catboost changelog

Changelog History

Page 1

• v0.22

  March 02, 2020

  🆕 New features:

  • 🚀 The main feature of the release is the support of non symmetric trees for training on CPU.
    Using non symmetric trees might be useful if one-hot encoding is present, or data has little noise.
    📄 To try non symmetric trees change grow_policy parameter.
    🚀 Starting from this release non symmetric trees are supported for both CPU and GPU training.
  • 👍 The next big feature improves catboost text features support.
    Now tokenization is done during training, you don't have to do lowercasing, digit extraction and other tokenization on your own, catboost does it for you.
  • 👍 Auto learning-rate is now supported in CPU MultiClass mode.
  • CatBoost class supports to_regressor and to_classifier methods.

  🚀 The release also contains a list of bug fixes.

• v0.21

  January 31, 2020

  🆕 New features:

  • The main feature of this release is the Stochastic Gradient Langevin Boosting (SGLB) mode that can improve quality of your models with non-convex loss functions. To use it specify langevin option and tune diffusion_temperature and model_shrink_rate. See the corresponding paper for details.

  👌 Improvements:

  • 0️⃣ Automatic learning rate is applied by default not only for Logloss objective, but also for RMSE (on CPU and GPU) and MultiClass (on GPU).
  • Class labels type information is stored in the model. Now estimators in python package return values of proper type in classes_ attribute and for prediction functions with prediction_type=Class. #305, #999, #1017.
    📄 Note: Class labels loaded from datasets in CatBoost dsv format always have string type now.

  🐛 Bug fixes:

  • 🛠 Fixed huge memory consumption for text features. #1107
  • 🛠 Fixed crash on GPU on big datasets with groups (hundred million+ groups).
  • 🛠 Fixed class labels consistency check and merging in model sums (now class names in binary classification are properly checked and added to the result as well)
  • 🛠 Fix for confusion matrix (PR #1152), thanks to @dmsivkov.
  • Fixed shap values calculation when boost_from_average=True. #1125
  • 🛠 Fixed use-after-free in fstr PredictionValuesChange with specified dataset
  • Target border and class weights are now taken from model when necessary for feature strength, metrics evaluation, roc_curve, object importances and calc_feature_statistics calculations.
  • 🛠 Fixed that L2 regularization was not applied for non symmetric trees for binary classification on GPU.
  • 🔋 [R-package] Fixed the bug that catboost.get_feature_importance did not work after model is loaded #1064
  • 📦 [R-package] Fixed the bug that catboost.train did not work when called with the single dataset parameter. #1162
  • 🛠 Fixed L2 score calculation on CPU

  Other:

  • 🚀 Starting from this release Java applier is released simultaneously with other components and has the same version.

  Compatibility:

  • 🚀 Models trained with this release require applier from this release or later to work correctly.

• v0.20.2

  December 25, 2019

  🆕 New features:

  • 👍 String class labels are now supported for binary classification
  • [CLI only] Timestamp column for the datasets can be provided in separate files.
  • [CLI only] Timesplit feature evaluation.
  • 🖨 Process groups of any size in block processing.

  🐛 Bug fixes:

  • classes_count and class_weight params can be now used with user-defined loss functions. #1119
  • 0️⃣ Form correct metric descriptions on GPU if use_weights gets value by default. #1106
  • Correct model.classes_ attribute for binary classification (proper labels instead of always 0 and 1). #984
  • Fix model.classes_ attribute when classes_count parameter was specified.
  • Proper error message when categorical features specified for MultiRMSE training. #1112
  • Block processing: It is valid for all groups in a single block to have weights equal to 0
  • 🛠 fix empty asymmetric tree index calculation. #1104

• v0.20.1

  December 11, 2019

  🆕 New features:

  • Have leaf_estimation_method=Exact the default for MAPE loss
  • 🌲 Add CatBoostClassifier.predict_log_proba(), PR #1095

  🐛 Bug fixes:

  • 🛠 Fix usability of read-only numpy arrays, #1101
  • 🔋 Fix python3 compatibility for get_feature_importance, PR #1090
  • Fix loading model from snapshot for boost_from_average mode

• v0.20

  November 28, 2019

  🆕 New submodule for text processing!
  It contains two classes to help you make text features ready for training:

  • Tokenizer -- use this class to split text into tokens (automatic lowercase and punctuation removal)
  • Dictionary -- with this class you create a dictionary which maps tokens to numeric identifiers. You then use these identifiers as new features.

  🆕 New features:

  • Enabled boost_from_average for MAPE loss function

  🐛 Bug fixes:

  • 🛠 Fixed Pool creation from pandas.DataFrame with discontinuous columns, #1079
  • 🛠 Fixed standalone_evaluator, PR #1083

  Speedups:

  • 📦 Huge speedup of preprocessing in python-package for datasets with many samples (>10 mln)

  🚀 We also release precompiled packages for Python 3.8

• v0.19.1

  November 19, 2019

  🆕 New features:

  • With this release we support Text features for classification on GPU. To specify text columns use text_features parameter. Achieve better quality by using text information of your dataset. See more in Learning CatBoost with text features
  • MultiRMSE loss function is now available on CPU. Labels for the multi regression mode should be specified in separate Label columns
  • MonoForest framework for model analysis, based on our NeurIPS 2019 paper. Learn more in MonoForest tutorial
  • boost_from_average is now True by default for Quantile and MAE loss functions, which improves the resulting quality

  Speedups:

  • Huge reduction of preprocessing time for datasets loaded from files and for datasets with many samples (> 10 million), which was a bottleneck for GPU training
  • 3x speedup for small datasets

• v0.18.1

  October 31, 2019

  🆕 New features:

  • Now datasets.msrank() returns full msrank dataset. Previously, it returned the first 10k samples.
    We have added msrank_10k() dataset implementing the past behaviour.

  🐛 Bug fixes:

  • get_object_importance() now respects parameter top_size, #1045 by @ibuda

• v0.18

  October 21, 2019

  • 🚀 The main feature of the release is huge speedup on small datasets. We now use MVS sampling for CPU regression and binary classification training by default, together with Plain boosting scheme for both small and large datasets. This change not only gives the huge speedup but also provides quality improvement!
  • The boost_from_average parameter is available in CatBoostClassifier and CatBoostRegressor
  • We have added new formats for describing monotonic constraints. For example, "(1,0,0,-1)" or "0:1,3:-1" or "FeatureName0:1,FeatureName3:-1" are all valid specifications. With Python and params-file json, lists and dictionaries can also be used

  🐛 Bugs fixed:

  • Error in Multiclass classifier training, #1040
  • 👻 Unhandled exception when saving quantized pool, #1021
  • Python 3.7: RuntimeError raised in StagedPredictIterator, #848

• v0.17.5

  October 10, 2019

  🐛 Bugs fixed:

  • 🏁 System of linear equations is not positive definite when training MultiClass on Windows, #1022
  • Cat feature values could be taken from floating-point data. We have forbidden this
  • Handling of numpy.ndarray features data with categorical features is corrected

• v0.17.4

  October 01, 2019

  👌 Improvements:

  • Massive 2x speedup for MultiClass with many classes
  • Updated MVS implementation. See Minimal Variance Sampling in Stochastic Gradient Boosting by Bulat Ibragimov and Gleb Gusev at NeurIPS 2019
  • ➕ Added sum_models in R-package, #1007

  🐛 Bugs fixed:

  • Multi model initialization in python, #995
  • Mishandling of 255 borders in training on GPU, #1010

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