* [Python] Require black and isort for new Python files.
- Require black and isort for spark and dask module.
These files are relatively new and are more conform to the black formatter. We will
convert the rest of the library as we move forward.
Other libraries including dask/distributed and optuna use the same formatting style and
have a more strict standard. The black formatter is indeed quite nice, automating it can
help us unify the code style.
- Gather Python checks into a single script.
* [PySpark] change the returning model type to string from binary
XGBoost pyspark can be can be accelerated by RAPIDS Accelerator seamlessly by
changing the returning model type from binary to string.
* Fix mypy error with latest dask.
Dask is adding type hints to its codebase and as the result, checks in XGBoost can be
performed more rigorously.
- Remove compatibility with old dask version where multi lock was missing.
- Restrict input of `X` to be non-series.
- Adopt latest definition of `Delayed`.
- Avoid passing optional `host_ip`.
- Avoid deprecated `worker.nthreads`.
Federated learning plugin for xgboost:
* A gRPC server to aggregate MPI-style requests (allgather, allreduce, broadcast) from federated workers.
* A Rabit engine for the federated environment.
* Integration test to simulate federated learning.
Additional followups are needed to address GPU support, better security, and privacy, etc.
* Extract partitioner from hist.
* Implement categorical data support by passing the gradient index directly into the partitioner.
* Organize/update document.
* Remove code for negative hessian.
* Fix copy for cv. This prevents inserting default callbacks into the input list.
* Clarify the behavior of callbacks in training/cv.
* Fix typos in doc.
* Cleanup some pylint errors.
* Cleanup pylint errors in rabit modules.
* Make data iter an abstract class and cleanup private access.
* Cleanup no-self-use for booster.
* Implement `MaxCategory` in quantile.
* Implement partition-based split for GPU evaluation. Currently, it's based on the existing evaluation function.
* Extract an evaluator from GPU Hist to store the needed states.
* Added some CUDA stream/event utilities.
* Update document with references.
* Fixed a bug in approx evaluator where the number of data points is less than the number of categories.
