* Re-implement ROC-AUC.
* Binary
* MultiClass
* LTR
* Add documents.
This PR resolves a few issues:
- Define a value when the dataset is invalid, which can happen if there's an
empty dataset, or when the dataset contains only positive or negative values.
- Define ROC-AUC for multi-class classification.
- Define weighted average value for distributed setting.
- A correct implementation for learning to rank task. Previous
implementation is just binary classification with averaging across groups,
which doesn't measure ordered learning to rank.
The [general documentation](https://xgboost.readthedocs.io/en/latest/parameter.html#parameters-for-tree-booster) clearly has alpha and lambda under its "Parameters for Tree Booster" heading. Furthermore, the R package clearly uses alpha and lambda when told to use the tree booster. This update adds those two parameters to the documentation for the R package.
Closed issue #6763.
* [dask] Use `distributed.MultiLock`
This enables training multiple models in parallel.
* Conditionally import `MultiLock`.
* Use async train directly in scikit learn interface.
* Use `worker_client` when available.
* Ensure RMM is 0.18 or later
* Add use_rmm flag to global configuration
* Modify XGBCachingDeviceAllocatorImpl to skip CUB when use_rmm=True
* Update the demo
* [CI] Pin NumPy to 1.19.4, since NumPy 1.19.5 doesn't work with latest Shap
* 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.
* Add ability to load booster direct from byte array
* fix compiler error
* move InputStream to byte-buffer conversion
- move it from Booster to XGBoost facade class
* Use normal predictor for dart booster.
* Implement `inplace_predict` for dart.
* Enable `dart` for dask interface now that it's thread-safe.
* categorical data should be working out of box for dart now.
The implementation is not very efficient as it has to pull back the data and
apply weight for each tree, but still a significant improvement over previous
implementation as now we no longer binary search for each sample.
* Fix output prediction shape on dataframe.
* Stop printing out message.
* Remove R specialization.
The printed message is not really useful anyway, without a reproducible example
there's no way to fix it. But if there's a reproducible example, we can always
obtain these information by a debugger. Removing the `printf` function avoids
creating the context in kernel.
* Add a new API function for predicting on `DMatrix`. This function aligns
with rest of the `XGBoosterPredictFrom*` functions on semantic of function
arguments.
* Purge `ntree_limit` from libxgboost, use iteration instead.
* [dask] Use `inplace_predict` by default for dask sklearn models.
* [dask] Run prediction shape inference on worker instead of client.
The breaking change is in the Python sklearn `apply` function, I made it to be
consistent with other prediction functions where `best_iteration` is used by
default.
