def __init__(
self,
dtype='train',
maxk=500,
):
self.dtye = dtype
self.seq_len = maxk
self.maxk = maxk
self.clusters = []
# 将pub_emb转换成clusters
# for author, author_dict in self.author_dict.items():
# for author_id, author_id_list in author_dict.items():
# for article in author_id_list:
# self.author.append([author, author_id, article])
#
# with open('./data/train_pub_new.json') as f:
# self.pub = json.loads(f.read())
if dtype == 'train':
pub_emb = load_json(rfdir='../data/', rfname='pub_emb.json')
authors = load_json(rfdir='../data/',
rfname='train_set_author.json')
for author in authors:
for nameid in authors[author]:
doc_set = []
for pid in authors[author][nameid]:
doc_set.append(pub_emb[pid])
self.clusters.append(doc_set)
def prepare_data(self):
self.name2pubs_train = data_utils.load_json(
settings.GLOBAL_DATA_DIR,
'name_to_pubs_train_500.json') # for test
self.name2pubs_test = data_utils.load_json(
settings.GLOBAL_DATA_DIR, 'name_to_pubs_test_100.json')
self.names_train = self.name2pubs_train.keys()
print('names train', len(self.names_train))
self.names_test = self.name2pubs_test.keys()
print('names test', len(self.names_test))
assert not set(self.names_train).intersection(set(self.names_test))
for name in self.names_train:
name_pubs_dict = self.name2pubs_train[name]
for aid in name_pubs_dict:
self.pids_train += name_pubs_dict[aid]
random.shuffle(self.pids_train)
self.n_pubs_train = len(self.pids_train)
print('pubs2train', self.n_pubs_train)
for name in self.names_test:
name_pubs_dict = self.name2pubs_test[name]
for aid in name_pubs_dict:
self.pids_test += name_pubs_dict[aid]
random.shuffle(self.pids_test)
self.n_pubs_test = len(self.pids_test)
print('pubs2test', self.n_pubs_test)
def dump_inter_emb():
"""
dump hidden embedding via trained global model for local model to use
"""
LMDB_NAME = "author_100.emb.weighted"
lc_input_train = LMDBClient(train_dataset_name, LMDB_NAME)
lc_input_test = LMDBClient(test_dataset_name, LMDB_NAME)
INTER_LMDB_NAME = 'author_triplets.emb'
lc_inter = LMDBClient(exp_name, INTER_LMDB_NAME)
global_model = GlobalTripletModel(train_dataset_name, data_scale=1000000)
trained_global_model = global_model.load_triplets_model()
name_to_pubs_test = {}
name_to_pubs_train = {}
TRAIN_NAME_LIST, _ = settings.get_split_name_list(train_dataset_name)
_, TEST_NAME_LIST = settings.get_split_name_list(test_dataset_name)
for case_name in TRAIN_NAME_LIST:
name_to_pubs_train[case_name] = data_utils.load_json(join(settings.get_raw_data_dir(train_dataset_name), case_name), "assignments.json")
for case_name in TEST_NAME_LIST:
name_to_pubs_test[case_name] = data_utils.load_json(join(settings.get_raw_data_dir(test_dataset_name), case_name), "assignments.json")
# name_to_pubs_test = data_utils.load_json(settings.get_global_data_dir(dataset_name), 'name_to_pubs_test_100.json')
for name in name_to_pubs_test:
print('name', name)
name_data = name_to_pubs_test[name]
embs_input = []
pids = []
for i, aid in enumerate(name_data.keys()):
# print(len(name_data[aid]))
if len(name_data[aid]) < 5: # n_pubs of current author is too small
continue
for pid in name_data[aid]:
cur_emb = lc_input_test.get(pid)
if cur_emb is None:
continue
embs_input.append(cur_emb)
pids.append(pid)
embs_input = np.stack(embs_input)
inter_embs = get_hidden_output(trained_global_model, embs_input)
for i, pid_ in enumerate(pids):
lc_inter.set(pid_, inter_embs[i])
for name in name_to_pubs_train:
print('name', name)
name_data = name_to_pubs_train[name]
embs_input = []
pids = []
for i, aid in enumerate(name_data.keys()):
# print(len(name_data[aid]))
if len(name_data[aid]) < 5: # n_pubs of current author is too small
continue
for pid in name_data[aid]:
cur_emb = lc_input_train.get(pid)
if cur_emb is None:
continue
embs_input.append(cur_emb)
pids.append(pid)
embs_input = np.stack(embs_input)
inter_embs = get_hidden_output(trained_global_model, embs_input)
for i, pid_ in enumerate(pids):
lc_inter.set(pid_, inter_embs[i])
def prepare_data(self):
self.name2pubs_train = data_utils.load_json(settings.GLOBAL_DATA_DIR, 'name_to_pubs_train_500.json') # for test
self.name2pubs_test = data_utils.load_json(settings.GLOBAL_DATA_DIR, 'name_to_pubs_test_100.json')
self.names_train = self.name2pubs_train.keys()
self.names_test = self.name2pubs_test.keys()
assert not set(self.names_train).intersection(set(self.names_test))
for authorName in self.names_train:
self.genPAPandPSP(authorName=authorName, idf_threshold=IDF_THRESHOLD)
def dump_inter_emb():
"""
dump hidden embedding via trained global_ model for local model to use
"""
Res = defaultdict(list)
LMDB_NAME = "author_100.emb.weighted"
lc_input = LMDBClient(LMDB_NAME)
INTER_LMDB_NAME = 'author_triplets.emb'
lc_inter = LMDBClient(INTER_LMDB_NAME)
global_model = GlobalTripletModel(data_scale=1000000)
trained_global_model = global_model.load_triplets_model()
name_to_pubs_test = data_utils.load_json(settings.GLOBAL_DATA_DIR,
'name_to_pubs_test_100.json')
# print(name_to_pubs_test)
for name in name_to_pubs_test:
name_data = name_to_pubs_test[name]
embs_input = []
pids = []
for i, aid in enumerate(name_data.keys()):
if len(name_data[aid]
) < 5: # n_pubs of current author is too small
continue
for pid in name_data[aid]:
cur_emb = lc_input.get(pid)
if cur_emb is None:
continue
embs_input.append(cur_emb)
pids.append(pid)
embs_input = np.stack(embs_input)
inter_embs = get_hidden_output(trained_global_model, embs_input)
for i, pid_ in enumerate(pids):
lc_inter.set(pid_, inter_embs[i])
Res[pid_].append(inter_embs[i])
# the same as the train data
name_to_pubs_train = data_utils.load_json(settings.GLOBAL_DATA_DIR,
'name_to_pubs_train_500.json')
for name in name_to_pubs_train:
name_data = name_to_pubs_train[name]
embs_input = []
pids = []
for i, aid in enumerate(name_data.keys()):
if len(name_data[aid]
) < 5: # n_pubs of current author is too small
continue
for pid in name_data[aid]:
cur_emb = lc_input.get(pid)
if cur_emb is None:
continue
embs_input.append(cur_emb)
pids.append(pid)
embs_input = np.stack(embs_input)
inter_embs = get_hidden_output(trained_global_model, embs_input)
for i, pid_ in enumerate(pids):
lc_inter.set(pid_, inter_embs[i])
def crop_to_size(dataset_type="STB", crop_size=320):
"""crop the dataset to crop_size"""
half_crop_size = crop_size / 2
dataset_path = data_path + dataset_path_dict[dataset_type]
image_names_json = dataset_json_dict[dataset_type][2]
image_names = load_json(dataset_path + "/" + image_names_json)
image_type = ".png" if dataset_type in ["RHD", "STB"] else ".jpg"
save_dir = dataset_path + "_" + str(crop_size)
begin = time.time()
print("crop {}:{} to size {}".format(dataset_type, len(image_names),
crop_size))
for _, image_name in enumerate(image_names):
img_raw = cv.imread(dataset_path + "/" + image_name + image_type)
#img_raw = cv.cvtColor(img_raw, cv.COLOR_BGR2RGB)
anno_infos = load_json(dataset_path + "/" + image_name + ".json")
joints = np.array(anno_infos['hand_pts']).astype("float32")
img_h, img_w, _ = img_raw.shape
crop_center = joints[:, :2][12].astype(int)
half_size = min(crop_center[0], crop_center[1], half_crop_size,
img_w - crop_center[0], img_h - crop_center[1])
x0, y0 = (crop_center - half_size).astype(int)
x1, y1 = (crop_center + half_size).astype(int)
img_crop = img_raw[y0:y1, x0:x1]
joints[:, :2] -= [x0, y0]
scale = half_crop_size / half_size
if scale > 1:
img_crop = cv.resize(img_crop, (0, 0),
fx=scale,
fy=scale,
interpolation=cv.INTER_CUBIC)
joints[:, :2] *= scale
if not os.path.exists(save_dir + "/" + image_name.split("/")[0]):
print("Make dirs:", save_dir + "/" + image_name.split("/")[0])
os.makedirs(save_dir + "/" + image_name.split("/")[0])
cv.imwrite(save_dir + "/" + image_name + image_type, img_crop)
anno_infos = {}
anno_infos['img_name'] = image_name
anno_infos['hand_pts'] = joints.tolist()
store_json(save_dir + "/" + image_name + ".json", anno_infos)
print("Done! Cost time:", time.time() - begin)
store_json(save_dir + "/" + image_names_json, image_names)
def gen_test(k=300, flatten=False): # 测试集 中 抽样 k个
name_to_pubs_test = data_utils.load_json(
settings.GLOBAL_DATA_DIR,
'name_to_pubs_test_100.json') # 测试集 name->aid->pid-j
#xs = []
xs, ys = [], [] # 特征 与 标记
names = []
for name in name_to_pubs_test: # 枚举名字 对于一个名字name 有重复抽样k个 文档
names.append(name) # 加入 名字列表 中
num_clusters = len(name_to_pubs_test[name]) # name 下 的 真实 聚类数
x = [] # 在name下 抽样k个 文档特征x^- 放入一个列表中
items = []
''' for item in name_to_pubs_test[name]: # 属于他的 文档id
items.append(item) '''
for c in name_to_pubs_test[name]: # one person 对于 name下 的 一个 实体 c
for item in name_to_pubs_test[name][c]: # 属于他的 文档id
items.append(item) # 加入 到 文档列表 中
sampled_points = [
items[p] for p in np.random.choice(len(items), k, replace=True)
] # 文档集 中 随机取样 k 个
for p in sampled_points:
if p in data_cache: # 在 cache 中
x.append(data_cache[p]) # 从 cache 中 取出 特征x^-
else:
x.append(lc.get(p)) # 否则 从 数据库 中 取出 特征x^-
if flatten:
xs.append(np.sum(x, axis=0))
else: # 条件走的是 这里
xs.append(np.stack(x)) # 数组堆积后 放入 xs
ys.append(num_clusters) # ys 存标记 即 实际聚类大小
xs = np.stack(
xs
) # 再堆积一次 此时 xs = array([ [(一个name下的若干文档) [100维特征向量(x^-)], ... ], [[...], ...], ...])
ys = np.stack(ys) # ys = array([聚类大小1, 聚类大小2...])
return names, xs, ys # 姓名name， 文档特征(x^-)， 聚类大小
def gen_test(dataset_name, k=300, flatten=False):
name_to_pubs_test = {}
_, TEST_NAME_LIST = settings.get_split_name_list(dataset_name)
for case_name in TEST_NAME_LIST:
name_to_pubs_test[case_name] = data_utils.load_json(
join(settings.get_raw_data_dir(dataset_name), case_name),
"assignments.json")
# name_to_pubs_test = data_utils.load_json(settings.get_global_data_dir(dataset_name), 'name_to_pubs_test_100.json')
xs, ys = [], []
names = []
for name in name_to_pubs_test:
names.append(name)
num_clusters = len(name_to_pubs_test[name])
x = []
items = []
for c in name_to_pubs_test[name]: # one person
for item in name_to_pubs_test[name][c]:
items.append(item)
sampled_points = [
items[p] for p in np.random.choice(len(items), k, replace=True)
]
for p in sampled_points:
if p in data_cache:
x.append(data_cache[p])
else:
x.append(lc.get(p))
if flatten:
xs.append(np.sum(x, axis=0))
else:
xs.append(np.stack(x))
ys.append(num_clusters)
xs = np.stack(xs)
ys = np.stack(ys)
return names, xs, ys
def run_rnn(dataset_name, k=300, seed=1106):
name_to_pubs_train = {}
TRAIN_NAME_LIST, _, _ = settings.get_split_name_list(dataset_name)
for case_name in TRAIN_NAME_LIST:
name_to_pubs_train[case_name] = data_utils.load_json(
join(settings.get_raw_data_dir(dataset_name), case_name),
"assignments.json")
# name_to_pubs_train = data_utils.load_json(settings.get_global_data_dir(dataset_name), 'name_to_pubs_train_500.json')
test_names, test_x, test_y = gen_test(dataset_name, k)
np.random.seed(seed)
clusters = []
for domain in name_to_pubs_train.values():
for cluster in domain.values():
clusters.append(cluster)
for i, c in enumerate(clusters):
if i % 100 == 0:
print(i, len(c), len(clusters))
for pid in c:
data_cache[pid] = lc.get(pid)
model = create_model()
# print(model.summary())
model.fit_generator(gen_train(clusters, k=300, batch_size=1000),
steps_per_epoch=100,
epochs=1000,
validation_data=(test_x, test_y))
kk = model.predict(test_x)
wf = open(join(settings.get_out_dir(dataset_name), 'n_clusters_rnn.txt'),
'w')
for i, name in enumerate(test_names):
wf.write('{}\t{}\t{}\n'.format(name, test_y[i], kk[i][0]))
wf.close()
def run_rnn(k=300, seed=1106, split=0.9):
np.random.seed(seed)
name_to_pubs = data_utils.load_json(settings.ASSIGNMENT_JSON)
names = list(name_to_pubs.keys())
num_train = int(len(names) * split)
names_train = names[:num_train]
name_to_pubs_test = dict((name, item)
for name, item in name_to_pubs.items()
if name not in names_train)
clusters = []
for name, pubs in name_to_pubs.items():
if name not in names_train:
continue
clusters.extend(pubs)
# for i, c in enumerate(clusters):
# if i % 100 == 0:
# print(i, len(c), len(clusters))
# for pid in c:
# v = lc.get(pid)
# if not v:
# data_cache[pid] = v
# print(model.summary())
model = create_model(k=k)
test_names, test_x, test_y = gen_test(name_to_pubs_test, k=k)
model.fit_generator(gen_train(clusters, k=k, batch_size=1000),
steps_per_epoch=100,
epochs=1000,
validation_data=(test_x, test_y))
kk = model.predict(test_x)
wf = open(join(settings.CLUSTER_SIZE), 'w')
for i, name in enumerate(test_names):
wf.write('{}\t{}\t{}\n'.format(name, test_y[i], kk[i][0]))
wf.close()
def dump_author_features_to_file():
"""
generate author features by raw publication data and dump to files
author features are defined by his/her paper attributes excluding the author's name
"""
pubs_dict = data_utils.load_json(settings.GLOBAL_DATA_DIR, 'pubs_raw.json')
print('n_papers', len(pubs_dict))
wf = codecs.open(join(settings.GLOBAL_DATA_DIR, 'author_features.txt'),
'w',
encoding='utf-8')
for i, pid in enumerate(pubs_dict):
if i % 1000 == 0:
print(i, datetime.now() - start_time)
paper = pubs_dict[pid]
if "title" not in paper or "authors" not in paper:
continue
if len(paper["authors"]) > 30:
print(i, pid, len(paper["authors"]))
if len(paper["authors"]) > 100:
continue
n_authors = len(paper.get('authors', []))
for j in range(n_authors):
author_feature = feature_utils.extract_author_features(paper, j)
aid = '{}-{}'.format(pid, j)
wf.write(aid + '\t' + ' '.join(author_feature) + '\n')
wf.close()
def dump_test_emb():
LMDB_NAME = "author_100.emb.weighted"
lc_input = LMDBClient(LMDB_NAME)
INTER_LMDB_NAME = 'author_triplets.emb'
lc_inter = LMDBClient(INTER_LMDB_NAME)
global_model = GlobalTripletModel(data_scale=1000000)
trained_global_model = global_model.load_triplets_model()
sna_valid_author_raw = data_utils.load_json(settings.SNA_PUB_DIR, 'sna_valid_author_raw.json')
for name in sna_valid_author_raw.keys():
if name == "j_yu":
continue
print ("name: ", name)
checkPids = sna_valid_author_raw[name]
embs_input = []
pids = []
for pid in checkPids:
cur_emb = lc_input.get(pid)
if cur_emb is None:
continue
embs_input.append(cur_emb)
pids.append(pid)
embs_input = np.stack(embs_input)
inter_embs = get_hidden_output(trained_global_model, embs_input)
for i, pid in enumerate(pids):
lc_inter.set(pid, inter_embs[i])
def gen_test(k=300, flatten=False):
name_to_pubs_test = data_utils.load_json(settings.GLOBAL_DATA_DIR,
'name_to_pubs_test.json')
xs, ys = [], []
names = []
# print (name_to_pubs_test)
for name in name_to_pubs_test:
# print ("name: ", name)
names.append(name)
num_clusters = len(name_to_pubs_test[name])
x = []
items = []
for c in name_to_pubs_test[name]: # one person
for item in name_to_pubs_test[name][c]:
if lc.get(item) is None:
continue
items.append(item)
if len(items) < k:
continue
sampled_points = [
items[p] for p in np.random.choice(len(items), k, replace=True)
]
for p in sampled_points:
x.append(lc.get(p))
# print ("name: ", name, "x: ", x)
if flatten:
xs.append(np.sum(x, axis=0))
else:
xs.append(np.stack(x))
ys.append(num_clusters)
xs = np.stack(xs)
ys = np.stack(ys)
return names, xs, ys
def gen_test(k=300, flatten=False):
name_to_pubs_test = data_utils.load_json(settings.GLOBAL_DATA_DIR,
'name_to_pubs_test_100.json')
xs, ys = [], []
names = []
for name in name_to_pubs_test:
names.append(name)
num_clusters = len(name_to_pubs_test[name])
x = []
items = []
for c in name_to_pubs_test[name]: # one person
for item in name_to_pubs_test[name][c]:
items.append(item)
sampled_points = [
items[p] for p in np.random.choice(len(items), k, replace=True)
]
for p in sampled_points:
if p in data_cache:
x.append(data_cache[p])
else:
x.append(lc.get(p))
if flatten:
xs.append(np.sum(x, axis=0))
else:
xs.append(np.stack(x))
ys.append(num_clusters)
xs = np.stack(xs)
ys = np.stack(ys)
return names, xs, ys
def dump_author_features_to_file(): #提取作者特征到文件中
"""
generate author features by raw publication data and dump to files
author features are defined by his/her paper attributes excluding the author's name
"""
pubs_dict = data_utils.load_json(settings.GLOBAL_DATA_DIR,
'pubs_raw.json') #原始数据 pubs_raw.json
print('n_papers', len(pubs_dict)) #论文数量
wf = codecs.open(join(settings.GLOBAL_DATA_DIR, 'author_features.txt'),
'w',
encoding='utf-8') #特征写入 author_features.txt
for i, pid in enumerate(pubs_dict): #枚举一篇论文 i, pid = 索引， 枚举对象
if i % 1000 == 0:
print(i, datetime.now() - start_time)
paper = pubs_dict[pid] # 某个paper 的信息
if "title" not in paper or "authors" not in paper:
continue
if len(paper["authors"]) > 30: # 合作者 人数
print(i, pid, len(paper["authors"]))
if len(paper["authors"]) > 100:
continue
n_authors = len(
paper.get('authors', [])
) #该论文的作者数 dict.get(key, default=None) 在字典中查询键值key 若不存在返回默认值default
for j in range(n_authors): #枚举每一位作者
if 'id' not in paper['authors'][j]:
continue
author_feature = feature_utils.extract_author_features(
paper, j) #提取论文paper中的作者j的特征 __$f_name$_$word$
aid = '{}-{}'.format(pid, j) #aid: pid-j
wf.write(aid + '\t' + ' '.join(author_feature) +
'\n') #往wf中写入特征信息 aid\t author_feature\n
wf.close()
def convert_to_TFRecords(dataset_list, state="train"):
"""Convert dataset in the list to TFRecords"""
dataset_list = dataset_list.split("/")
tfrecords_filename = data_path + "TFRecords/" + state + "_of_" + "_".join(
dataset_list) + "_num.tfrecords"
writer = tf.python_io.TFRecordWriter(tfrecords_filename)
print("Writing into ", tfrecords_filename)
samples = 0
begin = time.time()
for _, dataset_type in enumerate(dataset_list):
mid = time.time()
print("Process dataset:", dataset_type)
dataset_path = data_path + dataset_path_dict[dataset_type]
image_names_json = dataset_json_dict[dataset_type][
0] if state == "train" else dataset_json_dict[dataset_type][1]
image_names = load_json(dataset_path + "/" + image_names_json)
image_type = ".png" if dataset_type in ["RHD", "STB", "STB_320"
] else ".jpg"
for _, image_name in enumerate(image_names):
write_one_example(dataset_path, image_name, image_type, writer)
samples += 1
print("Cost time:", time.time() - mid)
writer.close()
os.rename(tfrecords_filename,
tfrecords_filename.replace("num", str(samples)))
print("ALL Done! Cost total time:", time.time() - begin)
def test_prepare_local_data(Name):
name_to_pubs_test = data_utils.load_json(settings.GLOBAL_DATA_DIR,
'name_to_pubs_test_100.json')
INTER_LMDB_NAME = 'author_triplets.emb'
lc_inter = LMDBClient(INTER_LMDB_NAME)
# cnt = 0
wf_contents = []
for i, name in enumerate(name_to_pubs_test):
if name != Name: continue
print(i, name)
cur_person_dict = name_to_pubs_test[name]
pids_set = set()
pids = []
pids2label = {}
# generate content
for i, aid in enumerate(cur_person_dict):
items = cur_person_dict[aid]
# if len(items) < 5:
# continue
for pid in items:
pids2label[pid] = aid
pids.append(pid)
shuffle(pids)
for pid in pids:
cur_pub_emb = lc_inter.get(pid)
if cur_pub_emb is not None:
pids_set.add(pid)
wf_contents.append({'pid': pid, 'label': pids2label[pid]})
# cur_pub_emb = list(map(str, cur_pub_emb))
# wf_content.write('{}\t'.format(pid))
# wf_content.write('\t'.join(cur_pub_emb))
# wf_content.write('\t{}\n'.format(pids2label[pid]))
PidsLabels = [x['label'] for x in wf_contents]
print(len(set(PidsLabels)))
def gen_sna(k=300):
name_to_pubs_test = data_utils.load_json(settings.SNA_PUB_DIR,
'sna_valid_author_raw.json')
xs = []
names = []
for name in name_to_pubs_test:
names.append(name)
x = []
items = []
for pid in name_to_pubs_test[name]:
if lc.get(pid) is not None:
items.append(pid)
if len(items) == 0:
continue
sampled_points = [
items[p] for p in np.random.choice(len(items), k, replace=True)
]
for p in sampled_points:
emb = lc.get(p)
# print ("emb: len :", len(emb))
x.append(emb)
xs.append(np.stack(x))
xs = np.stack(xs)
return names, xs
def run_rnn(k=300, seed=1106):
name_to_pubs_train = data_utils.load_json(settings.GLOBAL_DATA_DIR,
'name_to_pubs_train_500.json')
test_names, test_x, test_y = gen_test(k)
np.random.seed(seed)
clusters = []
for domain in name_to_pubs_train.values():
for cluster in domain.values():
clusters.append(cluster)
for i, c in enumerate(clusters):
if i % 100 == 0:
print(i, len(c), len(clusters))
for pid in c:
data_cache[pid] = lc.get(pid)
model = create_model()
# print(model.summary())
model.fit_generator(gen_train(clusters, k=300, batch_size=1000),
steps_per_epoch=100,
epochs=1000,
validation_data=(test_x, test_y))
kk = model.predict(test_x)
wf = open(join(settings.OUT_DIR, 'n_clusters_rnn.txt'), 'w')
for i, name in enumerate(test_names):
wf.write('{}\t{}\t{}\n'.format(name, test_y[i], kk[i][0]))
wf.close()
def check_labeled_zfj():
pairs = data_utils.load_json(settings.AFF_DATA_DIR,
"mag_aminer_hard_correct_zfj_copy.json")
n_label_zfj = 0
for pair in pairs:
if pair["label_zfj"]:
n_label_zfj += 1
print("labeled until now", n_label_zfj)
def load_id2papers(self, fold):
if os.path.isfile(
join(self.pairs_dir,
'clean-id2paper-test-{}.json'.format(fold))):
return data_utils.load_json(
self.paper_dir, 'clean-id2paper-test-{}.json'.format(fold))
else:
return self.gen_id2papers(fold)
def test(idf_threshold):
name_to_pubs_test = data_utils.load_json(settings.GLOBAL_DATA_DIR,
'name_to_pubs_test_100.json')
idf = data_utils.load_data(settings.GLOBAL_DATA_DIR, 'feature_idf.pkl')
INTER_LMDB_NAME = 'triplete_loss_lc_attention_network_embedding'
lc_inter = LMDBClient(INTER_LMDB_NAME)
LMDB_AUTHOR_FEATURE = "pub_authors.feature"
lc_feature = LMDBClient(LMDB_AUTHOR_FEATURE)
graph_dir = join(settings.DATA_DIR, 'local',
'graph-{}'.format(idf_threshold))
os.makedirs(graph_dir, exist_ok=True)
for i, name in enumerate(name_to_pubs_test):
print(i, name)
cur_person_dict = name_to_pubs_test[name]
pids_set = set()
pids = []
pids2label = {}
# 286 hongbin_li_pubs_content.txt
# generate content
for i, aid in enumerate(cur_person_dict):
items = cur_person_dict[aid]
if len(items) < 5:
continue
for pid in items:
pids2label[pid] = aid
pids.append(pid)
shuffle(pids)
for pid in pids:
cur_pub_emb = lc_inter.get(pid)
if cur_pub_emb is not None:
pids_set.add(pid)
# generate network1
# generate network1
all_idf_sum = 0
pathCnt = 0
pids_filter = list(pids_set)
n_pubs = len(pids_filter)
for i in range(n_pubs - 1):
author_feature1 = set(lc_feature.get(pids_filter[i]))
for j in range(i + 1, n_pubs):
author_feature2 = set(lc_feature.get(pids_filter[j]))
# print('author_feature2: ', author_feature2)
common_features = author_feature1.intersection(author_feature2)
idf_sum = 0
for f in common_features:
idf_sum += idf.get(f, idf_threshold)
all_idf_sum += idf_sum
if idf_sum >= idf_threshold:
pathCnt = pathCnt + 1
if name == "kexin_xu":
print("all_idf_sum: ", all_idf_sum)
print("pathCnt: ", pathCnt)
def dump_pub_features_to_file():
"""
generate author features by raw publication data and dump to files
author features are defined by his/her paper attributes excluding the author's name
"""
global _pubs_dict
# Load publication features
_pubs_dict = data_utils.load_json('./OAG_WhoIsWho_data', 'your_pub_file_name')
res = multithread_utils.processed_by_multi_thread(get_pub_feature, range(len(_pubs_dict)))
data_utils.dump_data(res, "Essential_Embeddings/", "pub.features")
def getPids():
name2pubs_train = data_utils.load_json(settings.GLOBAL_DATA_DIR, 'name_to_pubs_train_500.json') # for test
cntpapers = []
for name in name2pubs_train:
papers = name2pubs_train[name]
for aid in papers:
if len(papers[aid]) < 5:
continue
for pid in papers[aid]:
cntpapers.append(pid)
return cntpapers
def prepare_data(self):
self.name2pubs_train = {}
# self.name2pubs_val = {}
self.name2pubs_test = {}
TRAIN_NAME_LIST, TEST_NAME_LIST = settings.get_split_name_list(self.dataset_name)
for case_name in TRAIN_NAME_LIST:
self.name2pubs_train[case_name] = data_utils.load_json(join(settings.get_raw_data_dir(self.dataset_name), case_name),
"assignments.json")
# for case_name in VAL_NAME_LIST:
# self.name2pubs_val[case_name] = data_utils.load_json(join(settings.get_raw_data_dir(self.dataset_name), case_name),
# "assignments.json")
for case_name in TEST_NAME_LIST:
self.name2pubs_test[case_name] = data_utils.load_json(join(settings.get_raw_data_dir(self.dataset_name), case_name),
"assignments.json")
# self.name2pubs_train = data_utils.load_json(settings.get_global_data_dir(dataset_name), 'name_to_pubs_train_500.json') # for test
# self.name2pubs_test = data_utils.load_json(settings.get_global_data_dir(dataset_name), 'name_to_pubs_test_100.json')
# self.names_train = self.name2pubs_train.keys()
# print('names train', len(self.names_train))
# self.names_test = self.name2pubs_test.keys()
# print('names test', len(self.names_test))
self.names_train, self.names_test = settings.get_split_name_list(self.dataset_name)
assert not set(self.names_train).intersection(set(self.names_test))
# assert not set(self.names_train).intersection(set(self.names_val))
# assert not set(self.names_val).intersection(set(self.names_test))
for name in self.names_train:
name_pubs_dict = self.name2pubs_train[name]
for aid in name_pubs_dict:
self.pids_train += name_pubs_dict[aid]
random.shuffle(self.pids_train)
self.n_pubs_train = len(self.pids_train)
print('pubs2train', self.n_pubs_train)
for name in self.names_test:
name_pubs_dict = self.name2pubs_test[name]
for aid in name_pubs_dict:
self.pids_test += name_pubs_dict[aid]
random.shuffle(self.pids_test)
self.n_pubs_test = len(self.pids_test)
print('pubs2test', self.n_pubs_test)
def prepro_tacos(configs):
if not os.path.exists(configs.save_dir):
os.makedirs(configs.save_dir)
# train/test data format: (video_id, start_time, end_time, duration, words)
train_data, val_data, test_data = read_tacos_data(
configs.root, configs.max_position_length)
# load features and sample feature shapes if possible
features_path = os.path.join(
configs.root,
"tacos_features_{}/feature_shapes.json".format(configs.feature))
feature_shapes = dict()
for vid, length in load_json(features_path).items():
if configs.max_position_length is not None and length > configs.max_position_length:
length = configs.max_position_length
feature_shapes[vid] = length
# generate token dicts and load pre-trained vectors
word_counter, char_counter = Counter(), Counter()
for data in [train_data, val_data, test_data]:
for record in data:
words = record[-1]
for word in words:
word_counter[word] += 1
for char in list(word):
char_counter[char] += 1
word_dict, char_dict, word_vectors = create_vocabularies(
configs, word_counter, char_counter)
# generate datasets
train_set = generate_dataset(train_data, feature_shapes, word_dict,
char_dict, "train")
val_set = generate_dataset(val_data, feature_shapes, word_dict, char_dict,
"val")
test_set = generate_dataset(test_data, feature_shapes, word_dict,
char_dict, "test")
# save to directory
write_json(word_dict,
save_path=os.path.join(configs.save_dir, "word_dict.json"))
write_json(char_dict,
save_path=os.path.join(configs.save_dir, "char_dict.json"))
np.savez_compressed(os.path.join(configs.save_dir, "word_vectors.npz"),
vectors=word_vectors)
write_json(train_set,
save_path=os.path.join(configs.save_dir, "train_set.json"))
write_json(val_set,
save_path=os.path.join(configs.save_dir, "val_set.json"))
write_json(test_set,
save_path=os.path.join(configs.save_dir, "test_set.json"))
def load_aff_data():
file_dir = settings.AFF_DATA_DIR
pos_pairs = data_utils.load_json(file_dir,
"label_data_aff_zhoushao.json")[:600]
pos_pairs = [({
"name": p["affiliation"]
}, {
"DisplayName": p["label"]
}) for p in pos_pairs if p["label"] != "[NIF]"]
neg_pairs = data_utils.load_json(file_dir,
'train_negative_affi_clean.json')[:600]
neg_pairs = [(p['aminer_affi'], p['mag_affi']) for p in neg_pairs]
pairs_add = data_utils.load_json(file_dir,
"mag_aminer_hard_correct_zfj_copy.json")
print("add pairs", len(pairs_add))
pos_pairs += [(p['aminer_affi'], p['mag_affi']) for p in pairs_add
if p["label_zfj"] == "1"]
neg_pairs += [(p['aminer_affi'], p['mag_affi']) for p in pairs_add
if p["label_zfj"] == "0"]
pos_pairs = pos_pairs[-len(neg_pairs):]
labels = [1] * len(pos_pairs) + [0] * len(neg_pairs)
pairs = pos_pairs + neg_pairs # label balanced is important
return pairs, labels
def json2dataframe(rfpath, wfpath):
pubs = load_json(rfpath=rfpath)
names = []
values = []
for k, v in pubs.items():
names.extend([k] * len(v))
values.extend(v)
values = json_normalize(values)
values['name'] = names
pubs = values
pubs['org'] = pubs.authors.map(lambda x: list(map(lambda x: x['org'], x)))
pubs['authors'] = pubs.authors.map(
lambda x: list(map(lambda x: x['name'], x)))
pubs.to_parquet(wfpath, engine='fastparquet')
def prepare_corpus(self):
train_corpus_analyzed = []
analyzedDocument = namedtuple('AnalyzedDocument', 'words tags')
train_corpus = data_utils.load_json(self.train_data_dir, self.train_data_fname)
print('training documents loaded')
print('documents number: {}'.format(len(train_corpus)))
for i, text in enumerate(train_corpus):
if i % 10000 == 0:
print(i)
words = data_utils.get_words(text)
tags = [i]
train_corpus_analyzed.append(analyzedDocument(words=words, tags=tags))
# if i > 100000:
# break
return train_corpus_analyzed
def filter_aff_neg_pairs():
neg_pairs = data_utils.load_json(settings.AFF_DATA_DIR,
'train_negative_affi.json')
neg_pairs_cleaned = []
for i, pair in enumerate(neg_pairs):
if i % 100 == 0:
print("pair", i)
mag_aff = pair["mag_affi"]
aminer_aff = pair["aminer_affi"]
aff1 = mag_aff["NormalizedName"].split()
aff2 = aminer_aff["main_body"].split()
common = set(aff1).intersection(aff2)
if len(common) > 1:
neg_pairs_cleaned.append(pair)
print("after cleaned", len(neg_pairs_cleaned))
data_utils.dump_json(neg_pairs_cleaned, settings.AFF_DATA_DIR,
"train_negative_affi_clean.json")
