Empty partition is different from empty dataset. For the former case, each worker has
non-empty dask collections, but each collection might contain empty partition.
This PR prepares the GHistIndexMatrix to host the column matrix which is used by the hist tree method by accepting sparse_threshold parameter.
Some cleanups are made to ensure the correct batch param is being passed into DMatrix along with some additional tests for correctness of SimpleDMatrix.
* Replace all uses of deprecated function sklearn.datasets.load_boston
* More renaming
* Fix bad name
* Update assertion
* Fix n boosted rounds.
* Avoid over regularization.
* Rebase.
* Avoid over regularization.
* Whac-a-mole
Co-authored-by: fis <jm.yuan@outlook.com>
This is the one last PR for removing omp global variable.
* Add context object to the `DMatrix`. This bridges `DMatrix` with https://github.com/dmlc/xgboost/issues/7308 .
* Require context to be available at the construction time of booster.
* Add `n_threads` support for R csc DMatrix constructor.
* Remove `omp_get_max_threads` in R glue code.
* Remove threading utilities that rely on omp global variable.
- Add user configuration.
- Bring back to the logic of using scheduler address from dask. This was removed when we were trying to support GKE, now we bring it back and let xgboost try it if direct guess or host IP from user config failed.
* Implement ubjson.
This is a partial implementation of UBJSON with support for typed arrays. Some missing
features are `f64`, typed object, and the no-op.
This PR rewrites the approx tree method to use codebase from hist for better performance and code sharing.
The rewrite has many benefits:
- Support for both `max_leaves` and `max_depth`.
- Support for `grow_policy`.
- Support for mono constraint.
- Support for feature weights.
- Support for easier bin configuration (`max_bin`).
- Support for categorical data.
- Faster performance for most of the datasets. (many times faster)
- Support for prediction cache.
- Significantly better performance for external memory.
- Unites the code base between approx and hist.
Instead of accessing data from the `original_page_`, access the data from the first page of the available batch.
fix#7476
Co-authored-by: jiamingy <jm.yuan@outlook.com>
* Add num target model parameter, which is configured from input labels.
* Change elementwise metric and indexing for weights.
* Add demo.
* Add tests.
* Add a new ctor to tensor for `initilizer_list`.
* Change labels from host device vector to tensor.
* Rename the field from `labels_` to `labels` since it's a public member.
This PR changes base_margin into a 3-dim array, with one of them being reserved for multi-target classification. Also, a breaking change is made for binary serialization due to extra dimension along with a fix for saving the feature weights. Lastly, it unifies the prediction initialization between CPU and GPU. After this PR, the meta info setter in Python will be based on array interface.
