* [R-package] replace uses of T and F with TRUE and FALSE
* enable linting
* Remove skip
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Set output margin to True for custom objective in Python and R.
* Add a demo for writing multi-class custom objective function.
* Run tests on selected demos.
* Robust regularization of AFT gradient and hessian
* Fix AFT doc; expose it to tutorial TOC
* Apply robust regularization to uncensored case too
* Revise unit test slightly
* Fix lint
* Update test_survival.py
* Use GradientPairPrecise
* Remove unused variables
* [WIP] Add lower and upper bounds on the label for survival analysis
* Update test MetaInfo.SaveLoadBinary to account for extra two fields
* Don't clear qids_ for version 2 of MetaInfo
* Add SetInfo() and GetInfo() method for lower and upper bounds
* changes to aft
* Add parameter class for AFT; use enum's to represent distribution and event type
* Add AFT metric
* changes to neg grad to grad
* changes to binomial loss
* changes to overflow
* changes to eps
* changes to code refactoring
* changes to code refactoring
* changes to code refactoring
* Re-factor survival analysis
* Remove aft namespace
* Move function bodies out of AFTNormal and AFTLogistic, to reduce clutter
* Move function bodies out of AFTLoss, to reduce clutter
* Use smart pointer to store AFTDistribution and AFTLoss
* Rename AFTNoiseDistribution enum to AFTDistributionType for clarity
The enum class was not a distribution itself but a distribution type
* Add AFTDistribution::Create() method for convenience
* changes to extreme distribution
* changes to extreme distribution
* changes to extreme
* changes to extreme distribution
* changes to left censored
* deleted cout
* changes to x,mu and sd and code refactoring
* changes to print
* changes to hessian formula in censored and uncensored
* changes to variable names and pow
* changes to Logistic Pdf
* changes to parameter
* Expose lower and upper bound labels to R package
* Use example weights; normalize log likelihood metric
* changes to CHECK
* changes to logistic hessian to standard formula
* changes to logistic formula
* Comply with coding style guideline
* Revert back Rabit submodule
* Revert dmlc-core submodule
* Comply with coding style guideline (clang-tidy)
* Fix an error in AFTLoss::Gradient()
* Add missing files to amalgamation
* Address @RAMitchell's comment: minimize future change in MetaInfo interface
* Fix lint
* Fix compilation error on 32-bit target, when size_t == bst_uint
* Allocate sufficient memory to hold extra label info
* Use OpenMP to speed up
* Fix compilation on Windows
* Address reviewer's feedback
* Add unit tests for probability distributions
* Make Metric subclass of Configurable
* Address reviewer's feedback: Configure() AFT metric
* Add a dummy test for AFT metric configuration
* Complete AFT configuration test; remove debugging print
* Rename AFT parameters
* Clarify test comment
* Add a dummy test for AFT loss for uncensored case
* Fix a bug in AFT loss for uncensored labels
* Complete unit test for AFT loss metric
* Simplify unit tests for AFT metric
* Add unit test to verify aggregate output from AFT metric
* Use EXPECT_* instead of ASSERT_*, so that we run all unit tests
* Use aft_loss_param when serializing AFTObj
This is to be consistent with AFT metric
* Add unit tests for AFT Objective
* Fix OpenMP bug; clarify semantics for shared variables used in OpenMP loops
* Add comments
* Remove AFT prefix from probability distribution; put probability distribution in separate source file
* Add comments
* Define kPI and kEulerMascheroni in probability_distribution.h
* Add probability_distribution.cc to amalgamation
* Remove unnecessary diff
* Address reviewer's feedback: define variables where they're used
* Eliminate all INFs and NANs from AFT loss and gradient
* Add demo
* Add tutorial
* Fix lint
* Use 'survival:aft' to be consistent with 'survival:cox'
* Move sample data to demo/data
* Add visual demo with 1D toy data
* Add Python tests
Co-authored-by: Philip Cho <chohyu01@cs.washington.edu>
* Add OpenMP as CMake target
* Require CMake 3.12, to allow linking OpenMP target to objxgboost
* Specify OpenMP compiler flag for CUDA host compiler
* Require CMake 3.16+ if the OS is Mac OSX
* Use AppleClang in Mac tests.
* Update dmlc-core
* Don't set_params at the end of set_state.
* Also fix another issue found in dask prediction.
* Add note about prediction.
Don't support other prediction modes at the moment.
* Move get transpose into cc.
* Clean up headers in host device vector, remove thrust dependency.
* Move span and host device vector into public.
* Install c++ headers.
* Short notes for c and c++.
Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Refactor CMake scripts.
* Remove CMake CUDA wrapper.
* Bump CMake version for CUDA.
* Use CMake to handle Doxygen.
* Split up CMakeList.
* Export install target.
* Use modern CMake.
* Remove build.sh
* Workaround for gpu_hist test.
* Use cmake 3.12.
* Revert machine.conf.
* Move CLI test to gpu.
* Small cleanup.
* Support using XGBoost as submodule.
* Fix windows
* Fix cpp tests on Windows
* Remove duplicated find_package.
* add a demo of multi-class classification R version
* add a demo of multi-class classification result
* add intro to the demo readme
* Delete train.md
* Update README.md
* added xgbranker
* fixed predict method and ranking test
* reformatted code in accordance with pep8
* fixed lint error
* fixed docstring and added checks on objective
* added ranking demo for python
* fixed suffix in rank.py
