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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.
"""CodeBLEU metric."""
import evaluate
import datasets
from .my_codebleu import calc_codebleu
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {CodeBLEU: A Metric for Evaluating Code Generation},
authors={Sedykh, Ivan},
year={2022}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is an adaptation of the original CodeBLEU metric from CodexGLUE benchmark
for evaluating code generation.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of lists of references. Each list
should contain len(predictions) items.
lang: programming language in ['java','js','c_sharp','php','go','python','ruby']
tokenizer: tokenizer function str -> List[str], Defaults to lambda s: s.split()
params: str, weights for averaging(see CodeBLEU paper).
Defaults to equal weights "0.25,0.25,0.25,0.25".
Returns:
CodeBLEU: resulting score,
ngram_match_score: See paper CodeBLEU,
weighted_ngram_match_score: See paper CodeBLEU,
syntax_match_score: See paper CodeBLEU,
dataflow_match_score: See paper CodeBLEU,
Examples:
>>> codebleu = evaluate.load("my_new_module")
>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'accuracy': 1.0}
"""
# TODO: Define external resources urls if needed
# BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
@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"),
"references": datasets.Sequence(datasets.Value("string")),
}
),
# Homepage of the module for documentation
homepage="",
# Additional links to the codebase or references
codebase_urls=[],
reference_urls=[
"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
# source CodeBLEU/parser/build.sh
pass
def _compute(
self,
predictions,
references,
lang,
tokenizer=None,
params="0.25,0.25,0.25,0.25",
):
"""Returns the scores"""
res = calc_codebleu(
predictions=predictions,
references=references,
lang=lang,
tokenizer=tokenizer,
params=params,
)
return res
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