code: def test_filter_lr(generate_classification_data):
      # generate uplift classification data
      np.random.seed(RANDOM_SEED)
      df, X_names = generate_classification_data()
      y_name = CONVERSION
  
      # test LR filter
      method = "LR"
      filter_obj = FilterSelect()
      imp = filter_obj.get_importance(
          df, X_names, y_name, method, treatment_group="treatment1"
      )
  
      # each row represents the rank and importance score of each feature
      # and spot check if it's sorted properly
      assert imp.shape[0] == len(X_names)
      assert imp["rank"].values[0] == 1
      assert imp["score"].values[0] >= imp["score"].values[1]
  
imports: import numpy
  from causalml.feature_selection.filters import FilterSelect
  from .const import RANDOM_SEED, CONVERSION
label_desc: Use image node_id 2 for calling a global function with example usage: test_filter_lr(generate_classification_data) without return types