In the refactor to add base margins, #2532, all of the labels were lost
when creating the dmatrix. This became obvious as metrics like ndcg
always returned 1.0 regardless of the results.
Change-Id: I88be047e1c108afba4784bd3d892bfc9edeabe55
Training a model with the experimental rank:ndcg objective incorrectly
returns a Classification model. Adjust the classification check to
not recognize rank:* objectives as classification.
While writing tests for isClassificationTask also turned up that
obj_type -> regression was incorrectly identified as a classification
task so the function was slightly adjusted to pass the new tests.
When using xgboost4j-spark I had executors getting killed much more
often than i would expect by yarn for overrunning their memory limits,
based on the memoryOverhead provided. It looks like a significant
amount of this is because dmatrix's were being created but not released,
because they were only released when the GC decided it was time to
cleanup the references.
Rather than waiting for the GC, relesae the DMatrix's when we know
they are no longer necessary.
Storing and then loading a model loses any eval_metric that was
provided. This causes implementations that always store/load, like
xgboost4j-spark, to be unable to eval with the desired metric.
ebernhardson