roc_auc_curve.py

def take_roc_curve(X_test,model):
  y_preds = model.predict_proba(X_test)
  preds = y_preds[:,1]

  fpr, tpr, _ = metrics.roc_curve(y_test, preds)
  precision, recall, _ = metrics.precision_recall_curve(y_test, preds)
  auc_score = metrics.auc(fpr, tpr)

  plt.figure(figsize=(10,5))

  plt.subplot(1, 2, 1)
  plt.title('ROC Curve '+type(model).__name__)
  plt.plot(fpr, tpr, label='AUC = {:.2f}'.format(auc_score))
  plt.plot([0,1],[0,1],'r--')
  plt.xlim([-0.1,1.1])
  plt.ylim([-0.1,1.1])
  plt.ylabel('True Positive Rate')
  plt.xlabel('False Positive Rate')
  plt.legend(loc='lower right')
  
  plt.subplot(1, 2, 2)
  plt.step(recall, precision, color='orange', where='post')
  plt.xlabel('Recall')
  plt.ylabel('Precision')
  plt.ylim([0.0, 1.05])
  plt.xlim([0.0, 1.0])
  plt.title('Precision Recall Curve')
  plt.grid(True)

  plt.tight_layout()
  plt.show()