roc_auc_score - Only one class present in y_true

Question

I am doing a k-fold XV on an existing dataframe, and I need to get the AUC score. The problem is - sometimes the test data only contains 0s, and not 1s!

I tried using this example, but with different numbers:

import numpy as np
from sklearn.metrics import roc_auc_score
y_true = np.array([0, 0, 0, 0])
y_scores = np.array([1, 0, 0, 0])
roc_auc_score(y_true, y_scores)

And I get this exception:

ValueError: Only one class present in y_true. ROC AUC score is not defined in that case.

Is there any workaround that can make it work in such cases?

Answer 1

You could use try-except to prevent the error:

import numpy as np
from sklearn.metrics import roc_auc_score
y_true = np.array([0, 0, 0, 0])
y_scores = np.array([1, 0, 0, 0])
try:
    roc_auc_score(y_true, y_scores)
except ValueError:
    pass

Now you can also set the roc_auc_score to be zero if there is only one class present. However, I wouldn't do this. I guess your test data is highly unbalanced. I would suggest to use stratified K-fold instead so that you at least have both classes present.

Answer 2

As the error notes, if a class is not present in the ground truth of a batch,

ROC AUC score is not defined in that case.

I'm against either throwing an exception (about what? This is the expected behaviour) or returning another metric (e.g. accuracy). The metric is not broken per se.

I don't feel like solving a data imbalance "issue" with a metric "fix". It would probably be better to use another sampling, if possibile, or just join multiple batches that satisfy the class population requirement.