Added some more tests for the learner and fit_stump, for both column-wise distributed learning and vertical federated learning.
Also moved the `IsRowSplit` and `IsColumnSplit` methods from the `DMatrix` to the `MetaInfo` since in some places we only have access to the `MetaInfo`. Added a new convenience method `IsVerticalFederatedLearning`.
Some refactoring of the testing fixtures.
- Fix prediction range.
- Support prediction cache in mt-hist.
- Support model slicing.
- Make the booster a Python iterable by defining `__iter__`.
- Cleanup removed/deprecated parameters.
- A new field in the output model `iteration_indptr` for pointing to the ranges of trees for each iteration.
- Remove parameter serialization in the scikit-learn interface.
The scikit-lear interface `save_model` will save only the model and discard all
hyper-parameters. This is to align with the native XGBoost interface, which distinguishes
the hyper-parameter and model parameters.
With the scikit-learn interface, model parameters are attributes of the estimator. For
instance, `n_features_in_`, `n_classes_` are always accessible with
`estimator.n_features_in_` and `estimator.n_classes_`, but not with the
`estimator.get_params`.
- Define a `load_model` method for classifier to load its own attributes.
- Set n_estimators to None by default.
* 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.
- The new implementation is more strict as only binary labels are accepted. The previous implementation converts values greater than 1 to 1.
- Deterministic GPU. (no atomic add).
- Fix top-k handling.
- Precise definition of MAP. (There are other variants on how to handle top-k).
- Refactor GPU ranking tests.
* 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.
- Pass obj info into tree updater as const pointer.
This way we don't have to initialize the learner model param before configuring gbm, hence
breaking up the dependency of configurations.
- 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.
* Support sklearn cross validation for ranker.
- Add a convention for X to include a special `qid` column.
sklearn utilities consider only `X`, `y` and `sample_weight` for supervised learning
algorithms, but we need an additional qid array for ranking.
It's important to be able to support the cross validation function in sklearn since all
other tuning functions like grid search are based on cross validation.
* Update to C++17
* Turn off unity build
* Update CMake to 3.18
* Use MSVC 2022 + CUDA 11.8
* Re-create stack for worker images
* Allocate more disk space for Windows
* Tempiorarily disable clang-tidy
* RAPIDS now requires Python 3.10+
* Unpin cuda-python
* Use latest NCCL
* Use Ubuntu 20.04 in RMM image
* Mark failing mgpu test as xfail
* Fix CPU bin compression with categorical data.
* The bug causes the maximum category to be lesser than 256 or the maximum number of bins when
the input data is dense.
* Extract most of the functionality into `DMatrixCache`.
* Move API entry to independent file to reduce dependency on `predictor.h` file.
* Add test.
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Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
