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- import matplotlib.pyplot as plt
- from sklearn.metrics import roc_curve, auc
- def plot_roc_curve(test_labels: list, test_df: list, title: str, figsize=(8, 8), savefile = None):
- fpr, tpr, thresholds = roc_curve(test_labels, test_df)
- auc_score = auc(fpr, tpr)
- plt.figure(figsize=figsize)
- plt.plot(fpr, tpr, lw=1, label="ROC Curve")
- plt.plot([0, 1], [0, 1], color="lime", linestyle="--")
- plt.xlim([0.0, 1.0])
- plt.ylim([0.0, 1.05])
- plt.xlabel("FPR")
- plt.ylabel("TPR")
- plt.title(f"{title} (AUC = {auc_score})")
- plt.legend(loc="lower right")
- if savefile is not None:
- plt.savefig(f"{savefile}.png", bbox_inches="tight")
- plt.savefig(f"{savefile}.pdf", bbox_inches="tight")
- plt.show()
- return fpr, tpr, thresholds, auc_score
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