`bask`.BayesSearchCV¶

class bask.BayesSearchCV(estimator, search_spaces, optimizer_kwargs=None, n_iter=50, return_policy='best_setting', 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]¶

Fully Bayesian optimization over hyper parameters.

Wraps skopt.BayesSearchCV with a fully Bayesian estimation of the kernel hyperparameters, making it robust to very noisy target functions.

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 cross-validated 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 ———- estimator : estimator object.

A object of that type is instantiated for each search point. This object is assumed to implement the scikit-learn estimator api. Either estimator needs to provide a score function, or scoring 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.

return_policystring, default=’best_setting’

A string specifying which point should be considered the optimum at the end of the optimization. Should be one of

‘best_mean’: return the point maximizing the mean function of the Gaussian process. This is usually the best choice when the target function is noisy and a single observation might not be representative. Note, if the number of iterations n_iter is low, the expected optimum can be still be uncertain. Only use this setting when you only have one search space.

‘best_setting’: return the best setting tried so far. This is useful, if the target function is (almost) noise-free.

optimizer_kwargsdict, optional

Dict of arguments passed to Optimizer.

scoringstring, callable or None, default=None

A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score 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 times cv jobs available during each iteration.

n_pointsint, default=1

This is not implemented yet. Consider using the original skopt.BayesSearchCV for now. Number of parameter settings to sample in parallel. If this does not align with n_iter, the last iteration will sample less points. See also ask()

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 fast-running jobs, to avoid delays due to on-demand 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, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

None, to use the default 3-fold cross validation,

integer, to specify the number of folds in a (Stratified)KFold,

An object to be used as a cross-validation 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', the cv_results_ attribute will include training scores.

Examples¶

>>> from bask 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)
>>>
>>> # log-uniform: understand as search over p = exp(x) by varying x
>>> opt = BayesSearchCV(
...     SVC(),
...     {
...         'C': Real(1e-6, 1e+6, prior='log-uniform'),
...         'gamma': Real(1e-6, 1e+1, prior='log-uniform'),
...         '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...
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`` and
    ``std_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 cross-validation splits (folds/iterations).
Notes
-----
The parameters selected are those that maximize the score of the held-out
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 not `n_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 set `pre_dispatch`. Then, the memory is copied only
`pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
n_jobs`.
See Also
--------
:class:`skopt.BayesSearchCV`:
    This class wraps the original BayesSearchCV in skopt.
:class:`GridSearchCV`:
    Does exhaustive search over a grid of parameters.

Attributes:

classes_: Class labels.
n_features_in_: Number of features seen during fit.
optimizer_results_
total_iterations: Count total iterations that will be taken to explore all subspaces with fit method.

Methods

`decision_function`(X)	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_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(Xt)	Call inverse_transform on the estimator with the best found params.
`predict`(X)	Call predict on the estimator with the best found parameters.
`predict_log_proba`(X)	Call predict_log_proba on the estimator with the best found parameters.
`predict_proba`(X)	Call predict_proba on the estimator with the best found parameters.
`score`(X[, y])	Return the score on the given data, if the estimator has been refit.
`score_samples`(X)	Call score_samples on the estimator with the best found parameters.
`set_fit_request`(*[, callback, groups])	Request metadata passed to the `fit` method.
`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, return_policy='best_setting', 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]¶

property classes_¶

Class labels.

Only available when refit=True and the estimator is a classifier.

decision_function(X)¶

Call decision_function on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports decision_function.

Parameters:

Xindexable, length n_samples: Must fulfill the input assumptions of the underlying estimator.

Returns:

y_scorendarray of shape (n_samples,) or (n_samples, n_classes) or (n_samples, n_classes * (n_classes-1) / 2): Result of the decision function for X based on the estimator with the best found parameters.

fit(X, y=None, *, groups=None, callback=None, **fit_params)[source]¶

Run fit on the estimator with randomly drawn parameters.

Parameters:

Xarray-like or sparse matrix, shape = [n_samples, n_features]: The training input samples.
yarray-like, shape = [n_samples] or [n_samples, n_output]: Target relative to X for classification or regression (class labels should be integers or strings).
groupsarray-like, 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_metadata_routing()¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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 and refit=True.

Parameters:

Xtindexable, length n_samples: Must fulfill the input assumptions of the underlying estimator.

Returns:

X{ndarray, sparse matrix} of shape (n_samples, n_features): Result of the inverse_transform function for Xt based on the estimator with the best found parameters.

property n_features_in_¶

Number of features seen during fit.

Only available when refit=True.

predict(X)¶

Call predict on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict.

Parameters:

Xindexable, length n_samples: Must fulfill the input assumptions of the underlying estimator.

Returns:

y_predndarray of shape (n_samples,): The predicted labels or values for X based on the estimator with the best found parameters.

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 supports predict_log_proba.

Parameters:

Xindexable, length n_samples: Must fulfill the input assumptions of the underlying estimator.

Returns:

y_predndarray of shape (n_samples,) or (n_samples, n_classes): Predicted class log-probabilities for X based on the estimator with the best found parameters. The order of the classes corresponds to that in the fitted attribute classes_.

predict_proba(X)¶

Call predict_proba on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports predict_proba.

Parameters:

Xindexable, length n_samples: Must fulfill the input assumptions of the underlying estimator.

Returns:

y_predndarray of shape (n_samples,) or (n_samples, n_classes): Predicted class probabilities for X based on the estimator with the best found parameters. The order of the classes corresponds to that in the fitted attribute classes_.

score(X, y=None)¶

Return the score on the given data, if the estimator has been refit.

This uses the score defined by scoring where provided, and the best_estimator_.score method otherwise.

Parameters:

Xarray-like of shape (n_samples, n_features): Input data, where n_samples is the number of samples and n_features is the number of features.
yarray-like 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: The score defined by scoring if provided, and the best_estimator_.score method otherwise.

score_samples(X)¶

Call score_samples on the estimator with the best found parameters.

Only available if refit=True and the underlying estimator supports score_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,): The best_estimator_.score_samples method.

set_fit_request(*, callback: bool | None | str = '$UNCHANGED$', groups: bool | None | str = '$UNCHANGED$') → BayesSearchCV¶

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

Parameters:

callbackstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for callback parameter in fit.
groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for groups parameter in fit.

Returns:

selfobject: The updated object.

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.

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

transform(X)¶

Call transform on the estimator with the best found parameters.

Only available if the underlying estimator supports transform and refit=True.

Parameters:

Xindexable, length n_samples: Must fulfill the input assumptions of the underlying estimator.

Returns:

Xt{ndarray, sparse matrix} of shape (n_samples, n_features): X transformed in the new space based on the estimator with the best found parameters.

`bask`.BayesSearchCV¶

Examples¶

Bayes-skopt

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bask.BayesSearchCV¶

Examples¶

`bask`.BayesSearchCV¶