This helps reduce the memory copying needed for dense data. In addition, it helps reduce memory usage even if external memory is not used.
- Decouple the number of symbols needed in the compressor with the number of features when the data is dense.
- Remove the fetch call in the `at_end_` iteration.
- Reduce synchronization and kernel launches by using the `uvector` and ctx.
* 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.
