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XGBoost Change Log

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

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:
  • Ranking task now uses instance weights (#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