process.py

import json
import os
import tarfile
import zipfile
import gzip
import requests

from glob import glob
import gdown
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


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(return_validation_set: bool = False):
    """ 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 = {}
    all_relation_type = {}
    positives_score = {}
    # 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)
            if return_validation_set:
                positive_pairs = positive_pairs[min(top_n, len(positive_pairs)):]
                if len(positive_pairs) == 0:
                    continue
            else:
                positive_pairs = positive_pairs[:min(top_n, len(positive_pairs))]
            positives_score[relation_id] = positive_pairs
            positives[relation_id] = list(list(zip(*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[c]
            # negative_pairs += negatives[c]
        for c in child_negative:
            negative_pairs += positives[c]
            # negative_pairs += negatives[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 = []
    relation_pairs_3rd = []
    for p in all_relation_type.keys():
        positive_pairs = positives[p]
        negative_pairs = []
        # negative_pairs = negatives[p]
        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"
        }]

        negative_pairs = positive_pairs + 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": [(anchor, neg) for neg in negative_pairs[_n+k:]],
                        "relation_type": p,
                        "level": "child_prototypical"
                    }]

    return relation_pairs_1st + relation_pairs_2nd + relation_pairs_3rd


if __name__ == '__main__':
    data_train = get_training_data(return_validation_set=False)
    with open('dataset/train.jsonl', 'w') as f_writer:
        f_writer.write('\n'.join([json.dumps(i) for i in data_train]))
    data_valid = get_training_data(return_validation_set=True)
    with open('dataset/valid.jsonl', 'w') as f_writer:
        f_writer.write('\n'.join([json.dumps(i) for i in data_valid]))