import networkx as nx
from nodevectors import Node2Vec
import time
from gensim.models import KeyedVectors
g2v = Node2Vec.load(
'C:/Users/DI_Lab/Desktop/연구실 자료/국보연/전지원/word2vec_20000test.model.zip')
# Save model to gensim.KeyedVector format
g2v.save_vectors("wheel_model.bin")
# load in gensim
print(g2v)
model = KeyedVectors.load_word2vec_format("wheel_model.bin")
print(model)
model[str("cve-2019-1020019")]
pickle.dump(n2v, open("./graphs/n2v_sub_small1.pkl", "wb"))
train_comp_expensive = False
if train_comp_expensive:
graphs_subset = [
x for x in graphs if len(related[graphs.index(x)]) > 50
]
for i, graph in tqdm(enumerate(graphs_subset),
total=len(graphs_subset),
leave=False):
n2v = learn_embeddings(graph)
n2v.save(f"./graphs/n2v_sub_huge-{i+1}.pckl")
# pickle.dump(n2v, open(f"./graphs/n2v_sub_huge-{i+1}.pkl", "wb"))
save_huge_vecs = False
if save_huge_vecs:
graphs = [
Node2Vec.load(f"./graphs/huge_graphs/n2v_sub_huge-{i}.pckl.zip")
for i in range(1, 6)
]
for i, graph in enumerate(graphs):
graph.save_vectors(f"./vectors/wheel_mode_graph-{i}.bin")
save_small_vecs = False
if save_small_vecs:
if not save_huge_vecs:
i = 5
small_graphs = pickle.load(open("./graphs/n2v_sub_small1.pkl", "rb"))
for j, graph in enumerate(small_graphs):
graph.save_vectors(f"./vectors/wheel_mode_graph-{i+j}.bin")
from nodevectors import Node2Vec
model_name = "keywords_deep"
idx_file = "data/word_index.pickle"
keywords_file = "data/mag_cs_keywords.csv"
# Load in relevant data and modules
keywords_full_data = pd.read_csv(keywords_file)
keywords_full_data['normalizedName'] = keywords_full_data[
'normalizedName'].fillna('nan')
keywords_data = keywords_full_data['normalizedName']
with open(idx_file, 'rb') as f:
word_to_idx = pickle.load(f)
keyword_embs = Node2Vec.load(model_name + ".zip")
# Process word queries
while True:
print("Please enter a word to search: ")
query_word = input()
query_node_idx = -1
query_node = None
while query_node_idx < 0:
try:
query_node_idx = word_to_idx[query_word.lower()]
query_node = keyword_embs.predict(query_node_idx)
except:
print(