Skip to content

This is a stable release of 0.7 version
Compare
Choose a tag to compare
@hcho3 hcho3 released this 30 Dec 21:52
· 3920 commits to master since this release
v0.7
4aa346c
This commit was created on GitHub.com and signed with GitHub’s verified signature. The key has expired.
GPG key ID: 4AEE18F83AFDEB23
Expired
Learn about vigilant mode.

Changes

  • 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
Assets 2
Loading