# 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 }