* 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
* 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
* 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
* 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
* 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
* 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.
* 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
* [CORE] allow updating trees in an existing model
* [CORE] in refresh updater, allow keeping old leaf values and update stats only
* [R-package] xgb.train mod to allow updating trees in an existing model
* [R-package] added check for nrounds when is_update
* [CORE] merge parameter declaration changes; unify their code style
* [CORE] move the update-process trees initialization to Configure; rename default process_type to 'default'; fix the trees and trees_to_update sizes comparison check
* [R-package] unit tests for the update process type
* [DOC] documentation for process_type parameter; improved docs for updater, Gamma and Tweedie; added some parameter aliases; metrics indentation and some were non-documented
* fix my sloppy merge conflict resolutions
* [CORE] add a TreeProcessType enum
* whitespace fix
* 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.
* [TREE] Experimental version of monotone constraint
* Allow default detection of montone option
* loose the condition of strict check
* Update gbtree.cc
