create_queries.py

import pickle
import os.path as osp
import numpy as np
import click
from collections import defaultdict
import random
from copy import deepcopy
import time
import pdb
import logging
import os

def set_logger(save_path, query_name, print_on_screen=False):
    '''
    Write logs to checkpoint and console
    '''

    log_file = os.path.join(save_path, '%s.log'%(query_name))

    logging.basicConfig(
        format='%(asctime)s %(levelname)-8s %(message)s',
        level=logging.INFO,
        datefmt='%Y-%m-%d %H:%M:%S',
        filename=log_file,
        filemode='w'
    )
    if print_on_screen:
        console = logging.StreamHandler()
        console.setLevel(logging.INFO)
        formatter = logging.Formatter('%(asctime)s %(levelname)-8s %(message)s')
        console.setFormatter(formatter)
        logging.getLogger('').addHandler(console)

def set_global_seed(seed):
    np.random.seed(seed)
    random.seed(seed)

def index_dataset(dataset_name, force=False):
    print('Indexing dataset {0}'.format(dataset_name))
    base_path = 'data/{0}/'.format(dataset_name)
    # files = ['train.txt', 'valid.txt', 'test.txt']
    # indexified_files = ['train_indexified.txt', 'valid_indexified.txt', 'test_indexified.txt']
    files = ['train.txt']
    indexified_files = ['train_indexified.txt']
    return_flag = True
    for i in range(len(indexified_files)):
        if not osp.exists(osp.join(base_path, indexified_files[i])):
            return_flag = False
            break
    if return_flag and not force:
        print ("index file exists")
        return  

    ent2id, rel2id, id2rel, id2ent = {}, {}, {}, {}

    entid, relid = 0, 0

    with open(osp.join(base_path, files[0])) as f:
        lines = f.readlines()
        file_len = len(lines)

    for p, indexified_p in zip(files, indexified_files):
        fw = open(osp.join(base_path, indexified_p), "w")
        with open(osp.join(base_path, p), 'r') as f:
            for i, line in enumerate(f):
                print ('[%d/%d]'%(i, file_len), end='\r')
                e1, rel, e2 = line.split('\t')
                e1 = e1.strip()
                e2 = e2.strip()
                rel = rel.strip()
                rel_reverse = '-' + rel
                rel = '+' + rel
                # rel_reverse = rel+ '_reverse'

                if p == "train.txt":
                    if e1 not in ent2id.keys():
                        ent2id[e1] = entid
                        id2ent[entid] = e1
                        entid += 1

                    if e2 not in ent2id.keys():
                        ent2id[e2] = entid
                        id2ent[entid] = e2
                        entid += 1

                    if not rel in rel2id.keys():
                        rel2id[rel] = relid
                        id2rel[relid] = rel
                        assert relid % 2 == 0
                        relid += 1

                    if not rel_reverse in rel2id.keys():
                        rel2id[rel_reverse] = relid
                        id2rel[relid] = rel_reverse
                        assert relid % 2 == 1
                        relid += 1

                if e1 in ent2id.keys() and e2 in ent2id.keys():
                    fw.write("\t".join([str(ent2id[e1]), str(rel2id[rel]), str(ent2id[e2])]) + "\n")
                    fw.write("\t".join([str(ent2id[e2]), str(rel2id[rel_reverse]), str(ent2id[e1])]) + "\n")
        fw.close()

    with open(osp.join(base_path, "stats.txt"), "w") as fw:
        fw.write("numentity: " + str(len(ent2id)) + "\n")
        fw.write("numrelations: " + str(len(rel2id)))
    with open(osp.join(base_path, 'ent2id.pkl'), 'wb') as handle:
        pickle.dump(ent2id, handle, protocol=pickle.HIGHEST_PROTOCOL)
    with open(osp.join(base_path, 'rel2id.pkl'), 'wb') as handle:
        pickle.dump(rel2id, handle, protocol=pickle.HIGHEST_PROTOCOL)
    with open(osp.join(base_path, 'id2ent.pkl'), 'wb') as handle:
        pickle.dump(id2ent, handle, protocol=pickle.HIGHEST_PROTOCOL)
    with open(osp.join(base_path, 'id2rel.pkl'), 'wb') as handle:
        pickle.dump(id2rel, handle, protocol=pickle.HIGHEST_PROTOCOL)

    print ('num entity: %d, num relation: %d'%(len(ent2id), len(rel2id)))
    print ("indexing finished!!")

def construct_graph(base_path, indexified_files):
    #knowledge graph
    #kb[e][rel] = set([e, e, e])
    ent_in, ent_out = defaultdict(lambda: defaultdict(set)), defaultdict(lambda: defaultdict(set))
    for indexified_p in indexified_files:
        with open(osp.join(base_path, indexified_p)) as f:
            for i, line in enumerate(f):
                if len(line) == 0:
                    continue
                e1, rel, e2 = line.split('\t')
                e1 = int(e1.strip())
                e2 = int(e2.strip())
                rel = int(rel.strip())
                ent_out[e1][rel].add(e2)
                ent_in[e2][rel].add(e1)

    return ent_in, ent_out

def list2tuple(l):
    return tuple(list2tuple(x) if type(x)==list else x for x in l)

def tuple2list(t):
    return list(tuple2list(x) if type(x)==tuple else x for x in t)

def write_links(index_f, dataset, ent_out, small_ent_out, max_ans_num, name):
    queries = defaultdict(set)
    tp_answers = defaultdict(set)
    fn_answers = defaultdict(set)
    fp_answers = defaultdict(set)
    num_more_answer = 0
    for ent in ent_out:
        for rel in ent_out[ent]:
            if len(ent_out[ent][rel]) <= max_ans_num:
                queries[('e', ('r',))].add((ent, (rel,)))
                tp_answers[(ent, (rel,))] = small_ent_out[ent][rel]
                fn_answers[(ent, (rel,))] = ent_out[ent][rel]
            else:
                num_more_answer += 1

    with open('./data/%s/%s/%s-queries.pkl'%(dataset, index_f, name), 'wb') as f:
        pickle.dump(queries, f)
    with open('./data/%s/%s/%s-easy-answers.pkl'%(dataset, index_f, name), 'wb') as f:
        pickle.dump(tp_answers, f)
    with open('./data/%s/%s/%s-hard-answers.pkl'%(dataset, index_f, name), 'wb') as f:
        pickle.dump(fn_answers, f)
    with open('./data/%s/%s/%s-answers.pkl'%(dataset, index_f, name), 'wb') as f:
        pickle.dump(fp_answers, f)
    print (num_more_answer)

def ground_queries(index_f, dataset, query_structure, ent_in, ent_out, small_ent_in, small_ent_out, gen_num, max_ans_num, query_name, mode, ent2id, rel2id):
    num_sampled, num_try, num_repeat, num_more_answer, num_broken, num_no_extra_answer, num_no_extra_negative, num_empty = 0, 0, 0, 0, 0, 0, 0, 0
    tp_ans_num, fp_ans_num, fn_ans_num = [], [], []
    queries = defaultdict(set)
    tp_answers = defaultdict(set)
    fp_answers = defaultdict(set)
    fn_answers = defaultdict(set)
    s0 = time.time()
    old_num_sampled = -1
    while num_sampled < gen_num:
        if num_sampled != 0:
            if num_sampled % (gen_num//100) == 0 and num_sampled != old_num_sampled:
                logging.info('%s %s: [%d/%d], avg time: %s, try: %s, repeat: %s: more_answer: %s, broken: %s, no extra: %s, no negative: %s empty: %s'%(mode, 
                    query_structure, 
                    num_sampled, gen_num, (time.time()-s0)/num_sampled, num_try, num_repeat, num_more_answer, 
                    num_broken, num_no_extra_answer, num_no_extra_negative, num_empty))
                old_num_sampled = num_sampled
        print ('%s %s: [%d/%d], avg time: %s, try: %s, repeat: %s: more_answer: %s, broken: %s, no extra: %s, no negative: %s empty: %s'%(mode, 
            query_structure, 
            num_sampled, gen_num, (time.time()-s0)/(num_sampled+0.001), num_try, num_repeat, num_more_answer, 
            num_broken, num_no_extra_answer, num_no_extra_negative, num_empty), end='\r')
        num_try += 1
        empty_query_structure = deepcopy(query_structure)
        answer = random.sample(ent_in.keys(), 1)[0]
        broken_flag = fill_query(empty_query_structure, ent_in, ent_out, answer, ent2id, rel2id)
        if broken_flag:
            num_broken += 1
            continue
        query = empty_query_structure
        answer_set = achieve_answer(query, ent_in, ent_out)
        small_answer_set = achieve_answer(query, small_ent_in, small_ent_out)
        if len(answer_set) == 0:
            num_empty += 1
            continue
        if mode != 'train':
            if len(answer_set - small_answer_set) == 0:
                num_no_extra_answer += 1
                continue
            if 'n' in query_name:
                if len(small_answer_set - answer_set) == 0:
                    num_no_extra_negative += 1
                    continue
        if max(len(answer_set - small_answer_set), len(small_answer_set - answer_set)) > max_ans_num:
            num_more_answer += 1
            continue
        if list2tuple(query) in queries[list2tuple(query_structure)]:
            num_repeat += 1
            continue
        queries[list2tuple(query_structure)].add(list2tuple(query))
        tp_answers[list2tuple(query)] = small_answer_set
        fp_answers[list2tuple(query)] = small_answer_set - answer_set
        fn_answers[list2tuple(query)] = answer_set - small_answer_set
        num_sampled += 1
        tp_ans_num.append(len(tp_answers[list2tuple(query)]))
        fp_ans_num.append(len(fp_answers[list2tuple(query)]))
        fn_ans_num.append(len(fn_answers[list2tuple(query)]))

    print ()
    logging.info ("{} tp max: {}, min: {}, mean: {}, std: {}".format(mode, np.max(tp_ans_num), np.min(tp_ans_num), np.mean(tp_ans_num), np.std(tp_ans_num)))
    logging.info ("{} fp max: {}, min: {}, mean: {}, std: {}".format(mode, np.max(fp_ans_num), np.min(fp_ans_num), np.mean(fp_ans_num), np.std(fp_ans_num)))
    logging.info ("{} fn max: {}, min: {}, mean: {}, std: {}".format(mode, np.max(fn_ans_num), np.min(fn_ans_num), np.mean(fn_ans_num), np.std(fn_ans_num)))

    name_to_save = '%s'%(mode)
    with open('./data/%s/%s/%s-queries.pkl'%(dataset, index_f, name_to_save), 'wb') as f:
        pickle.dump(queries, f)
    with open('./data/%s/%s/%s-answers.pkl'%(dataset, index_f, name_to_save), 'wb') as f:
        pickle.dump(fp_answers, f)
    with open('./data/%s/%s/%s-hard-answers.pkl'%(dataset, index_f, name_to_save), 'wb') as f:
        pickle.dump(fn_answers, f)
    with open('./data/%s/%s/%s-easy-answers.pkl'%(dataset,index_f, name_to_save), 'wb') as f:
        pickle.dump(tp_answers, f)
    return queries, tp_answers, fp_answers, fn_answers

def generate_queries(index_f, dataset, query_structures, gen_num, max_ans_num, gen_train, gen_valid, gen_test, query_names, save_name):
    base_path = './data/%s'%dataset
    indexified_files = ['train.txt', 'valid.txt', 'test.txt']
    if gen_train or gen_valid:
        train_ent_in, train_ent_out = construct_graph(base_path, indexified_files[:1]) # ent_in 
    if gen_valid or gen_test:
        valid_ent_in, valid_ent_out = construct_graph(base_path, indexified_files[:2])
        valid_only_ent_in, valid_only_ent_out = construct_graph(base_path, indexified_files[1:2])
    if gen_test:
        test_ent_in, test_ent_out = construct_graph(base_path, indexified_files[:3])
        test_only_ent_in, test_only_ent_out = construct_graph(base_path, indexified_files[2:3])

    ent2id = pickle.load(open(os.path.join(base_path, "ent2id.pkl"), 'rb'))
    rel2id = pickle.load(open(os.path.join(base_path, "rel2id.pkl"), 'rb'))

    train_queries = defaultdict(set)
    train_tp_answers = defaultdict(set)
    train_fp_answers = defaultdict(set)
    train_fn_answers = defaultdict(set)
    valid_queries = defaultdict(set)
    valid_tp_answers = defaultdict(set)
    valid_fp_answers = defaultdict(set)
    valid_fn_answers = defaultdict(set)
    test_queries = defaultdict(set)
    test_answers = defaultdict(set)
    test_tp_answers = defaultdict(set)
    test_fp_answers = defaultdict(set)
    test_fn_answers = defaultdict(set)

    t1, t2, t3, t4, t5, t6 = 0, 0, 0, 0, 0, 0
    assert len(query_structures) == 1
    idx = 0
    query_structure = query_structures[idx]
    query_name = query_names[idx] if save_name else str(idx)
    print ('general structure is', query_structure, "with name", query_name)
    if query_structure == ['e', ['r']]:
        if gen_train:
            write_links(index_f, dataset, train_ent_out, defaultdict(lambda: defaultdict(set)), max_ans_num, 'train')
        if gen_valid:
            write_links(index_f, dataset, valid_only_ent_out, train_ent_out, max_ans_num, 'valid')
        if gen_test:
            write_links(index_f, dataset, test_only_ent_out, valid_ent_out, max_ans_num, 'test')
        print ("link prediction created!")
        exit(-1)
    
    name_to_save = query_name
    set_logger("./data/{}/".format(dataset), name_to_save)

    num_sampled, num_try, num_repeat, num_more_answer, num_broken, num_empty = 0, 0, 0, 0, 0, 0
    train_ans_num = []
    s0 = time.time()
    if gen_train:
        train_queries, train_tp_answers, train_fp_answers, train_fn_answers = ground_queries(index_f, dataset, query_structure,
            train_ent_in, train_ent_out, defaultdict(lambda: defaultdict(set)), defaultdict(lambda: defaultdict(set)), 
            gen_num[0], max_ans_num, query_name, 'train', ent2id, rel2id)
    if gen_valid:
        valid_queries, valid_tp_answers, valid_fp_answers, valid_fn_answers = ground_queries(index_f, dataset, query_structure,
            valid_ent_in, valid_ent_out, train_ent_in, train_ent_out, gen_num[1], max_ans_num, query_name, 'valid', ent2id, rel2id)
    if gen_test:
        test_queries, test_tp_answers, test_fp_answers, test_fn_answers = ground_queries(index_f, dataset, query_structure,
            test_ent_in, test_ent_out, valid_ent_in, valid_ent_out, gen_num[2], max_ans_num, query_name, 'test', ent2id, rel2id)
    print ('%s queries generated with structure %s'%(gen_num, query_structure))

def fill_query(query_structure, ent_in, ent_out, answer, ent2id, rel2id):
    assert type(query_structure[-1]) == list
    all_relation_flag = True
    for ele in query_structure[-1]:
        if ele not in ['r', 'n']:
            all_relation_flag = False
            break
    if all_relation_flag:
        r = -1
        for i in range(len(query_structure[-1]))[::-1]:
            if query_structure[-1][i] == 'n':
                query_structure[-1][i] = -2
                continue
            found = False
            for j in range(40):
               # print(ent_in[answer])
               # print(ent_in[answer])
               # if len(ent_in[answer].keys())<1:
                #    continue
                r_tmp = random.sample(ent_in[answer].keys(), 1)[0]
                if r_tmp // 2 != r // 2 or r_tmp == r:
                    r = r_tmp
                    found = True
                    break
            if not found:
                return True
            query_structure[-1][i] = r
            answer = random.sample(ent_in[answer][r], 1)[0]
        if query_structure[0] == 'e':
            query_structure[0] = answer
        else:
            return fill_query(query_structure[0], ent_in, ent_out, answer, ent2id, rel2id)
    else:
        same_structure = defaultdict(list)
        for i in range(len(query_structure)):
            same_structure[list2tuple(query_structure[i])].append(i)
        for i in range(len(query_structure)):
            if len(query_structure[i]) == 1 and query_structure[i][0] == 'u':
                assert i == len(query_structure) - 1
                query_structure[i][0] = -1
                continue
            broken_flag = fill_query(query_structure[i], ent_in, ent_out, answer, ent2id, rel2id)
            if broken_flag:
                return True
        for structure in same_structure:
            if len(same_structure[structure]) != 1:
                structure_set = set()
                for i in same_structure[structure]:
                    structure_set.add(list2tuple(query_structure[i]))
                if len(structure_set) < len(same_structure[structure]):
                    return True

def achieve_answer(query, ent_in, ent_out):
    assert type(query[-1]) == list
    all_relation_flag = True
    for ele in query[-1]:
        if (type(ele) != int) or (ele == -1):
            all_relation_flag = False
            break
    if all_relation_flag:
        if type(query[0]) == int:
            ent_set = set([query[0]])
        else:
            ent_set = achieve_answer(query[0], ent_in, ent_out)
        for i in range(len(query[-1])):
            if query[-1][i] == -2:
                ent_set = set(range(len(ent_in))) - ent_set
            else:
                ent_set_traverse = set()
                for ent in ent_set:
                    ent_set_traverse = ent_set_traverse.union(ent_out[ent][query[-1][i]])
                ent_set = ent_set_traverse
    else:   
        ent_set = achieve_answer(query[0], ent_in, ent_out)
        union_flag = False
        if len(query[-1]) == 1 and query[-1][0] == -1:
            union_flag = True
        for i in range(1, len(query)):
            if not union_flag:
                ent_set = ent_set.intersection(achieve_answer(query[i], ent_in, ent_out))
            else:
                if i == len(query) - 1:
                    continue
                ent_set = ent_set.union(achieve_answer(query[i], ent_in, ent_out))
    return ent_set

@click.command()
@click.option('--dataset', default="Yelp")
@click.option('--seed', default=0)
@click.option('--gen_train_num', default=0)
@click.option('--gen_valid_num', default=0)
@click.option('--gen_test_num', default=0)
@click.option('--max_ans_num', default=1e6)
@click.option('--reindex', is_flag=True, default=False)
@click.option('--gen_train', is_flag=True, default=False)
@click.option('--gen_valid', is_flag=True, default=False)
@click.option('--gen_test', is_flag=True, default=False)
@click.option('--gen_id', default=0)
@click.option('--save_name', is_flag=True, default=False)
@click.option('--index_only', is_flag=True, default=False)
def main(dataset, seed, gen_train_num, gen_valid_num, gen_test_num, max_ans_num, reindex, gen_train, gen_valid, gen_test, gen_id, save_name, index_only):
    train_num_dict = {'FB15k': 273710, "FB15k-237": 149689, "NELL": 107982,"Yelp": 15000}
    valid_num_dict = {'FB15k': 8000, "FB15k-237": 5000, "NELL": 4000, "Yelp": 4000}
    test_num_dict = {'FB15k': 8000, "FB15k-237": 5000, "NELL": 4000, "Yelp": 4000}
    gen_train = True
    gen_valid = True
    gen_test = True
    if gen_train and gen_train_num == 0:
        if 'FB15k-237' in dataset:
            gen_train_num = 149689
        elif 'FB15k' in dataset:
            gen_train_num = 273710
        elif 'NELL' in dataset:
            gen_train_num = 107982
        else:
            gen_train_num = train_num_dict[dataset]
    if gen_valid and gen_valid_num == 0:
        if 'FB15k-237' in dataset:
            gen_valid_num = 5000
        elif 'FB15k' in dataset:
            gen_valid_num = 8000
        elif 'NELL' in dataset:
            gen_valid_num = 4000
        else:
            gen_valid_num = valid_num_dict[dataset]
    if gen_test and gen_test_num == 0:
        if 'FB15k-237' in dataset:
            gen_test_num = 5000
        elif 'FB15k' in dataset:
            gen_test_num = 8000
        elif 'NELL' in dataset:
            gen_test_num = 4000
        else:
            gen_test_num = test_num_dict[dataset]
    if index_only:
        index_dataset(dataset, reindex)
        exit(-1)

    e = 'e'
    r = 'r'
    n = 'n'
    u = 'u'
    query_structures = [
                        [e, [r]],
                        [e, [r, r]],
                        [e, [r, r, r]],
                        [[e, [r]], [e, [r]]],
                        [[e, [r]], [e, [r]], [e, [r]]],
                        [[e, [r, r]], [e, [r]]],
                        [[[e, [r]], [e, [r]]], [r]],
                        # negation
                        [[e, [r]], [e, [r, n]]],
                        [[e, [r]], [e, [r]], [e, [r, n]]],
                        [[e, [r, r]], [e, [r, n]]],
                        [[e, [r, r, n]], [e, [r]]],
                        [[[e, [r]], [e, [r, n]]], [r]],
                        # union
                        [[e, [r]], [e, [r]], [u]],
                        [[[e, [r]], [e, [r]], [u]], [r]]
                       ]
    query_names = ['1p', '2p', '3p', '2i', '3i', 'pi', 'ip', '2in', '3in', 'pin', 'pni', 'inp', '2u', 'up']

    #gen_id = 0
    #generate_queries(0,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)
    gen_id = 1
    generate_queries(1,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)
    gen_id = 2
    generate_queries(2,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)
    gen_id = 3
    generate_queries(3,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)
    gen_id = 4
    generate_queries(4,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)
    gen_id = 5
    generate_queries(5,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)
    gen_id = 6
    generate_queries(6,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)
    gen_id = 12
    generate_queries(12,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)
    gen_id = 13
    generate_queries(13,dataset, query_structures[gen_id:gen_id+1], [gen_train_num, gen_valid_num, gen_test_num], max_ans_num, gen_train, gen_valid, gen_test, query_names[gen_id:gen_id+1], save_name)

if __name__ == '__main__':
    main()