* Port elementwise metrics to GPU.
* All elementwise metrics are converted to static polymorphic.
* Create a reducer for metrics reduction.
* Remove const of Metric::Eval to accommodate CubMemory.
* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage.
- added distributions to HostDeviceVector
- using HostDeviceVector for labels, weights and base margings in MetaInfo
- using HostDeviceVector for offset and data in SparsePage
- other necessary refactoring
* Added const version of HostDeviceVector API calls.
- const versions added to calls that can trigger data transfers, e.g. DevicePointer()
- updated the code that uses HostDeviceVector
- objective functions now accept const HostDeviceVector<bst_float>& for predictions
* Updated src/linear/updater_gpu_coordinate.cu.
* Added read-only state for HostDeviceVector sync.
- this means no copies are performed if both host and devices access
the HostDeviceVector read-only
* Fixed linter and test errors.
- updated the lz4 plugin
- added ConstDeviceSpan to HostDeviceVector
- using device % dh::NVisibleDevices() for the physical device number,
e.g. in calls to cudaSetDevice()
* Fixed explicit template instantiation errors for HostDeviceVector.
- replaced HostDeviceVector<unsigned int> with HostDeviceVector<int>
* Fixed HostDeviceVector tests that require multiple GPUs.
- added a mock set device handler; when set, it is called instead of cudaSetDevice()
* 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.
* add support for tweedie regression
* added back readme line that was accidentally deleted
* fixed linting errors
* add support for tweedie regression
* added back readme line that was accidentally deleted
* fixed linting errors
* rebased with upstream master and added R example
* changed parameter name to tweedie_variance_power
* linting error fix
* refactored tweedie-nloglik metric to be more like the other parameterized metrics
* added upper and lower bound check to tweedie metric
* add support for tweedie regression
* added back readme line that was accidentally deleted
* fixed linting errors
* added upper and lower bound check to tweedie metric
* added back readme line that was accidentally deleted
* rebased with upstream master and added R example
* rebased again on top of upstream master
* linting error fix
* added upper and lower bound check to tweedie metric
* rebased with master
* lint fix
* removed whitespace at end of line 186 - elementwise_metric.cc
* Add deviance metric for gamma regression
* Simplify the computation of nloglik for gamma regression
* Add a description for gamma-deviance
* Minor fix
* Add support for Gamma regression
* Use base_score to replace the lp_bias
* Remove the lp_bias config block
* Add a demo for running gamma regression in Python
* Typo fix
* Revise the description for objective
* Add a script to generate the autoclaims dataset
