code: def gini_c(target, prediction):
      target = np.asarray(
          target
      )  # In case someone passes Series or list
      n = len(target)
      t_s = target[np.argsort(prediction)]
      t_c = t_s.cumsum()
      gini_sum = t_c.sum() / t_s.sum() - (n + 1) / 2.0
      return gini_sum / n
  
file_path: hyperparameter_hunter/examples/feature_engineering_examples/olivier_porto_seguro_kaggle.py
imports: from hyperparameter_hunter import Environment, CVExperiment, FeatureEngineer, EngineerStep
  import numpy
  import pandas
  from sklearn.model_selection import StratifiedKFold
  from sklearn.preprocessing import LabelEncoder
  from xgboost import XGBClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: gini_c(target, prediction) and returns: unknown