Sklearn-genetic-opt

This is a small release for a minor bug fix

Features:
* Install TensorFlow when using `pip install sklearn-genetic-opt[all]`

Bug Fixes:
* Fixed a bug that wouldn’t allow cloning the GA classes when used inside a pipeline

This release brings support to python 3.10, it also comes with different API updates and algorithms optimization

API Changes:

* `GAFeatureSelectionCV` now mimics the scikit-learn FeatureSelection algorithms API instead of Grid Search, this enables easier implementation as a selection method that is closer to the scikit-learn API
* Improved `GAFeatureSelectionCV` candidate generation when `max_features` is set, it also ensures there is at least one feature selected
* `crossover_probability` and `mutation_probability` are now correctly passed to the mate and mutation functions inside GAFeatureSelectionCV
* Dropped support for python 3.7 and add support for python 3.10
* Update most important packages from dev-requirements.txt to more recent versions
* Update deprecated functions in tests

Thanks to the people who contributed with their ideas and suggestions

This release comes with new features and general performance improvements

Features:

* Introducing Adaptive Schedulers to enable adaptive mutation and crossover probabilities; currently, supported schedulers are: `ConstantAdapter`, `ExponentialAdapter`, `InverseAdapter`, and `PotentialAdapter`

* Add random_state parameter (default= None) in `Continuous`, `Categorical` and `Integer` classes from space to leave fixed the random seed during hyperparameters sampling.

API Changes:

* Changed the default values of mutation_probability and crossover_probability to 0.8 and 0.2, respectively.

* The weighted_choice function used in `GAFeatureSelectionCV` was re-written to give more probability to a number of features closer to the max_features parameter

* Removed unused and broken function plot_parallel_coordinates()

Bug Fixes

* Now, when using the plot_search_space() function, all the parameters get cast as np.float64 to avoid errors on the seaborn package while plotting bool values.

This release implements a change when the max_features parameter from class GAFeatureSelectionCV is set, the initial population is now sampled giving more probability to solutions with less than max_features features.

This release comes with some requested features and enhancements.

Features:

* Class `GAFeatureSelectionCV` now has a parameter called `max_features`, int, default=None. If it's not None, it will penalize individuals with more features than max_features, putting a "soft" upper bound to the number of features to be selected.

* Classes `GASearchCV` and `GAFeatureSelectionCV` now support multi-metric evaluation the same way scikit-learn does; you will see this reflected on the `logbook` and `cv_results_` objects, where now you get results for each metric. As in scikit-learn, if multi-metric is used, the `refit` parameter must be a str specifying the metric to evaluate the cv-scores.

* Training gracefully stops if interrupted by some of these exceptions: ``KeyboardInterrupt``, ``SystemExit``, ``StopIteration``.
When one of these exceptions is raised, the model finishes the current generation and saves the current best model. It only works if at least one generation has been completed.

API Changes:

* The following parameters changed their default values to create more extensive and different models with better results:

- population_size from 10 to 50

- generations from 40 to 80

- mutation_probability from 0.1 to 0.2

Docs:

* A new notebook called Iris_multimetric was added to showcase the new multi-metric capabilities.

This is an exciting release! It introduces features selection capabilities to the package

Features:

* `GAFeatureSelectionCV` class for feature selection along with any scikit-learn classifier or regressor. It optimizes the cv-score while minimizing the number of features to select. This class is compatible with the mlflow and tensorboard integration, the Callbacks, and the plot_fitness_evolution function.

API Changes:

The module mlflow was renamed to mlflow_log to avoid unexpected errors on name resolutions