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
This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a score function, or scoring must be passed.
Dictionary with parameters names (str) 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. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.
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 and factor. In this case, min_resources cannot be ‘exhaust’.
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.
'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.
The maximum number of resources that any candidate is allowed to use for a given iteration. By default, this is set n_samples when resource='n_samples' (default), else an error is raised.
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 when resource='n_samples' for a regression problem
n_classes * n_splits * 2 when resource='n_samples' for a classification problem
1 when resource != '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 than max_resources that is a multiple of both min_resources and factor. In general, using ‘exhaust’ leads to a more accurate estimator, but is slightly more time consuming. ‘exhaust’ isn’t available when n_candidates='exhaust'.
Note that the amount of resources used at each iteration is always a multiple of min_resources.
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. See Aggressive elimination of candidates for more details.
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 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 the cv’s random_state parameter to an integer.
Scoring method to use to evaluate the predictions on the test set.
str: see String name scorers for options.
callable: a scorer callable object (e.g., function) with signature scorer(estimator, X, y). See Callable scorers for details.
None: the estimator’s default evaluation criterion is used.
Refit an estimator using the best found parameters on the whole dataset.
Where there are considerations other than maximum score in choosing a best estimator, refit can be set to a function which returns the selected best_index_ given cv_results_. In that case, the best_estimator_ and best_params_ will be set according to the returned best_index_ while the best_score_ attribute will not be available.
The refitted estimator is made available at the best_estimator_ attribute and permits using predict directly on this HalvingRandomSearchCV instance.
See this example for an example of how to use refit=callable to balance model complexity and cross-validated score.
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.
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.
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.
Number of jobs to run in parallel. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
Controls the verbosity: the higher, the more messages.
The amount of resources used at each iteration.
The number of candidate parameters that were evaluated at each iteration.
The number of candidate parameters that are left after the last iteration. It corresponds to ceil(n_candidates[-1] / factor)
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 than max_resources_.
The amount of resources that are allocated for each candidate at the first iteration.
The actual number of iterations that were run. This is equal to n_required_iterations_ if aggressive_elimination is True. Else, this is equal to min(n_possible_iterations_, n_required_iterations_).
The number of iterations that are possible starting with min_resources_ resources and without exceeding max_resources_.
The number of iterations that are required to end up with less than factor candidates at the last iteration, starting with min_resources_ resources. This will be smaller than n_possible_iterations_ when there isn’t enough resources.
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 guide for details.
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.
Mean cross-validated score of the best_estimator.
Parameter setting that gave the best results on the hold out data.
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 used on the held out data to choose the best parameters for the model.
The number of cross-validation splits (folds/iterations).
Seconds used for refitting the best model on the whole dataset.
This is present only if refit is not False.
Whether or not the scorers compute several metrics.
classes_ndarray of shape (n_classes,)
Class labels.
n_features_in_int
Number of features seen during fit.
n_features_in_,)
Names of features seen during fit. Only defined if best_estimator_ is defined (see the documentation for the refit parameter for more details) and that best_estimator_ exposes feature_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}
Call decision_function on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports decision_function.
Must fulfill the input assumptions of the underlying estimator.
Result of the decision function for X based on the estimator with the best found parameters.
Run fit with all sets of parameters.
Training vector, where n_samples is the number of samples and n_features is the number of features.
Target relative to X for classification or regression; None for unsupervised learning.
Parameters passed to the fit method of the estimator.
Instance of fitted estimator.
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Added in version 1.4.
A MetadataRouter encapsulating routing information.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements inverse_transform and refit=True.
Must fulfill the input assumptions of the underlying estimator.
Result of the inverse_transform function for X based on the estimator with the best found parameters.
Call predict on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports predict.
Must fulfill the input assumptions of the underlying estimator.
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 supports predict_log_proba.
Must fulfill the input assumptions of the underlying estimator.
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 supports predict_proba.
Must fulfill the input assumptions of the underlying estimator.
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_.
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.
Input data, where n_samples is the number of samples and n_features is the number of features.
Target relative to X for classification or regression; None for unsupervised learning.
Parameters to be passed to the underlying scorer(s).
Added in version 1.4: Only available if enable_metadata_routing=True. See Metadata Routing User Guide for more details.
The score defined by scoring if provided, and the best_estimator_.score method otherwise.
Call score_samples on the estimator with the best found parameters.
Only available if refit=True and the underlying estimator supports score_samples.
Added in version 0.24.
Data to predict on. Must fulfill input requirements of the underlying estimator.
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.
Estimator parameters.
Estimator instance.
Call transform on the estimator with the best found parameters.
Only available if the underlying estimator supports transform and refit=True.
Must fulfill the input assumptions of the underlying estimator.
X transformed in the new space based on the estimator with the best found parameters.
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