- Implement a columnar adapter.
- Refactor Python pandas handling code to avoid converting into a single numpy array.
- Add support in R for transforming columns.
- Support R data.frame and factor type.
This aligns dask with pyspark, users need to explicitly call:
```
from xgboost.dask import DaskXGBClassifier
from xgboost import dask as dxgb
```
In future releases, we might stop using the default import and remove the lazy loader.
- Add `numBoostedRound` to jvm packages
- Remove rabit checkpoint version.
- Change the starting version of training continuation in JVM [breaking].
- Redefine the checkpoint version policy in jvm package. [breaking]
- Rename the Python check point callback parameter. [breaking]
- Unifies the checkpoint policy between Python and JVM.
- Use the `linalg::Matrix` for storing gradients.
- New API for the custom objective.
- Custom objective for multi-class/multi-target is now required to return the correct shape.
- Custom objective for Python can accept arrays with any strides. (row-major, column-major)
* Define `best_iteration` only if early stopping is used.
This is the behavior specified by the document but not honored in the actual code.
- Don't set the attributes if there's no early stopping.
- Clean up the code for callbacks, and replace assertions with proper exceptions.
- Assign the attributes when early stopping `save_best` is used.
- Turn the attributes into Python properties.
---------
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
- Rewrite GPU demos. notebook is converted to script to avoid committing additional png plots.
- Add GPU demos into the sphinx gallery.
- Add RMM demos into the sphinx gallery.
- Test for firing threads with different device ordinals.
* Handle the new `device` parameter in dask and demos.
- Check no ordinal is specified in the dask interface.
- Update demos.
- Update dask doc.
- Update the condition for QDM.
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 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.
* 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.
