code: def scoreOne(model, X, y, metric, predict_proba):
      """
      Scores one model fit using provided metric for given model on scoring data.
      :param model: The fitted model you wish to score
      :param X: The dependent variable data you wish to use for prediction
      :param y: The ground truth independent variable data you wish to compare the predictions to
      :param metric: The metric you wish to use to score the predictions using
      :param predict_proba: Choice between using 'predict_proba' and 'predict' for scoring routine.
          Default True means predict_proba and False means predict
      :return: Returns the score
      """
      if predict_proba:
          try:
              return metric(y, model.predict_proba(X)[:, 1])
          except:
              print(
                  "This model/metric cannot use predict_proba. Using predict for scoring instead."
              )
              return metric(y, model.predict(X))
      else:
          return metric(y, model.predict(X))
  
file_path: parfit/parfit/score.py
imports: from joblib import Parallel, delayed
  from sklearn.metrics import roc_auc_score
  import numpy
  import warnings
label_desc: Use image node_id 2 for calling a global function with example usage: scoreOne(model, X, y, metric, predict_proba) and returns: metric, metric, metric