* 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
* Bugfix 1: Fix segfault in multithreaded ApplySplitSparseData()
When there are more threads than rows in rowset, some threads end up
with empty ranges, causing them to crash. (iend - 1 needs to be
accessible as part of algorithm)
Fix: run only those threads with nonempty ranges.
* Add regression test for Bugfix 1
* Moving python_omp_test to existing python test group
Turns out you don't need to set "OMP_NUM_THREADS" to enable
multithreading. Just add nthread parameter.
* Bugfix 2: Fix corner case of ApplySplitSparseData() for categorical feature
When split value is less than all cut points, split_cond is set
incorrectly.
Fix: set split_cond = -1 to indicate this scenario
* Bugfix 3: Initialize data layout indicator before using it
data_layout_ is accessed before being set; this variable determines
whether feature 0 is included in feat_set.
Fix: re-order code in InitData() to initialize data_layout_ first
* Adding regression test for Bugfix 2
Unfortunately, no regression test for Bugfix 3, as there is no
way to deterministically assign value to an uninitialized variable.
* 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
* Support histogram-based algorithm + multiple tree growing strategy
* Add a brand new updater to support histogram-based algorithm, which buckets
continuous features into discrete bins to speed up training. To use it, set
`tree_method = fast_hist` to configuration.
* Support multiple tree growing strategies. For now, two policies are supported:
* `grow_policy=depthwise` (default): favor splitting at nodes closest to the
root, i.e. grow depth-wise.
* `grow_policy=lossguide`: favor splitting at nodes with highest loss change
* Improve single-threaded performance
* Unroll critical loops
* Introduce specialized code for dense data (i.e. no missing values)
* Additional training parameters: `max_leaves`, `max_bin`, `grow_policy`, `verbose`
* Adding a small test for hist method
* Fix memory error in row_set.h
When std::vector is resized, a reference to one of its element may become
stale. Any such reference must be updated as well.
* Resolve cross-platform compilation issues
* Versions of g++ older than 4.8 lacks support for a few C++11 features, e.g.
alignas(*) and new initializer syntax. To support g++ 4.6, use pre-C++11
initializer and remove alignas(*).
* Versions of MSVC older than 2015 does not support alignas(*). To support
MSVC 2012, remove alignas(*).
* For g++ 4.8 and newer, alignas(*) is enabled for performance benefits.
* Some old compilers (MSVC 2012, g++ 4.6) do not support template aliases
(which uses `using` to declate type aliases). So always use `typedef`.
* Fix a host of CI issues
* Remove dependency for libz on osx
* Fix heading for hist_util
* Fix minor style issues
* Add missing #include
* Remove extraneous logging
* Enable tree_method=hist in R
* Renaming HistMaker to GHistBuilder to avoid confusion
* Fix R integration
* Respond to style comments
* Consistent tie-breaking for priority queue using timestamps
* Last-minute style fixes
* Fix issuecomment-271977647
The way we quantize data is broken. The agaricus data consists of all
categorical values. When NAs are converted into 0's,
`HistCutMatrix::Init` assign both 0's and 1's to the same single bin.
Why? gmat only the smallest value (0) and an upper bound (2), which is twice
the maximum value (1). Add the maximum value itself to gmat to fix the issue.
* Fix issuecomment-272266358
* Remove padding from cut values for the continuous case
* For categorical/ordinal values, use midpoints as bin boundaries to be safe
* Fix CI issue -- do not use xrange(*)
* Fix corner case in quantile sketch
Signed-off-by: Philip Cho <chohyu01@cs.washington.edu>
* Adding a test for an edge case in quantile sketcher
max_bin=2 used to cause an exception.
* Fix fast_hist test
The test used to require a strictly increasing Test AUC for all examples.
One of them exhibits a small blip in Test AUC before achieving a Test AUC
of 1. (See bottom.)
Solution: do not require monotonic increase for this particular example.
[0] train-auc:0.99989 test-auc:0.999497
[1] train-auc:1 test-auc:0.999749
[2] train-auc:1 test-auc:0.999749
[3] train-auc:1 test-auc:0.999749
[4] train-auc:1 test-auc:0.999749
[5] train-auc:1 test-auc:0.999497
[6] train-auc:1 test-auc:1
[7] train-auc:1 test-auc:1
[8] train-auc:1 test-auc:1
[9] train-auc:1 test-auc:1
* 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.
* Changes for Mingw64 compilation to ensure long is a consistent size.
Mainly impacts the Java API which would not compile, but there may be
silent errors on Windows with large datasets before this patch (as long
is 32-bits when compiled with mingw64 even in 64-bit mode).
* Adding ifdefs to ensure it still compiles on MacOS
* Makefile and create_jni.bat changes for Windows.
* Switching XGDMatrixCreateFromCSREx JNI call to use size_t cast
* Fixing lint error, adding profile switching to jvm-packages build to make create-jni.bat get called, adding myself to Contributors.Md
