GASearchCV¶

Evolutionary optimization over hyperparameters. 
Call decision_function on the estimator with the best found parameters. 


Main method of GASearchCV, starts the optimization procedure with the hyperparameters of the given estimator 

Get parameters for this estimator. 
Call inverse_transform on the estimator with the best found params. 

Call predict on the estimator with the best found parameters. 

Call predict_proba on the estimator with the best found parameters. 


Returns the score on the given data, if the estimator has been refit. 
Call score_samples on the estimator with the best found parameters. 


Set the parameters of this estimator. 
Call transform on the estimator with the best found parameters. 
 class sklearn_genetic.GASearchCV(estimator, cv=3, param_grid=None, scoring=None, population_size=10, generations=40, crossover_probability=0.8, mutation_probability=0.1, tournament_size=3, elitism=True, verbose=True, keep_top_k=1, criteria='max', algorithm='eaMuPlusLambda', refit=True, n_jobs=1, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False, log_config=None)[source]¶
Evolutionary optimization over hyperparameters.
GASearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “predict_log_proba” if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by crossvalidated search over parameter settings.
 Parameters
 estimatorestimator object, default=None
estimator object implementing ‘fit’ The object to use to fit the data.
 cvint, crossvalidation generator or an iterable, default=None
Determines the crossvalidation splitting strategy. Possible inputs for cv are:  None, to use the default 5fold cross validation,  int, to specify the number of folds in a (Stratified)KFold,  CV splitter,  An iterable yielding (train, test) splits as arrays of indices. For int/None inputs, if the estimator is a classifier and
y
is either binary or multiclass,StratifiedKFold
is used. In all other cases,KFold
is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls. param_griddict, default=None
Grid with the parameters to tune, expects keys a valid name of hyperparameter based on the estimator selected and as values one of
Integer
,Categorical
Continuous
classes. At least two parameters are advised to be provided in order to successfully make an optimization routine. population_sizeint, default=10
Size of the initial population to sample randomly generated individuals.
 generationsint, default=40
Number of generations or iterations to run the evolutionary algorithm.
 crossover_probabilityfloat, default=0.8
Probability of crossover operation between two individuals.
 mutation_probabilityfloat, default=0.1
Probability of child mutation.
 tournament_sizeint, default=3
Number of individuals to perform tournament selection.
 elitismbool, default=True
If True takes the tournament_size best solution to the next generation.
 scoringstr or callable, default=None
A str (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
which should return only a single value. n_jobsint, default=None
Number of jobs to run in parallel. Training the estimator and computing the score are parallelized over the crossvalidation splits.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. verbosebool, default=True
If
True
, shows the metrics on the optimization routine. keep_top_kint, default=1
Number of best solutions to keep in the hof object. If a callback stops the algorithm before k iterations, it will return only one set of parameters per iteration.
 criteria{‘max’, ‘min’} , default=’max’
max
if a higher scoring metric is better,min
otherwise. algorithm{‘eaMuPlusLambda’, ‘eaMuCommaLambda’, ‘eaSimple’}, default=’eaMuPlusLambda’
Evolutionary algorithm to use. See more details in the deap algorithms documentation.
 refitbool, default=True
Refit an estimator using the best found parameters on the whole dataset. If
False
, it is not possible to make predictions using this GASearchCV instance after fitting. pre_dispatchint or str, default=’2*n_jobs’
Controls the number of jobs that get dispatched during parallel execution. Reducing this number can be useful to avoid an explosion of memory consumption when more jobs get dispatched than CPUs can process. This parameter can be:
None, in which case all the jobs are immediately created and spawned. Use this for lightweight and fastrunning jobs, to avoid delays due to ondemand spawning of the jobs
An int, giving the exact number of total jobs that are spawned
A str, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
 error_score‘raise’ or numeric, default=np.nan
Value to assign to the score if an error occurs in estimator fitting. If set to
'raise'
, the error is raised. If a numeric value is given, FitFailedWarning is raised. return_train_score: bool, default=False
If
False
, thecv_results_
attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting tradeoff. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. log_config
MLflowConfig
, default = None Configuration to log metrics and models to mlflow, of None, no mlflow logging will be performed
 Attributes
 logbook
DEAP.tools.Logbook
Contains the logs of every set of hyperparameters fitted with its average scoring metric.
 historydict
Dictionary of the form: {“gen”: [], “fitness”: [], “fitness_std”: [], “fitness_max”: [], “fitness_min”: []}
gen returns the index of the evaluated generations. Each entry on the others lists, represent the average metric in each generation.
 cv_results_dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be imported into a pandas
DataFrame
. best_estimator_estimator
Estimator that was chosen by the search, i.e. estimator which gave highest score on the left out data. Not available if
refit=False
. best_params_dict
Parameter setting that gave the best results on the hold out data.
 best_index_int
The index (of the
cv_results_
arrays) which corresponds to the best candidate parameter setting. The dict atsearch.cv_results_['params'][search.best_index_]
gives the parameter setting for the best model, that gives the highest mean score (search.best_score_
). scorer_function or a dict
Scorer function used on the held out data to choose the best parameters for the model.
 n_splits_int
The number of crossvalidation splits (folds/iterations).
 refit_time_float
Seconds used for refitting the best model on the whole dataset. This is present only if
refit
is not False.
 logbook
 decision_function(X)¶
Call decision_function on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportsdecision_function
. Parameters
 Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
 fit(X, y, callbacks=None)[source]¶
Main method of GASearchCV, starts the optimization procedure with the hyperparameters of the given estimator
 Parameters
 Xarraylike of shape (n_samples, n_features)
The data to fit. Can be for example a list, or an array.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs), default=None
The target variable to try to predict in the case of supervised learning.
 callbacks: list or callable
One or a list of the callbacks methods available in
callbacks
. The callback is evaluated after fitting the estimators from the generation 1.
 get_params(deep=True)¶
Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.
 inverse_transform(Xt)¶
Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements
inverse_transform
andrefit=True
. Parameters
 Xtindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
 predict(X)¶
Call predict on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportspredict
. Parameters
 Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
 predict_log_proba(X)¶
Call predict_log_proba on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportspredict_log_proba
. Parameters
 Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
 predict_proba(X)¶
Call predict_proba on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportspredict_proba
. Parameters
 Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
 score(X, y=None)¶
Returns the score on the given data, if the estimator has been refit.
This uses the score defined by
scoring
where provided, and thebest_estimator_.score
method otherwise. Parameters
 Xarraylike of shape (n_samples, n_features)
Input data, where n_samples is the number of samples and n_features is the number of features.
 yarraylike of shape (n_samples, n_output) or (n_samples,), default=None
Target relative to X for classification or regression; None for unsupervised learning.
 Returns
 scorefloat
 score_samples(X)¶
Call score_samples on the estimator with the best found parameters.
Only available if
refit=True
and the underlying estimator supportsscore_samples
.New in version 0.24.
 Parameters
 Xiterable
Data to predict on. Must fulfill input requirements of the underlying estimator.
 Returns
 y_scorendarray of shape (n_samples,)
 set_params(**params)¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.
 transform(X)¶
Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports
transform
andrefit=True
. Parameters
 Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.