Previously, we use `libsvm` as default when format is not specified. However, the dmlc
data parser is not particularly robust against errors, and the most common type of error
is undefined format.
Along with which, we will recommend users to use other data loader instead. We will
continue the maintenance of the parsers as it's currently used for many internal tests
including federated learning.
* Fix tests with pandas 2.0.
- `is_categorical` is replaced by `is_categorical_dtype`.
- one hot encoding returns boolean type instead of integer type.
- 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.
* 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.
* Use array interface for CSC matrix.
Use array interface for CSC matrix and align the interface with CSR and dense.
- Fix nthread issue in the R package DMatrix.
- Unify the behavior of handling `missing` with other inputs.
- Unify the behavior of handling `missing` around R, Python, Java, and Scala DMatrix.
- Expose `num_non_missing` to the JVM interface.
- Deprecate old CSR and CSC constructors.
* [CI] Fix CI with updated dependencies.
- Fix jvm package get iris.
* Skip SHAP test for now.
* Revert "Skip SHAP test for now."
This reverts commit 9aa28b4d8aee53fa95d92d2a879c6783ff4b2faa.
* Catch all exceptions.
* Add back xgboost.rabit for backwards compatibility
* fix my errors
* Fix lint
* Use FutureWarning
Co-authored-by: Hyunsu Philip Cho <chohyu01@cs.washington.edu>
