class_docstr: Box-Cox power transform.
  
  Box-Cox transformation is a power transformation that is used to
  make data more normally distributed and stabilize its variance based
  on the hyperparameter lambda. [1]_
  
  This transformer applies the Box-Cox-transform elementwise, where the lambda
  parameter is fitted by the specified method via ``method``.
  
  The Box-Cox-transform is defined as
  :math:`y\mapsto \frac{y^{\lambda}-1}{\lambda}, \lambda
  \ne 0 \text{ and } ln(y), \lambda = 0`,
  for positive :math:`y`.
  
  The :math:`\lambda` parameter is fitted per time series and instance and variable,
  by a method depending on the ``method`` parameter:
  
  * ``"pearsonr"`` - maximization of Pearson correlation between transformed
    and normalized untransformed. Direct interface to ``scipy.stats.boxcox_normmax``
    with ``method="pearsonr"``, with ``bracket=bounds``,  and otherwise defaults.
  * ``"mle"`` - maximization of the Box-Cox log-likelihood.
    Direct interface to ``scipy.stats.boxcox_normmax`` with ``method="mle"``
    with ``bracket=bounds``, and otherwise defaults.
  * ``"guerrero"`` - Guerrero's method with seasonal periodicity, see [2]_.
    this requires the seasonality parameter to be passed as ``sp``.
  * ``"fixed"`` - fixed, pre-specified :math:`\lambda`,
    which is passed as ``lambda_fixed``.
  
  If non-positive `:math:y` are present, they are by default replaced with their
  absolute values in ``fit``.
  In ``transform``, the signed Box-Cox-transform is applied, i.e., the sign is kept
  while the transform is applied to the value.
  
  Parameters
  ----------
  bounds : 2-tuple of finite float
      Initial bracket (lower, upper) for the optimization range
      when fitting the value of lambda. Default = unbounded.
      Ignored if ``method == "fixed"``.
      For half-open bounds pass a large bound value, e.g., (0, 1e12) for positive
      lambda. Infinity and nan as bound values are not supported.
  method : {"pearsonr", "mle", "guerrero", "fixed"}, default="mle"
      The optimization approach used to determine the lambda value used
      in the Box-Cox transformation.
  sp : int, optional, must be provided (only) if method="guerrero"
      Seasonal periodicity of the data in integer form. Only used if
      method="guerrero" is chosen. Must be an integer >= 2.
  lambda_fixed : float, optional, default = 0.0
      must be provided (only) if method="fixed"
      default means that BoxCoxTransformer behaves like logarithm
  enforce_positive : bool, optional, default = True
      If ``True`, in ``fit`` negative entries of ``X``
      are replaced by their absolute values. In ``transform``, the transform
      is applied to the absolute value while the sign is kept.
      If ``False``, any negative values will be passed unchanged to the
      underlying functions (possibly causing error).
  
  Attributes
  ----------
  lambda_ : float
      The Box-Cox lambda parameter that was fitted, based on the supplied
      `method` and data provided in `fit`.
  
  See Also
  --------
  LogTransformer :
      Transformer input data using natural log. Can help normalize data and
      compress variance of the series.
  sktime.transformations.series.exponent.ExponentTransformer :
      Transform input data by raising it to an exponent. Can help compress
      variance of series if a fractional exponent is supplied.
  sktime.transformations.series.exponent.SqrtTransformer :
      Transform input data by taking its square root. Can help compress
      variance of input series.
  
  References
  ----------
  .. [1] Box, G. E. P. & Cox, D. R. (1964) An analysis of transformations,
     Journal of the Royal Statistical Society, Series B, 26, 211-252.
  .. [2] V.M. Guerrero, "Time-series analysis supported by Power
     Transformations ", Journal of Forecasting, vol. 12, pp. 37-48, 1993.
  
  Examples
  --------
  >>> from sktime.transformations.series.boxcox import BoxCoxTransformer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  >>> transformer = BoxCoxTransformer()
  >>> y_hat = transformer.fit_transform(y)
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docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      There are currently no reserved values for transformers.
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
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code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          There are currently no reserved values for transformers.
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      params1 = {"method": "mle"}
      params2 = {"method": "pearsonr"}
      params3 = {"method": "guerrero", "sp": 2}
      params4 = {"method": "fixed", "lambda_fixed": 1}
      params5 = {"method": "fixed", "lambda_fixed": 0}
      params6 = {"method": "fixed", "lambda_fixed": -1}
  
      return [params1, params2, params3, params4, params5, params6]
  
imports: import numpy
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation import is_int
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 5 for calling the BoxCoxTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list