* 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
* fix rebase conflict
* [core] additional gblinear improvements
* [R] callback for gblinear coefficients history
* force eta=1 for gblinear python tests
* add top_k to GreedyFeatureSelector
* set eta=1 in shotgun test
* [core] fix SparsePage processing in gblinear; col-wise multithreading in greedy updater
* set sorted flag within TryInitColData
* gblinear tests: use scale, add external memory test
* fix multiclass for greedy updater
* fix whitespace
* fix typo
* Extended monotonic constraints support to 'hist' tree method.
* Added monotonic constraints tests.
* Fix the signature of NoConstraint::CalcSplitGain()
* Document monotonic constraint support in 'hist'
* Update signature of Update to account for latest refactor
* 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.
In line 461, the "size_t offset = 0;" should be declared before any calculation, otherwise will cause compilation error.
```
I:\Libraries\xgboost\src\c_api\c_api.cc(416): error C2146: Missing ";" before "offset" [I:\Libraries\xgboost\build\objxgboost.vcxproj]
```
* 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
- thrust::copy() called from dvec::copy() for gpairs invoked a GPU kernel instead of
cudaMemcpy()
- this resulted in illegal memory access if the GPU running the kernel could not access
the data being copied
- new version of dvec::copy() for thrust::device_ptr iterators calls cudaMemcpy(),
avoiding the problem.
* 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
* Fix#2905
* Fix gpu_exact test failures
* Fix bug in GPU prediction where multiple calls to batch prediction can produce incorrect results
* Fix GPU documentation formatting
- 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
* Fatal error if GPU algorithm selected without GPU support compiled
* Resolve type conversion warnings
* Fix gpu unit test failure
* Fix compressed iterator edge case
* Fix python unit test failures due to flake8 update on pip
Problem:
Fast histogram updater crashes whenever subsampling picks zero rows
Diagnosis:
Row set data structure uses "nullptr" internally to indicate a non-existent
row set. Since you cannot take the address of the first element of an empty
vector, a valid row set ends up getting "nullptr" as well.
Fix:
Use an arbitrary value (not equal to "nullptr") to bypass nullptr check.
* SHAP values for feature contributions
* Fix commenting error
* New polynomial time SHAP value estimation algorithm
* Update API to support SHAP values
* Fix merge conflicts with updates in master
* Correct submodule hashes
* Fix variable sized stack allocation
* Make lint happy
* Add docs
* Fix typo
* Adjust tolerances
* Remove unneeded def
* Fixed cpp test setup
* Updated R API and cleaned up
* Fixed test typo
* [R] MSVC compatibility
* [GPU] allow seed in BernoulliRng up to size_t and scale to uint32_t
* R package build with cmake and CUDA
* R package CUDA build fixes and cleanups
* always export the R package native initialization routine on windows
* update the install instructions doc
* fix lint
* use static_cast directly to set BernoulliRng seed
* [R] demo for GPU accelerated algorithm
* tidy up the R package cmake stuff
* R pack cmake: installs main dependency packages if needed
* [R] version bump in DESCRIPTION
* update NEWS
* added short missing/sparse values explanations to FAQ
* Removal of redundant code/files.
* Removal of exact namespace in GPU plugin
* Revert double precision histograms to single precision for performance on Maxwell/Kepler
* repared serialization after update process; fixes#2545
* non-stratified folds in python could omit some data instances
* Makefile: fixes for older makes on windows; clean R-package too
* make cub to be a shallow submodule
* improve $(MAKE) recovery
* for MinGW, drop the 'lib' prefix from shared library name
* fix defines for 'g++ 4.8 or higher' to include g++ >= 5
* fix compile warnings
* [Appveyor] add MinGW with python; remove redundant jobs
* [Appveyor] also do python build for one of msvc jobs
It has been reported that new parallel algorithm (#2493) results in excessive
message usage (see issue #2326). Until issues are resolved, XGBoost should use
the old parallel algorithm by default. The user would have to specify
`enable_feature_grouping=1` manually to enable the new algorithm.
* Patch to improve multithreaded performance scaling
Change parallel strategy for histogram construction.
Instead of partitioning data rows among multiple threads, partition feature
columns instead. Useful heuristics for assigning partitions have been adopted
from LightGBM project.
* Add missing header to satisfy MSVC
* Restore max_bin and related parameters to TrainParam
* Fix lint error
* inline functions do not require static keyword
* Feature grouping algorithm accepting FastHistParam
Feature grouping algorithm accepts many parameters (3+), and it gets annoying to
pass them one by one. Instead, simply pass the reference to FastHistParam. The
definition of FastHistParam has been moved to a separate header file to
accomodate this change.
