class_docstr: Partial auto-correlation transformer.
  
  The partial autocorrelation function measures the conditional correlation
  between a timeseries and its self at different lags. In particular,
  the correlation between a time period and a lag, is calculated conditional
  on all the points between the time period and the lag.
  
  The PartialAutoCorrelationTransformer returns
  these values as a series for each lag up to the `n_lags` specified.
  
  Parameters
  ----------
  n_lags : int, default=None
      Number of lags to return partial autocorrelation for. If None,
      statsmodels acf function uses min(10 * np.log10(nobs), nobs // 2 - 1).
  
  method : str, default="ywadjusted"
      Specifies which method for the calculations to use.
  
      - "yw" or "ywadjusted" : Yule-Walker with sample-size adjustment in
        denominator for acovf. Default.
      - "ywm" or "ywmle" : Yule-Walker without adjustment.
      - "ols" : regression of time series on lags of it and on constant.
      - "ols-inefficient" : regression of time series on lags using a single
        common sample to estimate all pacf coefficients.
      - "ols-adjusted" : regression of time series on lags with a bias
        adjustment.
      - "ld" or "ldadjusted" : Levinson-Durbin recursion with bias
        correction.
      - "ldb" or "ldbiased" : Levinson-Durbin recursion without bias
        correction.
  
  See Also
  --------
  AutoCorrelationTransformer
  
  Notes
  -----
  Provides wrapper around statsmodels
  `pacf <https://www.statsmodels.org/devel/generated/
  statsmodels.tsa.stattools.pacf.html>`_ function.
  
  
  Examples
  --------
  >>> from sktime.transformations.series.acf import PartialAutoCorrelationTransformer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()  # doctest: +SKIP
  >>> transformer = PartialAutoCorrelationTransformer(n_lags=12)  # doctest: +SKIP
  >>> y_hat = transformer.fit_transform(y)  # doctest: +SKIP
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.
  
  
  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`
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.
  
  
      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`
      """
      return [{}, {"n_lags": 1}]
  
file_path: sktime/sktime/transformations/series/acf.py
imports: import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the PartialAutoCorrelationTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list