Test case average of solutions in real dataset

[sindhura97]

Hi, Have you checked the ground truth solutions in the original dataset to make sure that they pass the test cases?

[xksteven]

We filtered when we collected the solutions to check if they pass the testcases however they may be horribly inefficient such as requiring several gigabytes of RAM to execute or take a really long time to execute (sometimes minutes). This varied based on the source of the ground truth solutions. We didn't filter the solutions further to only those that were optimal or near optimal.

[sindhura97]

I see. I was running evaluation by generating code by simply copying first ground truth solution this way:

from datasets import load_dataset
import json

ds = load_dataset("codeparrot/apps", split="train")
examples = {}
for eg in ds:
        for sol in eg['solutions'][2:-2].split('", "'):
            sol = sol.replace('\\n', '\n')
            examples[eg['problem_id']] = [sol]
            print ('='*10)
            print (sol)
            break
json.dump(examples, open('results/all_codes_orig_train.json','w'))

And when I run evaluation for these codes, I only got 60%. Does this seem right?

[xksteven]

You may need to select a different solution to test out. I can rerun the evaluation script to see how many optimal solutions. I might not be able to get to it for a while though as I'll need the compute in the background to re-evaluate all of the solutions and with sufficient RAM etc. So I can't really give an ETA on that.

[sindhura97]

Okay, btw I see that the low 60% is due to some problems with '' character used unnecessarily in solutions at some places.

[sindhura97]

Update: Doing sol = sol.replace('\n', '\n').replace('\"','"').replace('\r','').replace('\\','\').replace('\t','\t') has pushed it to >95% when I tested on first few training samples.

[xksteven]

Great thanks for the information. I thought we did that for our preprocessing but maybe something happened where it got removed.

Feel free to make a PR with the changes if you have time :)

[loubnabnl]

Hi this isn't an error in the dataset, to load it in HuggingFace hub and respect some format constraints we had to save the solutions and input_output columns in json format which led to this behaviour. But in the README of the dataset we show how to load the solutions and input_output columns correctly: https://huggingface.co/datasets/codeparrot/apps#how-to-use-it

[xksteven]

@loubnabnl Thanks for the input! Leaving the issue closed.