* add qid for https://github.com/dmlc/xgboost/issues/2748
* change names
* change spaces
* change qid to bst_uint type
* change qid type to size_t
* change qid first to SIZE_MAX
* change qid type from size_t to uint64_t
* update dmlc-core
* fix qids name error
* fix group_ptr_ error
* Style fix
* Add qid handling logic to SparsePage
* New MetaInfo format + backward compatibility fix
Old MetaInfo format (1.0) doesn't contain qid field. We still want to be able
to read from MetaInfo files saved in old format. Also, define a new format
(2.0) that contains the qid field. This way, we can distinguish files that
contain qid and those that do not.
* Update MetaInfo test
* Simply group assignment logic
* Explicitly set qid=nullptr in NativeDataIter
NativeDataIter's callback does not support qid field. Users of NativeDataIter
will need to call setGroup() function separately to set group information.
* Save qids_ in SaveBinary()
* Upgrade dmlc-core submodule
* Add a test for reading qid
* Add contributor
* Check the size of qids_
* Document qid format
* allow arbitrary cross validation fold indices
- use training indices passed to `folds` parameter in `training.cv`
- update doc string
* add tests for arbitrary fold indices
CI tests were failing because wget prompts "the user" for a response
whenever the google test archive is already on the disk.
Fix: Use `-nc` option to skip download when the archive already
exists
* Use sparse page as singular CSR matrix representation
* Simplify dmatrix methods
* Reduce statefullness of batch iterators
* BREAKING CHANGE: Remove prob_buffer_row parameter. Users are instead recommended to sample their dataset as a preprocessing step before using XGBoost.
* GPU binning and compression.
- binning and index compression are done inside the DeviceShard constructor
- in case of a DMatrix with multiple row batches, it is first converted into a single row batch
* Add option to use weights when evaluating metrics in validation sets
* Add test for validation-set weights functionality
* simplify case with no weights for test sets
* fix lint issues
* Update dmlc-core submodule
* Fix dense_parser to work with the latest dmlc-core
* Specify location of Google Test
* Add more source files in dmlc-minimum to get latest dmlc-core working
* Update dmlc-core submodule
* 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.
* 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
* 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
* 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
