- Use numpy stack for handling list of arrays.
- Reuse concat function from dask.
- Prepare for `QuantileDMatrix`.
- Remove unused code.
- Use iterator for prediction to avoid initializing xgboost model
There is a small typo in src/common/partition_builder.h.
Should read `canonical` rather than `cannonical`.
Signed-off-by: Tim Gates <tim.gates@iress.com>
* [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.
- Use `bst_bin_t` in batch param constructor.
- Use `StringView` to avoid `std::string` when appropriate.
- Avoid using `MetaInfo` in quantile constructor to limit the scope of parameter.
* Split up column matrix initialization.
This PR splits the column matrix initialization into 2 steps, the first one initializes
the storage while the second one does the transpose. By doing so, we can reuse the code
for Quantile DMatrix.
* 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`.
* [jvm-packages] fix executor crashing issue when transforming on xgboost4j-spark-gpu
the API XGBoosterSetParam is not thread-safe. Dring the phase of transforming,
XGBoost runs several transforming tasks at a time, and each of them will set
the "gpu_id" and "predictor" parameters, so if several tasks (multi-threads)
all XGBoosterSetParam simultaneously, it may cause the memory to be corrupted
and cause SIGSEGV.
This PR first get the booster from broadcast and set to the correct gpu_id
and predictor, and then all transforming taskes will use the same booster to
do the transforming.
