b4d5c80c83ec0502231ff3563fa8ac5b95717267,conceptnet5/vectors/transforms.py,,make_replacements,#,110

Before Change

    intersected = big_frame.loc[small_frame.index].dropna()
    index, index_map = build_annoy_tree(intersected, tree_depth)
    replacements = {}
    average_similarity = []
    for term in big_frame.index:
        if term not in small_frame.index:
            indices, scores = index.get_nns_by_vector(big_frame.loc[term], 2,
                                                   include_distances=True)

After Change

    replacements = {}
    for term in big_frame.index:
        if term not in small_frame.index:
            most_similar = similar_to_vec(intersected, big_frame.loc[term], limit=1)
            got = list(most_similar.index)
            if got:
                replacements[term] = got[0]
    return replacements


def choose_small_vocabulary(big_frame):

Instances