ValueError: Data is not binary and pos_label is not specified
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/0.15.X/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.
We have problem in y_true=np.array(['0', '1', '0', '0', '1', '1', '1', '1', '1'])Convert values of y_true to Boolean
y_true= '1' <= y_trueprint(y_true) # [False True False False True True True True True]