code: def test_ProtoDashExplainer(self):
      # load NHANES CDC Questionnaire data
      nhanes = CDCDataset()
  
      # load income questionnaire dataset
      df = nhanes.get_csv_file("INQ_H.csv")
      print(df.head(5))
  
      data = df.to_numpy()
      np.set_printoptions(suppress=True)
      print(data[:5])
  
      # One-hot encode the data
      original = data
  
      # replace nan's with 0's
      original[np.isnan(original)] = 0
  
      # delete 1st column (contains sequence numbers)
      original = original[:, 1:]
  
      # one hot encoding of all columns/features
      onehot_encoder = OneHotEncoder(sparse=False)
      onehot_encoded = onehot_encoder.fit_transform(original)
  
      print(onehot_encoded.shape, original.shape)
  
      # Obtain an explanation for data
      Y = onehot_encoded
      X = Y
      explainer = ProtodashExplainer()
      (W, S, _) = explainer.explain(X, Y, m=5)
  
      dfs = df.iloc[S].copy()
      dfs["Weight"] = W
      print(dfs)
  
      # Gaussian (simulated) data example
      # generate normalized gaussian data X, Y with 100 features and 300 & 4000 observations respectively
  
      (X, Y) = get_Gaussian_Data(100, 300, 4000)
      print(X.shape, Y.shape)
      (W, S, _) = explainer.explain(
          X, Y, m=5, kernelType="Gaussian", sigma=2
      )
  
      print(S, W)
  
file_path: AIX360/tests/protodash/test_PDASH.py
imports: import unittest
  from sklearn.preprocessing import OneHotEncoder
  import numpy
  from aix360.algorithms.protodash import ProtodashExplainer
  from aix360.algorithms.protodash import get_Gaussian_Data
  from aix360.datasets.cdc_dataset import CDCDataset
label_desc: Use image node_id 1 for calling the TestProtoDashExplainer obj's underlying member method code with example usage: obj.test_ProtoDashExplainer() without return types