Optbinning

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``.

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.

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``.

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.

Bugfixes:

- Fix setup.py packages using find_packages.

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.