class_docstr: Search over specified parameter values with successive halving.
  
  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.
  
  Read more in the :ref:`User guide <successive_halving_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 HalvingGridSearchCV
  
  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_grid : dict or list of dictionaries
      Dictionary with parameters names (string) as keys and lists of
      parameter settings to try as values, or a list of such
      dictionaries, in which case the grids spanned by each dictionary
      in the list are explored. This enables searching over any sequence
      of parameter settings.
  
  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 amount of resource that any candidate is allowed to use
      for a given iteration. By default, this is set to ``n_samples`` when
      ``resource='n_samples'`` (default), else an error is raised.
  
  min_resources : {'exhaust', 'smallest'} or int, default='exhaust'
      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.
  
      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
      :ref:`aggressive_elimination` for more details.
  
  cv : int, cross-validation generator or 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`,
      - :term:`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, :class:`StratifiedKFold` is used. In all
      other cases, :class:`KFold` is used. These splitters are instantiated
      with `shuffle=False` so the splits will be the same across calls.
  
      Refer :ref:`User Guide <cross_validation>` 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 :ref:`scoring_parameter`) or a callable
      (see :ref:`scoring`) 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
      ``HalvingGridSearchCV`` 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'`. Ignored otherwise.
      Pass an int for reproducible output across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  n_jobs : int or None, default=None
      Number of jobs to run in parallel.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      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 :ref:`User guide<successive_halving_cv_results>`
      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 :term:`fit`. Only defined if
      `best_estimator_` is defined (see the documentation for the `refit`
      parameter for more details) and that `best_estimator_` exposes
      `n_features_in_` when fit.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`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.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  :class:`HalvingRandomSearchCV`:
      Random search over a set 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 HalvingGridSearchCV
  ...
  >>> X, y = load_iris(return_X_y=True)
  >>> clf = RandomForestClassifier(random_state=0)
  ...
  >>> param_grid = {"max_depth": [3, None],
  ...               "min_samples_split": [5, 10]}
  >>> search = HalvingGridSearchCV(clf, param_grid, resource='n_estimators',
  ...                              max_resources=10,
  ...                              random_state=0).fit(X, y)
  >>> search.best_params_  # doctest: +SKIP
  {'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
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code: def __init__(
      self,
      estimator,
      param_grid,
      *,
      factor=3,
      resource="n_samples",
      max_resources="auto",
      min_resources="exhaust",
      aggressive_elimination=False,
      cv=5,
      scoring=None,
      refit=True,
      error_score=np.nan,
      return_train_score=True,
      random_state=None,
      n_jobs=None,
      verbose=0,
  ):
      super().__init__(
          estimator,
          scoring=scoring,
          n_jobs=n_jobs,
          refit=refit,
          verbose=verbose,
          cv=cv,
          random_state=random_state,
          error_score=error_score,
          return_train_score=return_train_score,
          max_resources=max_resources,
          resource=resource,
          factor=factor,
          min_resources=min_resources,
          aggressive_elimination=aggressive_elimination,
      )
      self.param_grid = param_grid
  
imports: from abc import abstractmethod
  from copy import deepcopy
  from math import ceil, floor, log
  from numbers import Integral, Real
  import numpy
  from ..base import _fit_context, is_classifier
  from ..metrics._scorer import get_scorer_names
  from ..utils import resample
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.multiclass import check_classification_targets
  from ..utils.validation import _num_samples
  from .None import ParameterGrid, ParameterSampler
  from ._search import BaseSearchCV
  from ._split import _yields_constant_splits, check_cv
label_desc: Use image node_id 1 to create a new HalvingGridSearchCV object from inherited base classes: BaseSuccessiveHalving with example: obj = HalvingGridSearchCV(estimator, param_grid)