* Implement GK sketching on GPU.
* Strong tests on quantile building.
* Handle sparse dataset by binary searching the column index.
* Hypothesis test on dask.
* Add thread local return entry for DMatrix.
* Save feature name and feature type in binary file.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Group aware GPU weighted sketching.
* Distribute group weights to each data point.
* Relax the test.
* Validate input meta info.
* Fix metainfo copy ctor.
* 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
* Set default dtor for SimpleDMatrix to initialize default copy ctor, which is
deleted due to unique ptr.
* Remove commented code.
* Remove warning for calling host function (std::max).
* Remove warning for initialization order.
* Remove warning for 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>
* Move thread local entry into Learner.
This is an attempt to workaround CUDA context issue in static variable, where
the CUDA context can be released before device vector.
* Add PredictionEntry to thread local entry.
This eliminates one copy of prediction vector.
* Don't define CUDA C API in a namespace.
* Use pre-rounding based method to obtain reproducible floating point
summation.
* GPU Hist for regression and classification are bit-by-bit reproducible.
* Add doc.
* Switch to thrust reduce for `node_sum_gradient`.
- move segment sorter to common
- this is the first of a handful of pr's that splits the larger pr #5326
- it moves this facility to common (from ranking objective class), so that it can be
used for metric computation
- it also wraps all the bald device pointers into span.
