code: def get_pr_roc_curves(y_true, y_pred_probs):
      """
      Compute precision/recall and receiver operating characteristic metrics for a
      binary class label.
  
      y_true: series of true class labels (only 1 or 0)
      y_pred_probs: series of probabilities generated by the model for the label class 1
      """
      results = {}
      roc = roc_curve(y_true, y_pred_probs, pos_label=1)
      results[Metrics.ROC_CURVE] = {
          "fprs": list(roc[0]),
          "tprs": list(roc[1]),
          "thresholds": list(roc[2]),
      }
  
      pr = precision_recall_curve(y_true, y_pred_probs, pos_label=1)
      results[Metrics.PR_CURVE] = {
          "precisions": list(pr[0]),
          "recalls": list(pr[1]),
          "thresholds": list(pr[2]),
      }
  
      return results
  
file_path: ATM/atm/metrics.py
imports: from __future__ import absolute_import, division, unicode_literals
  from builtins import range
  import numpy
  import pandas
  from sklearn.metrics import accuracy_score, average_precision_score, cohen_kappa_score, f1_score, matthews_corrcoef, precision_recall_curve, roc_auc_score, roc_curve
  from sklearn.model_selection import StratifiedKFold
  from atm.constants import METRICS_BINARY, METRICS_MULTICLASS, N_FOLDS_DEFAULT, Metrics
label_desc: Use image node_id 3 for calling a global function with example usage: get_pr_roc_curves(y_true, y_pred_probs) and returns: results