* 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.
* Port elementwise metrics to GPU.
* All elementwise metrics are converted to static polymorphic.
* Create a reducer for metrics reduction.
* Remove const of Metric::Eval to accommodate CubMemory.
* Fix#3342 and h2oai/h2o4gpu#625: Save predictor parameters in model file
This allows pickled models to retain predictor attributes, such as
'predictor' (whether to use CPU or GPU) and 'n_gpu' (number of GPUs
to use). Related: h2oai/h2o4gpu#625Closes#3342.
TODO. Write a test.
* Fix lint
* Do not load GPU predictor into CPU-only XGBoost
* Add a test for pickling GPU predictors
* Make sample data big enough to pass multi GPU test
* Update test_gpu_predictor.cu
* Clean up logic for converting tree_method to updater sequence
* Use C++11 enum class for extra safety
Compiler will give warnings if switch statements don't handle all
possible values of C++11 enum class.
Also allow enum class to be used as DMLC parameter.
* Fix compiler error + lint
* Address reviewer comment
* Better docstring for DECLARE_FIELD_ENUM_CLASS
* Fix lint
* Add C++ test to see if tree_method is recognized
* Fix clang-tidy error
* Add test_learner.h to R package
* Update comments
* Fix lint error
* DMatrix refactor 2
* Remove buffered rowset usage where possible
* Transition to c++11 style iterators for row access
* Transition column iterators to C++ 11
* Replaced std::vector with HostDeviceVector in MetaInfo and SparsePage.
- added distributions to HostDeviceVector
- using HostDeviceVector for labels, weights and base margings in MetaInfo
- using HostDeviceVector for offset and data in SparsePage
- other necessary refactoring
* Added const version of HostDeviceVector API calls.
- const versions added to calls that can trigger data transfers, e.g. DevicePointer()
- updated the code that uses HostDeviceVector
- objective functions now accept const HostDeviceVector<bst_float>& for predictions
* Updated src/linear/updater_gpu_coordinate.cu.
* Added read-only state for HostDeviceVector sync.
- this means no copies are performed if both host and devices access
the HostDeviceVector read-only
* Fixed linter and test errors.
- updated the lz4 plugin
- added ConstDeviceSpan to HostDeviceVector
- using device % dh::NVisibleDevices() for the physical device number,
e.g. in calls to cudaSetDevice()
* Fixed explicit template instantiation errors for HostDeviceVector.
- replaced HostDeviceVector<unsigned int> with HostDeviceVector<int>
* Fixed HostDeviceVector tests that require multiple GPUs.
- added a mock set device handler; when set, it is called instead of cudaSetDevice()
* Revert "Fix #3485, #3540: Don't use dropout for predicting test sets (#3556)"
This reverts commit 44811f233071c5805d70c287abd22b155b732727.
* Document behavior of predict() for DART booster
* Add notice to parameter.rst
* Save max_delta_step as an extra attribute of Booster
Fixes#3509 and #3026, where `max_delta_step` parameter gets lost during serialization.
* fix lint
* Use camel case for global constant
* disable local variable case in clang-tidy
* 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.
* 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
- 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
* Add prediction of feature contributions
This implements the idea described at http://blog.datadive.net/interpreting-random-forests/
which tries to give insight in how a prediction is composed of its feature contributions
and a bias.
* Support multi-class models
* Calculate learning_rate per-tree instead of using the one from the first tree
* Do not rely on node.base_weight * learning_rate having the same value as the node mean value (aka leaf value, if it were a leaf); instead calculate them (lazily) on-the-fly
* Add simple test for contributions feature
* Check against param.num_nodes instead of checking for non-zero length
* Loop over all roots instead of only the first
Storing and then loading a model loses any eval_metric that was
provided. This causes implementations that always store/load, like
xgboost4j-spark, to be unable to eval with the desired metric.
* 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.
* Add format to the params accepted by DumpModel
Currently, only the test format is supported when trying to dump
a model. The plan is to add more such formats like JSON which are
easy to read and/or parse by machines. And to make the interface
for this even more generic to allow other formats to be added.
Hence, we make some modifications to make these function generic
and accept a new parameter "format" which signifies the format of
the dump to be created.
* Fix typos and errors in docs
* plugin: Mention all the register macros available
Document the register macros currently available to the plugin
writers so they know what exactly can be extended using hooks.
* sparce_page_source: Use same arg name in .h and .cc
* gbm: Add JSON dump
The dump_format argument can be used to specify what type
of dump file should be created. Add functionality to dump
gblinear and gbtree into a JSON file.
The JSON file has an array, each item is a JSON object for the tree.
For gblinear:
- The item is the bias and weights vectors
For gbtree:
- The item is the root node. The root node has a attribute "children"
which holds the children nodes. This happens recursively.
* core.py: Add arg dump_format for get_dump()
