class_docstr: Exhaustive search over specified parameter values for an estimator.
  
  Adapts sklearn GridSearchCV for sktime time series classifiers
  
  Optimizes hyper-parameters of `estimators` by exhaustive grid search.
  
  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 (`str`) 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.
  
  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.
  
  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.
  
  refit : bool, str, or callable, default=True
      Refit an estimator using the best found parameters on the whole
      dataset. If ``False``, the ``predict`` and ``predict_probab`` will not work.
  
      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 ``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
      ``GridSearchCV`` 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.
  
  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.
  
  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'
  
  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.
  
  tune_by_variable : bool, optional (default=False)
      Whether to tune parameter by each time series variable separately,
      in case of multivariate data passed to the tuning estimator.
      Only applies if time series passed are strictly multivariate.
      If True, clones of the estimator will be fit to each variable separately,
      and are available in fields of the classifiers_ attribute.
      Has the same effect as applying ColumnEnsembleClassifier wrapper to self.
      If False, the same best parameter is selected for all variables.
  
  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 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``.
  
      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 present only if ``refit`` is
      specified.
  
      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 present only if ``refit`` is
      specified.
  
  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 present only if ``refit`` is
      specified.
  
  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.
  
  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.
  
  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.
  
  See Also
  --------
  ParameterGrid : Generates all the combinations of a hyperparameter grid.
  train_test_split : Utility function to split the data into a development
      set usable for fitting a GridSearchCV instance and an evaluation set
      for its final evaluation.
  sklearn.metrics.make_scorer : Make a scorer from a performance metric or
      loss function.
code: def __init__(
      self,
      estimator,
      param_grid,
      scoring=None,
      n_jobs=None,
      refit=True,
      cv=None,
      verbose=0,
      pre_dispatch="2*n_jobs",
      error_score=np.nan,
      return_train_score=False,
      tune_by_variable=False,
  ):
      self.estimator = estimator
      self.param_grid = param_grid
      self.scoring = scoring
      self.n_jobs = n_jobs
      self.refit = refit
      self.cv = cv
      self.verbose = verbose
      self.pre_dispatch = pre_dispatch
      self.error_score = error_score
      self.return_train_score = return_train_score
      self.tune_by_variable = tune_by_variable
  
      super().__init__()
  
      DELEGATED_PARAMS = [
          "estimator",
          "param_grid",
          "scoring",
          "n_jobs",
          "refit",
          "cv",
          "verbose",
          "pre_dispatch",
          "error_score",
          "return_train_score",
      ]
  
      gcsvargs = {k: getattr(self, k) for k in DELEGATED_PARAMS}
  
      self.estimator_ = GridSearchCV(**gcsvargs)
  
      if self.tune_by_variable:
          self.set_tags(**{"capability:multioutput": False})
  
file_path: sktime/sktime/classification/model_selection/_tune.py
imports: import numpy
  from sklearn.model_selection import GridSearchCV
  from sktime.classification._delegate import _DelegatedClassifier
label_desc: Use image node_id 1 to create a new TSCGridSearchCV object from inherited base classes: _DelegatedClassifier with example: obj = TSCGridSearchCV(estimator, param_grid, scoring, n_jobs, refit, cv, verbose, pre_dispatch, error_score, return_train_score, tune_by_variable)