Release Notes

Version 0.21.0 (2025-10-26)

New features:

  • Add transform method in scorecard (Issue 346).

  • Add to_dict method for binning table serialization (Issue 371).

  • Replace Boston dataset (Issue 372).

Bugfixes:

  • Use weighted min and max bin size, and correct decision tree hyperparameters when sample_weight is provided (Issue 359).

  • Fix ssym for std calculation (Issue 360).

Version 0.20.1 (2025-02-23)

Bugfixes:

  • Set the maximum ortools to be <9.12 to avoid CP-SAT incompatible changes (Issue 347).

Version 0.20.0 (2024-10-29)

Improvements:

  • Axis labels are cut off when saving the table plot to file (Issue 303).

  • Packaging improvements (Issue 335).

Bugfixes:

  • Legend missing in ScorecardMonitoring.psi_plot() (Issue 327).

Version 0.19.0 (2024-01-16)

Improvements:

  • Adjust plot size (Issue 244).

  • Save optimal binning object in JSON format (Issue 96).

  • Plot IV/WoE metric in binning table plot for binary and continuous target.

Bugfixes:

  • Keep pandas.DataFrame index in transform method (Issue 286).

  • Fix BinningProcess’s binning_transform_params=”bins” (Issue 266).

Version 0.18.0 (2023-09-22)

Bugfixes:

Version 0.17.3 (2023-02-12)

Improvements:

  • Implement sample_weight check in Scorecard class (Issue 228).

Bugfixes:

  • Fix metric_missing ignored in Scorecard class (Issue 226).

Dependencies:

  • Update RoPWR required version.

Version 0.17.2 (2022-12-15)

Improvements:

  • Modify max-pvalue and min_diff constraints for CP and MIP formulation to avoid suboptimal solutions.

Bugfixes:

  • Use keyword arguments in compute_class_weight (Issue 222).

  • Remove preprocessing step when monotonic trend in (ascending, descending) for scenario-based binning (Issue 216).

Dependencies:

  • Update scikit-learn and ortools required versions.

Version 0.17.1 (2022-11-20)

New features:

  • Add parameter cat_unknown to assign values to the unobserved categories during training.

Improvements:

  • Add method decision_function to Scorecard (Issue 198).

Version 0.17.0 (2022-11-06)

New features:

  • Optimize formulation of minimum difference constraints for all optimal binning classes and support these constraints regardless of the monotonic trend (Issue 201).

  • Implementation of sample weight for ContinuousOptimalBinning (Issue 131).

Bugfixes:

  • Fix ContinuousOptimalBinning prebinning step when no prebinning splits were generated (Issue 205).

Version 0.16.1 (2022-10-28)

New features:

  • Outlier detector YQuantileDetector for continuous target (Issue 203).

Improvements:

  • Add support to solver SCS and HIGHS for optimal piecewise binning classes.

  • Unit testing outlier detector methods.

Bugfixes:

  • Pass lb and ub as keyword arguments to RoPWR fit method (required since ropwr>=0.4.0).

Version 0.16.0 (2022-10-24)

New features:

  • Treatment of special codes separately for optimal piecewise binning classes (Issue 191).

Improvements:

  • Allow plot style="actual" for stochastic optimal binning.

  • Unit testing optimal piecewise binning classes (Issue 93).

  • Unit testing add macOS Monterey 12.

Bugfixes:

  • Fix sample weight for BinningProcess when n_jobs != 1 (Issue 190).

  • Fix transform method for optimal binning 2D when dtype is categorical (Issue 197).

  • Fix max_pvalue default value in documentation (Issue 199).

Version 0.15.1 (2022-09-06)

New features:

  • New parameter show_bin_labels for binning tables (Issue 180).

Version 0.15.0 (2022-07-20)

New features:

  • Optimal binning 2D support to categorical variables for binary and continuous target.

Improvements:

  • Integer intercept if rounding=True (Issue 165).

  • Parameter show_digits applies to scorecard table bin column (Issue 170).

Bugfixes:

  • Fix Scorecard.score method when there are special and missing bins. (Issue 179).

  • Fix x and y axis labels in OptimalBinning2D plots, x and y were interchanged.

Version 0.14.1 (2022-04-11)

Bugfixes:

  • Fix new setup function.

Version 0.14.0 (2022-04-10)

New features:

  • Optimal binning 2D with continuous target.

Improvements:

  • Set tdigest and pympler dependencies as optional. This change avoids accumulation-tree issues faced by several users. Remove dill dependency.

  • New continuous binning objective function leading to improvements in regression metrics.

Bugfixes:

  • Fix binning 2D minimum difference constraints.

Tutorials:

  • Tutorial: optimal binning 2D with continuous target

Version 0.13.1 (2022-02-18)

Bugfixes:

  • Fix binning process summary update (Issue 151).

  • Fix pandas 1.4.0 (python > 3.8) slicing issue with method at (Pull 148).

  • Fix minor typos (Pull 147).

  • Fix binning plot for multiple special values.

Version 0.13.0 (2021-11-24)

New features:

  • Treatment of special codes separately for optbinning classes (Issue 115).

Bugfixes:

  • Various bug fixes for the OptimalBinning2D class. See Issue 138, for instance.

Tutorials:

  • Tutorial: optimal binning 2D with binary target

Version 0.12.2 (2021-10-03)

Improvements:

  • Do not store optimization solver instance as class attribute.

  • Do not store logger as a class attribute.

Version 0.12.1 (2021-09-12)

New features:

  • Binning process supports sample_weight for binary target. Issue 124

  • Binning process can fix variables not satisfying selection criteria. Issue 123

Version 0.12.0 (2021-08-28)

New features:

  • Optimal binning 2D with binary target.

Improvements:

  • Update bin string format in binning tables.

  • Simplify logic when style="actual" in binning table plots.

API changes:

  • Scorecard fit method arguments changed to the usual (X, y): Issue 111

Version 0.11.0 (2021-05-28)

New features:

  • Counterfactual explanations for scorecard modelling.

Improvements:

  • Replace pickle by dill in save and load methods.

Bugfixes:

  • Parallel binning uses joblib: Issue 103

  • Fix custom metric_special and metric_missing in binning_transform_params.

Version 0.10.0 (2021-04-27)

New features:

  • Batch and streaming binning process.

Improvements:

  • Improve LocalSolver formulation for optimal binning with a binary target.

Bugfixes:

  • Fix MulticlassOptimalBinning when no prebins: Issue 94

  • Fix metric_missing and metric_special defined for fitting, but not for predictions or scorecard points: Issue 100

Version 0.9.2 (2021-03-12)

New features:

  • Binning process can update binned variables with new optimal binning object using method update_binned_variable.

Improvements:

  • Prevent large divisions to avoid overflow issues with int32 during Gini calculation.

Tutorials:

  • Tutorial: FICO Explainable Machine Learning Challenge - updating binning

Version 0.9.1 (2021-02-14)

New features:

  • Binning process can be constructed using OptimalBinning objects previously fitted. Method fit_from_dict.

  • Binning process can process large datasets directly on disk. Allowed file formats are csv and parquet. Methods fit_disk, fit_transform_disk and transform_disk.

Bugfixes:

  • Fix saving all OptBinning classes: Issue 77

Version 0.9.0 (2021-01-14)

New features:

  • Optimal piecewise polynomial binning.

  • New plotting option for binning table for binary and continuous target. Parameter style allows to represent the binning plot with the actual scale, i.e., actual bin widths.

Improvements:

  • Improve computation of p-values and binning table analysis for ContinuousOptimalBinning.

Tutorials:

  • Tutorial: optimal piecewise binning with binary target

  • Tutorial: optimal piecewise binning with continuous target

Bugfixes:

Version 0.8.0 (2020-09-18)

New features:

  • Scorecard monitoring supporting binning and continuous target.

  • OptimalBinning computes the Kolmogorov-Smirnov statistic.

  • Optimal binning classes show optimal monotonic trend information in the binning table analysis method.

  • ContinuousBinningTable adds method analysis.

  • Scorecard incorporates methods load and save to serialize and deserialize a scorecard using pickle module.

  • BinningProcess class supports multiprocessing via parameter n_jobs.

Tutorials:

  • Tutorial: Scorecard monitoring

Version 0.7.0 (2020-07-19)

New features:

  • Batch and streaming optimal binning.

  • New parameter divergence to select the divergence measure to maximize.

Tutorials:

  • Tutorial: optimal binning sketch with binary target

  • Tutorial: optimal binning sketch with binary target using PySpark

Bugfixes:

  • Catch error from Qhull library used by scipy.spatial.ConvexHull.

Version 0.6.1 (2020-06-07)

New features:

  • Options add_special and add_missing in all binning table plots.

  • Prebinning methods’ parameters are accessible via **prebinning_kwargs.

  • Add support MDLP algorithm for binary target.

Bugfixes:

  • Fix bug in solution when the status is not feasible or optimal for LocalSolver, solver="ls".

  • Fix several bugs for categorical variables with user_splits and user_splits_fixed.

  • Fix bug in binning process when passing user_splits and user_splits_fixed via parameter binning_fit_params.

Version 0.6.0 (2020-05-24)

New features:

  • Scorecard development supporting binary and continuous target.

  • Plotting functions: plot_auc_roc, plot_cap and plot_ks.

  • Optimal binning classes introduce sample_weight parameter in methods fit and fit_transform.

  • Optimal binning classes introduce two options for parameter metric in methods fit_transform and transform: metric="bins" and metric="indices".

Tutorials:

  • Tutorial: optimal binning with binary target - large scale.

  • Tutorial: Scorecard with binary target.

  • Tutorial: Scorecard with continuous target.

Version 0.5.0 (2020-04-13)

New features:

  • Scenario-based stochastic optimal binning.

  • New parameter user_split_fixed to force user-defined split points.

Tutorials:

  • Tutorial: Telco customer churn.

  • Tutorial: optimal binning with binary target under uncertainty.

Bugfixes:

  • Fix monotonic trend for non-auto mode in MulticlassOptimalBinning.

Version 0.4.0 (2020-03-22)

New features:

  • New monotonic_trend auto modes options: “auto_heuristic” and “auto_asc_desc”.

  • New monotonic_trend options: “peak_heuristic” and “valley_heuristic”. These options produce a remarkable speedup for large size instances.

  • Minimum Description Length Principle (MDLP) discretization algorithm.

Improvements:

  • BinningProcess now supports pandas.DataFrame as input X.

  • New unit test added.

Version 0.3.1 (2020-03-17)

Bugfixes:

  • Fix setup.py packages using find_packages.

Version 0.3.0 (2020-03-13)

New features:

  • Class OptBinning introduces a new constraint to reduce dominating bins, using parameter gamma.

  • Metrics HHI, HHI regularized and Cramer’s V added to binning_table.analysis method. Updated quality score.

  • Added column min/max target and zeros count to ContinuousOptimalBinning binning table.

  • Binning algorithms support univariate outlier detection methods.

Tutorials:

  • Tutorial: optimal binning with binary target. New section: Reduction of dominating bins.

  • Enhance binning process tutorials.

Version 0.2.0 (2020-02-02)

New features:

  • Binning process to support optimal binning of all variables in dataset.

  • Added print_output option to binning_table.analysis method.

Improvements:

  • New unit tests added.

Tutorials:

  • Tutorial: Binning process with Scikit-learn pipelines.

  • Tutorial: FICO Explainable Machine Learning Challenge using binning process.

Bugfixes:

  • Fix OptBinning.information print level default option.

  • Avoid numpy.digitize if no splits.

  • Compute Gini in binning_table.build method.

Version 0.1.1 (2020-01-24)

Bugfixes:

  • Fix a bug in OptimalBinning.fit_transform when calling tranform internally.

  • Replace np.int by np.int64 in model_data.py functions to guarantee 64-bit integer on Windows.

  • Fix a bug in _chech_metric_special_missing.

Version 0.1.0 (2020-01-22)

  • First release of OptBinning.