* Add interval accuracy
* De-virtualize AFT functions
* Lint
* Refactor AFT metric using GPU-CPU reducer
* Fix R build
* Fix build on Windows
* Fix copyright header
* Clang-tidy
* Fix crashing demo
* Fix typos in comment; explain GPU ID
* Remove unnecessary #include
* Add C++ test for interval accuracy
* Fix a bug in accuracy metric: use log pred
* Refactor AFT objective using GPU-CPU Transform
* Lint
* Fix lint
* Use Ninja to speed up build
* Use time, not /usr/bin/time
* Add cpu_build worker class, with concurrency = 1
* Use concurrency = 1 only for CUDA build
* concurrency = 1 for clang-tidy
* Address reviewer's feedback
* Update link to AFT paper
* [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>
* - create a gpu metrics (internal) registry
- the objective is to separate the cpu and gpu implementations such that they evolve
indepedently. to that end, this approach will:
- preserve the same metrics configuration (from the end user perspective)
- internally delegate the responsibility to the gpu metrics builder when there is a
valid device present
- decouple the gpu metrics builder from the cpu ones to prevent misuse
- move away from including the cuda file from within the cc file and segregate the code
via ifdef's
This makes GPU Hist robust in distributed environment as some workers might not
be associated with any data in either training or evaluation.
* Disable rabit mock test for now: See #5012 .
* Disable dask-cudf test at prediction for now: See #5003
* Launch dask job for all workers despite they might not have any data.
* Check 0 rows in elementwise evaluation metrics.
Using AUC and AUC-PR still throws an error. See #4663 for a robust fix.
* Add tests for edge cases.
* Add `LaunchKernel` wrapper handling zero sized grid.
* Move some parts of allreducer into a cu file.
* Don't validate feature names when the booster is empty.
* Sync number of columns in DMatrix.
As num_feature is required to be the same across all workers in data split
mode.
* Filtering in dask interface now by default syncs all booster that's not
empty, instead of using rank 0.
* Fix Jenkins' GPU tests.
* Install dask-cuda from source in Jenkins' test.
Now all tests are actually running.
* Restore GPU Hist tree synchronization test.
* Check UUID of running devices.
The check is only performed on CUDA version >= 10.x, as 9.x doesn't have UUID field.
* Fix CMake policy and project variables.
Use xgboost_SOURCE_DIR uniformly, add policy for CMake >= 3.13.
* Fix copying data to CPU
* Fix race condition in cpu predictor.
* Fix duplicated DMatrix construction.
* Don't download extra nccl in CI script.
* 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.
* 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 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
* - set the appropriate device before freeing device memory...
- pr #4532 added a global memory tracker/logger to keep track of number of (de)allocations
and peak memory usage on a per device basis.
- this pr adds the appropriate check to make sure that the (de)allocation counts and memory usages
makes sense for the device. since verbosity is typically increased on debug/non-retail builds.
* - pre-create cub allocators and reuse them
- create them once and not resize them dynamically. we need to ensure that these allocators
are created and destroyed exactly once so that the appropriate device id's are set
* Only define `gpu_id` and `n_gpus` in `LearnerTrainParam`
* Pass LearnerTrainParam through XGBoost vid factory method.
* Disable all GPU usage when GPU related parameters are not specified (fixes XGBoost choosing GPU over aggressively).
* Test learner train param io.
* Fix gpu pickling.
* Upgrade gtest for clang-tidy.
* Use CMake to install GTest instead of mv.
* Don't enforce clang-tidy to return 0 due to errors in thrust.
* Add a small test for tidy itself.
* Reformat.
* 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()
* rank_metric: add AUC-PR
Implementation of the AUC-PR calculation for weighted data, proposed by Keilwagen, Grosse and Grau (https://doi.org/10.1371/journal.pone.0092209)
* rank_metric: fix lint warnings
* Implement tests for AUC-PR and fix implementation
* add aucpr to documentation for other languages
* 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
* Add UpdatePredictionCache() option to updaters
Some updaters (e.g. fast_hist) has enough information to quickly compute
prediction cache for the training data. Each updater may override
UpdaterPredictionCache() method to update the prediction cache. Note: this
trick does not apply to validation data.
* Respond to code review
* Disable some debug messages by default
* Document UpdatePredictionCache() interface
* Remove base_margin logic from UpdatePredictionCache() implementation
* Do not take pointer to cfg, as reference may get stale
* Improve multi-threaded performance
* Use columnwise accessor to accelerate ApplySplit() step,
with support for a compressed representation
* Parallel sort for evaluation step
* Inline BuildHist() function
* Cache gradient pairs when building histograms in BuildHist()
* Add missing #if macro
* Respond to code review
* Use wrapper to enable parallel sort on Linux
* Fix C++ compatibility issues
* MSVC doesn't support unsigned in OpenMP loops
* gcc 4.6 doesn't support using keyword
* Fix lint issues
* Respond to code review
* Fix bug in ApplySplitSparseData()
* Attempting to read beyond the end of a sparse column
* Mishandling the case where an entire range of rows have missing values
* Fix training continuation bug
Disable UpdatePredictionCache() in the first iteration. This way, we can
accomodate the scenario where we build off of an existing (nonempty) ensemble.
* Add regression test for fast_hist
* Respond to code review
* Add back old version of ApplySplitSparseData
The GetWeight is a wrapper which sets the correct weight
if the weights vector is not provided. Hence accessing the default
weights vector is not recommended.
* 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 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
