Pythonpredictions-cobra

Summary of this 2023-03 release:

- It is no longer necessary to specify categorical variables yourself when calling Cobra's preprocessing, an automatic search of categorical variables can be done.
- It is not longer obligatory to specify a row ID column for the inputted basetable.
- We now automatically drop columns that only contain missing values, which in the past caused Cobra throwing exotic errors when preprocessing the basetable.
- We prevent fit_transform() of modifying the training data dataframe under some circumstances.
- As a short-term fix for pandas's 2.0 release, which breaks a small portion of Cobra's code, we impose that Cobra is installed with pandas < 2.0.0.

List of issues that were behind this: see the closed [2023-03 release milestone](https://github.com/PythonPredictions/cobra/milestone/3?closed=1).

We welcome the following new contributors!
* ZlaTanskY made their first contribution in https://github.com/PythonPredictions/cobra/pull/131
* patrickleonardy made their first contribution in https://github.com/PythonPredictions/cobra/pull/142

Full details (autogenerated by Github) of the associated issues and pull requests:

* feat: added files to encourage PEP8 by ZlaTanskY in https://github.com/PythonPredictions/cobra/pull/131
* Fix/mutable train data in fit transform by ZlaTanskY in https://github.com/PythonPredictions/cobra/pull/140
* Added drop of columns containing only NANs by patrickleonardy in https://github.com/PythonPredictions/cobra/pull/142
* Defaults id_column to None for PIGs & tests by patrickleonardy in https://github.com/PythonPredictions/cobra/pull/141
* issue 137/PR 145 automatically search for categorical variables by patrickleonardy in https://github.com/PythonPredictions/cobra/pull/145
* 137 automatically search for categorical variables by patrickleonardy in https://github.com/PythonPredictions/cobra/pull/147
* 151 add turorial while importing by patrickleonardy in https://github.com/PythonPredictions/cobra/pull/152
* Short term pandas 2.0 support by sandervh14 in https://github.com/PythonPredictions/cobra/pull/164
* Merging 2023-03 development branch to master for 2023-03 release (1.1.1) by sandervh14 in https://github.com/PythonPredictions/cobra/pull/162

**Full Changelog**: https://github.com/PythonPredictions/cobra/compare/v1.1.0...v1.1.1

New release includes:
- Added functionality for linear regression
- General bug fixing and overall consistency improvements
- Expanded documentation

Main issues dealt with can be found as part of this [Milestone](https://github.com/PythonPredictions/cobra/milestone/2?closed=1).

In this release, we added:
* added new logo (25)
* added CI/CD testing pipeline
* improved speed of train/test/validation split (53)
* optional number of deciles in evaluator plots (35)
* improve PIGs visualizaton (30 and 32)

In this release, we fixed:
* regrouping name (42)
* index error (40)
* error in disretizing (39)
* value trying to set on a copy warning (37)
* floating error in train/selection/validation split (33)
* AUC sorting inconsistency (28)

In this release, we:

- Fixed a bug caused by an edge-case in the forward selection procedure. When this bug occured, the output was still valid but too many models where trained during iterations causing a performance drop (in terms of speed)
- Added plotting functionality to plot PIG graphs
- Fixed a bug with labels in plotting functions
- Added unittest for forward selection and preprocessing
- Added docs source files to generate documentation using Sphinx
- Updated the `requirements.txt` and `setup.py` to publish the package to PyPi

We took the beta version of COBRA and transformed it into a scikit-like package with different modules that can separately be reused for other purposes. The package now has different modules for:

- preprocessing (both continuous and categorical data, incl. target encoding)
- building a logistic regression model using forward feature selection
- evaluation of the models using various metrics (both scalar & graphical)

This is a beta version of Cobra, were we took the original version based on notebooks and translated it into a package