332 lines
19 KiB
Markdown
332 lines
19 KiB
Markdown
XGBoost Change Log
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==================
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This file records the changes in xgboost library in reverse chronological order.
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## v0.80 (2018.08.13)
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* **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)
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- 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.
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- 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
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- A brand-new tutorial is [available](https://xgboost.readthedocs.io/en/release_0.80/jvm/xgboost4j_spark_tutorial.html) for XGBoost4J-Spark.
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- Latest API documentation is now hosted at https://xgboost.readthedocs.io/.
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* XGBoost documentation now keeps track of multiple versions:
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- Latest master: https://xgboost.readthedocs.io/en/latest
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- 0.80 stable: https://xgboost.readthedocs.io/en/release_0.80
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- 0.72 stable: https://xgboost.readthedocs.io/en/release_0.72
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* Ranking task now uses instance weights (#3379)
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* Fix inaccurate decimal parsing (#3546)
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* New functionality
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- Query ID column support in LIBSVM data files (#2749). This is convenient for performing ranking task in distributed setting.
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- Hinge loss for binary classification (`binary:hinge`) (#3477)
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- Ability to specify delimiter and instance weight column for CSV files (#3546)
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- Ability to use 1-based indexing instead of 0-based (#3546)
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* GPU support
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- Quantile sketch, binning, and index compression are now performed on GPU, eliminating PCIe transfer for 'gpu_hist' algorithm (#3319, #3393)
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- Upgrade to NCCL2 for multi-GPU training (#3404).
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- Use shared memory atomics for faster training (#3384).
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- Dynamically allocate GPU memory, to prevent large allocations for deep trees (#3519)
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- Fix memory copy bug for large files (#3472)
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* Python package
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- Importing data from Python datatable (#3272)
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- Pre-built binary wheels available for 64-bit Linux and Windows (#3424, #3443)
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- Add new importance measures 'total_gain', 'total_cover' (#3498)
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- Sklearn API now supports saving and loading models (#3192)
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- Arbitrary cross validation fold indices (#3353)
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- `predict()` function in Sklearn API uses `best_ntree_limit` if available, to make early stopping easier to use (#3445)
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- Informational messages are now directed to Python's `print()` rather than standard output (#3438). This way, messages appear inside Jupyter notebooks.
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* R package
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- Oracle Solaris support, per CRAN policy (#3372)
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* JVM packages
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- Single-instance prediction (#3464)
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- Pre-built JARs are now available from Maven Central (#3401)
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- Add NULL pointer check (#3021)
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- Consider `spark.task.cpus` when controlling parallelism (#3530)
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- Handle missing values in prediction (#3529)
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- Eliminate outputs of `System.out` (#3572)
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* Refactored C++ DMatrix class for simplicity and de-duplication (#3301)
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* Refactored C++ histogram facilities (#3564)
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* 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.
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* Statically link `libstdc++` for MinGW32 (#3430)
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* Enable loading from `group`, `base_margin` and `weight` (see [here](http://xgboost.readthedocs.io/en/latest/tutorials/input_format.html#auxiliary-files-for-additional-information)) for Python, R, and JVM packages (#3431)
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* Fix model saving for `count:possion` so that `max_delta_step` doesn't get truncated (#3515)
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* Fix loading of sparse CSC matrix (#3553)
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* Fix incorrect handling of `base_score` parameter for Tweedie regression (#3295)
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## v0.72.1 (2018.07.08)
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This version is only applicable for the Python package. The content is identical to that of v0.72.
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## v0.72 (2018.06.01)
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* Starting with this release, we plan to make a new release every two months. See #3252 for more details.
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* Fix a pathological behavior (near-zero second-order gradients) in multiclass objective (#3304)
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* Tree dumps now use high precision in storing floating-point values (#3298)
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* Submodules `rabit` and `dmlc-core` have been brought up to date, bringing bug fixes (#3330, #3221).
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* GPU support
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- Continuous integration tests for GPU code (#3294, #3309)
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- GPU accelerated coordinate descent algorithm (#3178)
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- Abstract 1D vector class now works with multiple GPUs (#3287)
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- Generate PTX code for most recent architecture (#3316)
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- Fix a memory bug on NVIDIA K80 cards (#3293)
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- Address performance instability for single-GPU, multi-core machines (#3324)
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* Python package
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- FreeBSD support (#3247)
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- Validation of feature names in `Booster.predict()` is now optional (#3323)
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* Updated Sklearn API
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- Validation sets now support instance weights (#2354)
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- `XGBClassifier.predict_proba()` should not support `output_margin` option. (#3343) See BREAKING CHANGES below.
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* R package:
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- Better handling of NULL in `print.xgb.Booster()` (#3338)
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- Comply with CRAN policy by removing compiler warning suppression (#3329)
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- Updated CRAN submission
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* JVM packages
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- JVM packages will now use the same versioning scheme as other packages (#3253)
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- Update Spark to 2.3 (#3254)
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- Add scripts to cross-build and deploy artifacts (#3276, #3307)
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- Fix a compilation error for Scala 2.10 (#3332)
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* BREAKING CHANGES
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- `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.
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## v0.71 (2018.04.11)
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* This is a minor release, mainly motivated by issues concerning `pip install`, e.g. #2426, #3189, #3118, and #3194.
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With this release, users of Linux and MacOS will be able to run `pip install` for the most part.
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* Refactored linear booster class (`gblinear`), so as to support multiple coordinate descent updaters (#3103, #3134). See BREAKING CHANGES below.
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* Fix slow training for multiclass classification with high number of classes (#3109)
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* Fix a corner case in approximate quantile sketch (#3167). Applicable for 'hist' and 'gpu_hist' algorithms
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* Fix memory leak in DMatrix (#3182)
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* New functionality
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- Better linear booster class (#3103, #3134)
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- Pairwise SHAP interaction effects (#3043)
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- Cox loss (#3043)
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- AUC-PR metric for ranking task (#3172)
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- Monotonic constraints for 'hist' algorithm (#3085)
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* GPU support
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- 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.
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- Fix minor bugs (#3051, #3217)
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- Fix compatibility error for CUDA 9.1 (#3218)
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* Python package:
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- Correctly handle parameter `verbose_eval=0` (#3115)
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* R package:
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- Eliminate segmentation fault on 32-bit Windows platform (#2994)
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* JVM packages
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- Fix a memory bug involving double-freeing Booster objects (#3005, #3011)
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- Handle empty partition in predict (#3014)
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- Update docs and unify terminology (#3024)
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- Delete cache files after job finishes (#3022)
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- Compatibility fixes for latest Spark versions (#3062, #3093)
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* 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.
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## v0.7 (2017.12.30)
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* **This version represents a major change from the last release (v0.6), which was released one year and half ago.**
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* Updated Sklearn API
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- Add compatibility layer for scikit-learn v0.18: `sklearn.cross_validation` now deprecated
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- Updated to allow use of all XGBoost parameters via `**kwargs`.
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- Updated `nthread` to `n_jobs` and `seed` to `random_state` (as per Sklearn convention); `nthread` and `seed` are now marked as deprecated
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- Updated to allow choice of Booster (`gbtree`, `gblinear`, or `dart`)
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- `XGBRegressor` now supports instance weights (specify `sample_weight` parameter)
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- Pass `n_jobs` parameter to the `DMatrix` constructor
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- Add `xgb_model` parameter to `fit` method, to allow continuation of training
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* Refactored gbm to allow more friendly cache strategy
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- Specialized some prediction routine
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* Robust `DMatrix` construction from a sparse matrix
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* Faster consturction of `DMatrix` from 2D NumPy matrices: elide copies, use of multiple threads
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* Automatically remove nan from input data when it is sparse.
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- This can solve some of user reported problem of istart != hist.size
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* Fix the single-instance prediction function to obtain correct predictions
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* Minor fixes
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- Thread local variable is upgraded so it is automatically freed at thread exit.
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- Fix saving and loading `count::poisson` models
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- Fix CalcDCG to use base-2 logarithm
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- Messages are now written to stderr instead of stdout
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- Keep built-in evaluations while using customized evaluation functions
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- Use `bst_float` consistently to minimize type conversion
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- Copy the base margin when slicing `DMatrix`
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- Evaluation metrics are now saved to the model file
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- Use `int32_t` explicitly when serializing version
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- In distributed training, synchronize the number of features after loading a data matrix.
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* Migrate to C++11
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- The current master version now requires C++11 enabled compiled(g++4.8 or higher)
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* Predictor interface was factored out (in a manner similar to the updater interface).
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* Makefile support for Solaris and ARM
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* Test code coverage using Codecov
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* Add CPP tests
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* Add `Dockerfile` and `Jenkinsfile` to support continuous integration for GPU code
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* New functionality
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- Ability to adjust tree model's statistics to a new dataset without changing tree structures.
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- Ability to extract feature contributions from individual predictions, as described in [here](http://blog.datadive.net/interpreting-random-forests/) and [here](https://arxiv.org/abs/1706.06060).
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- Faster, histogram-based tree algorithm (`tree_method='hist'`) .
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- GPU/CUDA accelerated tree algorithms (`tree_method='gpu_hist'` or `'gpu_exact'`), including the GPU-based predictor.
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- Monotonic constraints: when other features are fixed, force the prediction to be monotonic increasing with respect to a certain specified feature.
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- Faster gradient caculation using AVX SIMD
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- Ability to export models in JSON format
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- Support for Tweedie regression
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- Additional dropout options for DART: binomial+1, epsilon
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- Ability to update an existing model in-place: this is useful for many applications, such as determining feature importance
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* Python package:
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- New parameters:
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- `learning_rates` in `cv()`
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- `shuffle` in `mknfold()`
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- `max_features` and `show_values` in `plot_importance()`
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- `sample_weight` in `XGBRegressor.fit()`
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- Support binary wheel builds
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- Fix `MultiIndex` detection to support Pandas 0.21.0 and higher
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- Support metrics and evaluation sets whose names contain `-`
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- Support feature maps when plotting trees
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- Compatibility fix for Python 2.6
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- Call `print_evaluation` callback at last iteration
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- Use appropriate integer types when calling native code, to prevent truncation and memory error
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- Fix shared library loading on Mac OS X
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* R package:
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- New parameters:
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- `silent` in `xgb.DMatrix()`
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- `use_int_id` in `xgb.model.dt.tree()`
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- `predcontrib` in `predict()`
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- `monotone_constraints` in `xgb.train()`
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- Default value of the `save_period` parameter in `xgboost()` changed to NULL (consistent with `xgb.train()`).
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- It's possible to custom-build the R package with GPU acceleration support.
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- Enable JVM build for Mac OS X and Windows
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- Integration with AppVeyor CI
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- Improved safety for garbage collection
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- Store numeric attributes with higher precision
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- Easier installation for devel version
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- Improved `xgb.plot.tree()`
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- Various minor fixes to improve user experience and robustness
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- Register native code to pass CRAN check
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- Updated CRAN submission
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* JVM packages
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- Add Spark pipeline persistence API
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- Fix data persistence: loss evaluation on test data had wrongly used caches for training data.
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- Clean external cache after training
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- Implement early stopping
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- Enable training of multiple models by distinguishing stage IDs
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- Better Spark integration: support RDD / dataframe / dataset, integrate with Spark ML package
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- XGBoost4j now supports ranking task
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- Support training with missing data
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- Refactor JVM package to separate regression and classification models to be consistent with other machine learning libraries
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- Support XGBoost4j compilation on Windows
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- Parameter tuning tool
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- Publish source code for XGBoost4j to maven local repo
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- Scala implementation of the Rabit tracker (drop-in replacement for the Java implementation)
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- Better exception handling for the Rabit tracker
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- Persist `num_class`, number of classes (for classification task)
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- `XGBoostModel` now holds `BoosterParams`
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- libxgboost4j is now part of CMake build
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- Release `DMatrix` when no longer needed, to conserve memory
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- Expose `baseMargin`, to allow initialization of boosting with predictions from an external model
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- Support instance weights
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- Use `SparkParallelismTracker` to prevent jobs from hanging forever
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- Expose train-time evaluation metrics via `XGBoostModel.summary`
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- Option to specify `host-ip` explicitly in the Rabit tracker
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* Documentation
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- Better math notation for gradient boosting
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- Updated build instructions for Mac OS X
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- Template for GitHub issues
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- Add `CITATION` file for citing XGBoost in scientific writing
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- Fix dropdown menu in xgboost.readthedocs.io
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- Document `updater_seq` parameter
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- Style fixes for Python documentation
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- Links to additional examples and tutorials
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- Clarify installation requirements
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* Changes that break backward compatibility
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- [#1519](https://github.com/dmlc/xgboost/pull/1519) XGBoost-spark no longer contains APIs for DMatrix; use the public booster interface instead.
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- [#2476](https://github.com/dmlc/xgboost/pull/2476) `XGBoostModel.predict()` now has a different signature
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## v0.6 (2016.07.29)
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* Version 0.5 is skipped due to major improvements in the core
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* Major refactor of core library.
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- Goal: more flexible and modular code as a portable library.
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- Switch to use of c++11 standard code.
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- Random number generator defaults to ```std::mt19937```.
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- Share the data loading pipeline and logging module from dmlc-core.
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- Enable registry pattern to allow optionally plugin of objective, metric, tree constructor, data loader.
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- Future plugin modules can be put into xgboost/plugin and register back to the library.
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- Remove most of the raw pointers to smart ptrs, for RAII safety.
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* Add official option to approximate algorithm `tree_method` to parameter.
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- Change default behavior to switch to prefer faster algorithm.
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- User will get a message when approximate algorithm is chosen.
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* Change library name to libxgboost.so
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* Backward compatiblity
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- The binary buffer file is not backward compatible with previous version.
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- The model file is backward compatible on 64 bit platforms.
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* The model file is compatible between 64/32 bit platforms(not yet tested).
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* External memory version and other advanced features will be exposed to R library as well on linux.
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- Previously some of the features are blocked due to C++11 and threading limits.
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- The windows version is still blocked due to Rtools do not support ```std::thread```.
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* rabit and dmlc-core are maintained through git submodule
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- Anyone can open PR to update these dependencies now.
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* Improvements
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- Rabit and xgboost libs are not thread-safe and use thread local PRNGs
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- This could fix some of the previous problem which runs xgboost on multiple threads.
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* JVM Package
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- Enable xgboost4j for java and scala
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- XGBoost distributed now runs on Flink and Spark.
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* Support model attributes listing for meta data.
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- https://github.com/dmlc/xgboost/pull/1198
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- https://github.com/dmlc/xgboost/pull/1166
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* Support callback API
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- https://github.com/dmlc/xgboost/issues/892
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- https://github.com/dmlc/xgboost/pull/1211
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- https://github.com/dmlc/xgboost/pull/1264
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* Support new booster DART(dropout in tree boosting)
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- https://github.com/dmlc/xgboost/pull/1220
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* Add CMake build system
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- https://github.com/dmlc/xgboost/pull/1314
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## v0.47 (2016.01.14)
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* Changes in R library
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- fixed possible problem of poisson regression.
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- switched from 0 to NA for missing values.
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- exposed access to additional model parameters.
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* Changes in Python library
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- throws exception instead of crash terminal when a parameter error happens.
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- has importance plot and tree plot functions.
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- accepts different learning rates for each boosting round.
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- allows model training continuation from previously saved model.
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- allows early stopping in CV.
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- allows feval to return a list of tuples.
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- allows eval_metric to handle additional format.
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- improved compatibility in sklearn module.
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- additional parameters added for sklearn wrapper.
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- added pip installation functionality.
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- supports more Pandas DataFrame dtypes.
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- added best_ntree_limit attribute, in addition to best_score and best_iteration.
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* Java api is ready for use
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* Added more test cases and continuous integration to make each build more robust.
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## v0.4 (2015.05.11)
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* Distributed version of xgboost that runs on YARN, scales to billions of examples
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* Direct save/load data and model from/to S3 and HDFS
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* Feature importance visualization in R module, by Michael Benesty
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* Predict leaf index
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* Poisson regression for counts data
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* Early stopping option in training
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* Native save load support in R and python
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- xgboost models now can be saved using save/load in R
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- xgboost python model is now pickable
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* sklearn wrapper is supported in python module
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* Experimental External memory version
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## v0.3 (2014.09.07)
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* Faster tree construction module
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- Allows subsample columns during tree construction via ```bst:col_samplebytree=ratio```
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* Support for boosting from initial predictions
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* Experimental version of LambdaRank
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* Linear booster is now parallelized, using parallel coordinated descent.
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* Add [Code Guide](src/README.md) for customizing objective function and evaluation
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* Add R module
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## v0.2x (2014.05.20)
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* Python module
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* Weighted samples instances
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* Initial version of pairwise rank
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## v0.1 (2014.03.26)
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* Initial release
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