Changelogs » Optbinning

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Optbinning

0.8.0

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

0.7.0

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.

0.6.1

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``.

0.6.0

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.

0.5.0

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``.

0.4.0

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.

0.3.1

Bugfixes:
  
  - Fix setup.py packages using find_packages.

0.3.0

New additions:
  
  - 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.

0.2.0

New additions:
  
  - Binning process to support optimal binning of all variables in dataset.
  - Add ``print_output`` option to ``binning_table.analysis`` method.
  - 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.

0.1.1

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``.

0.1.0

First release of OptBinning.