class_docstr: Test cases for the whole feature selection algorithm.
class_docstr_tok: ["Test","cases","for","the","whole","feature","selection","algorithm","."]
code: def test_all_features_good(self):
      # Mixed case with real target
      y = pd.Series(np.random.normal(0, 1, 1000))
      X = pd.DataFrame(index=range(1000))
  
      z = (
          y
          - np.random.binomial(1, 0.20, 1000)
          + np.random.binomial(1, 0.20, 1000)
      )
      z[z == -1] = 0
      z[z == 2] = 1
      X["rel1"] = z
  
      z = (
          y
          - np.random.binomial(1, 0.10, 1000)
          + np.random.binomial(1, 0.10, 1000)
      )
      z[z == -1] = 0
      z[z == 2] = 1
      X["rel2"] = z
  
      df_bh = calculate_relevance_table(X, y)
      feat_rej = df_bh.loc[df_bh.relevant].feature
  
      # Make sure all selected variables are relevant
      for kept_feature in feat_rej:
          self.assertIn(kept_feature, ["rel1", "rel2"])
  
      self.assertGreater(len(feat_rej), 0)
  
file_path: tsfresh/tests/units/feature_selection/test_feature_significance.py
imports: from unittest import TestCase
  import numpy
  import pandas
  from tsfresh.feature_selection.relevance import calculate_relevance_table
label_desc: Use image node_id 7 for calling the FeatureSignificanceTestCase obj's underlying member method code with example usage: obj.test_all_features_good() without return types