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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""
import datasets
import evaluate
from codebleu import calc_codebleu
# TODO: Add BibTeX citation
_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}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
Unofficial `CodeBLEU` implementation with Linux and MacOS supports available with PyPI and HF HUB.
Based on original [CodeXGLUE/CodeBLEU](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans/evaluator/CodeBLEU) code -- refactored, build for macos, tested and fixed multiple crutches to make it more usable.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculate a weighted combination of `n-gram match (BLEU)`, `weighted n-gram match (BLEU-weighted)`, `AST match` and `data-flow match` scores.
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
language: programming language in ['java','js','c_sharp','php','go','python','ruby'].
weights: tuple of 4 floats to use as weights for scores. Defaults to (0.25, 0.25, 0.25, 0.25).
Returns:
codebleu: resulting `CodeBLEU` score,
ngram_match_score: resulting `n-gram match (BLEU)` score,
weighted_ngram_match_score: resulting `weighted n-gram match (BLEU-weighted)` score,
syntax_match_score: resulting `AST match` score,
dataflow_match_score: resulting `data-flow match` score,
Examples:
>>> metric = evaluate.load("k4black/codebleu")
>>> ref = "def sum ( first , second ) :\n return second + first"
>>> pred = "def add ( a , b ) :\n return a + b"
>>> results = metric.compute(references=[ref], predictions=[pred], language="python")
>>> print(results)
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class codebleu(evaluate.Metric):
"""CodeBLEU metric from CodexGLUE"""
def _info(self):
# TODO: Specifies the evaluate.EvaluationModuleInfo object
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=[
datasets.Features(
{
"predictions": datasets.Value("string", id="sequence"),
"references": datasets.Sequence(datasets.Value("string", id="sequence"), id="references"),
# "lang": datasets.Value("string"),
# "weights": datasets.Value("string"),
# "tokenizer": datasets.Value("string"),
}
),
datasets.Features(
{
"predictions": datasets.Value("string", id="sequence"),
"references": datasets.Value("string", id="sequence"),
# "lang": datasets.Value("string"),
# "weights": datasets.Value("string"),
# "tokenizer": datasets.Value("string"),
}
),
],
# Homepage of the module for documentation
homepage="https://github.com/k4black/codebleu",
# Additional links to the codebase or references
codebase_urls=["https://github.com/k4black/codebleu"],
reference_urls=[
"https://github.com/k4black/codebleu",
"https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-to-code-trans/evaluator",
"https://arxiv.org/abs/2009.10297",
],
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
pass
def _compute(self, predictions, references, lang, weights=(0.25, 0.25, 0.25, 0.25), tokenizer=None):
"""Returns the scores"""
return calc_codebleu(
references=references,
predictions=predictions,
lang=lang,
weights=weights,
tokenizer=tokenizer,
)
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