Following classes are added to support dataframe in java binding:
- `Column` is an abstract type for a single column in tabular data.
- `ColumnBatch` is an abstract type for dataframe.
- `CuDFColumn` is an implementaiton of `Column` that consume cuDF column
- `CudfColumnBatch` is an implementation of `ColumnBatch` that consumes cuDF dataframe.
- `DeviceQuantileDMatrix` is the interface for quantized data.
The Java implementation mimics the Python interface and uses `__cuda_array_interface__` protocol for memory indexing. One difference is on JVM package, the data batch is staged on the host as java iterators cannot be reset.
Co-authored-by: jiamingy <jm.yuan@outlook.com>
Fix bug introduced in 17913713b554d820a8ce94226d854b4a5f1d8bbc (allow loading from byte array)
When loading model from stream, only last buffer read from the input stream is used to construct the model.
This may work for models smaller than 1 MiB (if you are lucky enough to read the whole model at once), but will always fail if the model is larger.
* Add feature score support for linear model.
* Port R interface to the new implementation.
* Add linear model support in Python.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Add `XGBOOST_RABIT_TRACKER_IP_FOR_TEST` to set rabit tracker IP
* change spark and rabit tracker IP to 127.0.0.1on GitHub Action.
Co-authored-by: fis <jm.yuan@outlook.com>
* Add ability to load booster direct from byte array
* fix compiler error
* move InputStream to byte-buffer conversion
- move it from Booster to XGBoost facade class
* [java] extending the library loader to use both OS and CPU architecture.
* Simplifying create_jni.py's architecture detection.
* Tidying up the architecture detection in create_jni.py
CLI is not most developed interface. Putting them into correct directory can help new users to avoid it as most of the use cases are from a language binding.
* Now it's built as part of libxgboost.
* Set correct C API error in RABIT initialization and finalization.
* Remove redundant message.
* Guard the tracker print C API.
* [CI] Clean up build for JVM packages
* Use correct path for saving native lib
* Fix groupId of maven-surefire-plugin
* Fix stashing of xgboost4j_jar_gpu
* [CI] Don't run xgboost4j-tester with GPU, since it doesn't use gpu_hist
* Change DefaultEvalMetric of classification from error to logloss
* Change default binary metric in plugin/example/custom_obj.cc
* Set old error metric in python tests
* Set old error metric in R tests
* Fix missed eval metrics and typos in R tests
* Fix setting eval_metric twice in R tests
* Add warning for empty eval_metric for classification
* Fix Dask tests
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* Add getNumFeature to the Java API
* Add getNumFeature to the Scala API
* Add unit tests for getNumFeature
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* cancel job instead of killing SparkContext
This PR changes the default behavior that kills SparkContext. Instead, This PR
cancels jobs when coming across task failed. That means the SparkContext is
still alive even some exceptions happen.
* add a parameter to control if killing SparkContext
* cancel the jobs the failed task belongs to
* remove the jobId from the map when one job failed.
* resolve comments
We propose to only use the rowHashCode to compute the partitionKey, adding the FeatureValue hashCode does not bring more value and would make the computation slower. Even though a collision would appear at 0.2% with MurmurHash3 this is bearable for partitioning, this won't have any impact on the data balancing.
* [CI] Improve JVM test in GitHub Actions
* Use env var for Wagon options [skip ci]
* Move the retry flag to pom.xml [skip ci]
* Export env var RABIT_MOCK to run Spark tests [skip ci]
* Correct location of env var
* Re-try up to 5 times [skip ci]
* Don't run distributed training test on Windows
* Fix typo
* Update main.yml
The functions featureValueOfSparseVector or featureValueOfDenseVector could return a Float.NaN if the input vectore was containing any missing values. This would make fail the partition key computation and most of the vectors would end up in the same partition. We fix this by avoid returning a NaN and simply use the row HashCode in this case.
We added a test to ensure that the repartition is indeed now uniform on input dataset containing values by checking that the partitions size variance is below a certain threshold.
Signed-off-by: Anthony D'Amato <anthony.damato@hotmail.fr>
* Allow non-zero for missing value when training.
* Fix wrong method names.
* Add a unit test
* Move the getter/setter unit test to MissingValueHandlingSuite
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
