* Replaced std::vector-based interfaces with HostDeviceVector-based interfaces.
- replacement was performed in the learner, boosters, predictors,
updaters, and objective functions
- only interfaces used in training were replaced;
interfaces like PredictInstance() still use std::vector
- refactoring necessary for replacement of interfaces was also performed,
such as using HostDeviceVector in prediction cache
* HostDeviceVector-based interfaces for custom objective function example plugin.
* Add interaction effects and cox loss
* Minimize whitespace changes
* Cox loss now no longer needs a pre-sorted dataset.
* Address code review comments
* Remove mem check, rename to pred_interactions, include bias
* Make lint happy
* More lint fixes
* Fix cox loss indexing
* Fix main effects and tests
* Fix lint
* Use half interaction values on the off-diagonals
* Fix lint again
* Added GPU objective function and no-copy interface.
- xgboost::HostDeviceVector<T> syncs automatically between host and device
- no-copy interfaces have been added
- default implementations just sync the data to host
and call the implementations with std::vector
- GPU objective function, predictor, histogram updater process data
directly on GPU
- Implement colsampling, subsampling for gpu_hist_experimental
- Optimised multi-GPU implementation for gpu_hist_experimental
- Make nccl optional
- Add Volta architecture flag
- Optimise RegLossObj
- Add timing utilities for debug verbose mode
- Bump required cuda version to 8.0
* 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
* correct CalcDCG in rank_metric.cc
DCG use log base-2, however `std::log` returns log base-e.
* correct CalcDCG in rank_obj.cc
DCG use log base-2, however `std::log` returns log base-e.
* use std::log2 instead of std::log
make it more elegant
* use std::log2 instead of std::log
make it more elegant
* 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
