Some notes on new features in various releases
- Build-in integration with MLflow using the
~sklearn_genetic.mlflow.MLflowConfig
and the new parameter log_config from~sklearn_genetic.GASearchCV
- Implemented the callback
~sklearn_genetic.callbacks.LogbookSaver
which saves the estimator.logbook object with all the fitted hyperparameters and their cross-validation score - Added the parameter estimator to all the functions on the module
~sklearn_genetic.callbacks
- Added user guide "Integrating with MLflow"
- Update the tutorial "Custom Callbacks" for new API inheritance behavior
- Added a base class
~sklearn_genetic.callbacks.base.BaseCallback
from which all Callbacks must inherit from - Now coverage report doesn't take into account the lines with # pragma: no cover and # noqa
- Added user guide on "Understanding the evaluation process"
- Several guides on contributing, code of conduct
- Added important links
- Docs requirement are now independent of package requirements
- Changed test ci from travis to Github actions
- 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
- Added best_params_ and best_estimator_ properties after fitting GASearchCV
- Added optional parameters refit, pre_dispatch and error_score
- 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 replacing them with param_grid
- 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
- Enabled deap's eaMuPlusLambda algorithm for the optimization process, now is the default routine
- 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- Added the parameter keep_top_k to control the amount of solutions if the hall of fame (hof)
- 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
- Changed default parameters crossover_probability from 1 to 0.8 and generations from 50 to 40
~sklearn_genetic.GASearchCV
for hyperparameters tuning using custom genetic algorithm for scikit-learn classification and regression models~sklearn_genetic.plots.plot_fitness_evolution
function to see the average fitness values over generations