import matplotlib.pyplot as plt

def plot_roc_curve(roc_auc, fpr, tpr):
    # Function to plot ROC Curve
    # Inputs: 
    #     roc_auc - AU ROC value (float)
    #     fpr - false positive rate (output of roc_curve()) array
    #     tpr - true positive rate (output of roc_curve()) array
    plt.figure(figsize=(8,6))
    lw = 2
    plt.plot(fpr, tpr, color='orange', lw=lw, label='ROC curve (AUROC = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--', label='random')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.grid()
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()
    
import matplotlib.pyplot as plt

def plot_roc_curve(roc_auc, fpr, tpr):
    # Function to plot ROC Curve
    # Inputs: 
    #     roc_auc - AU ROC value (float)
    #     fpr - false positive rate (output of roc_curve()) array
    #     tpr - true positive rate (output of roc_curve()) array
    plt.figure(figsize=(8,6))
    lw = 2
    plt.plot(fpr, tpr, color='orange', lw=lw, label='ROC curve (AUROC = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--', label='random')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.grid()
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic example')
    plt.legend(loc="lower right")
    plt.show()
    
# Function to plot ROC Curve # Inputs: #     roc_auc - AU ROC value (float) #     fpr - false positive rate (output of roc_curve()) array #     tpr - true positive rate (output of roc_curve()) array