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Release Notes

Last updated on May 22, 2025

New Features in version 0.17.0¤

  • Adding the active_sample function, which refines the Coreset by adding new samples to it based on active sampling.

New Features, Improvements and Bug Fixes in version 0.16.0¤

  • Adding the build_from_databricks and partial_build_from_databricks functions, which allow building a coreset tree directly from a Databricks SQL query.
  • Adding native support for AWS, GCP, and Azure cloud storages. Any function receiving a path to a file or a directory, namely build_from_file, partial_build_from_file, get_coreset, save, load, and plot can now receive a path to cloud storage in addition to a local path..
  • Adding a CLI allowing to configure different parameters of the library.
  • Adding model_fit_params to grid_search, fit, and the validation functions, allowing to pass parameters to the model's fit function (such as early stopping and others).
  • Fixing an issue where partial_build with sample_weight did not work when the original build was done without sample_weight, and vice versa.
  • Fixing an issue where fit ignored the tree_idx parameter it received.
  • Fixing an issue where coreset_size was configured and chunk_size was not but the coreset_size was ignored.
  • Fixing issues in grid_search related to handling unbalanced Coreset trees and improving the logging message for the best hyperparameter combination.
  • get_coreset_size is no longer limited by license.
  • save_coreset was removed from the library’s API. get_coreset now receives a save_path argument if the Coreset needs to be saved.

New Features, Improvements and Bug Fixes in version 0.15.0¤

  • It is now possible to build multiple Coreset trees within one build call for data tuning and model training purposes, using the DataTuningParams structure, which can be passed when initializing the Coreset tree. The DataTuningParams structure is treated as a param_grid, and a Coreset tree will be built for each combination of parameters.
  • As a result of the DataTuningParams change, coreset_size and sample_all parameters can only be defined within the DataTuningParams structure and not directly within the list of parameters passed when initializing the Coreset tree.
  • As a result of the DataTuningParams change, the grid_search function can receive a list of tree_indices, specifying for which Coreset trees the grid search should be executed. If not specified, by default, grid_search will be executed on all Coreset trees.
  • As a result of the DataTuningParams change, a tree_idx parameter can be passed to the following functions, defining the index of the tree from which the coreset is extracted. The impacted functions are get_coreset, get_coreset_size, fit, get_hyperparameter_tuning_data, cross_validate, holdout_validate and seq_dependent_validate The default index is 0, which corresponds to the tree built according to the first DataTuningParams combination passed.
  • When defining a Coreset tree, the default value for the optimized_for parameter is now training.
  • Coresets can now be built when the features include a new data type, an array of integers (e.g., [3, 7, 19, 203, 579]). The library will automatically encode these array features into binary columns during the build process.
  • The default coreset_size and chunk_size calculation was improved in cases where the dataset includes categorical and array features.
  • It is now possible to pass n_instances and coreset_size as a float number when initializing the Coreset tree, without specifying the chunk_size. The library will calculate the optimal chunk_size, and the provided coreset_size will be treated as a ratio of the chunk_size.
  • Adding the build_from_file_insights, build_from_df_insights and build_insights functions, which provide insights into the Coreset tree(s) that would be built for the provided dataset.
  • Fixing an issue where build_from_file and partial_build_from_file received a directory or a list of directories, but the files were not processed in alphabetical order.
  • Adding the remove_by_seq function, which removes nodes from the Coreset tree within a given sequence.
  • When building the Coreset tree with a sequence column, it is now possible to define 'chunk_by': "every_build". In this case, every call to build or partial build will receive its own sequence, which can be assigned by the user or will be automatically assigned by the library.
  • When building the Coreset tree with a sequence column, it is now possible to define a sliding_window (e.g.: 'sliding_window': 10). In this case, the tree will only keep samples that are in the last n seq_column granularity units, where n is the value of the sliding_window.

Bug Fixes in version 0.14.1¤

  • Fixing compatibility issues with numpy version >= 2.0.0.

New Features, Improvements and Bug Fixes in version 0.14.0¤

  • Adding the capability to pass sample_weight to all the build functions.
  • preprocessing_stage=user is now the default when using CatBoost in fit, grid_search and the validation functions. In all other cases preprocessing_stage=auto is the default.
  • Fixing a rare problem that caused the build of the CoresetTreeServiceDTC and CoresetTreeServiceDTR to fail.

New Features, Improvements and Bug Fixes in version 0.13.0¤

  • Adding the support for a mixed categorical encoding strategy in binary classification problems, combining both Target Encoding (TE) and One Hot Encoding (OHE), which is now the default strategy.

Bug Fixes in version 0.12.1¤

  • Fixing the problem where predict and predict_proba sometimes worked incorrectly when the data included categorical features.

New Features, Improvements and Bug Fixes in version 0.12.0¤

  • Adding the support for Target Encoding, which is the default way to handle categorical features in binary classification problems.
  • Significantly improving the build functions’ runtime and their memory consumption, especially for datasets with categorical features and missing values.
  • Significantly improving the runtime of the auto_preprocessing, predict and predict_proba functions.
  • Fixing various problems concerning the handling of missing values.
  • Fixing the problem where incorrect data was saved for validation purposes when calling the save function.

Improvements and Bug Fixes in version 0.11.2¤

  • Solving a memory leak that happened sometimes during the various build and partial_build functions.
  • Improving the build function's runtime and their memory consumption.

Improvements and Bug Fixes in version 0.11.1¤

  • Improving the grid_search runtime, when XGBoost, LightGBM and CatBoost are used.
  • The get_coreset_size function is no longer limited by license.
  • Fixing the problem preventing the build of the CoresetTreeServiceDTR in some cases.

New Features, Improvements and Bug Fixes in version 0.11.0¤

  • Adding logging capabilities to the library, to improve its debugging capabilities.
  • Adding a verbose parameter to the various build and partial_build functions, to provide a better indication to the length of the operation.
  • Adding the get_hyperparameter_tuning_data function, which allows retrieving the data from the Coreset tree in such a format that allows running GridSearchCV, BayesSearchCV and other hyperparameter tuning methods.
  • Improving the structure returned by get_coreset.
  • Improving the build functions’ runtime and their memory consumption.
  • Fixing various problems when saving the Coreset tree.

New Features, Improvements and Bug Fixes in version 0.10.3¤

  • Improving grid_search runtime, by improving its parallelism. The number of jobs run in parallel during grid_search can be controlled by defining the n_jobs parameter passed to the function.
  • Fixing the problem when a Coreset tree was built with a seq_column, grid_search with refit=True would ignore the sequence-related parameters passed to the function.
  • Fixing the problem when a Coreset tree was built with a seq_column, fit and grid_search would not always select the optimal nodes from the Coreset tree.

New Features, Improvements and Bug Fixes in version 0.10.2¤

  • Allowing to configure the coreset_size also as a float, representing the ratio between each chunk and the resulting coreset.
  • Fixing the problem when the returned column types were incorrect in some cases when calling get_coreset, fit or grid_search with preprocessing_stage=user.

New Features, Improvements and Bug Fixes in version 0.10.1¤

  • Improving the grid_search time for datasets with categorical features and missing values.

New Features, Improvements and Bug Fixes in version 0.10.0¤

  • Adding an enhancement parameter to the fit, grid_search and the validation functions of the CoresetTreeServiceDTC and CoresetTreeServiceDTR. Setting a value of 1 to 3 for this parameter will enhance the default decision tree based training, which can improve the strength of the model, but will increase the training run time.

Bug Fixes in version 0.9.1¤

  • Fixing the problem when a Coreset tree was built with a seq_column on a dataset including categorical features, predict and predict_proba would sometimes fail.

New Features, Improvements and Bug Fixes in version 0.9.0¤

  • Allowing to execute get_coreset, fit, grid_search and the validation functions on a subset of the data of the Coreset tree (such as certain date ranges), by defining a sequence column (seq_column), in the DataParams structure passed during the initialization of the class, which can then be used to filter the data.
  • Improving the build time, by improving the parallelism of the Coreset Tree construction. The number of jobs run in parallel during build can be controlled by defining the n_jobs parameter passed to the function.

New Features, Improvements and Bug Fixes in version 0.8.1¤

  • Fixing a licensing issue.

New Features, Improvements and Bug Fixes in version 0.8.0¤

  • An additional CoresetTreeService for all decision tree regression-based problems has been added to the library. This service can be used to create regression-based Coresets for all libraries including: XGBoost, LightGBM, CatBoost, Scikit-learn and others.
  • Improving the default Coreset tree created when chunk_size and coreset_size are not provided during the initialization of the class.
  • grid_search was extended to return a Pandas DataFrame with the score of each hyperparameter combination and fold.
  • Improving the grid_search time.

New Features, Improvements and Bug Fixes in version 0.7.0¤

  • Coresets can now be built when the dataset has missing values. The library will automatically handle them during the build process (as Coresets can only be built without missing values).
  • Significantly improving the predict and predict_proba time for datasets with categorical features.
  • predict and predict_proba will now automatically preprocesses the data according to the preprocessing_stage used to train the model.
  • Improving the automatic detection of categorical features.
  • It is now possible to define the model class, used to train the model on the coreset, when initializing the CoresetTreeService class, using the model_cls parameter.
  • Enhancing the grid_search function to run on unsupervised datasets.
  • Fixing the problem when grid_search would fail after remove_samples or filter_out_samples were called.

New Features, Improvements and Bug Fixes in version 0.6.0¤

  • Replacing the save_all parameter passed when initializing all classes, with the chunk_sample_ratio parameter, which indicates the size of the sample that will be taken and saved from each chunk on top of the Coreset for the validation methods.
  • Significantly improving the build time for datasets with categorical features.
  • grid_search, cross_validate and holdout_validate all receive now the preprocessing_stage parameter, same as the fit function.
  • fit returns now the data in preprocessing_stage=auto by default when Scikit-learn or XGBoost are used to train the model and in preprocessing_stage=user by default when LightGBM or CatBoost are used to train the model.
  • Fixing the problem when both model and model_params were passed to fit, grid_search, cross_validate and holdout_validate, model_params were ignored.
  • Fixing the problem when a single Coreset was created for the entire dataset and get_cleaning_samples was called with class_size={"class XXX": "all"}, the returned result was faulty.

New Features and Improvements in version 0.5.0¤

  • Coresets can now be built using categorical features. The library will automatically one-hot encode them during the build process (as Coresets can only be built with numeric features).
  • get_coreset can now return the data according to three data preprocessing_stage=original – The dataset as it is handed to the Coreset’s build function. preprocessing_stage=user – The dataset after any user defined data preprocessing (default). preprocessing_stage=auto – The dataset after any automatic data preprocessing done by the library, such as one-hot encoding and converting Boolean fields to numeric. The features (X), can also be returned as a sparse matrix or an array, controlled by the sparse_output parameter (applicable only for preprocessing_stage=auto).
  • fit can now return the data according to two preprocessing_stage=auto is the default when Scikit-learn is used to train the model. preprocessing_stage=user is the default when XGBoost, LightGBM or CatBoost are used to train the model.
  • Adding a new auto_preprocessing function, allowing the user to preprocess the (test) data, as it was automatically done by the library during the Coreset’s build function.
  • Fixing the problem where the library’s Docstrings did not show up in some IDEs.

New Features and Improvements in version 0.4.0¤

  • Adding support for Python 3.11.
  • Allowing to create a CoresetTreeService, which can be used for both training and cleaning (optimized_for=['cleaning', 'training']).
  • The CoresetTreeService can now handle datasets that do not fit into the device’s memory also for cleaning purposes (for training purposes this was supported from the initial release).
  • The get_important_samples function was renamed to get_cleaning_samples to improve the clarity of its purpose.
  • Adding hyperparameter tuning capabilities to the library with the introduction of the grid_search() function, which works in a similar manner to Scikit-learn’s GridSearchCV class, only dramatically faster, as it utilizes the Coreset tree. Introducing also the cross_validate() and holdout_validate() functions, which can be used directly or as the validation method as part of the grid_search() function.
  • Further improving the error messages for some of the data processing problems users encountered.

Bug Fixes in version 0.3.1¤

  • build and build_partial now read the data in chunks in the size of chunk_size when the file format allows it (CSV, TSV), to reduce the memory footprint when building the Coreset tree.

New Features and Improvements in version 0.3.0¤

  • An additional CoresetTreeService for all decision tree classification-based problems has been added to the library. This service can be used to create classification-based Coresets for all libraries including: XGBoost, LightGBM, CatBoost, Scikit-learn and others.
  • Improving the results get_coreset returns in case the Coreset tree is not perfectly balanced.
  • Improving the data handling capabilities, when processing the input data provided to the different build functions, such as supporting pandas.BooleanDtype and pandas.Series and returning clearer error messages for some of the data processing problems encountered.

New Features and Improvements in version 0.2.0¤

  • Additional CoresetTreeServices for linear regression, K-Means, PCA and SVD have been added to the library.
  • Significantly reducing the memory footprint by up to 50% especially during the various build and partial_build functions.
  • Data is read in chunks in the size of chunk_size when the file format allows it (CSV, TSV), to reduce the memory footprint when building the Coreset tree.
  • Significantly improving the get_coreset time on large datasets.
  • Significantly improving the save time and changing the default save format to pickle.
  • Significantly improving the importance calculation when the number of data instances per class is lower than the number of features.
  • Allowing to save the entire dataset and not just the selected samples, by setting the save_all parameter during the initialization of the class. When optimized_for='cleaning' save_all is True by default and when optimized_for='training' it is False by default.
  • Allowing to define certain columns in the dataset as properties (props). Properties, won’t be used to compute the Coreset or train the model, but it is possible to filter_out_samples on them or to pass them in the select_from_function of get_important_samples.