code: def fbt_vs_fb_crime(
      iterations=30, treeNum=1, treeDepth=4, numThresh=9, filename=None
  ):
      X, y = shap.datasets.communitiesandcrime()
      y = (y >= np.percentile(y, 75)).astype(np.int)
      X.drop(columns=get_corr_columns(X), inplace=True)
      return fbt_vs_fb(
          X,
          y,
          iterations=iterations,
          treeNum=treeNum,
          treeDepth=treeDepth,
          numThresh=numThresh,
          filename=filename,
      )
  
imports: import warnings
  import pickle
  from time import time
  import numpy
  import pandas
  import shap
  from aix360.algorithms.rbm import BooleanRuleCG, BRCGExplainer, FeatureBinarizer, FeatureBinarizerFromTrees, GLRMExplainer, LogisticRuleRegression
  from aix360.datasets import HELOCDataset, MEPSDataset
  from aix360.datasets.heloc_dataset import nan_preprocessing
  from pandas import DataFrame
  from sklearn.datasets import load_breast_cancer
  from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 3 for calling a global function with example usage: fbt_vs_fb_crime(iterations, treeNum, treeDepth, numThresh, filename) and returns: fbt_vs_fb