code: def test_multiclass_importance_p_values(self):
      y0 = np.zeros(100)
      y1 = np.repeat(1, 100)
      y2 = np.repeat(2, 100)
      y = pd.Series(np.uint8(np.concatenate([y0, y1, y2])))
      X = pd.DataFrame(index=list(range(300)))
  
      X["irr1"] = np.random.normal(0, 1, 300)
      X["rel1"] = y
  
      selector = FeatureSelector(
          multiclass=True,
          ml_task="classification",
          n_significant=2,
          multiclass_p_values="all",
      )
      selector.fit(X, y)
  
      self.assertEqual(selector.p_values.shape, (2, 3))
      self.assertEqual(selector.feature_importances_.shape, (2, 3))
  
      selector = FeatureSelector(
          multiclass=True,
          ml_task="classification",
          n_significant=2,
          multiclass_p_values="min",
      )
      selector.fit(X, y)
  
      self.assertEqual(selector.p_values.shape, (2,))
      self.assertEqual(selector.feature_importances_.shape, (2,))
  
file_path: tsfresh/tests/units/transformers/test_feature_selector.py
imports: from builtins import range
  from unittest import TestCase
  import numpy
  import pandas
  from tsfresh.transformers.feature_selector import FeatureSelector
label_desc: Use image node_id 9 for calling the FeatureSelectorTestCase obj's underlying member method code with example usage: obj.test_multiclass_importance_p_values() without return types