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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 evaluate
import datasets
from .bleu import *
from .weighted_ngram_match import *
from .syntax_match import *
from .dataflow_match import *
from tree_sitter import Language, Parser
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""
# 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 reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Returns:
accuracy: description of the first score,
another_score: description of the second score,
Examples:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> my_new_module = 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):
"""TODO: Short description of my evaluation module."""
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('int64'),
'references': datasets.Value('int64'),
}),
# Homepage of the module for documentation
homepage="http://module.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
reference_urls=["http://path.to.reference.url/new_module"]
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
if self.config_name == "python":
Language.build_library('./parser/my-languages.so',['tree-sitter-python'])
elif self.config_name == "go":
Language.build_library('./parser/my-languages.so',['tree-sitter-go'])
elif self.config_name == "javascript":
Language.build_library('./parser/my-languages.so',['tree-sitter-javascript'])
elif self.config_name == "php":
Language.build_library('./parser/my-languages.so',['tree-sitter-php'])
elif self.config_name == "java":
Language.build_library('./parser/my-languages.so',['tree-sitter-java'])
elif self.config_name == "ruby":
Language.build_library('./parser/my-languages.so',['tree-sitter-ruby'])
elif self.config_name == "c-sharp":
Language.build_library('./parser/my-languages.so',['tree-sitter-c-sharp'])
elif self.config_name == "cpp":
Language.build_library('./parser/my-languages.so',['tree-sitter-cpp'])
def _compute(self, predictions, references, language, alpha=0.25, beta=0.25, gamma=0.25, theta=0.25):
# preprocess inputs
pre_references = [[x.strip() for x in open(file, 'r', encoding='utf-8').readlines()] \
for file in references]
hypothesis = [x.strip() for x in open(predictions, 'r', encoding='utf-8').readlines()]
for i in range(len(pre_references)):
assert len(hypothesis) == len(pre_references[i])
references = []
for i in range(len(hypothesis)):
ref_for_instance = []
for j in range(len(pre_references)):
ref_for_instance.append(pre_references[j][i])
references.append(ref_for_instance)
assert len(references) == len(pre_references)*len(hypothesis)
# calculate ngram match (BLEU)
tokenized_hyps = [x.split() for x in hypothesis]
tokenized_refs = [[x.split() for x in reference] for reference in references]
ngram_match_score = bleu.corpus_bleu(tokenized_refs,tokenized_hyps)
# calculate weighted ngram match
# from os import listdir
# from os.path import isfile, join
# onlyfiles = [f for f in listdir("./keywords") if isfile(join("keywords", f))]
# print(onlyfiles)
keywords = [x.strip() for x in open('./keywords/'+ language +'.txt', 'r', encoding='utf-8').readlines()]
def make_weights(reference_tokens, key_word_list):
return {token:1 if token in key_word_list else 0.2 \
for token in reference_tokens}
tokenized_refs_with_weights = [[[reference_tokens, make_weights(reference_tokens, keywords)]\
for reference_tokens in reference] for reference in tokenized_refs]
weighted_ngram_match_score = weighted_ngram_match.corpus_bleu(tokenized_refs_with_weights,tokenized_hyps)
# calculate syntax match
syntax_match_score = syntax_match.corpus_syntax_match(references, hypothesis, language)
# calculate dataflow match
dataflow_match_score = dataflow_match.corpus_dataflow_match(references, hypothesis, language)
code_bleu_score = alpha*ngram_match_score\
+ beta*weighted_ngram_match_score\
+ gamma*syntax_match_score\
+ theta*dataflow_match_score
return {
"ngram_match_score": ngram_match_score,
"weighted_ngram_match_score": weighted_ngram_match_score,
"syntax_match_score": syntax_match_score,
"dataflow_match_score": dataflow_match_score,
"code_bleu_score": code_bleu_score
} |