code: def test_1_categorical(self):
      df = pd.read_csv("tests/data/hdi.csv")
      target = "Development Index"
      predictor = self.setup_predictor(df, target)
      predictions = predictor.predict(df)
  
      self.assertTrue(
          balanced_accuracy_score(
              df[target].astype(int),
              predictions["prediction"].astype(int),
          )
          > 0.9
      )
      self.assertTrue(
          all([0 <= p <= 1 for p in predictions["confidence"]])
      )
  
      # test predict all mixers with some data
      predictions = predictor.predict(
          df[:10], args={"all_mixers": True}
      )
      assert "__mdb_mixer_Neural" in predictions.columns
  
      # predict single sample
      predictor.predict(df.iloc[[0]])
  
file_path: lightwood/tests/integration/basic/test_categorical.py
imports: import unittest
  import numpy
  import pandas
  from sklearn.metrics import balanced_accuracy_score
  from lightwood.api.types import ProblemDefinition
  from lightwood.api.high_level import predictor_from_problem
label_desc: Use image node_id 3 for calling the TestBasic obj's underlying member method code with example usage: obj.test_1_categorical() without return types