code: def auc(
      rating_true,
      rating_pred,
      col_user=DEFAULT_USER_COL,
      col_item=DEFAULT_ITEM_COL,
      col_rating=DEFAULT_RATING_COL,
      col_prediction=DEFAULT_PREDICTION_COL,
  ):
      """Calculate the Area-Under-Curve metric for implicit feedback typed
      recommender, where rating is binary and prediction is float number ranging
      from 0 to 1.
  
      https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
  
      Note:
          The evaluation does not require a leave-one-out scenario.
          This metric does not calculate group-based AUC which considers the AUC scores
          averaged across users. It is also not limited to k. Instead, it calculates the
          scores on the entire prediction results regardless the users.
  
      Args:
          rating_true (pandas.DataFrame): True data
          rating_pred (pandas.DataFrame): Predicted data
          col_user (str): column name for user
          col_item (str): column name for item
          col_rating (str): column name for rating
          col_prediction (str): column name for prediction
  
      Returns:
          float: auc_score (min=0, max=1)
      """
  
      y_true, y_pred = merge_rating_true_pred(
          rating_true=rating_true,
          rating_pred=rating_pred,
          col_user=col_user,
          col_item=col_item,
          col_rating=col_rating,
          col_prediction=col_prediction,
      )
      return roc_auc_score(y_true, y_pred)
  
imports: import numpy
  import pandas
  from functools import wraps
  from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score, roc_auc_score, log_loss
  from recommenders.utils.constants import DEFAULT_USER_COL, DEFAULT_ITEM_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL, DEFAULT_RELEVANCE_COL, DEFAULT_SIMILARITY_COL, DEFAULT_ITEM_FEATURES_COL, DEFAULT_ITEM_SIM_MEASURE, DEFAULT_K, DEFAULT_THRESHOLD
  from recommenders.datasets.pandas_df_utils import has_columns, has_same_base_dtype, lru_cache_df
label_desc: Use image node_id 7 for calling a global function with example usage: auc(rating_true, rating_pred, col_user, col_item, col_rating, col_prediction) and returns: roc_auc_score