docs: def transform(self, X):
          """
          After the fit step, it is known which features are relevant, Only extract those from the time series handed in
          with the function :func:`~set_timeseries_container`.
  
          If filter_only_tsfresh_features is False, also delete the irrelevant, already present features in the data frame.
  
          :param X: the data sample to add the relevant (and delete the irrelevant) features to.
          :type X: pandas.DataFrame or numpy.array
  
          :return: a data sample with the same information as X, but with added relevant time series features and
              deleted irrelevant information (only if filter_only_tsfresh_features is False).
          :rtype: pandas.DataFrame
          """
          if self.feature_selector.relevant_features is None:
              raise RuntimeError("You have to call fit before.")
  
          if self.timeseries_container is None:
              raise RuntimeError("You have to provide a time series using the set_timeseries_container function before.")
  
          self.feature_extractor.set_timeseries_container(self.timeseries_container)
  
          relevant_time_series_features = set(self.feature_selector.relevant_features) - set(pd.DataFrame(X).columns)
          relevant_extraction_settings = from_columns(relevant_time_series_features)
  
          # Set imputing strategy
          impute_function = partial(impute_dataframe_range, col_to_max=self.col_to_max,
                                    col_to_min=self.col_to_min, col_to_median=self.col_to_median)
  
          relevant_feature_extractor = FeatureAugmenter(kind_to_fc_parameters=relevant_extraction_settings,
                                                        default_fc_parameters={},
                                                        column_id=self.feature_extractor.column_id,
                                                        column_sort=self.feature_extractor.column_sort,
                                                        column_kind=self.feature_extractor.column_kind,
                                                        column_value=self.feature_extractor.column_value,
                                                        chunksize=self.feature_extractor.chunksize,
                                                        n_jobs=self.feature_extractor.n_jobs,
                                                        show_warnings=self.feature_extractor.show_warnings,
                                                        disable_progressbar=self.feature_extractor.disable_progressbar,
                                                        impute_function=impute_function,
                                                        profile=self.feature_extractor.profile,
                                                        profiling_filename=self.feature_extractor.profiling_filename,
                                                        profiling_sorting=self.feature_extractor.profiling_sorting)
  
          relevant_feature_extractor.set_timeseries_container(self.feature_extractor.timeseries_container)
  
          X_augmented = relevant_feature_extractor.transform(X)
  
          if self.filter_only_tsfresh_features:
              return X_augmented.copy().loc[:, self.feature_selector.relevant_features + X.columns.tolist()]
          else:
              return X_augmented.copy().loc[:, self.feature_selector.relevant_features]