code: def test_encoding_ordinal_features():
      """Assert that ordinal features are encoded correctly."""
      data = pycaret.datasets.get_data("employee")
      pc = pycaret.classification.setup(
          data=data,
          imputation_type=None,
          ordinal_features={"salary": ["low", "medium", "high"]},
      )
      X, _ = pc.pipeline.transform(pc.X, pc.y)
      mapping = pc.pipeline.steps[0][1].transformer.mapping
      assert mapping[0]["mapping"]["low"] == 0
      assert mapping[0]["mapping"]["medium"] == 1
      assert mapping[0]["mapping"]["high"] == 2
  
imports: import io
  import numpy
  import pandas
  import pytest
  from imblearn.over_sampling import ADASYN
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  import pycaret.classification
  import pycaret.datasets
  import pycaret.regression
label_desc: Use image node_id 24 for calling a global function with example usage: test_encoding_ordinal_features() without return types