CODEWITHSUNDEEP
pythonrocauc
Jaya A
Jaya A

Reputation: 163

How to plot average ROC and AUC in Python?

I need to perform a classification of users using binary classification (User 1 or 0 in each case).

I have 30 users and there are 30 sets of FPR and TPR.

I did not use roc_curve(y_test.ravel(), y_score.ravel()) to get FPR and TPF (there is a reason for this which I have to classify each of them using binary classification and generate FPR Aand TPF using my own code).

Actually, my setting was I did not store class labels as multi-class. What I did was I take one user as a positive class and the rest as negative class. I repeated for all other users. Then I calculated FPR and TPF using my own code without using roc_auc_score.

Let say I already have the values of FPR and TPF in alist.

I have these codes:

from sklearn.metrics import roc_curve, auc
import matplotlib.pyplot as plt
from scipy import interp

n_classes=30

# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr_svc[i] for i in range(n_classes)])) # Classified using SVC

# Then interpolate all ROC curves at this points
mean_tpr = np.zeros_like(all_fpr)
for i in range(n_classes):
    mean_tpr += interp(all_fpr, fpr_svc[i], tpr_svc[i])

# Finally average it and compute AUC
mean_tpr /= n_classes

fpr = all_fpr[:]
tpr = mean_tpr[:]

plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange',
         lw=lw)
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Acceptance Rate')
plt.ylabel('True Acceptance Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()

But, it produced this figure which is look weird. enter image description here

Moreover, how do I get the average AUC as well?

Upvotes: 2

Views: 1571

Answers (1)

dimid
dimid

Reputation: 7659

I'm not sure I understood your setting, but in general you can compute the average AUC for a multi-class classifier using sklearns roc_auc_score. If by average you mean to compare each class with every other class use ovo (One vs. One). Otherwise, if you prefer to compare each class to all the others together, use ovr (One vs. Rest). Here's the documentation for the multi_class param:

multi_class {‘raise’, ‘ovr’, ‘ovo’}, default=’raise’

Multiclass only. Determines the type of configuration to use. The default value raises an error, so either ovr or ovo must be passed explicitly.

'ovr': Computes the AUC of each class against the rest [3] [4]. This treats the multiclass case in the same way as the multilabel case. Sensitive to class imbalance even when average == 'macro', because class imbalance affects the composition of each of the ‘rest’ groupings.

'ovo': Computes the average AUC of all possible pairwise combinations of classes [5]. Insensitive to class imbalance when average == 'macro'.

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.htm

Upvotes: 0

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