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Support null value in CUDA array interface. #46
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* Support binary/multi-class classification, ranking. * Add documents. * Handle missing data.
We have 2 new custom objective demos covering both regression and classification with accompanying tutorials in documents.
* Replace -1 in pandas initializer. * Unify `IsValid` functor. * Mimic pandas data handling in cuDF glue code. * Check invalid categories. * Fix DDM sketching.
* Add new classifiers. * Typehint.
…mlc#6751) A new parameter `custom_metric` is added to `train` and `cv` to distinguish the behaviour from the old `feval`. And `feval` is deprecated. The new `custom_metric` receives transformed prediction when the built-in objective is used. This enables XGBoost to use cost functions from other libraries like scikit-learn directly without going through the definition of the link function. `eval_metric` and `early_stopping_rounds` in sklearn interface are moved from `fit` to `__init__` and is now saved as part of the scikit-learn model. The old ones in `fit` function are now deprecated. The new `eval_metric` in `__init__` has the same new behaviour as `custom_metric`. Added more detailed documents for the behaviour of custom objective and metric.
Spark 3.2 depends on 3.7.0-M11 which has changed some implicited functions' signatures. And it will result the xgboost4j built against spark 3.0/3.1 failed when saving the model.
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
Generated using `clang-format -style=google -dump-config > .clang-format`, with column width changed from 80 to 100 to be consistent with existing cpplint check.
* Fix span reverse iterator. * Disable `rbegin` on device code to avoid calling host function. * Add `trbegin` and friends.
This is already partially supported but never properly tested. So the only possible way to use it is calling `numpy.ndarray.flatten` with `base_margin` before passing it into XGBoost. This PR adds proper support for most of the data types along with tests.
* Support building with CTK11.5. * Require system cub installation for CTK11.4+. * Check thrust version for segmented sort.
* Move attribute setter to callback. * Remove the internal train function. * Remove unnecessary initialization.
* Add test for invalid categorical data values. * Add check during sketching.
Change from system Python to environment python3. For Ubuntu 20.04, only `python3` is available and there's no `python`. So at least `python3` is consistent with Python virtual env, Ubuntu and anaconda.
* Define the `ObjInfo` and pass it down to every tree updater.
* Replace existing matrix and vector view. This is to prepare for handling higher dimension data and prediction when we support multi-target models.
- Optionally switch to c++17 - Use rmm CMake target. - Workaround compiler errors. - Fix GPUMetric inheritance. - Run death tests even if it's built with RMM support. Co-authored-by: jakirkham <jakirkham@gmail.com> Co-authored-by: jakirkham <jakirkham@gmail.com>
…dmlc#8185) * Fix loading DMatrix binary in distributed env. (dmlc#8149) - Try to load DMatrix binary before trying to parse text input. - Remove some unmaintained code. * Fix.
Add missing Thrust header includes.
Update gputreeshap submodule.
…patch-1.6-cupy
Update to latest xgboost release_1.6.0 to include cupy compatibility patch
Merge branch-22.10 into branch-22.12 (Update to latest xgboost release_1.6.0 to include cupy compatibility patch)
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Port of dmlc#8486
cc @dantegd @wphicks @trivialfis