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All Samples(2)  |  Call(2)  |  Derive(0)  |  Import(0)

        def classify(repo):
    stars_per_day = score(repo)

    if stars_per_day > cutoff:
        return 'high'
    else:
        return 'low'
        


src/p/r/Predicting-Code-Popularity-HEAD/run_test.py   Predicting-Code-Popularity(Download)
    """
    class_to_id, id_to_class = utils.create_bimap(classes.classes)
    y = np.array([class_to_id[classes.classify(r)] for r in repos])
 
    # all features except imports are numerical;

src/p/r/Predicting-Code-Popularity-HEAD/module_test.py   Predicting-Code-Popularity(Download)
    vectorizer = DictVectorizer(sparse=False)
 
    y = np.array([class_to_id[classes.classify(r)] for r in repos])
    X = vectorizer.fit_transform(dict_repos)