My submission did not get scored. I do not see on the rankings. Submission #
It seems your submission failed on one of the leaderboard sets. This should have been clear upon your submission but it hasn’t for some reason. I will investigate further and get back to you here.
In the meantime I have sent you your error traceback privately as well.
I have resubmitted as I am not getting any error on my side. The submission number is 120039. If this does not work, can I just get the one from Week 6 scored and I will try to fix the issue next week.
same issue for my submission 119142, could you pls check for me @alfarzan?
The submission failed to generate prices during the evaluation. This error should have been caught during RMSE leaderboard evaluation. We are looking into this.
Also, shared the error traceback privately.
Looks like similar issue. I was able to submit and it was graded and I received a RMSE score, When I run it on jupyter notbook it works fine and i did not receive any error.
Hi, @jyotish I know the reason now, when calculating new features it lost one column in profit evaluation since there are not enough data comparing to rmse calculation
@zhisheng_wang, so how did you solve the issue when adding a new field?
Same question for submission 119612.
just prefix the features in the beginningg of the preprocess
for example: FEATURES=[‘x’,‘y’] then df[FEATURES]=0
Any update on the issue? Is it possible to score the Week 6 submission, I wanted to see how I did so that I can modify/update the model.
Apologies for the delay everyone!
For all the submissions listed here the problem was either:
- Feature mismatch between the expected data and the data that comes out of the proeprocessor. Or
- The model generated
Both of these should generally be caught upon submission and so if you fix these issues I may be able to privately generate the leaderboards for you, and if the rankings are not substantially different, give you the resulting feedback
To address the questions:
- @aalok_devkota I believe I replied privately, but the error remains the same. I suggest hardcoding the features as @zhisheng_wang has mentioned.
@MathB this submission generated
NaNvalues. Generally a check for this before sending out the prices would help fix the issue. We will also be checking from our side as well.