* Simplify DropTrees calling logic
* Add `training` parameter for prediction method.
* [Breaking]: Add `training` to C API.
* Change for R and Python custom objective.
* Correct comment.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
This PR fixes tree weights in dart being ignored when computing contributions.
* Fix ellpack page source link.
* Add tree weights to compute contribution.
* adding support for matrix slicing with query ID for cross-validation
* hail mary test of unrar installation for windows tests
* trying to modify tests to run in Github CI
* Remove dependency on wget and unrar
* Save error log from R test
* Relax assertion in test_training
* Use int instead of bool in C function interface
* Revise R interface
* Add XGDMatrixSliceDMatrixEx and keep old XGDMatrixSliceDMatrix for API compatibility
* 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.
* Unify logging facilities.
* Enhance `ConsoleLogger` to handle different verbosity.
* Override macros from `dmlc`.
* Don't use specialized gamma when building with GPU.
* Remove verbosity cache in monitor.
* Test monitor.
* Deprecate `silent`.
* Fix doc and messages.
* Fix python test.
* Fix silent tests.
**Symptom** Apple Clang's implementation of `std::shuffle` expects doesn't work
correctly when it is run with the random bit generator for R package:
```cpp
CustomGlobalRandomEngine::result_type
CustomGlobalRandomEngine::operator()() {
return static_cast<result_type>(
std::floor(unif_rand() * CustomGlobalRandomEngine::max()));
}
```
Minimial reproduction of failure (compile using Apple Clang 10.0):
```cpp
std::vector<int> feature_set(100);
std::iota(feature_set.begin(), feature_set.end(), 0);
// initialize with 0, 1, 2, 3, ..., 99
std::shuffle(feature_set.begin(), feature_set.end(), common::GlobalRandom());
// This returns 0, 1, 2, ..., 99, so content didn't get shuffled at all!!!
```
Note that this bug is platform-dependent; it does not appear when GCC or
upstream LLVM Clang is used.
**Diagnosis** Apple Clang's `std::shuffle` expects 32-bit integer
inputs, whereas `CustomGlobalRandomEngine::operator()` produces 64-bit
integers.
**Fix** Have `CustomGlobalRandomEngine::operator()` produce 32-bit integers.
Closes#3523.
* add interaction constraints
* enable both interaction and monotonic constraints at the same time
* fix lint
* add R test, fix lint, update demo
* Use dmlc::JSONReader to express interaction constraints as nested lists; Use sparse arrays for bookkeeping
* Add Python test for interaction constraints
* make R interaction constraints parameter based on feature index instead of column names, fix R coding style
* Fix lint
* Add BlueTea88 to CONTRIBUTORS.md
* Short circuit when no constraint is specified; address review comments
* Add tutorial for feature interaction constraints
* allow interaction constraints to be passed as string, remove redundant column_names argument
* Fix typo
* Address review comments
* Add comments to Python test
* Fix#3545: XGDMatrixCreateFromCSCEx silently discards empty trailing rows
Description: The bug is triggered when
1. The data matrix has empty rows at the bottom. More precisely, the rows
`n-k+1`, `n-k+2`, ..., `n` of the matrix have missing values in all
dimensions (`n` number of instances, `k` number of trailing rows)
2. The data matrix is given as Compressed Sparse Column (CSC) format.
Diagnosis: When the CSC matrix is converted to Compressed Sparse Row (CSR)
format (this is common format used for DMatrix), the trailing empty rows
are silently ignored. More specifically, the row pointer (`offset`) of the
newly created CSR matrix does not take account of these rows.
Fix: Modify the row pointer.
* Add regression test
* Refactor to allow for custom regularisation methods
* Implement compositional SplitEvaluator framework
* Fixed segfault when no monotone_constraints are supplied.
* Change pid to parentID
* test_monotone_constraints.py now passes
* Refactor ColMaker and DistColMaker to use SplitEvaluator
* Performance optimisation when no monotone_constraints specified
* Fix linter messages
* Fix a few more linter errors
* Update the amalgamation
* Add bounds check
* Add check for leaf node
* Fix linter error in param.h
* Fix clang-tidy errors on CI
* Fix incorrect function name
* Fix clang-tidy error in updater_fast_hist.cc
* Enable SSE2 for Win32 R MinGW
Addresses https://github.com/dmlc/xgboost/pull/3335#issuecomment-400535752
* Add contributor
* Fix print.xgb.Booster
valid_handle should be TRUE when x$handle is NOT null
* Update xgb.Booster.R
Modify is.null.handle to return TRUE for NULL handle
* For CRAN submission, remove all #pragma's that suppress compiler warnings
A few headers in dmlc-core contain #pragma's that disable compiler warnings,
which is against the CRAN submission policy. Fix the problem by removing
the offending #pragma's as part of the command `make Rbuild`.
This addresses issue #3322.
* Fix script to improve Cygwin/MSYS compatibility
We need this to pass rmingw CI test
* Remove remove_warning_suppression_pragma.sh from packaged tarball
