Cobra v1.1.1 (2023-03 release)

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.

We welcome the following new contributors!

• @ZlaTanskY made their first contribution in #131
• @patrickleonardy made their first contribution in #142

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

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

Full Changelog: v1.1.0...v1.1.1

Cobra v1.1.0

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.

Cobra v1.0.2

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)

Cobra v1.0.1

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

Cobra v1.0.0

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)

Cobra v0.0.1

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