Skip to content
This repository has been archived by the owner on May 7, 2020. It is now read-only.

Latest commit

 

History

History
792 lines (708 loc) · 51.8 KB

NEWS.md

File metadata and controls

792 lines (708 loc) · 51.8 KB
Raw

XGBoost Change Log

This file records the changes in xgboost library in reverse chronological order.

v0.90 (2019.05.18)

XGBoost Python package drops Python 2.x (#4379, #4381)

Python 2.x is reaching its end-of-life at the end of this year. Many scientific Python packages are now moving to drop Python 2.x.

XGBoost4J-Spark now requires Spark 2.4.x (#4377)

  • Spark 2.3 is reaching its end-of-life soon. See discussion at #4389.
  • Consistent handling of missing values (#4309, #4349, #4411): Many users had reported issue with inconsistent predictions between XGBoost4J-Spark and the Python XGBoost package. The issue was caused by Spark mis-handling non-zero missing values (NaN, -1, 999 etc). We now alert the user whenever Spark doesn't handle missing values correctly (#4309, #4349). See the tutorial for dealing with missing values in XGBoost4J-Spark. This fix also depends on the availability of Spark 2.4.x.

Roadmap: better performance scaling for multi-core CPUs (#4310)

  • Poor performance scaling of the hist algorithm for multi-core CPUs has been under investigation (#3810). #4310 optimizes quantile sketches and other pre-processing tasks. Special thanks to @SmirnovEgorRu.

Roadmap: Harden distributed training (#4250)

  • Make distributed training in XGBoost more robust by hardening Rabit, which implements the AllReduce primitive. In particular, improve test coverage on mechanisms for fault tolerance and recovery. Special thanks to @chenqin.

New feature: Multi-class metric functions for GPUs (#4368)

  • Metrics for multi-class classification have been ported to GPU: merror, mlogloss. Special thanks to @trivialfis.
  • With supported metrics, XGBoost will select the correct devices based on your system and n_gpus parameter.

New feature: Scikit-learn-like random forest API (#4148, #4255, #4258)

  • XGBoost Python package now offers XGBRFClassifier and XGBRFRegressor API to train random forests. See the tutorial. Special thanks to @canonizer

New feature: use external memory in GPU predictor (#4284, #4396, #4438, #4457)

  • It is now possible to make predictions on GPU when the input is read from external memory. This is useful when you want to make predictions with big dataset that does not fit into the GPU memory. Special thanks to @rongou, @canonizer, @sriramch.

    dtest = xgboost.DMatrix('test_data.libsvm#dtest.cache')
bst.set_param('predictor', 'gpu_predictor')
bst.predict(dtest)

  • Coming soon: GPU training (gpu_hist) with external memory

New feature: XGBoost can now handle comments in LIBSVM files (#4430)

  • Special thanks to @trivialfis and @hcho3

New feature: Embed XGBoost in your C/C++ applications using CMake (#4323, #4333, #4453)

  • It is now easier than ever to embed XGBoost in your C/C++ applications. In your CMakeLists.txt, add xgboost::xgboost as a linked library:

    find_package(xgboost REQUIRED)
add_executable(api-demo c-api-demo.c)
target_link_libraries(api-demo xgboost::xgboost)

    XGBoost C API documentation is available. Special thanks to @trivialfis

Performance improvements

  • Use feature interaction constraints to narrow split search space (#4341, #4428)
  • Additional optimizations for gpu_hist (#4248, #4283)
  • Reduce OpenMP thread launches in gpu_hist (#4343)
  • Additional optimizations for multi-node multi-GPU random forests. (#4238)
  • Allocate unique prediction buffer for each input matrix, to avoid re-sizing GPU array (#4275)
  • Remove various synchronisations from CUDA API calls (#4205)
  • XGBoost4J-Spark
    • Allow the user to control whether to cache partitioned training data, to potentially reduce execution time (#4268)

Bug-fixes

  • Fix node reuse in hist (#4404)
  • Fix GPU histogram allocation (#4347)
  • Fix matrix attributes not sliced (#4311)
  • Revise AUC and AUCPR metrics now work with weighted ranking task (#4216, #4436)
  • Fix timer invocation for InitDataOnce() in gpu_hist (#4206)
  • Fix R-devel errors (#4251)
  • Make gradient update in GPU linear updater thread-safe (#4259)
  • Prevent out-of-range access in column matrix (#4231)
  • Don't store DMatrix handle in Python object until it's initialized, to improve exception safety (#4317)
  • XGBoost4J-Spark
    • Fix non-deterministic order within a zipped partition on prediction (#4388)
    • Remove race condition on tracker shutdown (#4224)
    • Allow set the parameter maxLeaves. (#4226)
    • Allow partial evaluation of dataframe before prediction (#4407)
    • Automatically set maximize_evaluation_metrics if not explicitly given (#4446)

API changes

  • Deprecate reg:linear in favor of reg:squarederror. (#4267, #4427)
  • Add attribute getter and setter to the Booster object in XGBoost4J (#4336)

Maintenance: Refactor C++ code for legibility and maintainability

  • Fix clang-tidy warnings. (#4149)
  • Remove deprecated C APIs. (#4266)
  • Use Monitor class to time functions in hist. (#4273)
  • Retire DVec class in favour of c++20 style span for device memory. (#4293)
  • Improve HostDeviceVector exception safety (#4301)

Maintenance: testing, continuous integration, build system

  • Major refactor of CMakeLists.txt (#4323, #4333, #4453): adopt modern CMake and export XGBoost as a target
  • Major improvement in Jenkins CI pipeline (#4234)
    • Migrate all Linux tests to Jenkins (#4401)
    • Builds and tests are now de-coupled, to test an artifact against multiple versions of CUDA, JDK, and other dependencies (#4401)
    • Add Windows GPU to Jenkins CI pipeline (#4463, #4469)
  • Support CUDA 10.1 (#4223, #4232, #4265, #4468)
  • Python wheels are now built with CUDA 9.0, so that JIT is not required on Volta architecture (#4459)
  • Integrate with NVTX CUDA profiler (#4205)
  • Add a test for cpu predictor using external memory (#4308)
  • Refactor tests to get rid of duplication (#4358)
  • Remove test dependency on craigcitro/r-travis, since it's deprecated (#4353)
  • Add files from local R build to .gitignore (#4346)
  • Make XGBoost4J compatible with Java 9+ by revising NativeLibLoader (#4351)
  • Jenkins build for CUDA 10.0 (#4281)
  • Remove remaining silent and debug_verbose in Python tests (#4299)
  • Use all cores to build XGBoost4J lib on linux (#4304)
  • Upgrade Jenkins Linux build environment to GCC 5.3.1, CMake 3.6.0 (#4306)
  • Make CMakeLists.txt compatible with CMake 3.3 (#4420)
  • Add OpenMP option in CMakeLists.txt (#4339)
  • Get rid of a few trivial compiler warnings (#4312)
  • Add external Docker build cache, to speed up builds on Jenkins CI (#4331, #4334, #4458)
  • Fix Windows tests (#4403)
  • Fix a broken python test (#4395)
  • Use a fixed seed to split data in XGBoost4J-Spark tests, for reproducibility (#4417)
  • Add additional Python tests to test training under constraints (#4426)
  • Enable building with shared NCCL. (#4447)

Usability Improvements, Documentation

  • Document limitation of one-split-at-a-time Greedy tree learning heuristic (#4233)
  • Update build doc: PyPI wheel now support multi-GPU (#4219)
  • Fix docs for num_parallel_tree (#4221)
  • Fix document about colsample_by* parameter (#4340)
  • Make the train and test input with same colnames. (#4329)
  • Update R contribute link. (#4236)
  • Fix travis R tests (#4277)
  • Log version number in crash log in XGBoost4J-Spark (#4271, #4303)
  • Allow supression of Rabit output in Booster::train in XGBoost4J (#4262)
  • Add tutorial on handling missing values in XGBoost4J-Spark (#4425)
  • Fix typos (#4345, #4393, #4432, #4435)
  • Added language classifier in setup.py (#4327)
  • Added Travis CI badge (#4344)
  • Add BentoML to use case section (#4400)
  • Remove subtly sexist remark (#4418)
  • Add R vignette about parsing JSON dumps (#4439)

Acknowledgement

Contributors: Nan Zhu (@CodingCat), Adam Pocock (@Craigacp), Daniel Hen (@Daniel8hen), Jiaxiang Li (@JiaxiangBU), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), Andy Adinets (@canonizer), Jonas (@elcombato), Harry Braviner (@harrybraviner), Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), James Lamb (@jameslamb), Jean-Francois Zinque (@jeffzi), Yang Yang (@jokerkeny), Mayank Suman (@mayanksuman), jess (@monkeywithacupcake), Hajime Morrita (@omo), Ravi Kalia (@project-delphi), @ras44, Rong Ou (@rongou), Shaochen Shi (@shishaochen), Xu Xiao (@sperlingxx), @sriramch, Jiaming Yuan (@trivialfis), Christopher Suchanek (@wsuchy), Bozhao (@yubozhao)

Reviewers: Nan Zhu (@CodingCat), Adam Pocock (@Craigacp), Daniel Hen (@Daniel8hen), Jiaxiang Li (@JiaxiangBU), Laurae (@Laurae2), Rory Mitchell (@RAMitchell), Egor Smirnov (@SmirnovEgorRu), @alois-bissuel, Andy Adinets (@canonizer), Chen Qin (@chenqin), Harry Braviner (@harrybraviner), Philip Hyunsu Cho (@hcho3), Tong He (@hetong007), @jakirkham, James Lamb (@jameslamb), Julien Schueller (@jschueller), Mayank Suman (@mayanksuman), Hajime Morrita (@omo), Rong Ou (@rongou), Sara Robinson (@sararob), Shaochen Shi (@shishaochen), Xu Xiao (@sperlingxx), @sriramch, Sean Owen (@srowen), Sergei Lebedev (@superbobry), Yuan (Terry) Tang (@terrytangyuan), Theodore Vasiloudis (@thvasilo), Matthew Tovbin (@tovbinm), Jiaming Yuan (@trivialfis), Xin Yin (@xydrolase)

v0.82 (2019.03.03)

This release is packed with many new features and bug fixes.

Roadmap: better performance scaling for multi-core CPUs (#3957)

  • Poor performance scaling of the hist algorithm for multi-core CPUs has been under investigation (#3810). #3957 marks an important step toward better performance scaling, by using software pre-fetching and replacing STL vectors with C-style arrays. Special thanks to @Laurae2 and @SmirnovEgorRu.
  • See #3810 for latest progress on this roadmap.

New feature: Distributed Fast Histogram Algorithm (hist) (#4011, #4102, #4140, #4128)

  • It is now possible to run the hist algorithm in distributed setting. Special thanks to @CodingCat. The benefits include:
    1. Faster local computation via feature binning
    2. Support for monotonic constraints and feature interaction constraints
    3. Simpler codebase than approx, allowing for future improvement
  • Depth-wise tree growing is now performed in a separate code path, so that cross-node syncronization is performed only once per level.

New feature: Multi-Node, Multi-GPU training (#4095)

  • Distributed training is now able to utilize clusters equipped with NVIDIA GPUs. In particular, the rabit AllReduce layer will communicate GPU device information. Special thanks to @mt-jones, @RAMitchell, @rongou, @trivialfis, @canonizer, and @jeffdk.
  • Resource management systems will be able to assign a rank for each GPU in the cluster.
  • In Dask, users will be able to construct a collection of XGBoost processes over an inhomogeneous device cluster (i.e. workers with different number and/or kinds of GPUs).

New feature: Multiple validation datasets in XGBoost4J-Spark (#3904, #3910)

  • You can now track the performance of the model during training with multiple evaluation datasets. By specifying eval_sets or call setEvalSets over a XGBoostClassifier or XGBoostRegressor, you can pass in multiple evaluation datasets typed as a Map from String to DataFrame. Special thanks to @CodingCat.
  • See the usage of multiple validation datasets here

New feature: Additional metric functions for GPUs (#3952)

  • Element-wise metrics have been ported to GPU: rmse, mae, logloss, poisson-nloglik, gamma-deviance, gamma-nloglik, error, tweedie-nloglik. Special thanks to @trivialfis and @RAMitchell.
  • With supported metrics, XGBoost will select the correct devices based on your system and n_gpus parameter.

New feature: Column sampling at individual nodes (splits) (#3971)

  • Columns (features) can now be sampled at individual tree nodes, in addition to per-tree and per-level sampling. To enable per-node sampling, set colsample_bynode parameter, which represents the fraction of columns sampled at each node. This parameter is set to 1.0 by default (i.e. no sampling per node). Special thanks to @canonizer.
  • The colsample_bynode parameter works cumulatively with other colsample_by* parameters: for example, {'colsample_bynode':0.5, 'colsample_bytree':0.5} with 100 columns will give 25 features to choose from at each split.

Major API change: consistent logging level via verbosity (#3982, #4002, #4138)

  • XGBoost now allows fine-grained control over logging. You can set verbosity to 0 (silent), 1 (warning), 2 (info), and 3 (debug). This is useful for controlling the amount of logging outputs. Special thanks to @trivialfis.
  • Parameters silent and debug_verbose are now deprecated.
  • Note: Sometimes XGBoost tries to change configurations based on heuristics, which is displayed as warning message. If there's unexpected behaviour, please try to increase value of verbosity.

Major bug fix: external memory (#4040, #4193)

  • Clarify object ownership in multi-threaded prefetcher, to avoid memory error.
  • Correctly merge two column batches (which uses CSC layout).
  • Add unit tests for external memory.
  • Special thanks to @trivialfis and @hcho3.

Major bug fix: early stopping fixed in XGBoost4J and XGBoost4J-Spark (#3928, #4176)

  • Early stopping in XGBoost4J and XGBoost4J-Spark is now consistent with its counterpart in the Python package. Training stops if the current iteration is earlyStoppingSteps away from the best iteration. If there are multiple evaluation sets, only the last one is used to determinate early stop.
  • See the updated documentation here
  • Special thanks to @CodingCat, @yanboliang, and @mingyang.

Major bug fix: infrequent features should not crash distributed training (#4045)

  • For infrequently occuring features, some partitions may not get any instance. This scenario used to crash distributed training due to mal-formed ranges. The problem has now been fixed.
  • In practice, one-hot-encoded categorical variables tend to produce rare features, particularly when the cardinality is high.
  • Special thanks to @CodingCat.

Performance improvements

  • Faster, more space-efficient radix sorting in gpu_hist (#3895)
  • Subtraction trick in histogram calculation in gpu_hist (#3945)
  • More performant re-partition in XGBoost4J-Spark (#4049)

Bug-fixes

  • Fix semantics of gpu_id when running multiple XGBoost processes on a multi-GPU machine (#3851)
  • Fix page storage path for external memory on Windows (#3869)
  • Fix configuration setup so that DART utilizes GPU (#4024)
  • Eliminate NAN values from SHAP prediction (#3943)
  • Prevent empty quantile sketches in hist (#4155)
  • Enable running objectives with 0 GPU (#3878)
  • Parameters are no longer dependent on system locale (#3891, #3907)
  • Use consistent data type in the GPU coordinate descent code (#3917)
  • Remove undefined behavior in the CLI config parser on the ARM platform (#3976)
  • Initialize counters in GPU AllReduce (#3987)
  • Prevent deadlocks in GPU AllReduce (#4113)
  • Load correct values from sliced NumPy arrays (#4147, #4165)
  • Fix incorrect GPU device selection (#4161)
  • Make feature binning logic in hist aware of query groups when running a ranking task (#4115). For ranking task, query groups are weighted, not individual instances.
  • Generate correct C++ exception type for LOG(FATAL) macro (#4159)
  • Python package
    • Python package should run on system without PATH environment variable (#3845)
    • Fix coef_ and intercept_ signature to be compatible with sklearn.RFECV (#3873)
    • Use UTF-8 encoding in Python package README, to support non-English locale (#3867)
    • Add AUC-PR to list of metrics to maximize for early stopping (#3936)
    • Allow loading pickles without self.booster attribute, for backward compatibility (#3938, #3944)
    • White-list DART for feature importances (#4073)
    • Update usage of h2oai/datatable (#4123)
  • XGBoost4J-Spark
    • Address scalability issue in prediction (#4033)
    • Enforce the use of per-group weights for ranking task (#4118)
    • Fix vector size of rawPredictionCol in XGBoostClassificationModel (#3932)
    • More robust error handling in Spark tracker (#4046, #4108)
    • Fix return type of setEvalSets (#4105)
    • Return correct value of getMaxLeaves (#4114)

API changes

  • Add experimental parameter single_precision_histogram to use single-precision histograms for the gpu_hist algorithm (#3965)
  • Python package
    • Add option to select type of feature importances in the scikit-learn inferface (#3876)
    • Add trees_to_df() method to dump decision trees as Pandas data frame (#4153)
    • Add options to control node shapes in the GraphViz plotting function (#3859)
    • Add xgb_model option to XGBClassifier, to load previously saved model (#4092)
    • Passing lists into DMatrix is now deprecated (#3970)
  • XGBoost4J
    • Support multiple feature importance features (#3801)

Maintenance: Refactor C++ code for legibility and maintainability

  • Refactor hist algorithm code and add unit tests (#3836)
  • Minor refactoring of split evaluator in gpu_hist (#3889)
  • Removed unused leaf vector field in the tree model (#3989)
  • Simplify the tree representation by combining TreeModel and RegTree classes (#3995)
  • Simplify and harden tree expansion code (#4008, #4015)
  • De-duplicate parameter classes in the linear model algorithms (#4013)
  • Robust handling of ranges with C++20 span in gpu_exact and gpu_coord_descent (#4020, #4029)
  • Simplify tree training code (#3825). Also use Span class for robust handling of ranges.

Maintenance: testing, continuous integration, build system

  • Disallow std::regex since it's not supported by GCC 4.8.x (#3870)
  • Add multi-GPU tests for coordinate descent algorithm for linear models (#3893, #3974)
  • Enforce naming style in Python lint (#3896)
  • Refactor Python tests (#3897, #3901): Use pytest exclusively, display full trace upon failure
  • Address DeprecationWarning when using Python collections (#3909)
  • Use correct group for maven site plugin (#3937)
  • Jenkins CI is now using on-demand EC2 instances exclusively, due to unreliability of Spot instances (#3948)
  • Better GPU performance logging (#3945)
  • Fix GPU tests on machines with only 1 GPU (#4053)
  • Eliminate CRAN check warnings and notes (#3988)
  • Add unit tests for tree serialization (#3989)
  • Add unit tests for tree fitting functions in hist (#4155)
  • Add a unit test for gpu_exact algorithm (#4020)
  • Correct JVM CMake GPU flag (#4071)
  • Fix failing Travis CI on Mac (#4086)
  • Speed up Jenkins by not compiling CMake (#4099)
  • Analyze C++ and CUDA code using clang-tidy, as part of Jenkins CI pipeline (#4034)
  • Fix broken R test: Install Homebrew GCC (#4142)
  • Check for empty datasets in GPU unit tests (#4151)
  • Fix Windows compilation (#4139)
  • Comply with latest convention of cpplint (#4157)
  • Fix a unit test in gpu_hist (#4158)
  • Speed up data generation in Python tests (#4164)

Usability Improvements

  • Add link to InfoWorld 2019 Technology of the Year Award (#4116)
  • Remove outdated AWS YARN tutorial (#3885)
  • Document current limitation in number of features (#3886)
  • Remove unnecessary warning when gblinear is selected (#3888)
  • Document limitation of CSV parser: header not supported (#3934)
  • Log training parameters in XGBoost4J-Spark (#4091)
  • Clarify early stopping behavior in the scikit-learn interface (#3967)
  • Clarify behavior of max_depth parameter (#4078)
  • Revise Python docstrings for ranking task (#4121). In particular, weights must be per-group in learning-to-rank setting.
  • Document parameter num_parallel_tree (#4022)
  • Add Jenkins status badge (#4090)
  • Warn users against using internal functions of Booster object (#4066)
  • Reformat benchmark_tree.py to comply with Python style convention (#4126)
  • Clarify a comment in objectiveTrait (#4174)
  • Fix typos and broken links in documentation (#3890, #3872, #3902, #3919, #3975, #4027, #4156, #4167)

Acknowledgement

Contributors (in no particular order): Jiaming Yuan (@trivialfis), Hyunsu Cho (@hcho3), Nan Zhu (@CodingCat), Rory Mitchell (@RAMitchell), Yanbo Liang (@yanboliang), Andy Adinets (@canonizer), Tong He (@hetong007), Yuan Tang (@terrytangyuan)

First-time Contributors (in no particular order): Jelle Zijlstra (@JelleZijlstra), Jiacheng Xu (@jiachengxu), @ajing, Kashif Rasul (@kashif), @theycallhimavi, Joey Gao (@pjgao), Prabakaran Kumaresshan (@nixphix), Huafeng Wang (@huafengw), @lyxthe, Sam Wilkinson (@scwilkinson), Tatsuhito Kato (@stabacov), Shayak Banerjee (@shayakbanerjee), Kodi Arfer (@Kodiologist), @KyleLi1985, Egor Smirnov (@SmirnovEgorRu), @tmitanitky, Pasha Stetsenko (@st-pasha), Kenichi Nagahara (@keni-chi), Abhai Kollara Dilip (@abhaikollara), Patrick Ford (@pford221), @hshujuan, Matthew Jones (@mt-jones), Thejaswi Rao (@teju85), Adam November (@anovember)

First-time Reviewers (in no particular order): Mingyang Hu (@mingyang), Theodore Vasiloudis (@thvasilo), Jakub Troszok (@troszok), Rong Ou (@rongou), @Denisevi4, Matthew Jones (@mt-jones), Jeff Kaplan (@jeffdk)

v0.81 (2018.11.04)

New feature: feature interaction constraints

  • Users are now able to control which features (independent variables) are allowed to interact by specifying feature interaction constraints (#3466).
  • Tutorial is available, as well as R and Python examples.

New feature: learning to rank using scikit-learn interface

  • Learning to rank task is now available for the scikit-learn interface of the Python package (#3560, #3848). It is now possible to integrate the XGBoost ranking model into the scikit-learn learning pipeline.
  • Examples of using XGBRanker class is found at demo/rank/rank_sklearn.py.

New feature: R interface for SHAP interactions

  • SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. Previously, this feature was only available from the Python package; now it is available from the R package as well (#3636).

New feature: GPU predictor now use multiple GPUs to predict

  • GPU predictor is now able to utilize multiple GPUs at once to accelerate prediction (#3738)

New feature: Scale distributed XGBoost to large-scale clusters

  • Fix OS file descriptor limit assertion error on large cluster (#3835, dmlc/rabit#73) by replacing select() based AllReduce/Broadcast with poll() based implementation.
  • Mitigate tracker "thundering herd" issue on large cluster. Add exponential backoff retry when workers connect to tracker.
  • With this change, we were able to scale to 1.5k executors on a 12 billion row dataset after some tweaks here and there.

New feature: Additional objective functions for GPUs

  • New objective functions ported to GPU: hinge, multi:softmax, multi:softprob, count:poisson, reg:gamma, "reg:tweedie.
  • With supported objectives, XGBoost will select the correct devices based on your system and n_gpus parameter.

Major bug fix: learning to rank with XGBoost4J-Spark

  • Previously, repartitionForData would shuffle data and lose ordering necessary for ranking task.
  • To fix this issue, data points within each RDD partition is explicitly group by their group (query session) IDs (#3654). Also handle empty RDD partition carefully (#3750).

Major bug fix: early stopping fixed in XGBoost4J-Spark

  • Earlier implementation of early stopping had incorrect semantics and didn't let users to specify direction for optimizing (maximize / minimize)
  • A parameter maximize_evaluation_metrics is defined so as to tell whether a metric should be maximized or minimized as part of early stopping criteria (#3808). Also early stopping now has correct semantics.

API changes

  • Column sampling by level (colsample_bylevel) is now functional for hist algorithm (#3635, #3862)
  • GPU tag gpu: for regression objectives are now deprecated. XGBoost will select the correct devices automatically (#3643)
  • Add disable_default_eval_metric parameter to disable default metric (#3606)
  • Experimental AVX support for gradient computation is removed (#3752)
  • XGBoost4J-Spark
    • Add rank:ndcg and rank:map to supported objectives (#3697)
  • Python package
    • Add callbacks argument to fit() function of sciki-learn API (#3682)
    • Add XGBRanker to scikit-learn interface (#3560, #3848)
    • Add validate_features argument to predict() function of scikit-learn API (#3653)
    • Allow scikit-learn grid search over parameters specified as keyword arguments (#3791)
    • Add coef_ and intercept_ as properties of scikit-learn wrapper (#3855). Some scikit-learn functions expect these properties.

Performance improvements

  • Address very high GPU memory usage for large data (#3635)
  • Fix performance regression within EvaluateSplits() of gpu_hist algorithm. (#3680)

Bug-fixes

  • Fix a problem in GPU quantile sketch with tiny instance weights. (#3628)
  • Fix copy constructor for HostDeviceVectorImpl to prevent dangling pointers (#3657)
  • Fix a bug in partitioned file loading (#3673)
  • Fixed an uninitialized pointer in gpu_hist (#3703)
  • Reshared data among GPUs when number of GPUs is changed (#3721)
  • Add back max_delta_step to split evaluation (#3668)
  • Do not round up integer thresholds for integer features in JSON dump (#3717)
  • Use dmlc::TemporaryDirectory to handle temporaries in cross-platform way (#3783)
  • Fix accuracy problem with gpu_hist when min_child_weight and lambda are set to 0 (#3793)
  • Make sure that tree_method parameter is recognized and not silently ignored (#3849)
  • XGBoost4J-Spark
    • Make sure thresholds are considered when executing predict() method (#3577)
    • Avoid losing precision when computing probabilities by converting to Double early (#3576)
    • getTreeLimit() should return Int (#3602)
    • Fix checkpoint serialization on HDFS (#3614)
    • Throw ControlThrowable instead of InterruptedException so that it is properly re-thrown (#3632)
    • Remove extraneous output to stdout (#3665)
    • Allow specification of task type for custom objectives and evaluations (#3646)
    • Fix distributed updater check (#3739)
    • Fix issue when spark job execution thread cannot return before we execute first() (#3758)
  • Python package
    • Fix accessing DMatrix.handle before it is set (#3599)
    • XGBClassifier.predict() should return margin scores when output_margin is set to true (#3651)
    • Early stopping callback should maximize metric of form NDCG@n- (#3685)
    • Preserve feature names when slicing DMatrix (#3766)
  • R package
    • Replace nround with nrounds to match actual parameter (#3592)
    • Amend xgb.createFolds to handle classes of a single element (#3630)
    • Fix buggy random generator and make colsample_bytree functional (#3781)

Maintenance: testing, continuous integration, build system

  • Add sanitizers tests to Travis CI (#3557)
  • Add NumPy, Matplotlib, Graphviz as requirements for doc build (#3669)
  • Comply with CRAN submission policy (#3660, #3728)
  • Remove copy-paste error in JVM test suite (#3692)
  • Disable flaky tests in R-package/tests/testthat/test_update.R (#3723)
  • Make Python tests compatible with scikit-learn 0.20 release (#3731)
  • Separate out restricted and unrestricted tasks, so that pull requests don't build downloadable artifacts (#3736)
  • Add multi-GPU unit test environment (#3741)
  • Allow plug-ins to be built by CMake (#3752)
  • Test wheel compatibility on CPU containers for pull requests (#3762)
  • Fix broken doc build due to Matplotlib 3.0 release (#3764)
  • Produce xgboost.so for XGBoost-R on Mac OSX, so that make install works (#3767)
  • Retry Jenkins CI tests up to 3 times to improve reliability (#3769, #3769, #3775, #3776, #3777)
  • Add basic unit tests for gpu_hist algorithm (#3785)
  • Fix Python environment for distributed unit tests (#3806)
  • Test wheels on CUDA 10.0 container for compatibility (#3838)
  • Fix JVM doc build (#3853)

Maintenance: Refactor C++ code for legibility and maintainability

  • Merge generic device helper functions into GPUSet class (#3626)
  • Re-factor column sampling logic into ColumnSampler class (#3635, #3637)
  • Replace std::vector with HostDeviceVector in MetaInfo and SparsePage (#3446)
  • Simplify DMatrix class (#3395)
  • De-duplicate CPU/GPU code using Transform class (#3643, #3751)
  • Remove obsoleted QuantileHistMaker class (#3761)
  • Remove obsoleted NoConstraint class (#3792)

Other Features

  • C++20-compliant Span class for safe pointer indexing (#3548, #3588)
  • Add helper functions to manipulate multiple GPU devices (#3693)
  • XGBoost4J-Spark
    • Allow specifying host ip from the xgboost-tracker.properties file (#3833). This comes in handy when hosts files doesn't correctly define localhost.

Usability Improvements

  • Add reference to GitHub repository in pom.xml of JVM packages (#3589)
  • Add R demo of multi-class classification (#3695)
  • Document JSON dump functionality (#3600, #3603)
  • Document CUDA requirement and lack of external memory for GPU algorithms (#3624)
  • Document LambdaMART objectives, both pairwise and listwise (#3672)
  • Document aucpr evaluation metric (#3687)
  • Document gblinear parameters: feature_selector and top_k (#3780)
  • Add instructions for using MinGW-built XGBoost with Python. (#3774)
  • Removed nonexistent parameter use_buffer from documentation (#3610)
  • Update Python API doc to include all classes and members (#3619, #3682)
  • Fix typos and broken links in documentation (#3618, #3640, #3676, #3713, #3759, #3784, #3843, #3852)
  • Binary classification demo should produce LIBSVM with 0-based indexing (#3652)
  • Process data once for Python and CLI examples of learning to rank (#3666)
  • Include full text of Apache 2.0 license in the repository (#3698)
  • Save predictor parameters in model file (#3856)
  • JVM packages
    • Let users specify feature names when calling getModelDump and getFeatureScore (#3733)
    • Warn the user about the lack of over-the-wire encryption (#3667)
    • Fix errors in examples (#3719)
    • Document choice of trackers (#3831)
    • Document that vanilla Apache Spark is required (#3854)
  • Python package
    • Document that custom objective can't contain colon (:) (#3601)
    • Show a better error message for failed library loading (#3690)
    • Document that feature importance is unavailable for non-tree learners (#3765)
    • Document behavior of get_fscore() for zero-importance features (#3763)
    • Recommend pickling as the way to save XGBClassifier / XGBRegressor / XGBRanker (#3829)
  • R package
    • Enlarge variable importance plot to make it more visible (#3820)

BREAKING CHANGES

  • External memory page files have changed, breaking backwards compatibility for temporary storage used during external memory training. This only affects external memory users upgrading their xgboost version - we recommend clearing all *.page files before resuming training. Model serialization is unaffected.

Known issues

  • Quantile sketcher fails to produce any quantile for some edge cases (#2943)
  • The hist algorithm leaks memory when used with learning rate decay callback (#3579)
  • Using custom evaluation funciton together with early stopping causes assertion failure in XGBoost4J-Spark (#3595)
  • Early stopping doesn't work with gblinear learner (#3789)
  • Label and weight vectors are not reshared upon the change in number of GPUs (#3794). To get around this issue, delete the DMatrix object and re-load.
  • The DMatrix Python objects are initialized with incorrect values when given array slices (#3841)
  • The gpu_id parameter is broken and not yet properly supported (#3850)

Acknowledgement

Contributors (in no particular order): Hyunsu Cho (@hcho3), Jiaming Yuan (@trivialfis), Nan Zhu (@CodingCat), Rory Mitchell (@RAMitchell), Andy Adinets (@canonizer), Vadim Khotilovich (@khotilov), Sergei Lebedev (@superbobry)

First-time Contributors (in no particular order): Matthew Tovbin (@tovbinm), Jakob Richter (@jakob-r), Grace Lam (@grace-lam), Grant W Schneider (@grantschneider), Andrew Thia (@BlueTea88), Sergei Chipiga (@schipiga), Joseph Bradley (@jkbradley), Chen Qin (@chenqin), Jerry Lin (@linjer), Dmitriy Rybalko (@rdtft), Michael Mui (@mmui), Takahiro Kojima (@515hikaru), Bruce Zhao (@BruceZhaoR), Wei Tian (@weitian), Saumya Bhatnagar (@Sam1301), Juzer Shakir (@JuzerShakir), Zhao Hang (@cleghom), Jonathan Friedman (@jontonsoup), Bruno Tremblay (@meztez), Boris Filippov (@frenzykryger), @Shiki-H, @mrgutkun, @gorogm, @htgeis, @jakehoare, @zengxy, @KOLANICH

First-time Reviewers (in no particular order): Nikita Titov (@StrikerRUS), Xiangrui Meng (@mengxr), Nirmal Borah (@Nirmal-Neel)

v0.80 (2018.08.13)

  • JVM packages received a major upgrade: To consolidate the APIs and improve the user experience, we refactored the design of XGBoost4J-Spark in a significant manner. (#3387)
    • Consolidated APIs: It is now much easier to integrate XGBoost models into a Spark ML pipeline. Users can control behaviors like output leaf prediction results by setting corresponding column names. Training is now more consistent with other Estimators in Spark MLLIB: there is now one single method fit() to train decision trees.
    • Better user experience: we refactored the parameters relevant modules in XGBoost4J-Spark to provide both camel-case (Spark ML style) and underscore (XGBoost style) parameters
    • A brand-new tutorial is available for XGBoost4J-Spark.
    • Latest API documentation is now hosted at https://xgboost.readthedocs.io/.
  • XGBoost documentation now keeps track of multiple versions:
  • Support for per-group weights in ranking objective (#3379)
  • Fix inaccurate decimal parsing (#3546)
  • New functionality
    • Query ID column support in LIBSVM data files (#2749). This is convenient for performing ranking task in distributed setting.
    • Hinge loss for binary classification (binary:hinge) (#3477)
    • Ability to specify delimiter and instance weight column for CSV files (#3546)
    • Ability to use 1-based indexing instead of 0-based (#3546)
  • GPU support
    • Quantile sketch, binning, and index compression are now performed on GPU, eliminating PCIe transfer for 'gpu_hist' algorithm (#3319, #3393)
    • Upgrade to NCCL2 for multi-GPU training (#3404).
    • Use shared memory atomics for faster training (#3384).
    • Dynamically allocate GPU memory, to prevent large allocations for deep trees (#3519)
    • Fix memory copy bug for large files (#3472)
  • Python package
    • Importing data from Python datatable (#3272)
    • Pre-built binary wheels available for 64-bit Linux and Windows (#3424, #3443)
    • Add new importance measures 'total_gain', 'total_cover' (#3498)
    • Sklearn API now supports saving and loading models (#3192)
    • Arbitrary cross validation fold indices (#3353)
    • predict() function in Sklearn API uses best_ntree_limit if available, to make early stopping easier to use (#3445)
    • Informational messages are now directed to Python's print() rather than standard output (#3438). This way, messages appear inside Jupyter notebooks.
  • R package
    • Oracle Solaris support, per CRAN policy (#3372)
  • JVM packages
    • Single-instance prediction (#3464)
    • Pre-built JARs are now available from Maven Central (#3401)
    • Add NULL pointer check (#3021)
    • Consider spark.task.cpus when controlling parallelism (#3530)
    • Handle missing values in prediction (#3529)
    • Eliminate outputs of System.out (#3572)
  • Refactored C++ DMatrix class for simplicity and de-duplication (#3301)
  • Refactored C++ histogram facilities (#3564)
  • Refactored constraints / regularization mechanism for split finding (#3335, #3429). Users may specify an elastic net (L2 + L1 regularization) on leaf weights as well as monotonic constraints on test nodes. The refactor will be useful for a future addition of feature interaction constraints.
  • Statically link libstdc++ for MinGW32 (#3430)
  • Enable loading from group, base_margin and weight (see here) for Python, R, and JVM packages (#3431)
  • Fix model saving for count:possion so that max_delta_step doesn't get truncated (#3515)
  • Fix loading of sparse CSC matrix (#3553)
  • Fix incorrect handling of base_score parameter for Tweedie regression (#3295)

v0.72.1 (2018.07.08)

This version is only applicable for the Python package. The content is identical to that of v0.72.

v0.72 (2018.06.01)

  • Starting with this release, we plan to make a new release every two months. See #3252 for more details.
  • Fix a pathological behavior (near-zero second-order gradients) in multiclass objective (#3304)
  • Tree dumps now use high precision in storing floating-point values (#3298)
  • Submodules rabit and dmlc-core have been brought up to date, bringing bug fixes (#3330, #3221).
  • GPU support
    • Continuous integration tests for GPU code (#3294, #3309)
    • GPU accelerated coordinate descent algorithm (#3178)
    • Abstract 1D vector class now works with multiple GPUs (#3287)
    • Generate PTX code for most recent architecture (#3316)
    • Fix a memory bug on NVIDIA K80 cards (#3293)
    • Address performance instability for single-GPU, multi-core machines (#3324)
  • Python package
    • FreeBSD support (#3247)
    • Validation of feature names in Booster.predict() is now optional (#3323)
  • Updated Sklearn API
    • Validation sets now support instance weights (#2354)
    • XGBClassifier.predict_proba() should not support output_margin option. (#3343) See BREAKING CHANGES below.
  • R package:
    • Better handling of NULL in print.xgb.Booster() (#3338)
    • Comply with CRAN policy by removing compiler warning suppression (#3329)
    • Updated CRAN submission
  • JVM packages
    • JVM packages will now use the same versioning scheme as other packages (#3253)
    • Update Spark to 2.3 (#3254)
    • Add scripts to cross-build and deploy artifacts (#3276, #3307)
    • Fix a compilation error for Scala 2.10 (#3332)
  • BREAKING CHANGES
    • XGBClassifier.predict_proba() no longer accepts paramter output_margin. The paramater makes no sense for predict_proba() because the method is to predict class probabilities, not raw margin scores.

v0.71 (2018.04.11)

  • This is a minor release, mainly motivated by issues concerning pip install, e.g. #2426, #3189, #3118, and #3194. With this release, users of Linux and MacOS will be able to run pip install for the most part.
  • Refactored linear booster class (gblinear), so as to support multiple coordinate descent updaters (#3103, #3134). See BREAKING CHANGES below.
  • Fix slow training for multiclass classification with high number of classes (#3109)
  • Fix a corner case in approximate quantile sketch (#3167). Applicable for 'hist' and 'gpu_hist' algorithms
  • Fix memory leak in DMatrix (#3182)
  • New functionality
    • Better linear booster class (#3103, #3134)
    • Pairwise SHAP interaction effects (#3043)
    • Cox loss (#3043)
    • AUC-PR metric for ranking task (#3172)
    • Monotonic constraints for 'hist' algorithm (#3085)
  • GPU support
    • Create an abtract 1D vector class that moves data seamlessly between the main and GPU memory (#2935, #3116, #3068). This eliminates unnecessary PCIe data transfer during training time.
    • Fix minor bugs (#3051, #3217)
    • Fix compatibility error for CUDA 9.1 (#3218)
  • Python package:
    • Correctly handle parameter verbose_eval=0 (#3115)
  • R package:
    • Eliminate segmentation fault on 32-bit Windows platform (#2994)
  • JVM packages
    • Fix a memory bug involving double-freeing Booster objects (#3005, #3011)
    • Handle empty partition in predict (#3014)
    • Update docs and unify terminology (#3024)
    • Delete cache files after job finishes (#3022)
    • Compatibility fixes for latest Spark versions (#3062, #3093)
  • BREAKING CHANGES: Updated linear modelling algorithms. In particular L1/L2 regularisation penalties are now normalised to number of training examples. This makes the implementation consistent with sklearn/glmnet. L2 regularisation has also been removed from the intercept. To produce linear models with the old regularisation behaviour, the alpha/lambda regularisation parameters can be manually scaled by dividing them by the number of training examples.

v0.7 (2017.12.30)

  • This version represents a major change from the last release (v0.6), which was released one year and half ago.
  • Updated Sklearn API
    • Add compatibility layer for scikit-learn v0.18: sklearn.cross_validation now deprecated
    • Updated to allow use of all XGBoost parameters via **kwargs.
    • Updated nthread to n_jobs and seed to random_state (as per Sklearn convention); nthread and seed are now marked as deprecated
    • Updated to allow choice of Booster (gbtree, gblinear, or dart)
    • XGBRegressor now supports instance weights (specify sample_weight parameter)
    • Pass n_jobs parameter to the DMatrix constructor
    • Add xgb_model parameter to fit method, to allow continuation of training
  • Refactored gbm to allow more friendly cache strategy
    • Specialized some prediction routine
  • Robust DMatrix construction from a sparse matrix
  • Faster consturction of DMatrix from 2D NumPy matrices: elide copies, use of multiple threads
  • Automatically remove nan from input data when it is sparse.
    • This can solve some of user reported problem of istart != hist.size
  • Fix the single-instance prediction function to obtain correct predictions
  • Minor fixes
    • Thread local variable is upgraded so it is automatically freed at thread exit.
    • Fix saving and loading count::poisson models
    • Fix CalcDCG to use base-2 logarithm
    • Messages are now written to stderr instead of stdout
    • Keep built-in evaluations while using customized evaluation functions
    • Use bst_float consistently to minimize type conversion
    • Copy the base margin when slicing DMatrix
    • Evaluation metrics are now saved to the model file
    • Use int32_t explicitly when serializing version
    • In distributed training, synchronize the number of features after loading a data matrix.
  • Migrate to C++11
    • The current master version now requires C++11 enabled compiled(g++4.8 or higher)
  • Predictor interface was factored out (in a manner similar to the updater interface).
  • Makefile support for Solaris and ARM
  • Test code coverage using Codecov
  • Add CPP tests
  • Add Dockerfile and Jenkinsfile to support continuous integration for GPU code
  • New functionality
    • Ability to adjust tree model's statistics to a new dataset without changing tree structures.
    • Ability to extract feature contributions from individual predictions, as described in here and here.
    • Faster, histogram-based tree algorithm (tree_method='hist') .
    • GPU/CUDA accelerated tree algorithms (tree_method='gpu_hist' or 'gpu_exact'), including the GPU-based predictor.
    • Monotonic constraints: when other features are fixed, force the prediction to be monotonic increasing with respect to a certain specified feature.
    • Faster gradient caculation using AVX SIMD
    • Ability to export models in JSON format
    • Support for Tweedie regression
    • Additional dropout options for DART: binomial+1, epsilon
    • Ability to update an existing model in-place: this is useful for many applications, such as determining feature importance
  • Python package:
    • New parameters:
      • learning_rates in cv()
      • shuffle in mknfold()
      • max_features and show_values in plot_importance()
      • sample_weight in XGBRegressor.fit()
    • Support binary wheel builds
    • Fix MultiIndex detection to support Pandas 0.21.0 and higher
    • Support metrics and evaluation sets whose names contain -
    • Support feature maps when plotting trees
    • Compatibility fix for Python 2.6
    • Call print_evaluation callback at last iteration
    • Use appropriate integer types when calling native code, to prevent truncation and memory error
    • Fix shared library loading on Mac OS X
  • R package:
    • New parameters:
      • silent in xgb.DMatrix()
      • use_int_id in xgb.model.dt.tree()
      • predcontrib in predict()
      • monotone_constraints in xgb.train()
    • Default value of the save_period parameter in xgboost() changed to NULL (consistent with xgb.train()).
    • It's possible to custom-build the R package with GPU acceleration support.
    • Enable JVM build for Mac OS X and Windows
    • Integration with AppVeyor CI
    • Improved safety for garbage collection
    • Store numeric attributes with higher precision
    • Easier installation for devel version
    • Improved xgb.plot.tree()
    • Various minor fixes to improve user experience and robustness
    • Register native code to pass CRAN check
    • Updated CRAN submission
  • JVM packages
    • Add Spark pipeline persistence API
    • Fix data persistence: loss evaluation on test data had wrongly used caches for training data.
    • Clean external cache after training
    • Implement early stopping
    • Enable training of multiple models by distinguishing stage IDs
    • Better Spark integration: support RDD / dataframe / dataset, integrate with Spark ML package
    • XGBoost4j now supports ranking task
    • Support training with missing data
    • Refactor JVM package to separate regression and classification models to be consistent with other machine learning libraries
    • Support XGBoost4j compilation on Windows
    • Parameter tuning tool
    • Publish source code for XGBoost4j to maven local repo
    • Scala implementation of the Rabit tracker (drop-in replacement for the Java implementation)
    • Better exception handling for the Rabit tracker
    • Persist num_class, number of classes (for classification task)
    • XGBoostModel now holds BoosterParams
    • libxgboost4j is now part of CMake build
    • Release DMatrix when no longer needed, to conserve memory
    • Expose baseMargin, to allow initialization of boosting with predictions from an external model
    • Support instance weights
    • Use SparkParallelismTracker to prevent jobs from hanging forever
    • Expose train-time evaluation metrics via XGBoostModel.summary
    • Option to specify host-ip explicitly in the Rabit tracker
  • Documentation
    • Better math notation for gradient boosting
    • Updated build instructions for Mac OS X
    • Template for GitHub issues
    • Add CITATION file for citing XGBoost in scientific writing
    • Fix dropdown menu in xgboost.readthedocs.io
    • Document updater_seq parameter
    • Style fixes for Python documentation
    • Links to additional examples and tutorials
    • Clarify installation requirements
  • Changes that break backward compatibility
    • #1519 XGBoost-spark no longer contains APIs for DMatrix; use the public booster interface instead.
    • #2476 XGBoostModel.predict() now has a different signature

v0.6 (2016.07.29)

  • Version 0.5 is skipped due to major improvements in the core
  • Major refactor of core library.
    • Goal: more flexible and modular code as a portable library.
    • Switch to use of c++11 standard code.
    • Random number generator defaults to std::mt19937.
    • Share the data loading pipeline and logging module from dmlc-core.
    • Enable registry pattern to allow optionally plugin of objective, metric, tree constructor, data loader.
      • Future plugin modules can be put into xgboost/plugin and register back to the library.
    • Remove most of the raw pointers to smart ptrs, for RAII safety.
  • Add official option to approximate algorithm tree_method to parameter.
    • Change default behavior to switch to prefer faster algorithm.
    • User will get a message when approximate algorithm is chosen.
  • Change library name to libxgboost.so
  • Backward compatiblity
    • The binary buffer file is not backward compatible with previous version.
    • The model file is backward compatible on 64 bit platforms.
  • The model file is compatible between 64/32 bit platforms(not yet tested).
  • External memory version and other advanced features will be exposed to R library as well on linux.
    • Previously some of the features are blocked due to C++11 and threading limits.
    • The windows version is still blocked due to Rtools do not support std::thread.
  • rabit and dmlc-core are maintained through git submodule
    • Anyone can open PR to update these dependencies now.
  • Improvements
    • Rabit and xgboost libs are not thread-safe and use thread local PRNGs
    • This could fix some of the previous problem which runs xgboost on multiple threads.
  • JVM Package
    • Enable xgboost4j for java and scala
    • XGBoost distributed now runs on Flink and Spark.
  • Support model attributes listing for meta data.
  • Support callback API
  • Support new booster DART(dropout in tree boosting)
  • Add CMake build system

v0.47 (2016.01.14)

  • Changes in R library
    • fixed possible problem of poisson regression.
    • switched from 0 to NA for missing values.
    • exposed access to additional model parameters.
  • Changes in Python library
    • throws exception instead of crash terminal when a parameter error happens.
    • has importance plot and tree plot functions.
    • accepts different learning rates for each boosting round.
    • allows model training continuation from previously saved model.
    • allows early stopping in CV.
    • allows feval to return a list of tuples.
    • allows eval_metric to handle additional format.
    • improved compatibility in sklearn module.
    • additional parameters added for sklearn wrapper.
    • added pip installation functionality.
    • supports more Pandas DataFrame dtypes.
    • added best_ntree_limit attribute, in addition to best_score and best_iteration.
  • Java api is ready for use
  • Added more test cases and continuous integration to make each build more robust.

v0.4 (2015.05.11)

  • Distributed version of xgboost that runs on YARN, scales to billions of examples
  • Direct save/load data and model from/to S3 and HDFS
  • Feature importance visualization in R module, by Michael Benesty
  • Predict leaf index
  • Poisson regression for counts data
  • Early stopping option in training
  • Native save load support in R and python
    • xgboost models now can be saved using save/load in R
    • xgboost python model is now pickable
  • sklearn wrapper is supported in python module
  • Experimental External memory version

v0.3 (2014.09.07)

  • Faster tree construction module
    • Allows subsample columns during tree construction via bst:col_samplebytree=ratio
  • Support for boosting from initial predictions
  • Experimental version of LambdaRank
  • Linear booster is now parallelized, using parallel coordinated descent.
  • Add Code Guide for customizing objective function and evaluation
  • Add R module

v0.2x (2014.05.20)

  • Python module
  • Weighted samples instances
  • Initial version of pairwise rank

v0.1 (2014.03.26)

  • Initial release