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>
* [jvm-packages] add gpu_hist tree method
* change updater hist to grow_quantile_histmaker
* add gpu scheduling
* pass correct parameters to xgboost library
* remove debug info
* add use.cuda for pom
* add CI for gpu_hist for jvm
* add gpu unit tests
* use gpu node to build jvm
* use nvidia-docker
* Add CLI interface to create_jni.py using argparse
Co-authored-by: Hyunsu Cho <chohyu01@cs.washington.edu>
* fix type error
* Validate number of features.
* resolve comments
* add feature size for LabelPoint and DataBatch
* pass the feature size to native
* move feature size validating tests into a separate suite
* resolve comments
Co-authored-by: fis <jm.yuan@outlook.com>
* Automatically set maximize_evaluation_metrics if not explicitly given.
* When custom_eval is set, require maximize_evaluation_metrics.
* Update documents on early stop in XGBoost4J-Spark.
* Fix code error.
* fix the nan and non-zero missing value handling
* fix nan handling part
* add missing value
* Update MissingValueHandlingSuite.scala
* Update MissingValueHandlingSuite.scala
* stylistic fix
* [jvm-packages][hot-fix] fix column mismatch caused by zip actions at XGBooostModel.transformInternal
* apply minibatch in prediction
* an iterator-compatible minibatch prediction
* regressor impl
* continuous working on mini-batch prediction of xgboost4j-spark
* Update Booster.java
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* wrap iterators
* enable copartition training and validationset
* add parameters
* converge code path and have init unit test
* enable multi evals for ranking
* unit test and doc
* update example
* fix early stopping
* address the offline comments
* udpate doc
* test eval metrics
* fix compilation issue
* fix example
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* wrap iterators
* remove unused code
* refactor
* fix typo
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* temp
* add method for classifier and regressor
* update tutorial
* address the comments
* update
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* add back train method but mark as deprecated
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* fix scalastyle error
* sparjJobThread
* update
* fix issue when spark job execution thread cannot return before we execute first()
