utils.py

import numpy as np


class2label = {'Other': 0,
               'Message-Topic(e1,e2)': 1, 'Message-Topic(e2,e1)': 2,
               'Product-Producer(e1,e2)': 3, 'Product-Producer(e2,e1)': 4,
               'Instrument-Agency(e1,e2)': 5, 'Instrument-Agency(e2,e1)': 6,
               'Entity-Destination(e1,e2)': 7, 'Entity-Destination(e2,e1)': 8,
               'Cause-Effect(e1,e2)': 9, 'Cause-Effect(e2,e1)': 10,
               'Component-Whole(e1,e2)': 11, 'Component-Whole(e2,e1)': 12,
               'Entity-Origin(e1,e2)': 13, 'Entity-Origin(e2,e1)': 14,
               'Member-Collection(e1,e2)': 15, 'Member-Collection(e2,e1)': 16,
               'Content-Container(e1,e2)': 17, 'Content-Container(e2,e1)': 18}

label2class = {0: 'Other',
               1: 'Message-Topic(e1,e2)', 2: 'Message-Topic(e2,e1)',
               3: 'Product-Producer(e1,e2)', 4: 'Product-Producer(e2,e1)',
               5: 'Instrument-Agency(e1,e2)', 6: 'Instrument-Agency(e2,e1)',
               7: 'Entity-Destination(e1,e2)', 8: 'Entity-Destination(e2,e1)',
               9: 'Cause-Effect(e1,e2)', 10: 'Cause-Effect(e2,e1)',
               11: 'Component-Whole(e1,e2)', 12: 'Component-Whole(e2,e1)',
               13: 'Entity-Origin(e1,e2)', 14: 'Entity-Origin(e2,e1)',
               15: 'Member-Collection(e1,e2)', 16: 'Member-Collection(e2,e1)',
               17: 'Content-Container(e1,e2)', 18: 'Content-Container(e2,e1)'}


def load_word2vec(embedding_path, embedding_dim, vocab):
    # initial matrix with random uniform
    initW = np.random.randn(len(vocab.vocabulary_), embedding_dim).astype(np.float32) / np.sqrt(len(vocab.vocabulary_))
    # load any vectors from the word2vec
    print("Load word2vec file {0}".format(embedding_path))
    with open(embedding_path, "rb") as f:
        header = f.readline()
        vocab_size, layer_size = map(int, header.split())
        binary_len = np.dtype('float32').itemsize * layer_size
        for line in range(vocab_size):
            word = []
            while True:
                ch = f.read(1).decode('latin-1')
                if ch == ' ':
                    word = ''.join(word)
                    break
                if ch != '\n':
                    word.append(ch)
            idx = vocab.vocabulary_.get(word)
            if idx != 0:
                initW[idx] = np.fromstring(f.read(binary_len), dtype='float32')
            else:
                f.read(binary_len)
    return initW