HalvingRandomSearchCV (original) (raw)
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, or scoring must be passed.
param_distributionsdict or list of dicts
Dictionary with parameters names (str) as keys and distributions or lists of parameters to try. Distributions must provide a rvsmethod for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.
n_candidates“exhaust” or int, 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 onmin_resources, max_resources and factor. 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
r0to a small value:n_splits * 2whenresource='n_samples'for a regression problemn_classes * n_splits * 2whenresource='n_samples'for a classification problem1whenresource != 'n_samples'
- ‘exhaust’ will set
r0such that the last iteration uses as much resources as possible. Namely, the last iteration will use the highest value smaller thanmax_resourcesthat is a multiple of bothmin_resourcesandfactor. 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. If True, then the search process will ‘replay’ the first iteration for as long as needed until the number of candidates is small enough. This is False by default, which means that the last iteration may evaluate more than factor candidates. SeeAggressive 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, - CV splitter,
- 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 with shuffle=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 to cv.split(). For built-in scikit-learn iterators, this can be achieved by deactivating shuffling (shuffle=False), or by setting thecv’s random_state parameter to an integer.
scoringstr, callable, or None, default=None
A single string (see The scoring parameter: defining model evaluation rules) or a callable (see Callable scorers) 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 using predict 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, the cv_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 a joblib.parallel_backend context.-1 means using all processors. See Glossaryfor 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 ton_required_iterations_ if aggressive_elimination is True. Else, this is equal to min(n_possible_iterations_, n_required_iterations_).
**n_possible_iterations_**int
The number of iterations that are possible starting withmin_resources_ resources and without exceedingmax_resources_.
**n_required_iterations_**int
The number of iterations that are required to end up with less thanfactor 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 lots of information for analysing the results of a search. Please refer to the User guidefor 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,)
Class labels.
Number of features seen during fit.
**feature_names_in_**ndarray of shape (n_features_in_,)
Names of features seen during fit. Only defined ifbest_estimator_ is defined (see the documentation for the refitparameter for more details) and that best_estimator_ exposesfeature_names_in_ when fit.
Added in version 1.0.
Notes
The parameters selected are those that maximize the score of the held-out data, according to the scoring parameter.
All parameter combinations scored with a NaN will share the lowest rank.
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 import numpy as np ... 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}
property classes_#
Class labels.
Only available when refit=True and the estimator is a classifier.
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.
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, **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 andn_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.
**paramsdict of string -> object
Parameters passed to the fit method of the estimator.
Returns:
selfobject
Instance of fitted estimator.
get_metadata_routing()[source]#
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Added in version 1.4.
Returns:
routingMetadataRouter
A MetadataRouter encapsulating routing information.
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(X=None, Xt=None)[source]#
Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implementsinverse_transform and refit=True.
Parameters:
Xindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Xtindexable, length n_samples
Must fulfill the input assumptions of the underlying estimator.
Deprecated since version 1.5: Xt was deprecated in 1.5 and will be removed in 1.7. Use X instead.
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.
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.
Returns:
y_predndarray of shape (n_samples,)
The predicted labels or values for X based on the estimator with the best found parameters.
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.
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_.
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.
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, **params)[source]#
Return 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 andn_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.
**paramsdict
Parameters to be passed to the underlying scorer(s).
Added in version 1.4: Only available if enable_metadata_routing=True. SeeMetadata Routing User Guide for more details.
Returns:
scorefloat
The score defined by scoring if provided, and thebest_estimator_.score method otherwise.
Call score_samples on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supportsscore_samples.
Added 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 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.
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.
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.