Optbinning

New features:

- Batch and streaming binning process.

Improvements:

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

Bugfixes:

- Fix MulticlassOptimalBinning when no prebins: 94
- Fix metric_missing and metric_special defined for fitting, but not for predictions or scorecard points: 100

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

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: 77

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:

- Fix sample weights bug: 64

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

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.