README.md

---
title: Code Eval Stdio
datasets:
  - giulio98/xlcost-single-prompt
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
  The stdio version of of the ["code
  eval"](https://huggingface.co/spaces/evaluate-metric/code_eval) metrics, which
  handles python programs that read inputs from STDIN and print answers to
  STDOUT, which is common in competitive programming (e.g. CodeForce, USACO) : )
---

# Metric Card for Code Eval StdIO

The stdio version of of the ["code
eval"](https://huggingface.co/spaces/evaluate-metric/code_eval) metrics, which
handles python programs that read inputs from STDIN and print answers to
STDOUT, which is common format in competitive programming

## Metric description

This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).

The CodeEval metric estimates the pass@k metric for code synthesis. 

It implements the evaluation harness for the HumanEval problem solving dataset described in the paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374).


## How to use 

The Code Eval metric calculates how good are predictions given a set of references. Its arguments are:

`predictions`: a list of candidates to evaluate. Each candidate should be a list of strings with several code candidates to solve the problem.

`references`: a list of Dict of [str, str], each dict has two keys "input" and "reference_output". e.g. [{"input": "1 2", "reference_output": "3"}]

`k`: number of code candidates to consider in the evaluation. The default value is `[1, 10, 100]`.

`num_workers`: the number of workers used to evaluate the candidate programs (The default value is `4`).

`timeout`: The maximum time taken to produce a prediction before it is considered a "timeout". The default value is `3.0` (i.e. 3 seconds).

```python
from evaluate import load
code_eval_stdio = load("hage2000/code_eval_stdio")
references = [{"input":"2 3", "reference_output":"5})]
candidates = [[ "nums = list(map(int, input().split()))\nprint(sum(nums))"]]
pass_at_k, results = code_eval_stdio.compute(references=references, predictions=candidates, k=[1, 2])
```

N.B.
This metric exists to run untrusted model-generated code. Users are strongly encouraged not to do so outside of a robust security sandbox. Before running this metric and once you've taken the necessary precautions, you will need to set the `HF_ALLOW_CODE_EVAL` environment variable. Use it at your own risk:
```python
import os
os.environ["HF_ALLOW_CODE_EVAL"] = "1"` 
```

## Output values

The Code Eval metric outputs two things:

`pass_at_k`: a dictionary with the pass rates for each k value defined in the arguments.

`results`: a dictionary with granular results of each unit test.

### Values from popular papers
The [original CODEX paper](https://arxiv.org/pdf/2107.03374.pdf) reported that the CODEX-12B model had a pass@k score of 28.8% at `k=1`, 46.8% at `k=10` and 72.3% at `k=100`. However, since the CODEX model is not open source, it is hard to verify these numbers. 



## Examples 

Full match at `k=1`:

```python
from evaluate import load
code_eval_stdio = load("hage2000/code_eval_stdio")
references = [{"inputs":["2 3"], "reference_outputs":["5"]}]
candidates = [[ "nums = list(map(int, input().split()))\nprint(sum(nums))"]]
pass_at_k, results = code_eval_stdio.compute(references=references, predictions=candidates, k=[1, 2])
print(pass_at_k)
{'pass@1': 1.0}
```

No match for k = 1:

```python
from evaluate import load
code_eval_stdio = load("hage2000/code_eval_stdio")
references = [{"inputs":["2 3"], "reference_outputs":["5"]}]
candidates = [[ "nums = list(map(int, input().split()))\nprint(nums[0]*nums[1])"]]
pass_at_k, results = code_eval_stdio.compute(references=references, predictions=candidates, k=[1, 2])
print(pass_at_k)
{'pass@1': 0.0}
```

Partial match at k=1, full match at k=2:

```python
from evaluate import load
code_eval_stdio = load("hage2000/code_eval_stdio")
references = [{"inputs": ["2 3"]:, "reference_outputs":["5"]}]
candidates = [[ "nums = list(map(int, input().split()))\nprint(sum(nums))", "nums = list(map(int, input().split()))\nprint(nums[0]*nums[1])"]]
pass_at_k, results = code_eval_stdio.compute(references=references, predictions=candidates, k=[1, 2])
print(pass_at_k)
{'pass@1': 0.5, 'pass@2': 1.0}
```

## Limitations and bias

As per the warning included in the metric code itself:
> This program exists to execute untrusted model-generated code. Although it is highly unlikely that model-generated code will do something overtly malicious in response to this test suite, model-generated code may act destructively due to a lack of model capability or alignment. Users are strongly encouraged to sandbox this evaluation suite so that it does not perform destructive actions on their host or network. For more  information on how OpenAI sandboxes its code, see the accompanying paper. Once you have read this disclaimer and taken appropriate precautions, uncomment the following line and proceed at your own risk:

More information about the limitations of the code can be found on the [Human Eval Github repository](https://github.com/openai/human-eval).

## Citation

```bibtex
@misc{chen2021evaluating,
      title={Evaluating Large Language Models Trained on Code},
      author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
      year={2021},
      eprint={2107.03374},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```
    
## Further References 

- [Human Eval Github repository](https://github.com/openai/human-eval)
- [OpenAI Codex website](https://openai.com/blog/openai-codex/)