Catboost

Breaking changes
- We fixed bug in CatBoost. Pool initialization from `numpy.ndarray` and `pandas.dataframe` with string values that can cause slight inconsistence while using trained model from older versions. Around 1% of cat feature hashes were treated incorrectly. If you expirience quality drop after update you should consider retraining your model.

Major Features And Improvements
- Algorithm for finding most influential training samples for a given object from the 'Finding Influential Training Samples for Gradient Boosted Decision Trees' [paper](https://arxiv.org/pdf/1802.06640.pdf) is implemented. This mode for every object from input pool calculates scores for every object from train pool. A positive score means that the given train object has made a negative contribution to the given test object prediction. And vice versa for negative scores. The higher score modulo - the higher contribution.
See `get_object_importance` model method in Python package and `ostr` mode in cli-version. Tutorial for Python is available [here](https://github.com/catboost/tutorials/blob/master/model_analysis/object_importance_tutorial.ipynb).
More details and examples will be published in documentation soon.
- We have implemented new way of exploring feature importance - Shap values from [paper](https://arxiv.org/pdf/1706.06060.pdf). This allows to understand which features are most influent for a given object. You can also get more insite about your model, see details in a [tutorial](https://github.com/catboost/tutorials/blob/master/model_analysis/shap_values_tutorial.ipynb).
- Save model as code functionality published. For now you could save model as Python code with categorical features and as C++ code w/o categorical features.

Bug Fixes and Other Changes
- Fix `_catboost` reinitialization issues 268 and 269.
- Python module `catboost.util` extended with `create_cd`. It creates column description file.
- Now it's possible to load titanic and amazon (Kaggle Amazon Employee Access Challenge) datasets from Python code. Use `catboost.datasets`.
- GPU parameter `use_cpu_ram_for_cat_features` renamed to `gpu_cat_features_storage` with posible values `CpuPinnedMemory` and `GpuRam`. Default is `GpuRam`.

Thanks to our Contributors
This release contains contributions from CatBoost team.

As usual we are grateful to all who filed issues or helped resolve them, asked and answered questions.

Major Features And Improvements
- GPU: New `DocParallel` mode for tasks without categorical features and or with categorical features and `—max-ctr-complextiy 1`. Provides best performance for pools with big number of documents.
- GPU: Distributed training on several GPU host via MPI. See instruction how to build binary [here](https://tech.yandex.com/catboost/doc/dg/concepts/cli-installation-docpage/#multi-node-installation).
- GPU: Up to 30% learning speed-up for Maxwell and later GPUs with binarization level > 32

Bug Fixes and Other Changes
- Hotfixes for GPU version of python wrapper.

Major Features And Improvements
- Python wrapper: added methods to download datasets titanic and amazon, to make it easier to try the library (`catboost.datasets`).
- Python wrapper: added method to write column desctiption file (`catboost.utils.create_cd`).
- Made improvements to visualization.
- Support non-numeric values in `GroupId` column.
- [Tutorials](https://github.com/catboost/tutorials/blob/master/README.md) section updated.

Bug Fixes and Other Changes
- Fixed problems with eval_metrics (issue 285)
- Other fixes

Breaking changes
- Changed parameter order in [`train()`](https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_train-docpage/) function to be consistant with other GBDT libraries.
- `use_best_model` is set to True by default if `eval_set` labels are present.

Major Features And Improvements
- New ranking mode [`YetiRank`](https://tech.yandex.com/catboost/doc/dg/concepts/loss-functions-docpage/#loss-functions__ranking) optimizes `NDGC` and `PFound`.
- New visualisation for `eval_metrics` and `cv` in Jupyter notebook.
- Improved per document feature importance.
- Supported `verbose`=`int`: if `verbose` > 1, `metric_period` is set to this value.
- Supported type(`eval_set`) = list in python. Currently supporting only single `eval_set`.
- Binary classification leaf estimation defaults are changed for weighted datasets so that training converges for any weights.
- Add `model_size_reg` parameter to control model size. Fix `ctr_leaf_count_limit` parameter, also to control model size.
- Beta version of distributed CPU training with only float features support.
- Add `subgroupId` to [Python](https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_pool-docpage/)/[R-packages](https://tech.yandex.com/catboost/doc/dg/concepts/r-reference_catboost-load_pool-docpage/).
- Add groupwise metrics support in `eval_metrics`.

Thanks to our Contributors
This release contains contributions from CatBoost team.

We are grateful to all who filed issues or helped resolve them, asked and answered questions.

Breaking changes
- `boosting_type` parameter value `Dynamic` is renamed to `Ordered`.
- Data visualisation functionality in Jupyter Notebook requires ipywidgets 7.x+ now.
- `query_id` parameter renamed to `group_id` in Python and R wrappers.
- cv returns pandas.DataFrame by default if Pandas installed. See new parameter [`as_pandas`](https://tech.yandex.com/catboost/doc/dg/concepts/python-reference_cv-docpage/).

Major Features And Improvements
- CatBoost build with make file. Now it’s possible to build command-line CPU version of CatBoost under Linux with [make file](https://tech.yandex.com/catboost/doc/dg/concepts/cli-installation-docpage/#make-install).
- In column description column name `Target` is changed to `Label`. It will still work with previous name, but it is recommended to use the new one.
- `eval-metrics` mode added into cmdline version. Metrics can be calculated for a given dataset using a previously [trained model](https://tech.yandex.com/catboost/doc/dg/concepts/cli-reference_eval-metrics-docpage/).
- New classification metric `CtrFactor` is [added](https://tech.yandex.com/catboost/doc/dg/concepts/loss-functions-docpage/).
- Load CatBoost model from memory. You can load your CatBoost model from file or initialize it from buffer [in memory](https://github.com/catboost/catboost/blob/master/catboost/CatboostModelAPI.md).
- Now you can run `fit` function using file with dataset: `fit(train_path, eval_set=eval_path, column_description=cd_file)`. This will reduce memory consumption by up to two times.
- 12% speedup for training.

Bug Fixes and Other Changes
- JSON output data format is [changed](https://tech.yandex.com/catboost/doc/dg/concepts/output-data_training-log-docpage/).
- Python whl binaries with CUDA 9.1 support for Linux OS published into the release assets.
- Added `bootstrap_type` parameter to `CatBoostClassifier` and `Regressor` (issue 263).

Thanks to our Contributors
This release contains contributions from newbfg and CatBoost team.

We are grateful to all who filed issues or helped resolve them, asked and answered questions.

Major Features And Improvements
- **BETA** version of distributed mulit-host GPU via MPI training
- Added possibility to import coreml model with oblivious trees. Makes possible to migrate pre-flatbuffers model (with float features only) to current format (issue 235)
- Added QuerySoftMax loss function

Bug Fixes and Other Changes
- Fixed GPU models bug on pools with both categorical and float features (issue 241)
- Use all available cores by default
- Fixed not querywise loss for pool with `QueryId`
- Default float features binarization method set to `GreedyLogSum`