def load_train_features(self):
print("start to dump train_features")
features = {}
out_feature_path = self.args['feature_train_path']
for name in tqdm(self.train_author_data):
pubs = []
for authorid in self.train_author_data[name]:
for clusters in self.train_author_data[name][authorid]:
pubs.append(clusters)
# print(pubs)
name_pubs_raw = {}
for i, pid in enumerate(pubs):
name_pubs_raw[pid] = self.train_pub_data[pid]
save_relation(name_pubs_raw, name)
ptext_emb = load_data('gene', 'ptext_emb.pkl')
tembs = []
for i, pid in enumerate(pubs):
# tembs.append (ptext_emb[pid])
features[pid] = ptext_emb[pid]
train_dataframe = pd.DataFrame(features)
train_dataframe.to_pickle(out_feature_path)
def load_train_local_features(self):
print("start to dump train_features")
local_feaures = {}
out_feature_path = self.args['feature_local_train_path']
for name in tqdm(self.train_author_data):
pubs = []
for authorid in self.train_author_data[name]:
for clusters in self.train_author_data[name][authorid]:
pubs.append(clusters)
# print(pubs)
name_pubs_raw = {}
for i, pid in enumerate(pubs):
name_pubs_raw[pid] = self.train_pub_data[pid]
save_relation(name_pubs_raw, name)
mpg = MetaPathGenerator()
mpg.read_data("gene")
rw_num = 10
cp = set()
for k in range(rw_num):
mpg.generate_WMRW("gene/RW.txt", 5, 20)
sentences = word2vec.Text8Corpus(r'gene/RW.txt')
model = word2vec.Word2Vec(sentences,
size=128,
negative=25,
min_count=1,
window=10)
#embs = []
for i, pid in enumerate(pubs):
if pid in model:
embs = model[pid]
else:
cp.add(i)
embs = np.zeros(128)
if pid not in local_feaures:
local_feaures[pid] = [embs]
else:
local_feaures[pid].append(embs)
for pid in local_feaures:
local_feaures[pid] = np.array(local_feaures[pid])
local_feaures[pid] = np.mean(local_feaures[pid], axis=0)
train_Dataframe = pd.DataFrame(local_feaures)
train_Dataframe.to_pickle(out_feature_path)
def train_val(self):
result = {}
for n, name in tqdm(enumerate(self.val_author_data)):
pubs = []
#get the author's all paper
for clusters in self.val_author_data[name]:
pubs.append(clusters)
#print(pubs)
name_pubs_raw = {}
for i, pid in enumerate(pubs):
name_pubs_raw[pid] = self.val_pub_data[pid]
#load the author's features
save_relation(name_pubs_raw, name)
mpg = MetaPathGenerator()
mpg.read_data("gene")
all_embs = []
rw_num = 10
cp = set()
#start to random walk
for k in range(rw_num):
mpg.generate_WMRW("gene/RW.txt", 5, 20)
sentences = word2vec.Text8Corpus(r'gene/RW.txt')
##########use word2vec to train the paper's embedding###############
model = word2vec.Word2Vec(sentences,
size=128,
negative=25,
min_count=1,
window=10)
embs = []
for i, pid in enumerate(pubs):
if pid in model:
embs.append(model[pid])
else:
cp.add(i)
embs.append(np.zeros(128))
all_embs.append(embs)
all_embs = np.array(all_embs)
##########################loading the sematic feautures#################
ptext_emb = load_data('gene', 'ptext_emb.pkl')
tcp = load_data('gene', 'tcp.pkl')
tembs = []
for i, pid in enumerate(pubs):
#tembs.append (ptext_emb[pid])
tembs.append(self.val_features[pid])
##############get the paper's connection's cosine matrix####################
sk_sim = np.zeros((len(pubs), len(pubs)))
for k in range(rw_num):
sk_sim = sk_sim + pairwise_distances(all_embs[k],
metric="cosine")
sk_sim = sk_sim / rw_num
##############get the paper's semantic's cosine matrix####################
tembs = pairwise_distances(tembs, metric="cosine")
w = 0.25
sim = (np.array(sk_sim) + w * np.array(tembs)) / (1 + w)
pre = DBSCAN(eps=0.2, min_samples=3,
metric="precomputed").fit_predict(sim)
pre = np.array(pre)
##离群论文集
outlier = set()
for i in range(len(pre)):
if pre[i] == -1:
outlier.add(i)
for i in cp:
outlier.add(i)
for i in tcp:
outlier.add(i)
##基于阈值的相似性匹配
paper_pair = generate_pair(pubs, outlier)
paper_pair1 = paper_pair.copy()
K = len(set(pre))
for i in range(len(pre)):
if i not in outlier:
continue
j = np.argmax(paper_pair[i])
while j in outlier:
paper_pair[i][j] = -1
j = np.argmax(paper_pair[i])
if paper_pair[i][j] >= 1.5:
pre[i] = pre[j]
else:
pre[i] = K
K = K + 1
for ii, i in enumerate(outlier):
for jj, j in enumerate(outlier):
if jj <= ii:
continue
else:
if paper_pair1[i][j] >= 1.5:
pre[j] = pre[i]
#print (pre, len (set (pre)))
result[name] = []
for i in set(pre):
oneauthor = []
for idx, j in enumerate(pre):
if i == j:
oneauthor.append(pubs[idx])
result[name].append(oneauthor)
json.dump(result,
open(self.args['val_result'], 'w', encoding='utf-8'),
indent=4)
f1 = f1_score(result, self.args)
print("f1:", f1)
