metrics.py

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import roc_curve, auc
from scipy import interp
from itertools import cycle

def roc_auc(y_test, y_score, n_classes):  
    """Plots ROC curve for micro and macro averaging."""
    
    # Compute ROC curve and ROC area for each class
    fpr = {}
    tpr = {}
    roc_auc = {}
    for i in range(n_classes):
        fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i])
        roc_auc[i] = auc(fpr[i], tpr[i])
    
    # Compute micro-average ROC curve and ROC area
    fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel())
    roc_auc["micro"] = auc(fpr["micro"], tpr["micro"])
    
    # Compute macro-average ROC curve and ROC area     
    all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)]))
    mean_tpr = np.zeros_like(all_fpr)
    for i in range(n_classes):
        mean_tpr += interp(all_fpr, fpr[i], tpr[i])
    mean_tpr /= n_classes 
    fpr["macro"] = all_fpr
    tpr["macro"] = mean_tpr
    roc_auc["macro"] = auc(fpr["macro"], tpr["macro"])
    
    # Plot all ROC curves
    plt.figure()
    plt.plot(fpr["micro"], tpr["micro"], 
             label='micro-average ROC curve (area = {0:0.2f})'.format(roc_auc["micro"]),
             color='deeppink', linestyle=':', linewidth=4)
    
    plt.plot(fpr["macro"], tpr["macro"], 
             label='macro-average ROC curve (area = {0:0.2f})'.format(roc_auc["macro"]),
             color='navy', linestyle=':', linewidth=4)
    
    colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
    for i, color in zip(range(0,3), colors):
        plt.plot(fpr[i], tpr[i], color=color, lw=2, 
                 label='ROC curve of class {0} (area = {1:0.2f})'.format(i, roc_auc[i]))
    
    plt.plot([0, 1], [0, 1], 'k--', lw=2)
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Some extension of ROC to multi-class')
    plt.legend(loc="lower right")
    plt.show()