code: def test_UpliftTreeClassifier_feature_importance(
      generate_classification_data,
  ):
      # test if feature importance is working as expected
      df, x_names = generate_classification_data()
      df_train, df_test = train_test_split(
          df, test_size=0.2, random_state=RANDOM_SEED
      )
  
      # Train the upLift classifier
      uplift_model = UpliftTreeClassifier(
          control_name=TREATMENT_NAMES[0], random_state=RANDOM_SEED
      )
      uplift_model.fit(
          df_train[x_names].values,
          treatment=df_train["treatment_group_key"].values,
          y=df_train[CONVERSION].values,
      )
  
      assert hasattr(uplift_model, "feature_importances_")
      assert np.all(uplift_model.feature_importances_ >= 0)
      num_non_zero_imp_features = sum(
          [
              1 if imp > 0 else 0
              for imp in uplift_model.feature_importances_
          ]
      )
  
      def getNonleafCount(node):
          # base case
          if node is None or (
              node.trueBranch is None and node.falseBranch is None
          ):
              return 0
          # If root is Not None and its one of its child is also not None
          return (
              1
              + getNonleafCount(node.trueBranch)
              + getNonleafCount(node.falseBranch)
          )
  
      num_non_leaf_nodes = getNonleafCount(
          uplift_model.fitted_uplift_tree
      )
      # Check if the features with positive importance is not more than number of nodes
      # the reason is, each non-leaf node evaluates only one feature, and some of the nodes
      # would evaluate the same feature, thus the number of features with importance value
      # shouldn't be larger than the number of non-leaf node
      assert num_non_zero_imp_features <= num_non_leaf_nodes
  
file_path: causalml/tests/test_uplift_trees.py
imports: import cProfile
  import numpy
  import pandas
  import pytest
  import pstats
  from joblib import parallel_backend
  from sklearn.model_selection import train_test_split
  from causalml.inference.tree import UpliftTreeClassifier, UpliftRandomForestClassifier
  from causalml.metrics import get_cumgain
  from causalml.dataset import make_uplift_classification
  from causalml.inference.tree import uplift_tree_string, uplift_tree_plot
  from .const import RANDOM_SEED, N_SAMPLE, CONTROL_NAME, TREATMENT_NAMES, CONVERSION
label_desc: Use image node_id 6 for calling a global function with example usage: test_UpliftTreeClassifier_feature_importance(generate_classification_data) and returns: unknown, int