process.py

import json
import os
import tarfile
import zipfile
import gzip
import requests
from itertools import chain
from glob import glob
import gdown
from random import seed, shuffle
from datasets import load_dataset

k = 10  # the 3rd level negative-distance ranking
m = 5  # the 3rd level negative-distance ranking
top_n = 10  # threshold of positive pairs in the 1st and 2nd relation
seed(42)


def wget(url, cache_dir: str = './cache', gdrive_filename: str = None):
    """ wget and uncompress data_iterator """
    os.makedirs(cache_dir, exist_ok=True)
    if url.startswith('https://drive.google.com'):
        assert gdrive_filename is not None, 'please provide fileaname for gdrive download'
        gdown.download(url, f'{cache_dir}/{gdrive_filename}', quiet=False)
        filename = gdrive_filename
    else:
        filename = os.path.basename(url)
    with open(f'{cache_dir}/{filename}', "wb") as f:
        r = requests.get(url)
        f.write(r.content)
    path = f'{cache_dir}/{filename}'

    if path.endswith('.tar.gz') or path.endswith('.tgz') or path.endswith('.tar'):
        if path.endswith('.tar'):
            tar = tarfile.open(path)
        else:
            tar = tarfile.open(path, "r:gz")
        tar.extractall(cache_dir)
        tar.close()
        os.remove(path)
    elif path.endswith('.zip'):
        with zipfile.ZipFile(path, 'r') as zip_ref:
            zip_ref.extractall(cache_dir)
        os.remove(path)
    elif path.endswith('.gz'):
        with gzip.open(path, 'rb') as f:
            with open(path.replace('.gz', ''), 'wb') as f_write:
                f_write.write(f.read())
        os.remove(path)


def get_training_data():
    """ Get RelBERT training data

    Returns
    -------
    pairs: dictionary of list (positive pairs, negative pairs)
    {'1b': [[0.6, ('office', 'desk'), ..], [[-0.1, ('aaa', 'bbb'), ...]]
    """
    cache_dir = 'cache'
    os.makedirs(cache_dir, exist_ok=True)
    remove_relation = None
    path_answer = f'{cache_dir}/Phase2Answers'
    path_scale = f'{cache_dir}/Phase2AnswersScaled'
    url = 'https://drive.google.com/u/0/uc?id=0BzcZKTSeYL8VYWtHVmxUR3FyUmc&export=download'
    filename = 'SemEval-2012-Platinum-Ratings.tar.gz'
    if not (os.path.exists(path_scale) and os.path.exists(path_answer)):
        wget(url, gdrive_filename=filename, cache_dir=cache_dir)
    files_answer = [os.path.basename(i) for i in glob(f'{path_answer}/*.txt')]
    files_scale = [os.path.basename(i) for i in glob(f'{path_scale}/*.txt')]
    assert files_answer == files_scale, f'files are not matched: {files_scale} vs {files_answer}'
    positives = {}
    negatives = {}
    positives_limit = {}
    all_relation_type = {}
    # score_range = [90.0, 88.7]  # the absolute value of max/min prototypicality rating
    for i in files_scale:
        relation_id = i.split('-')[-1].replace('.txt', '')
        if remove_relation and int(relation_id[:-1]) in remove_relation:
            continue
        with open(f'{path_answer}/{i}', 'r') as f:
            lines_answer = [_l.replace('"', '').split('\t') for _l in f.read().split('\n')
                            if not _l.startswith('#') and len(_l)]
            relation_type = list(set(list(zip(*lines_answer))[-1]))
            assert len(relation_type) == 1, relation_type
            relation_type = relation_type[0]
        with open(f'{path_scale}/{i}', 'r') as f:
            # list of tuple [score, ("a", "b")]
            scales = [[float(_l[:5]), _l[6:].replace('"', '')] for _l in f.read().split('\n')
                      if not _l.startswith('#') and len(_l)]
            scales = sorted(scales, key=lambda _x: _x[0])
            # positive pairs are in the reverse order of prototypicality score
            positive_pairs = [[s, tuple(p.split(':'))] for s, p in filter(lambda _x: _x[0] > 0, scales)]
            positive_pairs = sorted(positive_pairs, key=lambda x:  x[0], reverse=True)
            positives[relation_id] = list(list(zip(*positive_pairs))[1])
            positives_limit[relation_id] = list(list(zip(*positive_pairs[:min(top_n, len(positive_pairs))]))[1])
            negatives[relation_id] = [tuple(p.split(':')) for s, p in filter(lambda _x: _x[0] < 0, scales)]
        all_relation_type[relation_id] = relation_type
    parent = list(set([i[:-1] for i in all_relation_type.keys()]))

    # 1st level relation contrast (among parent relations)
    relation_pairs_1st = []
    for p in parent:
        child_positive = list(filter(lambda x: x.startswith(p), list(all_relation_type.keys())))
        child_negative = list(filter(lambda x: not x.startswith(p), list(all_relation_type.keys())))
        positive_pairs = []
        negative_pairs = []
        for c in child_positive:
            positive_pairs += positives_limit[c]
        for c in child_negative:
            negative_pairs += positives_limit[c]

        relation_pairs_1st += [{
            "positives": positive_pairs, "negatives": negative_pairs, "relation_type": p, "level": "parent"
        }]

    # 2nd level relation contrast (among child relations) & 3rd level relation contrast (within child relations)
    relation_pairs_2nd = []
    for p in all_relation_type.keys():
        positive_pairs = positives_limit[p]
        negative_pairs = []
        for n in all_relation_type.keys():
            if p == n:
                continue
            negative_pairs += positives[n]

        relation_pairs_2nd += [{
            "positives": positive_pairs, "negatives": negative_pairs, "relation_type": p, "level": "child"
        }]

    relation_pairs_3rd = []
    for p in all_relation_type.keys():
        positive_pairs = positives[p]
        negative_pairs = positives[p] + negatives[p]
        for n, anchor in enumerate(positive_pairs):
            if n > m:
                continue
            for _n, posi in enumerate(positive_pairs):
                if n < _n and len(negative_pairs) > _n + k:
                    relation_pairs_3rd += [{
                        "positives": [anchor, posi],
                        "negatives": negative_pairs[_n + k:],
                        "relation_type": p,
                        "level": "child_prototypical"
                    }]
                print(len(relation_pairs_3rd))

    train = relation_pairs_1st + relation_pairs_2nd + relation_pairs_3rd

    # conceptnet as the validation set
    cn = load_dataset('relbert/conceptnet_high_confidence_v2')
    valid = list(chain(*cn.values()))
    for i in valid:
        i['level'] = 'N/A'
    return train, valid


if __name__ == '__main__':
    data_train, data_validation = get_training_data()
    print(f"- training data     : {len(data_train)}")
    print(f"- validation data   : {len(data_validation)}")
    with open('dataset/train.jsonl', 'w') as f_writer:
        f_writer.write('\n'.join([json.dumps(i) for i in data_train]))
    with open('dataset/valid.jsonl', 'w') as f_writer:
        f_writer.write('\n'.join([json.dumps(i) for i in data_validation]))