Sklearn-genetic-opt

Features:

* Implemented callbacks module to stop the optimization process based in the current iteration metrics, currently implemented:
`sklearn_genetic.callbacks.ThresholdStopping` , `sklearn_genetic.callbacks.ConsecutiveStopping` and `sklearn_genetic.callbacks.DeltaThreshold`.
* The algorithms 'eaSimple', 'eaMuPlusLambda', 'eaMuCommaLambda' are now implemented in the module `sklearn_genetic.algorithms`
for more control over their options, rather that taking the `deap.algorithms module`.
* Implemented the `sklearn_genetic.plots` module and added the function `sklearn_genetic.plots.plot_search_space`,
this function plots a mixed counter, scatter and histogram plots over all the fitted hyperparameters and their cross-validation score.
* Documentation based in rst with Sphinx to host in read the docs. It includes public classes and functions documentation as well
as several tutorials on how to use the package, link: https://sklearn-genetic-opt.readthedocs.io/
* Added `best_params_` and `best_estimator_` properties after fitting GASearchCV.
* Added optional parameters `refit`, `pre_dispatch` and `error_score`.

API Changes:

* Removed support for python 3.6, changed the libraries supported versions to be the same as scikit-learn current version.
* Several internal changes on the documentation and variables naming style to be compatible with Sphinx.
* Removed the parameters `continuous_parameters`, `categorical_parameters` and `integer_parameters` in GASearchCV, replacing them with `param_grid`.

Features:
* Added the space module to control better the data types and ranges of each hyperparameter, their distribution to sample random values from, and merge all data types in one Space class that can work with the new param_grid parameter
* Changed the continuous_parameters, categorical_parameters and integer_parameters for the param_grid, the first ones still work but will be removed in a next version
* Added the option to use the eaMuCommaLambda algorithm from deap
* The mu and lambda_ parameters of the internal eaMuPlusLambda and eaMuCommaLambda now are in terms of the initial population size and not the number of generations

Features:
* Enabled deap's eaMuPlusLambda algorithm for the optimization process, now is the default routine
* Added the parameter keep_top_k to control the amout of solutions if the hall of fame (hof)
* Changed default parameters crossover_probability from 1 to 0.8 and generations from 50 to 40

Internal
* Changed parameters with pre-defined options to use pydantic models

Fixes
* Fix log of the scoring metric in logbook, now is part of the parameters and is show only once

Features:
* Added a logbook and history properties to the fitted GASearchCV to make post-fit analysis
* Elitism = False now implements a roulette selection instead of ignoring the parameter


API Changes:
* Refactored the optimization algorithm to use deap package instead of a custom implementation, this causes the removal of several methods, properties and variables inside the GASearchCV class
* The parameter encoding_length has been removed, it's not longer required to the GASearchCV class
* Renamed the property of the fitted estimator from best_params_ to best_params
* The verbosity now prints the deap log of the fitness function, it's standard deviation, max and min values from each generation
* The variable GASearchCV._best_solutions was removed and it's meant to be replaced with GASearchCV.logbook and GASearchCV.history

Bug Fixes:
* Fixs unexpected overwrites over if statements
* Correct validation when parameters dicts are empty

Enchacements:
* Criteria parameter to control if it's a minimization or maximization problem with respect to the scoring metric
* Plot fitness function over generations
* Unit tests for all the package
* Examples with regression problems
* Implementation of some magic methods
* Documentation of the GASearchCV class parameters

Features:
* GASearchCV for sklearn classifiers and regressors hyperparameters search using genetic algorithms and cross validation