sklearn.model_selection
.HalvingRandomSearchCV¶
- class sklearn.model_selection.HalvingRandomSearchCV(estimator, param_distributions, *, n_candidates='exhaust', factor=3, resource='n_samples', max_resources='auto', min_resources='smallest', aggressive_elimination=False, cv=5, scoring=None, refit=True, error_score=nan, return_train_score=True, random_state=None, n_jobs=None, verbose=0)[source]¶
Randomized search on hyper parameters.
The search strategy starts evaluating all the candidates with a small amount of resources and iteratively selects the best candidates, using more and more resources.
The candidates are sampled at random from the parameter space and the number of sampled candidates is determined by
n_candidates
.Read more in the User guide.
Note
This estimator is still experimental for now: the predictions and the API might change without any deprecation cycle. To use it, you need to explicitly import
enable_halving_search_cv
:>>> # explicitly require this experimental feature >>> from sklearn.experimental import enable_halving_search_cv # noqa >>> # now you can import normally from model_selection >>> from sklearn.model_selection import HalvingRandomSearchCV
- Parameters
- estimatorestimator object.
This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a
score
function, orscoring
must be passed.- param_distributionsdict
Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a
rvs
method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly.- n_candidatesint, default=’exhaust’
The number of candidate parameters to sample, at the first iteration. Using ‘exhaust’ will sample enough candidates so that the last iteration uses as many resources as possible, based on
min_resources
,max_resources
andfactor
. In this case,min_resources
cannot be ‘exhaust’.- factorint or float, default=3
The ‘halving’ parameter, which determines the proportion of candidates that are selected for each subsequent iteration. For example,
factor=3
means that only one third of the candidates are selected.- resource
'n_samples'
or str, default=’n_samples’ Defines the resource that increases with each iteration. By default, the resource is the number of samples. It can also be set to any parameter of the base estimator that accepts positive integer values, e.g. ‘n_iterations’ or ‘n_estimators’ for a gradient boosting estimator. In this case
max_resources
cannot be ‘auto’ and must be set explicitly.- max_resourcesint, default=’auto’
The maximum number of resources that any candidate is allowed to use for a given iteration. By default, this is set
n_samples
whenresource='n_samples'
(default), else an error is raised.- min_resources{‘exhaust’, ‘smallest’} or int, default=’smallest’
The minimum amount of resource that any candidate is allowed to use for a given iteration. Equivalently, this defines the amount of resources
r0
that are allocated for each candidate at the first iteration.‘smallest’ is a heuristic that sets
r0
to a small value:n_splits * 2
whenresource='n_samples'
for a regression problemn_classes * n_splits * 2
whenresource='n_samples'
for a classification problem1
whenresource != 'n_samples'
‘exhaust’ will set
r0
such that the last iteration uses as much resources as possible. Namely, the last iteration will use the highest value smaller thanmax_resources
that is a multiple of bothmin_resources
andfactor
. In general, using ‘exhaust’ leads to a more accurate estimator, but is slightly more time consuming. ‘exhaust’ isn’t available whenn_candidates='exhaust'
.
Note that the amount of resources used at each iteration is always a multiple of
min_resources
.- aggressive_eliminationbool, default=False
This is only relevant in cases where there isn’t enough resources to reduce the remaining candidates to at most
factor
after the last iteration. IfTrue
, then the search process will ‘replay’ the first iteration for as long as needed until the number of candidates is small enough. This isFalse
by default, which means that the last iteration may evaluate more thanfactor
candidates. See Aggressive elimination of candidates for more details.- cvint, cross-validation generator or an iterable, default=5
Determines the cross-validation splitting strategy. Possible inputs for cv are:
integer, to specify the number of folds in a
(Stratified)KFold
,An iterable yielding (train, test) splits as arrays of indices.
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. These splitters are instantiated withshuffle=False
so the splits will be the same across calls.Refer User Guide for the various cross-validation strategies that can be used here.
Note
Due to implementation details, the folds produced by
cv
must be the same across multiple calls tocv.split()
. For built-inscikit-learn
iterators, this can be achieved by deactivating shuffling (shuffle=False
), or by setting thecv
’srandom_state
parameter to an integer.- scoringstring, callable, or None, default=None
A single string (see The scoring parameter: defining model evaluation rules) or a callable (see Defining your scoring strategy from metric functions) to evaluate the predictions on the test set. If None, the estimator’s score method is used.
- refitbool, default=True
If True, refit an estimator using the best found parameters on the whole dataset.
The refitted estimator is made available at the
best_estimator_
attribute and permits usingpredict
directly on thisHalvingRandomSearchCV
instance.- error_score‘raise’ 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. Default is
np.nan
- return_train_scorebool, 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 trade-off. 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.- random_stateint, RandomState instance or None, default=None
Pseudo random number generator state used for subsampling the dataset when
resources != 'n_samples'
. Also used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary.- n_jobsint or None, default=None
Number of jobs to run in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.- verboseint
Controls the verbosity: the higher, the more messages.
- Attributes
- n_resources_list of int
The amount of resources used at each iteration.
- n_candidates_list of int
The number of candidate parameters that were evaluated at each iteration.
- n_remaining_candidates_int
The number of candidate parameters that are left after the last iteration. It corresponds to
ceil(n_candidates[-1] / factor)
- max_resources_int
The maximum number of resources that any candidate is allowed to use for a given iteration. Note that since the number of resources used at each iteration must be a multiple of
min_resources_
, the actual number of resources used at the last iteration may be smaller thanmax_resources_
.- min_resources_int
The amount of resources that are allocated for each candidate at the first iteration.
- n_iterations_int
The actual number of iterations that were run. This is equal to
n_required_iterations_
ifaggressive_elimination
isTrue
. Else, this is equal tomin(n_possible_iterations_, n_required_iterations_)
.- n_possible_iterations_int
The number of iterations that are possible starting with
min_resources_
resources and without exceedingmax_resources_
.- n_required_iterations_int
The number of iterations that are required to end up with less than
factor
candidates at the last iteration, starting withmin_resources_
resources. This will be smaller thann_possible_iterations_
when there isn’t enough resources.- 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
. It contains many informations for analysing the results of a search. Please refer to the User guide for details.- best_estimator_estimator or dict
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
.- best_score_float
Mean cross-validated score of the best_estimator.
- 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 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 cross-validation 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.- multimetric_bool
Whether or not the scorers compute several metrics.
- classes_ndarray of shape (n_classes,)
The classes labels. This is present only if
refit
is specified and the underlying estimator is a classifier.- n_features_in_int
Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.
New in version 0.24.
See also
HalvingGridSearchCV
Search over a grid of parameters using successive halving.
Notes
The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter.
Examples
>>> from sklearn.datasets import load_iris >>> from sklearn.ensemble import RandomForestClassifier >>> from sklearn.experimental import enable_halving_search_cv # noqa >>> from sklearn.model_selection import HalvingRandomSearchCV >>> from scipy.stats import randint ... >>> X, y = load_iris(return_X_y=True) >>> clf = RandomForestClassifier(random_state=0) >>> np.random.seed(0) ... >>> param_distributions = {"max_depth": [3, None], ... "min_samples_split": randint(2, 11)} >>> search = HalvingRandomSearchCV(clf, param_distributions, ... resource='n_estimators', ... max_resources=10, ... random_state=0).fit(X, y) >>> search.best_params_ {'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
Methods
Call decision_function on the estimator with the best found parameters.
fit
(X[, y, groups])Run fit with all sets of 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.
Call score_samples on the estimator with the best found parameters.
set_params
(**params)Set the parameters of this estimator.
transform
(X)Call transform on the estimator with the best found parameters.
- 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, **fit_params)[source]¶
Run fit with all sets of parameters.
- Parameters
- Xarray-like, shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features.
- yarray-like, shape (n_samples,) or (n_samples, n_output), optional
Target relative to X for classification or regression; None for unsupervised learning.
- groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g.,
GroupKFold
).- **fit_paramsdict of string -> object
Parameters passed to the
fit
method of the estimator
- 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
- paramsdict
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
- 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
- score_samples(X)[source]¶
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)[source]¶
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.