catboost changelog

Changelog History

Page 3

• v0.17 Changes

  September 10, 2019

  🆕 New features:

  • 📜 Sparse data support
  • We've implemented and set to default boost_from_average in RMSE mode. It gives a boost in quality especially for a small number of iterations.

  👌 Improvements:

  • Quantile regression on CPU
  • 0️⃣ default parameters for Poisson regression

  Speedups:

  • A number of speedups for training on CPU
  • Huge speedups for loading datasets with categorical features represented as pandas.Categorical.
    Hint: use pandas.Categorical instead of object to speed up loading up to 200x.

• v0.16.5 Changes

  August 20, 2019

  💥 Breaking changes:

  • 0️⃣ All metrics except for AUC metric now use weights by default.

  🆕 New features:

  • Added boost_from_average parameter for RMSE training on CPU which might give a boost in quality.
  • Added conversion from ONNX to CatBoost. Now you can convert XGBoost or LightGBM model to ONNX, then convert it to CatBoost and use our fast applier. Use model.load_model(model_path, format="onnx") for that.

  Speed ups:

  • Training is ~15% faster for datasets with categorical features.

  🐛 Bug fixes:

  • 🔋 R language: get_features_importance with ShapValues for MultiClass, #868
  • NormalizedGini was not calculated, #962
  • 🐛 Bug in leaf calculation which could result in slightly worse quality if you use weights in binary classification mode
  • Fixed __builtins__ import in Python3 in PR #957, thanks to @AbhinavanT

• v0.16.4 Changes

  August 14, 2019

  🐛 Bug fixes:

  • 🔖 Versions 0.16.* had a bug in python applier with categorical features for applying on more than 128 documents.

  🆕 New features:

  • It is now possible to use pairwise modes for datasets without groups

  👌 Improvements:

  • 1.8x Evaluation speed on asymmetrical trees

• v0.16.3 Changes

  August 11, 2019

  💥 Breaking changes:

  • 🔋 Renamed column Feature Index to Feature Id in prettified output of python method get_feature_importance(), because it supports feature names now
  • Renamed option per_float_feature_binarization (--per-float-feature-binarization) to per_float_feature_quantization (--per-float-feature-quantization)
  • ✂ Removed parameter inverted from python cv method. Added type parameter instead, which can be set to Inverted
  • Method get_features() now works only for datasets without categorical features

  🆕 New features

  • A new multiclass version of AUC metric, called AUC Mu, which was proposed by Ross S. Kleiman on NeurIPS 2019, link
  • ➕ Added time series cv
  • ➕ Added MeanWeightedTarget in fstat
  • Added utils.get_confusion_matrix()
  • Now feature importance can be calculated for non-symmetric trees

• v0.16.2 Changes

  August 02, 2019

  💥 Breaking changes:

  • Removed get_group_id() and get_features() methods of Pool class

  🆕 New model analysis tools:

  • 🔋 Added PredictionDiff type of get_feature_importance() method, which is a new method for model analysis. The method shows how the features influenced the fact that among two samples one has a higher prediction. It allows to debug ranking models: you find a pair of samples ranked incorrectly and you look at what features have caused that.
  • ➕ Added plot_predictions() method

  🆕 New features:

  • 🔋 model.set_feature_names() method in Python
  • ➕ Added stratified split to parameter search methods
  • 👌 Support catboost.load_model() from CPU snapshots for numerical-only datasets
  • 👍 CatBoostClassifier.score() now supports y as DataFrame
  • Added sampling_frequency, per_float_feature_binarization, monotone_constraints parameters to CatBoostClassifier and CatBoostRegresssor

  Speedups:

  • 2x speedup of multi-classification mode

  🛠 Bugfixes:

  • 🛠 Fixed score() for multiclassification, #924
  • Fixed get_all_params() function, #926

  Other improvements:

  • Clear error messages when a model cannot be saved

• v0.16.1 Changes

  July 30, 2019

  💥 Breaking changes:

  • parameter fold_count is now called cv in grid_search() and randomized_search
  • cv results are now returned from grid_search() and randomized_search() in res['cv_results'] field

  🆕 New features:

  • 👍 R-language function catboost.save_model() now supports PMML, ONNX and other formats
  • Parameter monotone_constraints in python API allows specifying numerical features that the prediction shall depend on monotonically

  🐛 Bug fixes:

  • 🛠 Fixed eval_metric calculation for training with weights (in release 0.16 evaluation of a metric that was equal to an optimized loss did not use weights by default, so overfitting detector worked incorrectly)

  👌 Improvements:

  • Added option verbose to grid_search() and randomized_search()
  • Added tutorial on grid_search() and randomized_search()

• v0.16 Changes

  July 24, 2019

  💥 Breaking changes:

  • MultiClass loss has now the same sign as Logloss. It had the other sign before and was maximized, now it is minimized.
  • CatBoostRegressor.score now returns the value of R² metric instead of RMSE to be more consistent with the behavior of scikit-learn regressors.
  • 🔄 Changed metric parameter use_weights default value to false (except for ranking metrics)

  🆕 New features:

  • It is now possible to apply model on GPU
  • We have published two new realworld datasets with monotonic constraints, catboost.datasets.monotonic1() and catboost.datasets.monotonic2(). Before that there was only california_housing dataset in open-source with monotonic constraints. Now you can use these two to benchmark algorithms with monotonic constraints.
  • We've added several new metrics to catboost, including DCG, FairLoss, HammingLoss, NormalizedGini and FilteredNDCG
  • Introduced efficient GridSearch and RandomSearch implementations.
  • get_all_params() Python function returns the values of all training parameters, both user-defined and default.
  • ➕ Added more synonyms for training parameters to be more compatible with other GBDT libraries.

  Speedups:

  • AUC metric is computationally very expensive. We've implemented parallelized calculation of this metric, now it can be calculated on every iteration (or every k-th iteration) about 4x faster.

  Educational materials:

  • We've improved our command-line tutorial, now it has examples of files and more information.

  🛠 Fixes:

  • Automatic Logloss or MultiClass loss function deduction for CatBoostClassifier.fit now also works if the training dataset is specified as Pool or filename string.
  • 🛠 And some other fixes

• v0.15.2 Changes

  June 28, 2019

  💥 Breaking changes:

  • 🔋 Function get_feature_statistics is replaced by calc_feature_statistics
  • Scoring function Correlation is renamed to Cosine
  • Parameter efb_max_conflict_fraction is renamed to sparse_features_conflict_fraction

  🆕 New features:

  • Models can be saved in PMML format now.

  Note: PMML does not have full categorical features support, so to have the model in PMML format for datasets with categorical features you need to use set one_hot_max_size parameter to some large value, so that all categorical features are one-hot encoded

  • 🔋 Feature names can be used to specify ignored features

  🐛 Bug fixes, including:

  • 🛠 Fixed restarting of CV on GPU for datasets without categorical features
  • 🛠 Fixed learning continuation errors with changed dataset (#879) and with model loaded from file (#884)
  • 🛠 Fixed NativeLib for JDK 9+ (PR #857)

• v0.15.1 Changes

  May 31, 2019

  🐛 Bug fixes:

  • ⏪ restored parameter fstr_type in Python and R interfaces

• v0.15 Changes

  May 27, 2019

  💥 Breaking changes

  • 0️⃣ cv is now stratified by default for Logloss, MultiClass and MultiClassOneVsAll.
  • 🚚 We have removed border parameter of Logloss metric. You need to use target_border as a separate training parameter now.
  • CatBoostClassifier now runs MultiClass if more than 2 different values are present in training dataset labels.
  • model.best_score_["validation_0"] is replaced with model.best_score_["validation"] if a single validation dataset is present.
  • get_object_importance function parameter ostr_type is renamed to type in Python and R.

  Model analysis

  • Tree visualisation by @karina-usmanova.
  • 🆕 New feature analysis: plotting information about how a feature was used in the model by @alexrogozin12.
  • Added plot parameter to get_roc_curve, get_fpr_curve and get_fnr_curve functions from catboost.utils.
  • 👌 Supported prettified format for all types of feature importances.

  🆕 New ways of doing predictions

  • Rust applier by @shuternay.
  • DotNet applier by @17minutes.
  • One-hot encoding for categorical features in CatBoost CoreML model by Kseniya Valchuk and Ekaterina Pogodina.

  🆕 New objectives

  • Expectile Regression by @david-waterworth.
  • Huber loss by @atsky.

  Speedups

  • Speed up of shap values calculation for single object or for small number of objects by @Lokutrus.
  • Cheap preprocessing and no fighting of overfitting if there is little amount of iterations (since you will not overfit anyway).

  🆕 New functionality

  • Prediction of leaf indices.

  🆕 New educational materials

  • Rust tutorial by @shuternay.
  • C# tutorial.
  • Leaf indices.
  • Tree visualisation tutorial by @karina-usmanova.
  • Google Colab tutorial for regression in catboost by @col14m.

  🛠 And a set of fixes for your issues.

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