Tag version 0.7 (#2991)

Document all changes made in year 2017

NEWS.md

@@ -3,17 +3,23 @@ XGBoost Change Log
 
 This file records the changes in xgboost library in reverse chronological order.
 
-## v0.7 (2017.12.26)
+## 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
+  - 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).
+  - 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
-* Elide copies when building `DMatrix` from 2D NumPy matrices
+* 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
@@ -21,30 +27,42 @@ This file records the changes in xgboost library in reverse chronological order.
   - 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
+* 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.
-  - Extracting feature contributions to individual predictions.
+  - 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).
   - 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
-  - Fix early stopping for evaluation sets whose names contain `-`
+  - 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()`
@@ -53,32 +71,53 @@ This file records the changes in xgboost library in reverse chronological order.
     - `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
-  - Updated CRAN submission
   - 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.
-  - Make `IEvaluation` serializable
+  - 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 installation instructions for Mac OS X
+  - 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
-* Backward compatiblity
-  - XGBoost-spark no longer contains APIs for DMatrix (#1519); use the public booster interface instead.
+  - Links to additional examples and tutorials
+  - Clarify installation requirements
+* Changes that break backward compatibility
+  - [#1519](https://github.com/dmlc/xgboost/pull/1519) XGBoost-spark no longer contains APIs for DMatrix; use the public booster interface instead.
+  - [#2476](https://github.com/dmlc/xgboost/pull/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