class_docstr: Randomized search on hyper parameters.
  
  RandomizedSearchCV implements a "fit" and a "score" method.
  It also implements "score_samples", "predict", "predict_proba",
  "decision_function", "transform" and "inverse_transform" if they are
  implemented in the estimator used.
  
  The parameters of the estimator used to apply these methods are optimized
  by cross-validated search over parameter settings.
  
  In contrast to GridSearchCV, not all parameter values are tried out, but
  rather a fixed number of parameter settings is sampled from the specified
  distributions. The number of parameter settings that are tried is
  given by n_iter.
  
  If all parameters are presented as a list,
  sampling without replacement is performed. If at least one parameter
  is given as a distribution, sampling with replacement is used.
  It is highly recommended to use continuous distributions for continuous
  parameters.
  
  Read more in the :ref:`User Guide <randomized_parameter_search>`.
  
  .. versionadded:: 0.14
  
  Parameters
  ----------
  estimator : estimator object
      An object of that type is instantiated for each grid point.
      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_iter : int, default=10
      Number of parameter settings that are sampled. n_iter trades
      off runtime vs quality of the solution.
  
  scoring : str, callable, list, tuple or dict, default=None
      Strategy to evaluate the performance of the cross-validated model on
      the test set.
  
      If `scoring` represents a single score, one can use:
  
      - a single string (see :ref:`scoring_parameter`);
      - a callable (see :ref:`scoring`) that returns a single value.
  
      If `scoring` represents multiple scores, one can use:
  
      - a list or tuple of unique strings;
      - a callable returning a dictionary where the keys are the metric
        names and the values are the metric scores;
      - a dictionary with metric names as keys and callables a values.
  
      See :ref:`multimetric_grid_search` for an example.
  
      If None, the estimator's score method is used.
  
  n_jobs : int, 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.
  
      .. versionchanged:: v0.20
         `n_jobs` default changed from 1 to None
  
  refit : bool, str, or callable, default=True
      Refit an estimator using the best found parameters on the whole
      dataset.
  
      For multiple metric evaluation, this needs to be a `str` denoting the
      scorer that would be used to find the best parameters for refitting
      the estimator at the end.
  
      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 the ``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
      ``RandomizedSearchCV`` instance.
  
      Also for multiple metric evaluation, the attributes ``best_index_``,
      ``best_score_`` and ``best_params_`` will only be available if
      ``refit`` is set and all of them will be determined w.r.t this specific
      scorer.
  
      See ``scoring`` parameter to know more about multiple metric
      evaluation.
  
      .. versionchanged:: 0.20
          Support for callable added.
  
  cv : int, cross-validation generator or an iterable, default=None
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
  
      - None, to use the default 5-fold cross validation,
      - 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.
  
      .. versionchanged:: 0.22
          ``cv`` default value if None changed from 3-fold to 5-fold.
  
  verbose : int
      Controls the verbosity: the higher, the more messages.
  
      - >1 : the computation time for each fold and parameter candidate is
        displayed;
      - >2 : the score is also displayed;
      - >3 : the fold and candidate parameter indexes are also displayed
        together with the starting time of the computation.
  
  pre_dispatch : int, or str, default='2*n_jobs'
      Controls the number of jobs that get dispatched during parallel
      execution. Reducing this number can be useful to avoid an
      explosion of memory consumption when more jobs get dispatched
      than CPUs can process. This parameter can be:
  
          - None, in which case all the jobs are immediately
            created and spawned. Use this for lightweight and
            fast-running jobs, to avoid delays due to on-demand
            spawning of the jobs
  
          - An int, giving the exact number of total jobs that are
            spawned
  
          - A str, giving an expression as a function of n_jobs,
            as in '2*n_jobs'
  
  random_state : int, RandomState instance or None, default=None
      Pseudo random number generator state 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 :term:`Glossary <random_state>`.
  
  error_score : 'raise' or numeric, default=np.nan
      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.
  
  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.
  
      .. versionadded:: 0.19
  
      .. versionchanged:: 0.21
          Default value was changed from ``True`` to ``False``
  
  Attributes
  ----------
  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``.
  
      For instance the below given table
  
      +--------------+-------------+-------------------+---+---------------+
      | param_kernel | param_gamma | split0_test_score |...|rank_test_score|
      +==============+=============+===================+===+===============+
      |    'rbf'     |     0.1     |       0.80        |...|       1       |
      +--------------+-------------+-------------------+---+---------------+
      |    'rbf'     |     0.2     |       0.84        |...|       3       |
      +--------------+-------------+-------------------+---+---------------+
      |    'rbf'     |     0.3     |       0.70        |...|       2       |
      +--------------+-------------+-------------------+---+---------------+
  
      will be represented by a ``cv_results_`` dict of::
  
          {
          'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'],
                                        mask = False),
          'param_gamma'  : masked_array(data = [0.1 0.2 0.3], mask = False),
          'split0_test_score'  : [0.80, 0.84, 0.70],
          'split1_test_score'  : [0.82, 0.50, 0.70],
          'mean_test_score'    : [0.81, 0.67, 0.70],
          'std_test_score'     : [0.01, 0.24, 0.00],
          'rank_test_score'    : [1, 3, 2],
          'split0_train_score' : [0.80, 0.92, 0.70],
          'split1_train_score' : [0.82, 0.55, 0.70],
          'mean_train_score'   : [0.81, 0.74, 0.70],
          'std_train_score'    : [0.01, 0.19, 0.00],
          'mean_fit_time'      : [0.73, 0.63, 0.43],
          'std_fit_time'       : [0.01, 0.02, 0.01],
          'mean_score_time'    : [0.01, 0.06, 0.04],
          'std_score_time'     : [0.00, 0.00, 0.00],
          'params'             : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...],
          }
  
      NOTE
  
      The key ``'params'`` is used to store a list of parameter
      settings dicts for all the parameter candidates.
  
      The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and
      ``std_score_time`` are all in seconds.
  
      For multi-metric evaluation, the scores for all the scorers are
      available in the ``cv_results_`` dict at the keys ending with that
      scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
      above. ('split0_test_precision', 'mean_train_precision' etc.)
  
  best_estimator_ : estimator
      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``.
  
      For multi-metric evaluation, this attribute is present only if
      ``refit`` is specified.
  
      See ``refit`` parameter for more information on allowed values.
  
  best_score_ : float
      Mean cross-validated score of the best_estimator.
  
      For multi-metric evaluation, this is not available if ``refit`` is
      ``False``. See ``refit`` parameter for more information.
  
      This attribute is not available if ``refit`` is a function.
  
  best_params_ : dict
      Parameter setting that gave the best results on the hold out data.
  
      For multi-metric evaluation, this is not available if ``refit`` is
      ``False``. See ``refit`` parameter for more information.
  
  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_``).
  
      For multi-metric evaluation, this is not available if ``refit`` is
      ``False``. See ``refit`` parameter for more information.
  
  scorer_ : function or a dict
      Scorer function used on the held out data to choose the best
      parameters for the model.
  
      For multi-metric evaluation, this attribute holds the validated
      ``scoring`` dict which maps the scorer key to the scorer callable.
  
  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.
  
      .. versionadded:: 0.20
  
  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
  --------
  GridSearchCV : Does exhaustive search over a grid of parameters.
  ParameterSampler : A generator over parameter settings, constructed from
      param_distributions.
  
  Notes
  -----
  The parameters selected are those that maximize the score of the held-out
  data, according to the scoring parameter.
  
  If `n_jobs` was set to a value higher than one, the data is copied for each
  parameter setting(and not `n_jobs` times). This is done for efficiency
  reasons if individual jobs take very little time, but may raise errors if
  the dataset is large and not enough memory is available.  A workaround in
  this case is to set `pre_dispatch`. Then, the memory is copied only
  `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
  n_jobs`.
  
  Examples
  --------
  >>> from sklearn.datasets import load_iris
  >>> from sklearn.linear_model import LogisticRegression
  >>> from sklearn.model_selection import RandomizedSearchCV
  >>> from scipy.stats import uniform
  >>> iris = load_iris()
  >>> logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200,
  ...                               random_state=0)
  >>> distributions = dict(C=uniform(loc=0, scale=4),
  ...                      penalty=['l2', 'l1'])
  >>> clf = RandomizedSearchCV(logistic, distributions, random_state=0)
  >>> search = clf.fit(iris.data, iris.target)
  >>> search.best_params_
  {'C': 2..., 'penalty': 'l1'}
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code: def __init__(
      self,
      estimator,
      param_distributions,
      *,
      n_iter=10,
      scoring=None,
      n_jobs=None,
      refit=True,
      cv=None,
      verbose=0,
      pre_dispatch="2*n_jobs",
      random_state=None,
      error_score=np.nan,
      return_train_score=False,
  ):
      self.param_distributions = param_distributions
      self.n_iter = n_iter
      self.random_state = random_state
      super().__init__(
          estimator=estimator,
          scoring=scoring,
          n_jobs=n_jobs,
          refit=refit,
          cv=cv,
          verbose=verbose,
          pre_dispatch=pre_dispatch,
          error_score=error_score,
          return_train_score=return_train_score,
      )
  
imports: import numbers
  import operator
  import time
  import warnings
  from abc import ABCMeta, abstractmethod
  from collections import defaultdict
  from collections.abc import Iterable, Mapping, Sequence
  from functools import partial, reduce
  from itertools import product
  import numpy
  from numpy.ma import MaskedArray
  from scipy.stats import rankdata
  from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier
  from ..exceptions import NotFittedError
  from ..metrics import check_scoring
  from ..metrics._scorer import _check_multimetric_scoring, _MultimetricScorer, get_scorer_names
  from ..utils import Bunch, check_random_state
  from ..utils._param_validation import HasMethods, Interval, StrOptions
  from ..utils._tags import _safe_tags
  from ..utils.metadata_routing import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
  from ..utils.metaestimators import available_if
  from ..utils.parallel import Parallel, delayed
  from ..utils.random import sample_without_replacement
  from ..utils.validation import _check_method_params, check_is_fitted, indexable
  from ._split import check_cv
  from ._validation import _aggregate_score_dicts, _fit_and_score, _insert_error_scores, _normalize_score_results, _warn_or_raise_about_fit_failures
label_desc: Use image node_id 1 to create a new RandomizedSearchCV object from inherited base classes: BaseSearchCV with example: obj = RandomizedSearchCV(estimator, param_distributions)