88b754261ee28f8e4143a573135a0f33da42d249,bugbug/similarity.py,NeighborsSimilarity,__init__,#NeighborsSimilarity#Any#Any#Any#,205

Before Change

class NeighborsSimilarity(BaseSimilarity):
    def __init__(self, k=10, vectorizer=TfidfVectorizer(), cleanup_urls=True):
        super().__init__(cleanup_urls=cleanup_urls)
        self.vectorizer = vectorizer
        self.similarity_calculator = NearestNeighbors(n_neighbors=k)
        text = []
        self.bug_ids = []

        for bug in bugzilla.get_bugs():
            text.append(self.text_preprocess(self.get_text(bug), join=True))
            self.bug_ids.append(bug["id"])

        self.vectorizer.fit(text)
        self.similarity_calculator.fit(self.vectorizer.transform(text))

    def get_similar_bugs(self, query):

        processed_query = self.vectorizer.transform([self.get_text(query)])

After Change

class Word2VecWmdSimilarity(BaseSimilarity):
    def __init__(self, cut_off=0.2, cleanup_urls=True, nltk_tokenizer=False):
        super().__init__(cleanup_urls=cleanup_urls, nltk_tokenizer=nltk_tokenizer)
        self.corpus = []
        self.bug_ids = []
        self.cut_off = cut_off
        for bug in bugzilla.get_bugs():
            self.corpus.append(self.text_preprocess(self.get_text(bug)))
            self.bug_ids.append(bug["id"])

        indexes = list(range(len(self.corpus)))
        random.shuffle(indexes)
        self.corpus = [self.corpus[idx] for idx in indexes]
        self.bug_ids = [self.bug_ids[idx] for idx in indexes]

        self.w2vmodel = Word2Vec(self.corpus, size=100, min_count=5)
        self.w2vmodel.init_sims(replace=True)

    // word2vec.wmdistance calculates only the euclidean distance. To get the cosine distance,
    // we"re using the function with a few subtle changes. We compute the cosine distances

Instances