There is a small typo in src/common/partition_builder.h.
Should read `canonical` rather than `cannonical`.
Signed-off-by: Tim Gates <tim.gates@iress.com>
- Use `bst_bin_t` in batch param constructor.
- Use `StringView` to avoid `std::string` when appropriate.
- Avoid using `MetaInfo` in quantile constructor to limit the scope of parameter.
* Split up column matrix initialization.
This PR splits the column matrix initialization into 2 steps, the first one initializes
the storage while the second one does the transpose. By doing so, we can reuse the code
for Quantile DMatrix.
- Remove unused parameters. There are still many warnings that are not yet
addressed. Currently, the warnings in dmlc-core dominate the error log.
- Remove `distributed` parameter from metric.
- Fixes some warnings about signed comparison.
- 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>
* Pass sparse page as adapter, which prepares for quantile dmatrix.
* Remove old external memory code like `rbegin` and extra `Init` function.
* Simplify type dispatch.
Federated learning plugin for xgboost:
* A gRPC server to aggregate MPI-style requests (allgather, allreduce, broadcast) from federated workers.
* A Rabit engine for the federated environment.
* Integration test to simulate federated learning.
Additional followups are needed to address GPU support, better security, and privacy, etc.
Support adaptive tree, a feature supported by both sklearn and lightgbm. The tree leaf is recomputed based on residue of labels and predictions after construction.
For l1 error, the optimal value is the median (50 percentile).
This is marked as experimental support for the following reasons:
- The value is not well defined for distributed training, where we might have empty leaves for local workers. Right now I just use the original leaf value for computing the average with other workers, which might cause significant errors.
- Some follow-ups are required, for exact, pruner, and optimization for quantile function. Also, we need to calculate the initial estimation.
* Make sure the task is initialized before construction of tree updater.
This is a quick fix meant to be backported to 1.6, for a full fix we should pass the model
param into tree updater by reference instead.
