class_docstr: Base adapter class for tsfresh transformations.
class_docstr_tok: ["Base","adapter","class","for","tsfresh","transformations","."]
docstr: Set default parameters from tsfresh.
docstr_tok: ["Set","default","parameters","from","tsfresh","."]
code: def _get_extraction_params(self):
      """Set default parameters from tsfresh."""
      # make n_jobs compatible with scikit-learn
      n_jobs = self.n_jobs
      self.n_jobs = check_n_jobs(self.n_jobs)
  
      # lazy imports to avoid hard dependency
      from tsfresh.defaults import (
          CHUNKSIZE,
          DISABLE_PROGRESSBAR,
          N_PROCESSES,
          PROFILING,
          PROFILING_FILENAME,
          PROFILING_SORTING,
          SHOW_WARNINGS,
      )
      from tsfresh.feature_extraction.settings import (
          ComprehensiveFCParameters,
          EfficientFCParameters,
          MinimalFCParameters,
          from_columns,
      )
      from tsfresh.utilities.dataframe_functions import impute
  
      # Set defaults from tsfresh
      extraction_params = {
          "n_jobs": N_PROCESSES,
          "chunksize": CHUNKSIZE,
          "show_warnings": SHOW_WARNINGS,
          "disable_progressbar": DISABLE_PROGRESSBAR,
          "impute_function": impute,
          "profiling_sorting": PROFILING_SORTING,
          "profiling_filename": PROFILING_FILENAME,
          "profile": PROFILING,
      }
  
      # Replace defaults with user defined parameters
      for name in extraction_params.keys():
          if hasattr(self, name):
              value = getattr(self, name)
              if value is not None:
                  extraction_params[name] = value
  
          # Fixes key mismatch between tsfresh and sktime
          # tsfresh uses "profile" while sktime uses "profiling"
          # This fix keeps compatibility
          if name == "profile":
              extraction_params[name] = self.profiling
  
      self.n_jobs = n_jobs
  
      # Convert convenience string arguments to tsfresh parameters classes
      fc_param_lookup = {
          "minimal": MinimalFCParameters(),
          "efficient": EfficientFCParameters(),
          "comprehensive": ComprehensiveFCParameters(),
      }
      if isinstance(self.default_fc_parameters, str):
          if self.default_fc_parameters not in fc_param_lookup:
              raise ValueError(
                  f"If `default_fc_parameters` is passed as a "
                  f"string, "
                  f"it must be one of"
                  f" {fc_param_lookup.keys()}, but found: "
                  f"{self.default_fc_parameters}"
              )
          else:
              fc_parameters = fc_param_lookup[
                  self.default_fc_parameters
              ]
      else:
          fc_parameters = self.default_fc_parameters
      extraction_params["default_fc_parameters"] = fc_parameters
  
      # creates mapping from kind names to fc_parameter objects
      if self.kind_to_fc_parameters is not None:
          self.kind_to_fc_parameters_ = from_columns(
              self.kind_to_fc_parameters
          )
      else:
          self.kind_to_fc_parameters_ = self.kind_to_fc_parameters
      extraction_params[
          "kind_to_fc_parameters"
      ] = self.kind_to_fc_parameters_
  
      return extraction_params
  
file_path: sktime/sktime/transformations/panel/tsfresh.py
imports: from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation import check_n_jobs
label_desc: Use image node_id 2 for calling the _TSFreshFeatureExtractor obj's underlying member method code with example usage: obj._get_extraction_params() and returns: extraction_params