# ValueError: Data is not binary and pos_label is not specified

I am trying to calculate roc_auc_score, but I am getting following error.

"ValueError: Data is not binary and pos_label is not specified"

My code snippet is as follows:

import numpy as np from sklearn.metrics import roc_auc_score y_scores=np.array([ 0.63, 0.53, 0.36, 0.02, 0.70 ,1 , 0.48, 0.46, 0.57]) y_true=np.array(['0', '1', '0', '0', '1', '1', '1', '1', '1']) roc_auc_score(y_true, y_scores)

Please tell me what is wrong with it.

## Answers

You only need to change y_trueso it looks like this:

y_true=np.array([0, 1, 0, 0, 1, 1, 1, 1, 1])

**Explanation:**
If you take a look to what roc_auc_score functions does in https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/metrics/metrics.py you will see that y_true is evaluated as follows:

classes = np.unique(y_true) if (pos_label is None and not (np.all(classes == [0, 1]) or np.all(classes == [-1, 1]) or np.all(classes == [0]) or np.all(classes == [-1]) or np.all(classes == [1]))): raise ValueError("Data is not binary and pos_label is not specified")

At the moment of the execution pos_label is None, but as long as your are defining y_true as an array of characters the np.all are always false and as all of them are negated then the if condition is trueand the exception is raised.