code: def evaluate_print(clf_name, y, y_pred):
      """Utility function for evaluating and printing the results for examples.
      Default metrics include ROC and Precision @ n
  
      Parameters
      ----------
      clf_name : str
          The name of the detector.
  
      y : list or numpy array of shape (n_samples,)
          The ground truth. Binary (0: inliers, 1: outliers).
  
      y_pred : list or numpy array of shape (n_samples,)
          The raw outlier scores as returned by a fitted model.
  
      """
  
      y = column_or_1d(y)
      y_pred = column_or_1d(y_pred)
      check_consistent_length(y, y_pred)
  
      print(
          "{clf_name} ROC:{roc}, precision @ rank n:{prn}".format(
              clf_name=clf_name,
              roc=np.round(roc_auc_score(y, y_pred), decimals=4),
              prn=np.round(precision_n_scores(y, y_pred), decimals=4),
          )
      )
  
file_path: pyod/pyod/utils/data.py
imports: from __future__ import division
  from __future__ import print_function
  from warnings import warn
  import numpy
  from sklearn.datasets import make_blobs
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.utils import check_X_y
  from sklearn.utils import check_consistent_length
  from sklearn.utils import check_random_state
  from sklearn.utils import column_or_1d
  from .utility import check_parameter
  from .utility import precision_n_scores
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate_print(clf_name, y, y_pred) without return types