* Disallow importing non-dask estimators from xgboost.dask
This is mostly a style change, but also avoids a user error (that I have
committed on a few occasions). Since `XGBRegressor` and `XGBClassifier`
are imported as parent classes for the `dask` estimators, without
defining an `__all__`, autocomplete (or muscle) memory will produce the
following with little prompting:
```
from xgboost.dask import XGBClassifier
```
There's nothing inherently wrong with that, but given that
`XGBClassifier` is not `dask` enabled, it can lead to confusing behavior
until you figure out you should've typed
```
from xgboost.dask import DaskXGBClassifier
```
Another option is to alias import the existing non-dask estimators.
* Remove base/iter class, add train predict funcs
* Support categorical data for dask functional interface and DQM.
* Implement categorical data support for GPU GK-merge.
* Add support for dask functional interface.
* Add support for DQM.
* Get newer cupy.
* [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.
* 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.
* 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.
* Accept array interface for csr and array.
* Accept an optional proxy dmatrix for metainfo.
This constructs an explicit `_ProxyDMatrix` type in Python.
* Remove unused doc.
* Add strict output.
This PR changes predict and inplace_predict to accept a Future of model, to avoid sending models to workers repeatably.
* Document is updated to reflect functionality additions in recent changes.
* [dask] Use a 1 line sample to infer output shape.
This is for inferring shape with direct prediction (without DaskDMatrix).
There are a few things that requires known output shape before carrying out
actual prediction, including dask meta data, output dataframe columns.
* Infer output shape based on local prediction.
* Remove set param in predict function as it's not thread safe nor necessary as
we now let dask to decide the parallelism.
* Simplify prediction on `DaskDMatrix`.
This PR ensures all DMatrix types have a common interface.
* Fix logic in avoiding duplicated DMatrix in sklearn.
* Check for consistency between DMatrix types.
* Add doc for bounds.
* Initial support for distributed LTR using dask.
* Support `qid` in libxgboost.
* Refactor `predict` and `n_features_in_`, `best_[score/iteration/ntree_limit]`
to avoid duplicated code.
* Define `DaskXGBRanker`.
The dask ranker doesn't support group structure, instead it uses query id and
convert to group ptr internally.
* For sklearn:
- Handles user defined objective function.
- Handles `softmax`.
* For dask:
- Use the implementation from sklearn, the previous implementation doesn't perform any extra handling.
* Add management functions for global configuration: XGBSetGlobalConfig(), XGBGetGlobalConfig().
* Add Python interface: set_config(), get_config(), and config_context().
* Add unit tests for Python
* Add R interface: xgb.set.config(), xgb.get.config()
* Add unit tests for R
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
Deprecate positional arguments in following functions:
- `__init__` for all classes in sklearn module.
- `fit` method for all classes in sklearn module.
- dask interface.
- `set_info` for `DMatrix` class.
Refactor the evaluation matrices handling.
