Python DataSet.init_data_matrices方法代码示例

# 需要导入模块: from dataset import DataSet [as 别名]
# 或者: from dataset.DataSet import init_data_matrices [as 别名]

#.........这里部分代码省略.........
    # we have no terminal tokens in ifttt
    all_terminal_tokens = []
    terminal_vocab = gen_vocab(all_terminal_tokens, vocab_size=4000, freq_cutoff=WORD_FREQ_CUT_OFF)

    # now generate the dataset!

    train_data = DataSet(annot_vocab, terminal_vocab, grammar, 'ifttt.train_data')
    dev_data = DataSet(annot_vocab, terminal_vocab, grammar, 'ifttt.dev_data')
    test_data = DataSet(annot_vocab, terminal_vocab, grammar, 'ifttt.test_data')

    all_examples = []

    can_fully_reconstructed_examples_num = 0
    examples_with_empty_actions_num = 0

    for entry in data:
        idx = entry['id']
        query_tokens = entry['query_tokens']
        code = entry['code']
        parse_tree = entry['parse_tree']

        # check if query tokens are valid
        query_token_ids = [annot_vocab[token] for token in query_tokens if token not in string.punctuation]
        valid_query_tokens_ids = [tid for tid in query_token_ids if tid != annot_vocab.unk]

        # remove examples with rare words from train and dev, avoid overfitting
        if len(valid_query_tokens_ids) == 0 and 0 <= idx < 77495 + 5171:
            continue

        rule_list, rule_parents = parse_tree.get_productions(include_value_node=True)

        actions = []
        can_fully_reconstructed = True
        rule_pos_map = dict()

        for rule_count, rule in enumerate(rule_list):
            if not grammar.is_value_node(rule.parent):
                assert rule.value is None
                parent_rule = rule_parents[(rule_count, rule)][0]
                if parent_rule:
                    parent_t = rule_pos_map[parent_rule]
                else:
                    parent_t = 0

                rule_pos_map[rule] = len(actions)

                d = {'rule': rule, 'parent_t': parent_t, 'parent_rule': parent_rule}
                action = Action(APPLY_RULE, d)

                actions.append(action)
            else:
                raise RuntimeError('no terminals should be in ifttt dataset!')

        if len(actions) == 0:
            examples_with_empty_actions_num += 1
            continue

        example = DataEntry(idx, query_tokens, parse_tree, code, actions,
                            {'str_map': None, 'raw_code': entry['raw_code']})

        if can_fully_reconstructed:
            can_fully_reconstructed_examples_num += 1

        # train, valid, test splits
        if 0 <= idx < 77495:
            train_data.add(example)
        elif idx < 77495 + 5171:
            dev_data.add(example)
        else:
            test_data.add(example)

        all_examples.append(example)

    # print statistics
    max_query_len = max(len(e.query) for e in all_examples)
    max_actions_len = max(len(e.actions) for e in all_examples)

    # serialize_to_file([len(e.query) for e in all_examples], 'query.len')
    # serialize_to_file([len(e.actions) for e in all_examples], 'actions.len')

    logging.info('train_data examples: %d', train_data.count)
    logging.info('dev_data examples: %d', dev_data.count)
    logging.info('test_data examples: %d', test_data.count)

    logging.info('examples that can be fully reconstructed: %d/%d=%f',
                 can_fully_reconstructed_examples_num, len(all_examples),
                 can_fully_reconstructed_examples_num / len(all_examples))
    logging.info('empty_actions_count: %d', examples_with_empty_actions_num)

    logging.info('max_query_len: %d', max_query_len)
    logging.info('max_actions_len: %d', max_actions_len)

    train_data.init_data_matrices(max_query_length=40, max_example_action_num=6)
    dev_data.init_data_matrices()
    test_data.init_data_matrices()

    serialize_to_file((train_data, dev_data, test_data),
                      'data/ifttt.freq{WORD_FREQ_CUT_OFF}.bin'.format(WORD_FREQ_CUT_OFF=WORD_FREQ_CUT_OFF))

    return train_data, dev_data, test_data

# 需要导入模块: from dataset import DataSet [as 别名]
# 或者: from dataset.DataSet import init_data_matrices [as 别名]

#.........这里部分代码省略.........
        for rule_count, rule in enumerate(rule_list):
            if not grammar.is_value_node(rule.parent):
                assert rule.value is None
                parent_rule = rule_parents[(rule_count, rule)][0]
                if parent_rule:
                    parent_t = rule_pos_map[parent_rule]
                else:
                    parent_t = 0

                rule_pos_map[rule] = len(actions)

                d = {'rule': rule, 'parent_t': parent_t, 'parent_rule': parent_rule}
                action = Action(APPLY_RULE, d)

                actions.append(action)
            else:
                assert rule.is_leaf

                parent_rule = rule_parents[(rule_count, rule)][0]
                parent_t = rule_pos_map[parent_rule]

                terminal_val = rule.value
                terminal_str = str(terminal_val)
                terminal_tokens = get_terminal_tokens(terminal_str)

                # assert len(terminal_tokens) > 0

                for terminal_token in terminal_tokens:
                    term_tok_id = terminal_vocab[terminal_token]
                    tok_src_idx = -1
                    try:
                        tok_src_idx = query_tokens.index(terminal_token)
                    except ValueError:
                        pass

                    d = {'literal': terminal_token, 'rule': rule, 'parent_rule': parent_rule, 'parent_t': parent_t}

                    # cannot copy, only generation
                    # could be unk!
                    if tok_src_idx < 0 or tok_src_idx >= MAX_QUERY_LENGTH:
                        action = Action(GEN_TOKEN, d)
                        if terminal_token not in terminal_vocab:
                            if terminal_token not in query_tokens:
                                # print terminal_token
                                can_fully_reconstructed = False
                    else:  # copy
                        if term_tok_id != terminal_vocab.unk:
                            d['source_idx'] = tok_src_idx
                            action = Action(GEN_COPY_TOKEN, d)
                        else:
                            d['source_idx'] = tok_src_idx
                            action = Action(COPY_TOKEN, d)

                    actions.append(action)

                d = {'literal': '<eos>', 'rule': rule, 'parent_rule': parent_rule, 'parent_t': parent_t}
                actions.append(Action(GEN_TOKEN, d))

        if len(actions) == 0:
            examples_with_empty_actions_num += 1
            continue

        example = DataEntry(idx, query_tokens, parse_tree, code, actions, {'str_map': None, 'raw_code': entry['raw_code']})

        if can_fully_reconstructed:
            can_fully_reconstructed_examples_num += 1

        # train, valid, test splits
        if 0 <= idx < 533:
            train_data.add(example)
        elif idx < 599:
            dev_data.add(example)
        else:
            test_data.add(example)

        all_examples.append(example)

    # print statistics
    max_query_len = max(len(e.query) for e in all_examples)
    max_actions_len = max(len(e.actions) for e in all_examples)

    # serialize_to_file([len(e.query) for e in all_examples], 'query.len')
    # serialize_to_file([len(e.actions) for e in all_examples], 'actions.len')

    logging.info('examples that can be fully reconstructed: %d/%d=%f',
                 can_fully_reconstructed_examples_num, len(all_examples),
                 can_fully_reconstructed_examples_num / len(all_examples))
    logging.info('empty_actions_count: %d', examples_with_empty_actions_num)

    logging.info('max_query_len: %d', max_query_len)
    logging.info('max_actions_len: %d', max_actions_len)

    train_data.init_data_matrices(max_query_length=70, max_example_action_num=350)
    dev_data.init_data_matrices(max_query_length=70, max_example_action_num=350)
    test_data.init_data_matrices(max_query_length=70, max_example_action_num=350)

    serialize_to_file((train_data, dev_data, test_data),
                      'data/hs.freq{WORD_FREQ_CUT_OFF}.max_action350.pre_suf.unary_closure.bin'.format(WORD_FREQ_CUT_OFF=WORD_FREQ_CUT_OFF))

    return train_data, dev_data, test_data