Issue with custom scorer

[mirix]

Hello,

I have an unbalanced dataset and I am trying to create a custom scorer that finds the best possible recall above a given precision for the minority class.

The opposite seems to work well. When I feed the following score to shap-hypertune, it produces consistent results for the precision:

def precision_at_recall(y_true, y_hat):
	precision, recall, thresholds = precision_recall_curve(y_true, y_hat, pos_label=1)
	ix = np.argmax(precision[recall >= .9])
	return 'precision_at_recall', precision[ix], True

The recall and precision for the minority class at a threshold of 0.5 are both around 0.85. If we set a recall above 0.9, the precision decreases accordingly, as expected.

However, the following does not work:

def recall_at_precision(y_true, y_hat):
	precision, recall, thresholds = precision_recall_curve(y_true, y_hat, pos_label=1)
	ix = np.argmax(recall[precision >= .9])
	return 'recall_at_precision', recall[ix], True

It always produces a perfect recall (1), regardless of the precision, even if the precision is set to 1.

[mirix]

Axis issue...