMatrix refactor 2
* Remove buffered rowset usage where possible
* Transition to c++11 style iterators for row access
* Transition column iterators to C++ 11
* Add basic Span class based on ISO++20.
* Use Span<Entry const> instead of Inst in SparsePage.
* Add DeviceSpan in HostDeviceVector, use it in regression obj.
* Use sparse page as singular CSR matrix representation
* Simplify dmatrix methods
* Reduce statefullness of batch iterators
* BREAKING CHANGE: Remove prob_buffer_row parameter. Users are instead recommended to sample their dataset as a preprocessing step before using XGBoost.
* Fix various typos
* Add override to functions that are overridden
gcc gives warnings about functions that are being overridden by not
being marked as oveirridden. This fixes it.
* Use bst_float consistently
Use bst_float for all the variables that involve weight,
leaf value, gradient, hessian, gain, loss_chg, predictions,
base_margin, feature values.
In some cases, when due to additions and so on the value can
take a larger value, double is used.
This ensures that type conversions are minimal and reduces loss of
precision.
