Thank you for developing this fantastic package and the very recent update. I have not tried it yet, but I guess the predict step will be much faster.
I have some questions regarding to multi-task shap score generation.
For profile shap scores: in line 286 of shap_scores.py, the option stranded=True is hard-coded. I think it will work fine for single-task (whether it is stranded or not). But for multi-tasks (unstranded), will it pick up the wrong task? For example, I have 2 tasks (unstranded). Based on the code, task 0 will use output[0:2] (0 and 1) and task 1 will use [2:4] (2 and 3, which will be out of bounds since there are only 2 outputs).
For counts shap scores: in line 280 of shap_scores.py, it seems to use all count outputs. Again this is fine for single task. But for multi-tasks, will it make all tasks having the same scores?
Thank you for your time. Let me know if you have any questions or comments.
Thank you for developing this fantastic package and the very recent update. I have not tried it yet, but I guess the predict step will be much faster.
I have some questions regarding to multi-task shap score generation.
For profile shap scores: in line 286 of shap_scores.py, the option stranded=True is hard-coded. I think it will work fine for single-task (whether it is stranded or not). But for multi-tasks (unstranded), will it pick up the wrong task? For example, I have 2 tasks (unstranded). Based on the code, task 0 will use output[0:2] (0 and 1) and task 1 will use [2:4] (2 and 3, which will be out of bounds since there are only 2 outputs).
For counts shap scores: in line 280 of shap_scores.py, it seems to use all count outputs. Again this is fine for single task. But for multi-tasks, will it make all tasks having the same scores?
Thank you for your time. Let me know if you have any questions or comments.