- Update SparseDMatrix comment.
- Use a pointer in the bitfield. We will replace the `std::vector<bool>` in `ColumnMatrix` with bitfield.
- Clean up the page source. The timer is removed as it's inaccurate once we swap the mmap pointer into the page.
* Implement multi-target for hist.
- Add new hist tree builder.
- Move data fetchers for tests.
- Dispatch function calls in gbm base on the tree type.
* Make tree model param a private member.
* Number of features and targets are immutable after construction.
This is to reduce the number of places where we can run configuration.
- Define a new tree struct embedded in the `RegTree`.
- Provide dispatching functions in `RegTree`.
- Fix some c++-17 warnings about the use of nodiscard (currently we disable the warning on
the CI).
- Use uint32_t instead of size_t for `bst_target_t` as it has a defined size and can be used
as part of dmlc parameter.
- Hide the `Segment` struct inside the categorical split matrix.
This PR rewrites the approx tree method to use codebase from hist for better performance and code sharing.
The rewrite has many benefits:
- Support for both `max_leaves` and `max_depth`.
- Support for `grow_policy`.
- Support for mono constraint.
- Support for feature weights.
- Support for easier bin configuration (`max_bin`).
- Support for categorical data.
- Faster performance for most of the datasets. (many times faster)
- Support for prediction cache.
- Significantly better performance for external memory.
- Unites the code base between approx and hist.
* Categorical prediction with CPU predictor and GPU predict leaf.
* Implement categorical prediction for CPU prediction.
* Implement categorical prediction for GPU predict leaf.
* Refactor the prediction functions to have a unified get next node function.
Co-authored-by: Shvets Kirill <kirill.shvets@intel.com>
* Save feature info in booster in JSON model.
* [breaking] Remove automatic feature name generation in `DMatrix`.
This PR is to enable reliable feature validation in Python package.
* fixed some endian issues
* Use dmlc::ByteSwap() to simplify code
* Fix lint check
* [CI] Add test for s390x
* Download latest CMake on s390x
* Fix a bug in my code
* Save magic number in dmatrix with byteswap on big-endian machine
* Save version in binary with byteswap on big-endian machine
* Load scalar with byteswap in MetaInfo
* Add a debugging message
* Handle arrays correctly when byteswapping
* EOF can also be 255
* Handle magic number in MetaInfo carefully
* Skip Tree.Load test for big-endian, since the test manually builds little-endian binary model
* Handle missing packages in Python tests
* Don't use boto3 in model compatibility tests
* Add s390 Docker file for local testing
* Add model compatibility tests
* Add R compatibility test
* Revert "Add R compatibility test"
This reverts commit c2d2bdcb7dbae133cbb927fcd20f7e83ee2b18a8.
Co-authored-by: Qi Zhang <q.zhang@ibm.com>
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Use `UpdateAllowUnknown' for non-model related parameter.
Model parameter can not pack an additional boolean value due to binary IO
format. This commit deals only with non-model related parameter configuration.
* Add tidy command line arg for use-dmlc-gtest.
* Apply Configurable to objective functions.
* Apply Model to Learner and Regtree, gbm.
* Add Load/SaveConfig to objs.
* Refactor obj tests to use smart pointer.
* Dummy methods for Save/Load Model.
* Implement tree model dump with a code generator.
* Split up generators.
* Implement graphviz generator.
* Use pattern matching.
* [Breaking] Return a Source in `to_graphviz` instead of Digraph in Python package.
Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Increase precision of bst_float values in tree dumps
* Increase precision of bst_float values in tree dumps
* Fix lint error and switch precision to right float variable
* Fix clang-tidy error
* 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.
