Release Notes¶
Some notes on new features in various releases
What’s new in 0.4.1¶
Docs:¶
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
Internal:¶
Changed test ci from travis to Github actions
What’s new in 0.4¶
Features:¶
Implemented callbacks module to stop the optimization process based in the current iteration metrics, currently implemented:
ThresholdStopping
,ConsecutiveStopping
andDeltaThreshold
.The algorithms ‘eaSimple’, ‘eaMuPlusLambda’, ‘eaMuCommaLambda’ are now implemented in the module
algorithms
for more control over their options, rather that taking the deap.algorithms moduleImplemented the
plots
module and added the functionplot_search_space()
, this function plots a mixed counter, scatter and histogram plots over all the fitted hyperparameters and their cross-validation scoreDocumentation 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
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 replacing them with param_grid
What’s new in 0.3¶
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
What’s new in 0.2¶
Features:¶
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 parameterAdded the parameter keep_top_k to control the amount of solutions if the hall of fame (hof)
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
Changed default parameters crossover_probability from 1 to 0.8 and generations from 50 to 40
What’s new in 0.1¶
Features:¶
GASearchCV
for hyperparameters tuning using custom genetic algorithm for scikit-learn classification and regression modelsplot_fitness_evolution()
function to see the average fitness values over generations