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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# -*- coding:utf-8 -*-
import argparse
from .bleu import corpus_bleu
from .weighted_ngram_match import corpus_weighted_ngram_match
from .syntax_match import corpus_syntax_match
from .dataflow_match import corpus_dataflow_match
import os
def calculate(predictions, references, language="python", alpha=0.25, beta=0.25, gamma=0.25, theta=0.25):
# preprocess inputs
pre_references = [[s.strip() for s in my_list] for my_list in references]
hypothesis = [s.strip() for s in predictions]
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 = 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)
curr_path = os.path.dirname(os.path.abspath(__file__))
keywords = [x.strip() for x in open(curr_path + "/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 = corpus_weighted_ngram_match(tokenized_refs_with_weights,tokenized_hyps)
# calculate syntax match
syntax_match_score = corpus_syntax_match(references, hypothesis, language)
# calculate dataflow match
dataflow_match_score = 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
}
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