def main(args):
t1 = time.time()
g = Graph()
print("Reading...")
if args.graph_format == 'adjlist':
g.read_adjlist(filename=args.input)
elif args.graph_format == 'edgelist':
g.read_edgelist(filename=args.input,
weighted=args.weighted,
directed=args.directed)
if args.method == 'dngr':
model = DNGR(graph=g, Kstep=args.kstep, dim=args.representation_size)
model.show()
t2 = time.time()
print(t2 - t1)
if args.method != 'gcn':
print("Saving embeddings...")
model.save_embeddings(args.output)
if args.label_file and args.method != 'gcn':
vectors = model.vectors
X, Y = read_node_label(args.label_file)
print("Training classifier using {:.2f}% nodes...".format(
args.clf_ratio * 100))
clf = Classifier(vectors=vectors, clf=LogisticRegression())
clf.split_train_evaluate(X, Y, args.clf_ratio)
def main(args):
t1 = time.time()
g = Graph()
singluar_node_file = "singluar_nodes.txt"
print("Reading...")
if args.graph_format == 'adjlist':
g.read_adjlist(filename=args.input)
elif args.graph_format == 'edgelist':
g.read_edgelist(filename=args.input, weighted=args.weighted, directed=args.directed)
if args.method == 'node2vec':
model = node2vec.Node2vec(graph=g, path_length=args.walk_length,
num_paths=args.number_walks, dim=args.representation_size,
workers=args.workers, p=args.p, q=args.q, window=args.window_size)
elif args.method == 'line':
if args.label_file and not args.no_auto_save:
model = line.LINE(g, epoch = args.epochs, rep_size=args.representation_size, order=args.order,
label_file=args.label_file, clf_ratio=args.clf_ratio)
else:
model = line.LINE(g, epoch = args.epochs, rep_size=args.representation_size, order=args.order)
elif args.method == 'deepWalk':
model = node2vec.Node2vec(graph=g, path_length=args.walk_length,
num_paths=args.number_walks, dim=args.representation_size,
workers=args.workers, window=args.window_size, dw=True)
elif args.method == 'tadw':
# assert args.label_file != ''
assert args.feature_file != ''
# g.read_node_label(args.label_file)
g.read_node_features(args.feature_file)
fout = open(singluar_node_file, "w+")
for node_idx in g.sgl_node_list:
fout.write("{}\n".format(node_idx))
fout.close()
model = tadw.TADW(graph=g, dim=args.representation_size, lamb=args.lamb)
# model = tadw_gpu.TADW_GPU(graph=g, dim=args.representation_size, lamb=args.lamb)
elif args.method == 'gcn':
assert args.label_file != ''
assert args.feature_file != ''
g.read_node_label(args.label_file)
g.read_node_features(args.feature_file)
model = gcnAPI.GCN(graph=g, dropout=args.dropout,
weight_decay=args.weight_decay, hidden1=args.hidden,
epochs=args.epochs, clf_ratio=args.clf_ratio)
elif args.method == 'grarep':
model = GraRep(graph=g, Kstep=args.kstep, dim=args.representation_size)
t2 = time.time()
print("time: ", t2-t1)
if args.method != 'gcn':
print("Saving embeddings...")
model.save_embeddings(args.output)
if args.label_file and args.method != 'gcn':
vectors = model.vectors
X, Y = read_node_label(args.label_file)
print("Training classifier using {:.2f}% nodes...".format(args.clf_ratio*100))
clf = Classifier(vectors=vectors, clf=LogisticRegression())
clf.split_train_evaluate(X, Y, args.clf_ratio)
def main(args):
t1 = time.time()
g = Graph()
print("Reading...")
X, Y = read_node_label(args.label_file)
training_size = int(args.clf_ratio * len(X))
shuffle_indices = np.random.permutation(np.arange(len(X)))
X_train = [X[shuffle_indices[i]] for i in range(training_size)]
Y_train = [Y[shuffle_indices[i]] for i in range(training_size)]
X_test = [X[shuffle_indices[i]] for i in range(training_size, len(X))]
Y_test = [Y[shuffle_indices[i]] for i in range(training_size, len(X))]
if args.graph_format == 'adjlist':
g.read_adjlist(filename=args.input)
elif args.graph_format == 'edgelist':
g.read_edgelist(filename=args.input, weighted=args.weighted, directed=args.directed)
if args.method == 'dngr':
model = DNGR(graph=g, Kstep=args.kstep, dim=args.representation_size, XY=[X_train, Y_train])
model.show()
if args.method == 'vaedngr':
model = VAEDNGR(graph=g, Kstep=args.kstep, dim=args.representation_size, XY=[X_train, Y_train])
model.show()
if args.method == 'sdngr':
model= SDNGR(graph=g, Kstep=args.kstep, dim=args.representation_size, XY=[X_train, Y_train])
if args.method == 'vaesdngr':
model = VAESDNGR(graph=g, Kstep=args.kstep, dim=args.representation_size, XY=[X_train, Y_train])
model.show()
t2 = time.time()
print(t2-t1)
if args.method != 'gcn':
print("Saving embeddings...")
model.save_embeddings(args.method+'_'+args.output)
# if args.label_file and args.method != 'gcn':
vectors = model.vectors
print("Training classifier using {:.2f}% nodes...".format(args.clf_ratio*100))
clf = Classifier(vectors=vectors, clf=LogisticRegression())
clf.my_evaluate(X_train, Y_train, X_test, Y_test)
import numpy as np
import random
from sklearn.linear_model import LogisticRegression
from libnrl.graph import *
from libnrl import node2vec
from libnrl.classify import Classifier, read_node_label
import matplotlib as plt
g = Graph()
g.read_edgelist(filename='../data/load_rename.csv',
weighted=True,
directed=True)
#调参
X, Y = read_node_label('../data/load_label.csv')
tuned_parameters = {
'path_length': [20, 100],
'num_paths': [10, 20, 50],
'dim': [30, 80, 200],
'p': [0.25, 0.5, 1, 2, 4],
'q': [0.25, 0.5, 1, 2, 4]
}
test_scores = {}
for p in tuned_parameters['p']:
for q in tuned_parameters['q']:
model = node2vec.Node2vec(graph=g,
path_length=80,
num_paths=10,
dim=30,
p=p,
q=q,
window=20)
def main(args):
t1 = time.time()
g = Graph()
print "Reading..."
if args.graph_format == 'adjlist':
g.read_adjlist(filename=args.input)
elif args.graph_format == 'edgelist':
g.read_edgelist(filename=args.input,
weighted=args.weighted,
directed=args.directed)
if args.method == 'node2vec':
model = node2vec.Node2vec(graph=g,
path_length=args.walk_length,
num_paths=args.number_walks,
dim=args.representation_size,
workers=args.workers,
p=args.p,
q=args.q,
window=args.window_size)
elif args.method == 'line':
if args.label_file:
model = line.LINE(g,
lr=args.lr,
batch_size=args.batch_size,
epoch=args.epochs,
rep_size=args.representation_size,
order=args.order,
label_file=args.label_file,
clf_ratio=args.clf_ratio,
auto_stop=args.no - auto_stop)
else:
model = line.LINE(g,
lr=args.lr,
batch_size=args.batch_size,
epoch=args.epochs,
rep_size=args.representation_size,
order=args.order)
elif args.method == 'deepWalk':
model = node2vec.Node2vec(graph=g,
path_length=args.walk_length,
num_paths=args.number_walks,
dim=args.representation_size,
workers=args.workers,
window=args.window_size,
dw=True)
elif args.method == 'tadw':
assert args.label_file != ''
assert args.feature_file != ''
g.read_node_label(args.label_file)
g.read_node_features(args.feature_file)
model = tadw.TADW(graph=g,
dim=args.representation_size,
lamb=args.lamb)
elif args.method == 'gcn':
assert args.label_file != ''
assert args.feature_file != ''
g.read_node_label(args.label_file)
g.read_node_features(args.feature_file)
model = gcnAPI.GCN(graph=g,
dropout=args.dropout,
weight_decay=args.weight_decay,
hidden1=args.hidden,
epochs=args.epochs,
clf_ratio=args.clf_ratio)
elif args.method == 'grarep':
model = GraRep(graph=g, Kstep=args.kstep, dim=args.representation_size)
t2 = time.time()
print t2 - t1
if args.method != 'gcn':
print "Saving embeddings..."
model.save_embeddings(args.output)
if args.label_file and args.method != 'gcn':
vectors = model.vectors
X, Y = read_node_label(args.label_file)
print "Training classifier using {:.2f}% nodes...".format(
args.clf_ratio * 100)
clf = Classifier(vectors=vectors, clf=LogisticRegression())
clf.split_train_evaluate(X, Y, args.clf_ratio)
