README.md

metadata

title: codebleu
tags:
  - evaluate
  - metric
  - code
  - codebleu
description: >-
  Unofficial `CodeBLEU` implementation with Linux and MacOS supports available
  with PyPI and HF HUB.
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false

Metric Card for codebleu

Module Card Instructions: Fill out the following subsections. Feel free to take a look at existing metric cards if you'd like examples.

Metric Description

Unofficial CodeBLEU implementation with Linux and MacOS supports available with PyPI and HF HUB.

An ideal evaluation metric should consider the grammatical correctness and the logic correctness. We propose weighted n-gram match and syntactic AST match to measure grammatical correctness, and introduce semantic data-flow match to calculate logic correctness.
(from CodeXGLUE repo)

In a nutshell, CodeBLEU is a weighted combination of n-gram match (BLEU), weighted n-gram match (BLEU-weighted), AST match and data-flow match scores.

The metric has shown higher correlation with human evaluation than BLEU and accuracy metrics.

How to Use

Give general statement of how to use the metric

Provide simplest possible example for using the metric

Inputs

• refarences (list[str] or list[list[str]]): reference code
• predictions (list[str]) predicted code
• lang (str): code language, see codebleu.AVAILABLE_LANGS for available languages (python, c_sharp, java at the moment)
• weights (tuple[float,float,float,float]): weights of the ngram_match, weighted_ngram_match, syntax_match, and dataflow_match respectively, defaults to (0.25, 0.25, 0.25, 0.25)
• tokenizer (callable): to split code string to tokens, defaults to s.split()

Output Values

The metric outputs the dict[str, float] with following fields:

• codebleu: the final CodeBLEU score
• ngram_match_score: ngram_match score (BLEU)
• weighted_ngram_match_score: weighted_ngram_match score (BLEU-weighted)
• syntax_match_score: syntax_match score (AST match)
• dataflow_match_score: dataflow_match score

Each of the scores is in range [0, 1], where 1 is the best score.

Examples

Using pip package (pip install codebleu):

from codebleu import calc_codebleu

prediction = "def add ( a , b ) :\n return a + b"
reference = "def sum ( first , second ) :\n return second + first"

result = calc_codebleu([reference], [prediction], lang="python", weights=(0.25, 0.25, 0.25, 0.25), tokenizer=None)
print(result)
# {
#   'codebleu': 0.5537, 
#   'ngram_match_score': 0.1041, 
#   'weighted_ngram_match_score': 0.1109, 
#   'syntax_match_score': 1.0, 
#   'dataflow_match_score': 1.0
# }

Or using evaluate library (package required):

import evaluate
metric = evaluate.load("dvitel/codebleu")

prediction = "def add ( a , b ) :\n return a + b"
reference = "def sum ( first , second ) :\n return second + first"

result = metric.compute([reference], [prediction], lang="python", weights=(0.25, 0.25, 0.25, 0.25), tokenizer=None)

Note: language is required;

Limitations and Bias

As this library require so file compilation it is platform dependent.

Currently available for Linux (manylinux) and MacOS on Python 3.8+.

Citation

@misc{ren2020codebleu,
      title={CodeBLEU: a Method for Automatic Evaluation of Code Synthesis}, 
      author={Shuo Ren and Daya Guo and Shuai Lu and Long Zhou and Shujie Liu and Duyu Tang and Neel Sundaresan and Ming Zhou and Ambrosio Blanco and Shuai Ma},
      year={2020},
      eprint={2009.10297},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}

Further References

This implementation is Based on original CodeXGLUE/CodeBLEU code -- refactored, build for macos, tested and fixed multiple crutches to make it more usable.

The source code is available at GitHub k4black/codebleu repository.