code: def test_shuffled_data(self, iris_test_data, iris_edge_list):
      df = iris_test_data.copy()
      df = df.sample(frac=0.5, random_state=2020)
      ground_truth = np.array(
          [
              [2, 0, 1, 2, 2, 1, 2, 0, 0, 0, 2, 1, 0, 2, 2],
              [0, 1, 2, 2, 0, 0, 1, 2, 0, 2, 1, 1, 2, 0, 0],
              [2, 0, 0, 0, 2, 0, 0, 1, 0, 1, 0, 2, 1, 0, 2],
              [2, 1, 2, 2, 0, 0, 1, 1, 0, 2, 1, 2, 2, 1, 1],
              [0, 0, 0, 0, 1, 2, 0, 0, 1, 2, 2, 0, 0, 2, 0],
          ]
      )
      supervised_param = {
          "sepal width (cm)": {"max_depth": 2, "random_state": 2020},
          "petal length (cm)": {"max_depth": 2, "random_state": 2020},
          "petal width (cm)": {"max_depth": 2, "random_state": 2020},
      }
  
      label = df["sepal length (cm)"]
      df.drop(["sepal length (cm)"], axis=1, inplace=True)
      clf = BayesianNetworkClassifier(
          iris_edge_list,
          discretiser_kwargs=supervised_param,
          discretiser_alg={
              "sepal width (cm)": "tree",
              "petal length (cm)": "tree",
              "petal width (cm)": "tree",
          },
      )
      clf.fit(df, label)
      output = clf.predict(df)
      assert np.isnan(output).sum() == 0
      assert (ground_truth == output.reshape(5, 15)).all()
  
file_path: causalnex/tests/test_network_model.py
imports: import math
  import numpy
  import pytest
  from causalnex.network.sklearn import BayesianNetworkClassifier
label_desc: Use image node_id 10 for calling the TestBayesianCPDs obj's underlying member method code with example usage: obj.test_shuffled_data(iris_test_data, iris_edge_list) without return types