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prepare_MIND_dataset.py
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import os
import json
import shutil
import random
import numpy as np
import shutil
import collections
random.seed(0)
np.random.seed(0)
MIND_small_dataset_root = '../MIND-small'
MIND_large_dataset_root = '../MIND-large'
MIND_200k_dataset_root = '../MIND-200k'
def download_extract_MIND_small():
if not os.path.exists(MIND_small_dataset_root):
os.mkdir(MIND_small_dataset_root)
if not os.path.exists(MIND_small_dataset_root + '/download'):
os.mkdir(MIND_small_dataset_root + '/download')
if not os.path.exists(MIND_small_dataset_root + '/download/train'):
if not os.path.exists(MIND_small_dataset_root + '/download/MINDsmall_train.zip'):
os.system('wget https://mind201910small.blob.core.windows.net/release/MINDsmall_train.zip -P %s/download' % MIND_small_dataset_root)
assert os.path.exists(MIND_small_dataset_root + '/download/MINDsmall_train.zip'), 'Train set zip not found'
os.mkdir(MIND_small_dataset_root + '/download/train')
os.system('unzip %s/download/MINDsmall_train.zip -d %s/download/train' % (MIND_small_dataset_root, MIND_small_dataset_root))
if not os.path.exists(MIND_small_dataset_root + '/download/dev'):
if not os.path.exists(MIND_small_dataset_root + '/download/MINDsmall_dev.zip'):
os.system('wget https://mind201910small.blob.core.windows.net/release/MINDsmall_dev.zip -P %s/download' % MIND_small_dataset_root)
assert os.path.exists(MIND_small_dataset_root + '/download/MINDsmall_dev.zip'), 'Dev set zip not found'
os.mkdir(MIND_small_dataset_root + '/download/dev')
os.system('unzip %s/download/MINDsmall_dev.zip -d %s/download/dev' % (MIND_small_dataset_root, MIND_small_dataset_root))
if not os.path.exists(MIND_small_dataset_root + '/download/wikidata-graph'):
if not os.path.exists(MIND_small_dataset_root + '/download/wikidata-graph.zip'):
os.system('wget https://mind201910.blob.core.windows.net/knowledge-graph/wikidata-graph.zip -P %s/download' % MIND_small_dataset_root)
os.system('unzip %s/download/wikidata-graph.zip -d %s/download' % (MIND_small_dataset_root, MIND_small_dataset_root))
def download_extract_MIND_large(mode):
assert mode in ['200k', 'large']
if not os.path.exists('../MIND-%s' % mode):
os.mkdir('../MIND-%s' % mode)
if not os.path.exists('../MIND-%s/train' % mode):
os.mkdir('../MIND-%s/train' % mode)
if not os.path.exists('../MIND-%s/dev' % mode):
os.mkdir('../MIND-%s/dev' % mode)
if not os.path.exists('../MIND-%s/test' % mode):
os.mkdir('../MIND-%s/test' % mode)
dataset_root = MIND_200k_dataset_root if mode == '200k' else MIND_large_dataset_root
if not os.path.exists(dataset_root):
os.mkdir(dataset_root)
if not os.path.exists(dataset_root + '/download'):
os.mkdir(dataset_root + '/download')
if (not os.path.exists(dataset_root + '/train/news.tsv') or not os.path.exists(dataset_root + '/train/behaviors.tsv') or not os.path.exists(dataset_root + '/train/entity_embedding.vec')) and \
not os.path.exists(dataset_root + '/download/train'):
if not os.path.exists(dataset_root + '/download/MINDlarge_train.zip'):
os.system('wget https://mind201910small.blob.core.windows.net/release/MINDlarge_train.zip -P %s/download' % dataset_root)
assert os.path.exists(dataset_root + '/download/MINDlarge_train.zip'), 'Train set zip not found'
os.mkdir(dataset_root + '/download/train')
os.system('unzip %s/download/MINDlarge_train.zip -d %s/download/train' % (dataset_root, dataset_root))
if (not os.path.exists(dataset_root + '/dev/news.tsv') or not os.path.exists(dataset_root + '/dev/behaviors.tsv') or not os.path.exists(dataset_root + '/dev/entity_embedding.vec')) and \
not os.path.exists(dataset_root + '/download/dev'):
if not os.path.exists(dataset_root + '/download/MINDlarge_dev.zip'):
os.system('wget https://mind201910small.blob.core.windows.net/release/MINDlarge_dev.zip -P %s/download' % dataset_root)
assert os.path.exists(dataset_root + '/download/MINDlarge_dev.zip'), 'Dev set zip not found'
os.mkdir(dataset_root + '/download/dev')
os.system('unzip %s/download/MINDlarge_dev.zip -d %s/download/dev' % (dataset_root, dataset_root))
if (not os.path.exists(dataset_root + '/test/news.tsv') or not os.path.exists(dataset_root + '/test/behaviors.tsv') or not os.path.exists(dataset_root + '/test/entity_embedding.vec')) and \
not os.path.exists(dataset_root + '/download/test'):
if not os.path.exists(dataset_root + '/download/MINDlarge_test.zip'):
os.system('wget https://mind201910small.blob.core.windows.net/release/MINDlarge_test.zip -P %s/download' % dataset_root)
assert os.path.exists(dataset_root + '/download/MINDlarge_test.zip'), 'Test set zip not found'
os.mkdir(dataset_root + '/download/test')
os.system('unzip %s/download/MINDlarge_test.zip -d %s/download/test' % (dataset_root, dataset_root))
if not os.path.exists(dataset_root + '/download/wikidata-graph'):
if not os.path.exists(dataset_root + '/download/wikidata-graph.zip'):
os.system('wget https://mind201910.blob.core.windows.net/knowledge-graph/wikidata-graph.zip -P %s/download' % dataset_root)
os.system('unzip %s/download/wikidata-graph.zip -d %s/download' % (dataset_root, dataset_root))
if mode == 'large':
for data in ['train', 'dev', 'test']:
if not os.path.exists('../MIND-large/%s/news.tsv' % data):
shutil.copyfile('../MIND-large/download/%s/news.tsv' % data, '../MIND-large/%s/news.tsv' % data)
if not os.path.exists('../MIND-large/%s/behaviors.tsv' % data):
shutil.copyfile('../MIND-large/download/%s/behaviors.tsv' % data, '../MIND-large/%s/behaviors.tsv' % data)
def split_training_behaviors():
MIND_small_train_ratio = 0.95
train_behavior_lines = []
dev_behavior_lines = []
behavior_lines = []
with open(MIND_small_dataset_root + '/download/train/behaviors.tsv', 'r', encoding='utf-8') as f:
for line in f:
if len(line.strip()) > 0:
behavior_lines.append(line)
random.shuffle(behavior_lines)
behavior_num = len(behavior_lines)
behavior_id = [i for i in range(behavior_num)]
random.shuffle(behavior_id)
train_num = int(behavior_num * MIND_small_train_ratio)
train_behavior_id = random.sample(behavior_id, train_num)
train_behavior_id = set(train_behavior_id)
for i, line in enumerate(behavior_lines):
if i in train_behavior_id:
train_behavior_lines.append(line)
else:
dev_behavior_lines.append(line)
return train_behavior_lines, dev_behavior_lines
def preprocess_MIND_small():
train_behavior_lines, dev_behavior_lines = split_training_behaviors()
# train set
train_set_root = MIND_small_dataset_root + '/train'
if not os.path.exists(train_set_root):
os.mkdir(train_set_root)
with open(train_set_root + '/behaviors.tsv', 'w', encoding='utf-8') as f:
for line in train_behavior_lines:
f.write(line)
if not os.path.exists(train_set_root + '/news.tsv'):
shutil.copyfile(MIND_small_dataset_root + '/download/train/news.tsv', train_set_root + '/news.tsv')
# dev set
dev_set_root = MIND_small_dataset_root + '/dev'
if not os.path.exists(dev_set_root):
os.mkdir(dev_set_root)
with open(dev_set_root + '/behaviors.tsv', 'w', encoding='utf-8') as f:
for line in dev_behavior_lines:
f.write(line)
if not os.path.exists(dev_set_root):
os.mkdir(dev_set_root)
if not os.path.exists(dev_set_root + '/news.tsv'):
shutil.copyfile(MIND_small_dataset_root + '/download/train/news.tsv', dev_set_root + '/news.tsv')
# test set
test_set_root = MIND_small_dataset_root + '/test'
if not os.path.exists(test_set_root):
os.mkdir(test_set_root)
if not os.path.exists(test_set_root + '/behaviors.tsv'):
shutil.copyfile(MIND_small_dataset_root + '/download/dev/behaviors.tsv', test_set_root + '/behaviors.tsv')
if not os.path.exists(test_set_root + '/news.tsv'):
shutil.copyfile(MIND_small_dataset_root + '/download/dev/news.tsv', test_set_root + '/news.tsv')
def sampling_MIND_dataset():
sample_num = 200000
# 1. randomly sample by users
user_set = set()
with open('../MIND-200k/download/train/behaviors.tsv', 'r', encoding='utf-8') as f:
for line in f:
impression_ID, user_ID, time, history, impressions = line.strip().split('\t')
user_set.add(user_ID)
with open('../MIND-200k/download/dev/behaviors.tsv', 'r', encoding='utf-8') as f:
for line in f:
impression_ID, user_ID, time, history, impressions = line.strip().split('\t')
user_set.add(user_ID)
user_list = list(user_set)
random.shuffle(user_list)
assert sample_num <= len(user_list), 'sample num must be less than or equal to 1000000'
sample_user_list = random.sample(user_list, sample_num)
with open('../MIND-200k/sample_users.json', 'w', encoding='utf-8') as f:
json.dump(sample_user_list, f)
sampled_user_set = set(sample_user_list)
# 2. write sampled behavior file
with open('../MIND-200k/download/train/behaviors.tsv', 'r', encoding='utf-8') as f:
with open('../MIND-200k/train/behaviors.tsv', 'w', encoding='utf-8') as train_f:
for line in f:
impression_ID, user_ID, time, history, impressions = line.strip().split('\t')
if user_ID in sampled_user_set:
train_f.write(line)
cnt = 0
with open('../MIND-200k/download/dev/behaviors.tsv', 'r', encoding='utf-8') as f:
with open('../MIND-200k/dev/behaviors.tsv', 'w', encoding='utf-8') as dev_f:
with open('../MIND-200k/test/behaviors.tsv', 'w', encoding='utf-8') as test_f:
for line in f:
impression_ID, user_ID, time, history, impressions = line.strip().split('\t')
if user_ID in sampled_user_set:
if cnt % 2 == 0:
dev_f.write(line) # half-split for dev
else:
test_f.write(line) # half-split for test
cnt += 1
# 3. write sampled news file
for mode in ['train', 'dev', 'test']:
with open('../MIND-200k/%s/behaviors.tsv' % (mode), 'r', encoding='utf-8') as f:
news_set = set()
for line in f:
impression_ID, user_ID, time, history, impressions = line.strip().split('\t')
if len(history) > 0:
news = history.split(' ')
for n in news:
news_set.add(n)
if len(impressions) > 0:
news = impressions.split(' ')
for n in news:
news_set.add(n[:-2])
with open('../MIND-200k/download/%s/news.tsv' % ('dev' if mode == 'test' else mode), 'r', encoding='utf-8') as _f:
with open('../MIND-200k/%s/news.tsv' % mode, 'w', encoding='utf-8') as __f:
for line in _f:
news_ID, category, subCategory, title, abstract, _, _, _ = line.split('\t')
if news_ID in news_set:
__f.write(line)
def generate_knowledge_entity_embedding(data_mode):
assert data_mode in ['200k', 'small', 'large']
# 1. copy entity embedding file
if not os.path.exists('../MIND-%s/train/entity_embedding.vec' % data_mode):
shutil.copyfile('../MIND-%s/download/train/entity_embedding.vec' % data_mode, '../MIND-%s/train/entity_embedding.vec' % data_mode)
if not os.path.exists('../MIND-%s/dev/entity_embedding.vec' % data_mode):
shutil.copyfile('../MIND-%s/download/dev/entity_embedding.vec' % data_mode, '../MIND-%s/dev/entity_embedding.vec' % data_mode)
if data_mode in ['200k', 'small']:
if not os.path.exists('../MIND-%s/test/entity_embedding.vec' % data_mode):
shutil.copyfile('../MIND-%s/download/dev/entity_embedding.vec' % data_mode, '../MIND-%s/test/entity_embedding.vec' % data_mode)
else:
if not os.path.exists('../MIND-large/test/entity_embedding.vec'):
shutil.copyfile('../MIND-large/download/test/entity_embedding.vec', '../MIND-large/test/entity_embedding.vec')
# 2. generate context embedding file
entity_embeddings = {}
entity_embedding_files = ['../MIND-%s/%s/entity_embedding.vec' % (data_mode, mode) for mode in ['train', 'dev', 'test']]
for entity_embedding_file in entity_embedding_files:
with open(entity_embedding_file, 'r', encoding='utf-8') as f:
for line in f:
if len(line.strip()) > 0:
terms = line.strip().split('\t')
assert len(terms) == 101
entity_embeddings[terms[0]] = list(map(float, terms[1:]))
entity_embedding_relation = collections.defaultdict(set)
with open('../MIND-%s/download/wikidata-graph/wikidata-graph.tsv' % data_mode, 'r', encoding='utf-8') as wikidata_graph_f:
for line in wikidata_graph_f:
if len(line.strip()) > 0:
terms = line.strip().split('\t')
entity_embedding_relation[terms[0]].add(terms[2])
entity_embedding_relation[terms[2]].add(terms[0])
context_embeddings = {}
for entity in entity_embeddings:
entity_embedding = entity_embeddings[entity]
context_embedding = [entity_embedding[i] for i in range(100)]
cnt = 1
for _entity in entity_embedding_relation[entity]:
if _entity in entity_embeddings:
embedding = entity_embeddings[_entity]
for i in range(100):
context_embedding[i] += embedding[i]
cnt += 1
for i in range(100):
context_embedding[i] /= cnt
context_embeddings[entity] = context_embedding
for mode in ['train', 'dev', 'test']:
with open('../MIND-%s/%s/entity_embedding.vec' % (data_mode, mode), 'r', encoding='utf-8') as entity_embedding_f:
with open('../MIND-%s/%s/context_embedding.vec' % (data_mode, mode), 'w', encoding='utf-8') as context_embedding_f:
for line in entity_embedding_f:
if len(line.strip()) > 0:
entity = line.split('\t')[0]
context_embedding_f.write(entity + '\t' + '\t'.join(list(map(str, context_embeddings[entity]))) + '\n')
def prepare_MIND_small():
download_extract_MIND_small()
preprocess_MIND_small()
generate_knowledge_entity_embedding('small')
def prepare_MIND_large():
download_extract_MIND_large('large')
generate_knowledge_entity_embedding('large')
def prepare_MIND_200k():
download_extract_MIND_large('200k')
sampling_MIND_dataset()
generate_knowledge_entity_embedding('200k')
if __name__ == '__main__':
prepare_MIND_small()
prepare_MIND_large()
prepare_MIND_200k()