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# `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)

  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:
    estimator estimator 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_distributions dict or list of dicts

      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.

    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 on `min_resources`, `max_resources` and `factor`. In this case, `min_resources` cannot be ‘exhaust’.

    factor int 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_resources int, 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` when `resource='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` 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`.

    aggressive_elimination bool, 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. See Aggressive elimination of candidates for more details.

    cv int, 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 the `cv`’s `random_state` parameter to an integer.

    scoring str, 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.

    refit bool, 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 this `HalvingRandomSearchCV` 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_score bool, 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_state int, 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_jobs int 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 Glossary for more details.

    verbose int

      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 than `max_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_` 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 with `min_resources_` resources and without exceeding `max_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 with `min_resources_` resources. This will be smaller than `n_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 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,)

      Class labels.

    `n_features_in_`int

      Number of features seen during fit.

    feature_names_in_ndarray of shape (`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.

      New in version 1.0.

  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.

  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}

  Methods

  `decision_function`(X) | Call decision_function on the estimator with the best found parameters.
  `fit`(X[, y, groups]) | Run fit with all sets of 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`(*[, 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.

  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:
      X indexable, length n_samples

        Must fulfill the input assumptions of the underlying estimator.

    Returns:
      y_score ndarray 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, **fit_params)

    Run fit with all sets of parameters.

    Parameters:
      X array-like, shape (n_samples, n_features)

        Training vector, where `n_samples` is the number of samples and `n_features` is the number of features.

      y array-like, shape (n_samples,) or (n_samples, n_output), optional

        Target relative to X for classification or regression; None for unsupervised learning.

      groups array-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_params dict of string -> object

        Parameters passed to the `fit` method of the estimator.

    Returns:
      self object

        Instance of fitted estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        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:
      Xt indexable, 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:
      X indexable, length n_samples

        Must fulfill the input assumptions of the underlying estimator.

    Returns:
      y_pred ndarray 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:
      X indexable, length n_samples

        Must fulfill the input assumptions of the underlying estimator.

    Returns:
      y_pred ndarray 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:
      X indexable, length n_samples

        Must fulfill the input assumptions of the underlying estimator.

    Returns:
      y_pred ndarray 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:
      X array-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.

      y array-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:
      score float

        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:
      X iterable

        Data to predict on. Must fulfill input requirements of the underlying estimator.

    Returns:
      y_score ndarray of shape (n_samples,)

        The `best_estimator_.score_samples` method.

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

    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`. Otherwise it has no effect.

    Parameters:
      groups str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

        Metadata routing for `groups` parameter in `fit`.

    Returns:
      self object

        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:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

  transform(X)

    Call transform on the estimator with the best found parameters.

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

    Parameters:
      X indexable, 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.

## Examples using `sklearn.model_selection.HalvingRandomSearchCV`

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Prediction Intervals for Gradient Boosting Regression

Prediction Intervals for Gradient Boosting Regression

Successive Halving Iterations

Successive Halving Iterations