code: def test_multiclass_selects_correct_n_significant(self):
      df = pd.DataFrame()
      N = 10
      constants = [N] * N
      increase = list(range(N))
  
      df["f1"] = constants + increase + increase
      df["f2"] = increase + constants + increase
      df["f3"] = increase + increase + constants
      df["y"] = [0] * N + [1] * N + [2] * N
  
      y = df.y
      X = df.drop(["y"], axis=1)
      X_relevant = select_features(
          X,
          y,
          ml_task="classification",
          multiclass=True,
          n_significant=1,
          fdr_level=0.01,
      )
      assert {"f1", "f2", "f3"} == set(X_relevant.columns)
      X_relevant = select_features(
          X,
          y,
          ml_task="classification",
          multiclass=True,
          n_significant=2,
          fdr_level=0.01,
      )
      assert len(X_relevant.columns) == 0
      X_relevant = select_features(
          X,
          y,
          ml_task="classification",
          multiclass=True,
          n_significant=3,
          fdr_level=0.01,
      )
      assert len(X_relevant.columns) == 0
  
file_path: tsfresh/tests/units/feature_selection/test_selection.py
imports: import numpy
  import pandas
  import pytest
  from tsfresh.feature_selection.selection import select_features
label_desc: Use image node_id 8 for calling the TestSelectFeatures obj's underlying member method code with example usage: obj.test_multiclass_selects_correct_n_significant() without return types