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Releases: catboost/catboost

Release 0.6.1

01 Feb 15:01
@kizill kizill
v0.6.1
b176bbf
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Release 0.6.1

Bug Fixes and Other Changes

  • Fixed critical bugs in formula evaluation code (issue #236)
  • Added scale_pos_weight parameter
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Release 0.6

30 Jan 02:56
@sab sab
v0.6
be74162
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Release 0.6

Speedups

  • 25% speedup of the model applier
  • 43% speedup for training on large datasets.
  • 15% speedup for QueryRMSE and calculation of querywise metrics.
  • Large speedups when using binary categorical features.
  • Significant (x200 on 5k trees and 50k lines dataset) speedup for plot and stage predict calculations in cmdline.
  • Compilation time speedup.

Major Features And Improvements

  • Industry fastest applier implementation.
  • Introducing new parameter boosting-type to switch between standard boosting scheme and dynamic boosting, described in paper "Dynamic boosting".
  • Adding new bootstrap types bootstrap_type, subsample. Using Bernoulli bootstrap type with subsample < 1 might increase the training speed.
  • Better logging for cross-validation, added parameter logging_level and metric_period (should be set in training parameters) to cv.
  • Added a separate train function that receives the parameters and returns a trained model.
  • Ranking mode QueryRMSE now supports default settings for dynamic boosting.
  • R-package pre-build binaries are included into release.
  • We added many synonyms to our parameter names, now it is more convenient to try CatBoost if you are used to some other library.

Bug Fixes and Other Changes

  • Fix for CPU QueryRMSE with weights.
  • Adding several missing parameters into wrappers.
  • Fix for data split in querywise modes.
  • Better logging.
  • From this release we'll provide pre-build R-binaries.
  • More parallelisation.
  • Memory usage improvements.
  • And some other bug fixes.

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.

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Release 0.5.2.1

09 Jan 12:27
@sab sab
v0.5.2.1
27b8574
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Release 0.5.2.1

Hot fixes

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Release 0.5.2

28 Dec 13:53
@sab sab
v0.5.2
e764811
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Release 0.5.2

Release 0.5.2

Major Features And Improvements

  • We've made single document formula applier 4 times faster!
  • model.shrink function added in Python and R wrappers.
  • Added new training parameter metric_period that controls output frequency.
  • Added new ranking metric QueryAverage.
  • This version contains an easy way to implement new user metrics in C++. How-to example is provided.

Bug Fixes and Other Changes

  • Stability improvements and bug fixes

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

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Release 0.5.1

19 Dec 10:57
@sab sab
v0.5.1
5bc723e
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Release 0.5.1

Hot fixes

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Release 0.5

29 Nov 23:24
@kizill kizill
v0.5
674abef
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Release 0.5

Major Features And Improvements

  • In Python we added a new method eval_metrics: now it's possible for a given model to calculate specified metric values for each iteration on specified dataset.
  • One command-line binary for CPU and GPU: in CatBoost you can switch between CPU and GPU training by changing single parameter value task-type CPU or GPU (task_type 'CPU', 'GPU' in python bindings). Windows build still contains two binaries.
  • We have speed up the training up to 30% for datasets with a lot of objects.
  • Up to 10% speed-up of GPU implementation on Pascal cards

Breaking Changes

Cmdline:

  • Training parameter gradient-iterations renamed to leaf-estimation-iterations.
  • border option removed. If you want to specify border for binary classification mode you need to specify it in the following way: loss-function Logloss:Border=0.5
  • CTR parameters are changed:
    • Removed priors, per-feature-priors, ctr-binarization;
    • Added simple-ctr, combintations-ctr, per-feature-ctr;
      More details will be published in our documentation.

Python and R:

  • Training parameter gradient_iterations renamed to leaf_estimation_iterations.
  • border option removed. If you want to specify border for binary classification mode you need to specify it in the following way: loss_function='Logloss:Border=0.5'
  • CTR parameters are changed:
    • Removed priors, per_feature_priors, ctr_binarization;
    • Added simple_ctr, combintations_ctr, per_feature_ctr;
      More details will be published in our documentation.

Bug Fixes and Other Changes

  • Stability improvements and bug fixes

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

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Release 0.4.1

23 Nov 13:10
@sab sab
v0.4.1
6cafa10
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Release 0.4.1

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Release 0.4

22 Nov 08:38
@sab sab
v0.4
977da22
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Release 0.4

Breaking Changes

FlatBuffers model format: new CatBoost versions wouldn’t break model compatibility anymore.

Major Features And Improvements

  • Training speedups: we have speed up the training by 33%.
  • Two new ranking modes are available:
    • PairLogit - pairwise comparison of objects from the input dataset. Algorithm maximises probability correctly reorder all dataset pairs.
    • QueryRMSE - mix of regression and ranking. It’s trying to make best ranking for each dataset query by input labels.

Bug Fixes and Other Changes

  • We have fixed a bug that caused quality degradation when using weights < 1.
  • Verbose flag is now deprecated, please use logging_level instead. You could set the following levels: Silent, Verbose, Info, Debug.
  • And some other bugs.

Thanks to our Contributors

This release contains contributions from: avidale, newbfg, KochetovNicolai and CatBoost team.

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

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Release 0.3

02 Nov 08:10
@sab sab
v.0.3
0104b00
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Release 0.3

Major Features And Improvements

GPU CUDA support is available. CatBoost supports multi-GPU training. Our GPU implementation is 2 times faster then LightGBM and more then 20 times faster then XGBoost one. Check out the news with benchmarks on our site.

Bug Fixes and Other Changes

Stability improvements and bug fixes

Thanks to our Contributors

This release contains contributions from: daskol and CatBoost team.

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

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Release 0.2

14 Sep 11:23
@sab sab
v0.2
27bbcec
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Release 0.2

Breaking Changes

  • R library interface significantly changed
  • New model format: CatBoost v0.2 model binary not compatible with previous versions
  • Cross-validation parameters c 4AC5 hanges: we changed overfitting detector parameters of CV in python so that it is same as those in training.
  • CTR types: MeanValue => BinarizedTargetMeanValue

Major Features And Improvements

  • Training speedups: we have speed up the training by 20-30%.
  • Accuracy improvement with categoricals: we have changed computation of statistics for categorical features, which leads to better quality.
  • New type of overfitting detector: Iter. This type of detector was requested by our users. So now you can also stop training by a simple criterion: if after a fixed number of iterations there is no improvement of your evaluation function.
  • TensorBoard support: this is another way of looking on the graphs of different error functions both during training and after training has finished. To look at the metrics you need to provide train_dir when training your model and then run "tensorboard --logdir={train_dir}"
  • Jupyter notebook improvements: for our Python library users that experiment with Jupyter notebooks, we have improved our visualisation tool. Now it is possible to save image of the graph. We also have changed scrolling behaviour so that it is more convenient to scroll the notebook.
  • NaN features support: we also have added simple but effective way of dealing with NaN features. If you have some NaNs in the train set, they will be changed to a value that is less than the minimum value or greater than the maximum value in the dataset (this is configurable), so that it is guaranteed that they are in their own bin, and a split would separates NaN values from all other values. By default, no NaNs are allowed, so you need to use option nan_mode for that. When applying a model, NaNs will be treated in the same way for the features where NaN values were seen in train. It is not allowed to have NaN values in test if no NaNs in train for this feature were provided.
  • Snapshotting: we have added snapshotting to our Python and R libraries. So if you think that something can happen with your training, for example machine can reboot, you can use snapshot_file parameter - this way after you restart your training it will start from the last completed iteration.
  • R library tutorial: we have added tutorial.
  • Logging customization: we have added allow_writing_files parameter. By default some files with logging and diagnostics are written on disc, but you can turn it off using by setting this flag to False.
  • Multiclass mode improvements: we have added a new objective for multiclass mode - MultiClassOneVsAll. We also added class_names param - now you don't have to renumber your classes to be able to use multiclass. And we have added two new metrics for multiclass: TotalF1 and MCC metrics.
    You can use the metrics to look how its values are changing during training or to use overfitting detection or cutting the model by best value of a given metric.
  • Any delimeters support: in addition to datasets in tsv format, CatBoost now supports files with any delimeters

Bug Fixes and Other Changes

  • Stability improvements and bug fixes

Thanks to our Contributors

This release contains contributions from: grayskripko, hadjipantelis and CatBoost team.

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

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