docs: def fit(self, X, y):
          """
          Extract the information, which of the features are relevent using the given target.
  
          For more information, please see the :func:`~tsfresh.festure_selection.festure_selector.check_fs_sig_bh`
          function. All columns in the input data sample are treated as feature. The index of all
          rows in X must be present in y.
  
          :param X: data sample with the features, which will be classified as relevant or not
          :type X: pandas.DataFrame or numpy.array
  
          :param y: target vector to be used, to classify the features
          :type y: pandas.Series or numpy.array
  
          :return: the fitted estimator with the information, which features are relevant
          :rtype: FeatureSelector
          """
          if not isinstance(X, pd.DataFrame):
              X = pd.DataFrame(X.copy())
  
          if not isinstance(y, pd.Series):
              y = pd.Series(y.copy())
  
          relevance_table = calculate_relevance_table(
                                  X, y, ml_task=self.ml_task, n_jobs=self.n_jobs,
                                  chunksize=self.chunksize, fdr_level=self.fdr_level,
                                  hypotheses_independent=self.hypotheses_independent,
                                  test_for_binary_target_real_feature=self.test_for_binary_target_real_feature)
          self.relevant_features = relevance_table.loc[relevance_table.relevant].feature.tolist()
          self.feature_importances_ = 1.0 - relevance_table.p_value.values
          self.p_values = relevance_table.p_value.values
          self.features = relevance_table.index.tolist()
  
          return self