skopt
.BayesSearchCV¶

class
skopt.
BayesSearchCV
(estimator, search_spaces, optimizer_kwargs=None, n_iter=50, scoring=None, fit_params=None, n_jobs=1, n_points=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=False)[source][source]¶ Bayesian optimization over hyper parameters.
BayesSearchCV implements a “fit” and a “score” method. It also implements “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” 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.
In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.
Parameters are presented as a list of skopt.space.Dimension objects.
 Parameters
 estimatorestimator object.
A object of that type is instantiated for each search point. This object is assumed to implement the scikitlearn estimator api. Either estimator needs to provide a
score
function, orscoring
must be passed. search_spacesdict, list of dict or list of tuple containing
(dict, int). One of these cases: 1. dictionary, where keys are parameter names (strings) and values are skopt.space.Dimension instances (Real, Integer or Categorical) or any other valid value that defines skopt dimension (see skopt.Optimizer docs). Represents search space over parameters of the provided estimator. 2. list of dictionaries: a list of dictionaries, where every dictionary fits the description given in case 1 above. If a list of dictionary objects is given, then the search is performed sequentially for every parameter space with maximum number of evaluations set to self.n_iter. 3. list of (dict, int > 0): an extension of case 2 above, where first element of every tuple is a dictionary representing some search subspace, similarly as in case 2, and second element is a number of iterations that will be spent optimizing over this subspace.
 n_iterint, default=50
Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. Consider increasing
n_points
if you want to try more parameter settings in parallel. optimizer_kwargsdict, optional
Dict of arguments passed to
Optimizer
. For example,{'base_estimator': 'RF'}
would use a Random Forest surrogate instead of the default Gaussian Process. scoringstring, callable or None, default=None
A string (see model evaluation documentation) or a scorer callable object / function with signature
scorer(estimator, X, y)
. IfNone
, thescore
method of the estimator is used. fit_paramsdict, optional
Parameters to pass to the fit method.
 n_jobsint, default=1
Number of jobs to run in parallel. At maximum there are
n_points
timescv
jobs available during each iteration. n_pointsint, default=1
Number of parameter settings to sample in parallel. If this does not align with
n_iter
, the last iteration will sample less points. See alsoask()
 pre_dispatchint, or string, optional
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 string, giving an expression as a function of n_jobs, as in ‘2*n_jobs’
 iidboolean, default=True
If True, the data is assumed to be identically distributed across the folds, and the loss minimized is the total loss per sample, and not the mean loss across the folds.
 cvint, crossvalidation generator or an iterable, optional
Determines the crossvalidation splitting strategy. Possible inputs for cv are:
None, to use the default 3fold cross validation,
integer, to specify the number of folds in a
(Stratified)KFold
,An object to be used as a crossvalidation generator.
An iterable yielding train, test splits.
For integer/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. refitboolean, default=True
Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this RandomizedSearchCV instance after fitting.
 verboseinteger
Controls the verbosity: the higher, the more messages.
 random_stateint or RandomState
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions.
 error_score‘raise’ (default) or numeric
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. This parameter does not affect the refit step, which will always raise the error.
 return_train_scoreboolean, default=False
If
'True'
, thecv_results_
attribute will include training scores.
 Attributes
 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
.For instance the below given table
param_kernel
param_gamma
split0_test_score
…
rank_test_score
‘rbf’
0.1
0.8
…
2
‘rbf’
0.2
0.9
…
1
‘rbf’
0.3
0.7
…
1
will be represented by a
cv_results_
dict of:{ 'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'], mask = False), 'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False), 'split0_test_score' : [0.8, 0.9, 0.7], 'split1_test_score' : [0.82, 0.5, 0.7], 'mean_test_score' : [0.81, 0.7, 0.7], 'std_test_score' : [0.02, 0.2, 0.], 'rank_test_score' : [3, 1, 1], 'split0_train_score' : [0.8, 0.9, 0.7], 'split1_train_score' : [0.82, 0.5, 0.7], 'mean_train_score' : [0.81, 0.7, 0.7], 'std_train_score' : [0.03, 0.03, 0.04], 'mean_fit_time' : [0.73, 0.63, 0.43, 0.49], 'std_fit_time' : [0.01, 0.02, 0.01, 0.01], 'mean_score_time' : [0.007, 0.06, 0.04, 0.04], 'std_score_time' : [0.001, 0.002, 0.003, 0.005], 'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...], }
NOTE that the key
'params'
is used to store a list of parameter settings dict for all the parameter candidates.The
mean_fit_time
,std_fit_time
,mean_score_time
andstd_score_time
are all in seconds. best_estimator_estimator
Estimator that was chosen by the search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. Not available if refit=False.
 optimizer_results_list of
OptimizeResult
Contains a
OptimizeResult
for each search space. The search space parameter are sorted by its name. best_score_float
Score of best_estimator on the left out data.
 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 at
search.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
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).
See also
GridSearchCV
Does exhaustive search over a grid of parameters.
Notes
The parameters selected are those that maximize the score of the heldout data, according to the scoring parameter.
If
n_jobs
was set to a value higher than one, the data is copied for each parameter setting(and notn_jobs
times). This is done for efficiency reasons if individual jobs take very little time, but may raise errors if the dataset is large and not enough memory is available. A workaround in this case is to setpre_dispatch
. Then, the memory is copied onlypre_dispatch
many times. A reasonable value forpre_dispatch
is2 * n_jobs
.Examples
>>> from skopt import BayesSearchCV >>> # parameter ranges are specified by one of below >>> from skopt.space import Real, Categorical, Integer >>> >>> from sklearn.datasets import load_iris >>> from sklearn.svm import SVC >>> from sklearn.model_selection import train_test_split >>> >>> X, y = load_iris(True) >>> X_train, X_test, y_train, y_test = train_test_split(X, y, ... train_size=0.75, ... random_state=0) >>> >>> # loguniform: understand as search over p = exp(x) by varying x >>> opt = BayesSearchCV( ... SVC(), ... { ... 'C': Real(1e6, 1e+6, prior='loguniform'), ... 'gamma': Real(1e6, 1e+1, prior='loguniform'), ... 'degree': Integer(1,8), ... 'kernel': Categorical(['linear', 'poly', 'rbf']), ... }, ... n_iter=32, ... random_state=0 ... ) >>> >>> # executes bayesian optimization >>> _ = opt.fit(X_train, y_train) >>> >>> # model can be saved, used for predictions or scoring >>> print(opt.score(X_test, y_test)) 0.973...
Methods
Call decision_function on the estimator with the best found parameters.
fit
(X[, y, groups, callback])Run fit on the estimator with randomly drawn parameters.
get_params
([deep])Get parameters for this estimator.
Call inverse_transform on the estimator with the best found params.
predict
(X)Call predict on the estimator with the best found parameters.
Call predict_log_proba on the estimator with the best found parameters.
Call predict_proba on the estimator with the best found parameters.
score
(X[, y])Returns the score on the given data, if the estimator has been refit.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Call transform on the estimator with the best found parameters.

__init__
(estimator, search_spaces, optimizer_kwargs=None, n_iter=50, scoring=None, fit_params=None, n_jobs=1, n_points=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None, error_score='raise', return_train_score=False)[source][source]¶ Initialize self. See help(type(self)) for accurate signature.

decision_function
(X)[source]¶ 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=None, groups=None, callback=None)[source][source]¶ Run fit on the estimator with randomly drawn parameters.
 Parameters
 Xarraylike or sparse matrix, shape = [n_samples, n_features]
The training input samples.
 yarraylike, shape = [n_samples] or [n_samples, n_output]
Target relative to X for classification or regression (class labels should be integers or strings).
 groupsarraylike, with shape (n_samples,), optional
Group labels for the samples used while splitting the dataset into train/test set.
 callback: [callable, list of callables, optional]
If callable then
callback(res)
is called after each parameter combination tested. If list of callables, then each callable in the list is called.

get_params
(deep=True)[source]¶ 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
 paramsmapping of string to any
Parameter names mapped to their values.

inverse_transform
(Xt)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). 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
 selfobject
Estimator instance.

property
total_iterations
¶ Count total iterations that will be taken to explore all subspaces with
fit
method. Returns
 max_iter: int, total number of iterations to explore