docstr: Test FIGS with categorical data
docstr_tok: ["Test","FIGS","with","categorical","data"]
code: def test_categorical(self):
      """Test FIGS with categorical data"""
      categories = ["cat", "dog", "bird", "fish"]
      categories_2 = ["bear", "chicken", "cow"]
  
      self.X_cat = pd.DataFrame(self.X)
      self.X_cat["pet1"] = np.random.choice(
          categories, size=(self.n, 1)
      )
      self.X_cat["pet2"] = np.random.choice(
          categories_2, size=(self.n, 1)
      )
  
      figs_reg = FIGSRegressor()
      figs_cls = FIGSClassifier()
  
      figs_reg.fit(
          self.X_cat, self.y_reg, categorical_features=["pet1", "pet2"]
      )
      figs_reg.predict(
          self.X_cat, categorical_features=["pet1", "pet2"]
      )
  
      figs_cls.fit(
          self.X_cat, self.y_reg, categorical_features=["pet1", "pet2"]
      )
      figs_cls.predict_proba(
          self.X_cat, categorical_features=["pet1", "pet2"]
      )
  
file_path: imodels/tests/figs_test.py
imports: import os
  import random
  from functools import partial
  import numpy
  import pandas
  from sklearn.tree import DecisionTreeRegressor
  from imodels import FIGSClassifier, FIGSRegressor, FIGSClassifierCV, FIGSRegressorCV
  from imodels.experimental.figs_ensembles import FIGSExtRegressor, FIGSExtClassifier
  from sklearn.ensemble import StackingRegressor, VotingRegressor, BaggingClassifier
label_desc: Use image node_id 3 for calling the TestFIGS obj's underlying member method code with example usage: obj.test_categorical() without return types