* [jvm-packages] Fixed test/train persistence
Prior to this patch both data sets were persisted in the same directory,
i.e. the test data replaced the training one which led to
* training on less data (since usually test < train) and
* test loss being exactly equal to the training loss.
Closes#2945.
* Cleanup file cache after the training
* Addressed review comments
* [jvm-packages] Exposed train-time evaluation metrics
They are accessible via 'XGBoostModel.summary'. The summary is not
serialized with the model and is only available after the training.
* Addressed review comments
* Extracted model-related tests into 'XGBoostModelSuite'
* Added tests for copying the 'XGBoostModel'
* [jvm-packages] Fixed a subtle bug in train/test split
Iterator.partition (naturally) assumes that the predicate is deterministic
but this is not the case for
r.nextDouble() <= trainTestRatio
therefore sometimes the DMatrix(...) call got a NoSuchElementException
and crashed the JVM due to lack of exception handling in
XGBoost4jCallbackDataIterNext.
* Make sure train/test objectives are different
* Allowed subsampling test from the training data frame/RDD
The implementation requires storing 1 - trainTestRatio points in memory
to make the sampling work.
An alternative approach would be to construct the full DMatrix and then
slice it deterministically into train/test. The peak memory consumption
of such scenario, however, is twice the dataset size.
* Removed duplication from 'XGBoost.train'
Scala callers can (and should) use names to supply a subset of
parameters. Method overloading is not required.
* Reuse XGBoost seed parameter to stabilize train/test splitting
* Added early stopping support to non-distributed XGBoost
Closes#1544
* Added early-stopping to distributed XGBoost
* Moved construction of 'watches' into a separate method
This commit also fixes the handling of 'baseMargin' which previously
was not added to the validation matrix.
* Addressed review comments
* Converted ml.dmlc.xgboost4j.LabeledPoint to Scala
This allows to easily integrate LabeledPoint with Spark DataFrame APIs,
which support encoding/decoding case classes out of the box. Alternative
solution would be to keep LabeledPoint in Java and make it a Bean by
generating boilerplate getters/setters. I have decided against that, even
thought the conversion in this PR implies a public API change.
I also had to remove the factory methods fromSparseVector and
fromDenseVector because a) they would need to be duplicated to support
overloaded calls with extra data (e.g. weight); and b) Scala would expose
them via mangled $.MODULE$ which looks ugly in Java.
Additionally, this commit makes it possible to switch to LabeledPoint in
all public APIs and effectively to pass initial margin/group as part of
the point. This seems to be the only reliable way of implementing distributed
learning with these data. Note that group size format used by single-node
XGBoost is not compatible with that scenario, since the partition split
could divide a group into two chunks.
* Switched to ml.dmlc.xgboost4j.LabeledPoint in RDD-based public APIs
Note that DataFrame-based and Flink APIs are not affected by this change.
* Removed baseMargin argument in favour of the LabeledPoint field
* Do a single pass over the partition in buildDistributedBoosters
Note that there is no formal guarantee that
val repartitioned = rdd.repartition(42)
repartitioned.zipPartitions(repartitioned.map(_ + 1)) { it1, it2, => ... }
would do a single shuffle, but in practice it seems to be always the case.
* Exposed baseMargin in DataFrame-based API
* Addressed review comments
* Pass baseMargin to XGBoost.trainWithDataFrame via params
* Reverted MLLabeledPoint in Spark APIs
As discussed, baseMargin would only be supported for DataFrame-based APIs.
* Cleaned up baseMargin tests
- Removed RDD-based test, since the option is no longer exposed via
public APIs
- Changed DataFrame-based one to check that adding a margin actually
affects the prediction
* Pleased Scalastyle
* Addressed more review comments
* Pleased scalastyle again
* Fixed XGBoost.fromBaseMarginsToArray
which always returned an array of NaNs even if base margin was not
specified. Surprisingly this only failed a few tests.
* Deduplicated DataFrame creation in XGBoostDFSuite
* Extracted dermatology.data into MultiClassification
* Moved cache cleaning to SharedSparkContext
Cache files are prefixed with appName therefore this seems to be just the
place to delete them.
* Removed redundant JMatrix calls in xgboost4j-spark
* Slightly more readable buildDenseRDD in XGBoostGeneralSuite
* Generalized train/test DataFrame construction in XGBoostDFSuite
* Changed SharedSparkContext to setup a new context per-test
Hence the new name: PerTestSparkSession :)
* Fused Utils into PerTestSparkSession
* Whitespace fix in XGBoostDFSuite
* Ensure SparkSession is always eagerly created in PerTestSparkSession
* Renamed PerTestSparkSession->PerTest
because it was doing slightly more than creating/stopping the session.
* [jvm-packages] Deduplicated train/test data access in tests
All datasets are now available via a unified API, e.g. Agaricus.test.
The only exception is the dermatology data which requires parsing a
CSV file.
* Inlined Utils.buildTrainingRDD
The default number of partitions for local mode is equal to the number
of available CPUs.
* Replaced dataset names with problem types
Prior to this commit XGBoostModel.predict produced an RDD with
an array of predictions for each partition, effectively changing
the shape wrt the input RDD. A more natural contract for prediction
API is that given an RDD it returns a new RDD with the same number
of elements. This allows the users to easily match inputs with
predictions.
This commit removes one layer of nesting in XGBoostModel.predict output.
Even though the change is clearly non-backward compatible, I still
think it is well justified. See discussion in 06bd5dca for motivation.
* Disabled excessive Spark logging in tests
* Fixed a singature of XGBoostModel.predict
Prior to this commit XGBoostModel.predict produced an RDD with
an array of predictions for each partition, effectively changing
the shape wrt the input RDD. A more natural contract for prediction
API is that given an RDD it returns a new RDD with the same number
of elements. This allows the users to easily match inputs with
predictions.
This commit removes one layer of nesting in XGBoostModel.predict output.
Even though the change is clearly non-backward compatible, I still
think it is well justified.
* Removed boxing in XGBoost.fromDenseToSparseLabeledPoints
* Inlined XGBoost.repartitionData
An if is more explicit than an opaque method name.
* Moved XGBoost.convertBoosterToXGBoostModel to XGBoostModel
* Check the input dimension in DMatrix.setBaseMargin
Prior to this commit providing an array of incorrect dimensions would
have resulted in memory corruption. Maybe backport this to C++?
* Reduced nesting in XGBoost.buildDistributedBoosters
* Ensured consistent naming of the params map
* Cleaned up DataBatch to make it easier to comprehend
* Made scalastyle happy
* Added baseMargin to XGBoost.train and trainWithRDD
* Deprecated XGBoost.train
It is ambiguous and work only for RDDs.
* Addressed review comments
* Revert "Fixed a singature of XGBoostModel.predict"
This reverts commit 06bd5dcae7780265dd57e93ed7d4135f4e78f9b4.
* Addressed more review comments
* Fixed NullPointerException in buildDistributedBoosters
* Fixed DLL name on Windows in ``xgboost.libpath``
* Added support for OS X to ``xgboost.libpath``
* Use .dylib for shared library on OS X
This does not affect the JNI library, because it is not trully
cross-platform in the Makefile-build anyway.
* [jvm-packages] Ensure the native library is loaded once
Previously any class using XGBoostJNI queried NativeLibLoader to make
sure the native library is loaded. This commit moves the initXGBoost
call to XGBoostJNI, effectively delegating the initialization to the class
loader.
Note also, that now XGBoostJNI would NOT suppress an IOException if it
occured in initXGBoost.
* [jvm-packages] Fused JNIErrorHandle with XGBoostJNI
There was no reason for having a separate class.
* [jvm-packages] Fixed compilation on Windows
* [jvm-packages] Build the JNI bindings on Appveyor
* [jvm-packages] Build & test on OS X
* [jvm-packages] Re-applied the CMake build changes reverted by #2395
* Fixed Appveyor JVM build
* Muted Maven on Travis
* Don't link with libawt
* "linux2"->"linux"
Python2.x and 3.X use slightly different values for ``sys.platform``.
* [jvm-packages] Fixed JNI_OnLoad overload
It does not compile on Windows without proper export flags.
* [jvm-packages] Use JNI types directly where appropriate
* Removed lib hack from CMake build
Prior to this commit the CMake build use hardcoded lib prefix for
libxgboost and libxgboost4j. Unfortunatelly this did not play well with
Windows, which does not use the lib- prefix.
* [jvm-packages] Replaced create_jni.{bat,sh} with a Python version
This allows to have a single script for all platforms.
* [jvm-packages] Added all configuration options to create_jni.py
* [jvm-packages] Added libxgboost4j to CMake build
* [jvm-packages] Wired CMake build into create_jni.sh
* User newer CMake version on Travis
* Lowered CMake version constraints
* Fixed various quirks in the new CMake build
* Specified 'exec-maven-plugin' version
* Changed 'create_jni.sh' to fail on error
and also report each of the executed commands, which makes it easier
to debug.
* Fix compilation on OS X with GCC 7
Compilation failed with
In file included from src/tree/tree_updater.cc:6:0:
include/xgboost/tree_updater.h:75:46: error: 'function' is not a member of 'std'
std::function<TreeUpdater* ()> > {
caused by a missing <functional> include.
* Fixed another occurence of that issue spotted by @ClimberPG
Sergei Lebedev