* add back train method but mark as deprecated
* fix scalastyle error
* change class to object in examples
* fix compilation error
* fix mis configuration
* make DMatrix._init_from_npy2d only copy data when necessary
When creating DMatrix from a 2d ndarray, it can unnecessarily copy the input data. This can be problematic when the data is already very large--running out of memory. The copy is temporary (going out of scope at the end of this function) but it still adds to peak memory usage.
``numpy.array`` copies its input no matter what by default. By adding ``copy=False``, it will only do so when necessary. Since XGDMatrixCreateFromMat is readonly on the input buffer, this copy is not needed.
Also added comments explaining when a copy can happen (if data ordering/layout is wrong or if type is not 32-bit float).
* remove whitespace
* correct CalcDCG in rank_metric.cc
DCG use log base-2, however `std::log` returns log base-e.
* correct CalcDCG in rank_obj.cc
DCG use log base-2, however `std::log` returns log base-e.
* use std::log2 instead of std::log
make it more elegant
* use std::log2 instead of std::log
make it more elegant
*Fix 1439
*Fix python_wrapper when eval set name contain '-' will cause early_stop maximize variable con't set to True propely
Change-Id: Ib0595afd4ae7b445a84c00a3a8faeccc506c6d13
* Changes for Mingw64 compilation to ensure long is a consistent size.
Mainly impacts the Java API which would not compile, but there may be
silent errors on Windows with large datasets before this patch (as long
is 32-bits when compiled with mingw64 even in 64-bit mode).
* Adding ifdefs to ensure it still compiles on MacOS
* Makefile and create_jni.bat changes for Windows.
* Switching XGDMatrixCreateFromCSREx JNI call to use size_t cast
* Fixing lint error, adding profile switching to jvm-packages build to make create-jni.bat get called, adding myself to Contributors.Md
add_library(libxgboost SHARED ${SOURCES}) builds a library named
liblibxgboost.so; However, simply changing it to add_library(xgboost ...)
won't work, as add_executable(xgboost ...) and add_library(xgbboost ...)
will then have the same target name. This patch correctly handles the
same-name situation through SET_TARGET_PROPERTIES.
* add scikit-learn v0.18 compatibility
import KFold & StratifiedKFold from sklearn.model_selection instead of sklearn.cross_validation
* change DeprecationWarning to ImportError
DeprecationWarning isn't an exception, so it should work the other way around.
ml.dmlc.xgboost4j.scala.spark.XGBoost.scala:51
values is empty when we meet it at first time, so values(0) throw an IndexOutOfBoundsException.
It should be dVector.values(i) instead of values(i).
* bump up to scala 2.11
* framework of data frame integration
* test consistency between RDD and DataFrame
* order preservation
* test order preservation
* example code and fix makefile
* improve type checking
* improve APIs
* user docs
* work around travis CI's limitation on log length
* adjust test structure
* integrate with Spark -1 .x
* spark 2.x integration
* remove spark 1.x implementation but provide instructions on how to downgrade
* [TREE] Experimental version of monotone constraint
* Allow default detection of montone option
* loose the condition of strict check
* Update gbtree.cc
Fixed to work with future versions of visual studio i.e., 2015
MSVC has it's own section for setting compile parameters, it shouldn't need to fall into section below i.e., checking for c++11 as this is definitely already supported, though this isn't an issue for Visual Studio 2012, it breaks for later versions
of visual studio i.e., 2015 when the default c++ is version 14. Though still backward compatible with c++11
