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main.py
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import argparse
import os.path as osp
import random
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
import contrastive_loss
from torch_geometric.nn import GCNConv, SGConv, SAGEConv, GATConv, GraphConv, GINConv
from torch_geometric.utils import to_dense_adj
from dataset import get_dataset
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cluster
from scipy.sparse.linalg import svds
from sklearn.preprocessing import normalize
from sklearn.utils import check_random_state, check_array, check_symmetric
import scipy.sparse as sparse
import clustering_metric
from simple_param.sp import SimpleParam
from model import Encoder, Decoder, Model
from sklearn.neighbors import NearestNeighbors
def get_base_model(name: str):
def gat_wrapper(in_channels, out_channels):
return GATConv(
in_channels=in_channels,
out_channels=out_channels // 1,
heads=1
)
def gin_wrapper(in_channels, out_channels):
mlp = nn.Sequential(
nn.Linear(in_channels, 2 * out_channels),
nn.ELU(),
nn.Linear(2 * out_channels, out_channels)
)
return GINConv(mlp)
base_models = {
'GCNConv': GCNConv,
'SGConv': SGConv,
'SAGEConv': SAGEConv,
'GATConv': gat_wrapper,
'GraphConv': GraphConv,
'GINConv': gin_wrapper
}
return base_models[name]
def get_activation(name: str):
activations = {
'relu': F.relu,
'hardtanh': F.hardtanh,
'elu': F.elu,
'leakyrelu': F.leaky_relu,
'prelu': torch.nn.PReLU(),
'rrelu': F.rrelu
}
return activations[name]
# def thrC(C, alpha):
# # if alpha < 1:
# # N = C.shape[1]
# # Cp = np.zeros((N, N))
# # S = np.abs(np.sort(-np.abs(C), axis=0))
# # Ind = np.argsort(-np.abs(C), axis=0)
# # for i in range(N):
# # cL1 = np.sum(S[:, i]).astype(float)
# # stop = False
# # csum = 0
# # t = 0
# # while (stop == False):
# # csum = csum + S[t, i]
# # if csum > alpha * cL1:
# # stop = True
# # Cp[Ind[0:t + 1, i], i] = C[Ind[0:t + 1, i], i]
# # t = t + 1
# # else:
# # Cp = C
# # return Cp
# #
# #
# # def post_proC(C, K, d, ro):
# # # C: coefficient matrix, K: number of clusters, d: dimension of each subspace
# # n = C.shape[0]
# # C = 0.5 * (C + C.T)
# # # C = C - np.diag(np.diag(C)) + np.eye(n, n) # good for coil20, bad for orl
# # r = d * K + 1
# # U, S, _ = svds(C, r, v0=np.ones(n))
# # U = U[:, ::-1]
# # S = np.sqrt(S[::-1])
# # S = np.diag(S)
# # U = U.dot(S)
# # U = normalize(U, norm='l2', axis=1)
# # Z = U.dot(U.T)
# # Z = Z * (Z > 0)
# # L = np.abs(Z ** ro)
# # L = L / L.max()
# # L = 0.5 * (L + L.T)
# # spectral = cluster.SpectralClustering(n_clusters=K, eigen_solver='arpack', affinity='precomputed',
# # assign_labels='discretize')
# # spectral.fit(L)
# # grp = spectral.fit_predict(L)
# # return grp, L
# def spectral_clustering_1(C, K, d, alpha, ro):
# C = thrC(C, alpha)
# y, _ = post_proC(C, K, d, ro)
# return y
def post_proC(C, K, d=6, alpha=8):
# C: coefficient matrix, K: number of clusters, d: dimension of each subspace
C = 0.5 * (C + C.T)
r = d * K + 1
U, S, _ = svds(C, r, v0=np.ones(C.shape[0]))
U = U[:, ::-1]
S = np.sqrt(S[::-1])
S = np.diag(S)
U = U.dot(S)
U = normalize(U, norm='l2', axis=1)
Z = U.dot(U.T)
Z = Z * (Z > 0)
L = np.abs(Z ** alpha)
L = L / L.max()
L = 0.5 * (L + L.T)
spectral = cluster.SpectralClustering(n_clusters=K, eigen_solver='arpack', affinity='precomputed',
assign_labels='discretize')
spectral.fit(L)
grp = spectral.fit_predict(L) + 1
return grp, L
def thrC(C, ro):
if ro < 1:
N = C.shape[1]
Cp = np.zeros((N, N))
S = np.abs(np.sort(-np.abs(C), axis=0))
Ind = np.argsort(-np.abs(C), axis=0)
for i in range(N):
cL1 = np.sum(S[:, i]).astype(float)
stop = False
csum = 0
t = 0
while stop == False:
csum = csum + S[t, i]
if csum > ro * cL1:
stop = True
Cp[Ind[0:t + 1, i], i] = C[Ind[0:t + 1, i], i]
t = t + 1
else:
Cp = C
return Cp
# def spectral_clustering_2(affinity_matrix_, n_clusters, k, seed=1, n_init=20):
# affinity_matrix_ = check_symmetric(affinity_matrix_)
# random_state = check_random_state(seed)
#
# laplacian = sparse.csgraph.laplacian(affinity_matrix_, normed=True)
# _, vec = sparse.linalg.eigsh(sparse.identity(laplacian.shape[0]) - laplacian,
# k=k, sigma=None, which='LA')
# embedding = normalize(vec)
# _, labels_, _ = cluster.k_means(embedding, n_clusters,
# random_state=seed, n_init=n_init)
# return labels_
def clustering_evaluation(predict_labels, true_labels):
acc, nmi, ari = clustering_metric.evaluationClusterModelFromLabel(true_labels, predict_labels)
return acc, nmi, ari
def regularizer(c, lmbd=1.0):
return lmbd * torch.abs(c).sum() + (1.0 - lmbd) / 2.0 * torch.pow(c, 2).sum()
def sim(z1: torch.Tensor, z2: torch.Tensor):
z1 = F.normalize(z1)
z2 = F.normalize(z2)
return torch.mm(z1, z2.t())
def semi_loss(z1: torch.Tensor, z2: torch.Tensor):
f = lambda x: torch.exp(x /param['tau'])
refl_sim = f(sim(z1, z1))
between_sim = f(sim(z1, z2))
return -torch.log(
between_sim.diag()
/ (refl_sim.sum(1) + between_sim.sum(1) - refl_sim.diag()))
def instanceloss(z1: torch.Tensor, z2: torch.Tensor, mean: bool = True):
l1 = semi_loss(z1, z2)
l2 = semi_loss(z2, z1)
ret = (l1 + l2) * 0.5
ret = ret.mean() if mean else ret.sum()
return ret
def knbrsloss(H, k):
nbrs = NearestNeighbors(n_neighbors=k+1, algorithm="auto").fit(H.cpu().detach().numpy())
_, indices = nbrs.kneighbors(H.cpu().detach().numpy())
f = lambda x: torch.exp(x / param['tau_knbrs'])
refl_sim = f(sim(H, H))
V = torch.zeros((param['instance_number'], k)).to(device)
for i in range(param['instance_number']):
for j in range(k):
V[i][j] += refl_sim[i][indices[i][j + 1]]
ret = -torch.log(
V.sum(1) / (refl_sim.sum(1) - refl_sim.diag()))
ret = ret.mean()
return ret
def train():
model.train()
optimizer.zero_grad()
H, CH, Coefficient, X_ = model(data.x, data.edge_index)
loss_knbrs = knbrsloss(H, k = 10)
# C_diag = torch.diag(torch.diag(Coefficient))
# A_ = torch.sigmoid(torch.mm(CH, CH.t()))
# pos_weight = float(A.shape[0] * A.shape[0] - A.sum()) / A.sum()
# norm = A.shape[0] * A.shape[0] / float((A.shape[0] * A.shape[0] - A.sum()) * 2)
# loss_edge = norm * F.binary_cross_entropy_with_logits(A_, A, pos_weight=pos_weight)
# rec_loss = torch.sum(torch.pow(H - CH, 2))
# loss_instance = criterion_instance(H, CH)
rec_loss = torch.sum(torch.pow(data.x - X_, 2))
# rec_loss = instanceloss(data.x, X_)
loss_instance = instanceloss(H, CH)
loss_coef = torch.sum(torch.pow(Coefficient, 2))
# loss_coef = torch.mean(torch.pow(Coefficient, 2))
# loss_coef = regularizer(Coefficient)
loss = 1.0 * loss_instance + 1.0 * loss_knbrs + 1.0 * loss_coef + 0.1 * rec_loss
loss.backward()
optimizer.step()
return loss_instance.item(), loss_coef.item(), loss_knbrs.item(), rec_loss.item(), loss.item(), Coefficient
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cuda:0')
parser.add_argument('--dataset', type=str, default='Cora')
parser.add_argument('--param', type=str, default='local:Cora.json')
parser.add_argument('--seed', type=int, default=39788)
default_param = {
'learning_rate': 0.01,
'num_hidden': 256,
'activation': 'prelu',
'base_model': 'GCNConv',
'num_layers': 2,
'num_epochs': 3000,
'weight_decay': 1e-5,
}
# add hyper-parameters into parser
param_keys = default_param.keys()
for key in param_keys:
parser.add_argument(f'--{key}', type=type(default_param[key]), nargs='?')
args = parser.parse_args()
# parse param
sp = SimpleParam(default=default_param)
param = sp(source=args.param, preprocess='nni')
# merge cli arguments and parsed param
for key in param_keys:
if getattr(args, key) is not None:
param[key] = getattr(args, key)
use_nni = args.param == 'nni'
if use_nni and args.device != 'cpu':
args.device = 'cuda'
torch_seed = args.seed
torch.manual_seed(torch_seed)
random.seed(12345)
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device = torch.device(args.device)
path = osp.expanduser('~/datasets')
path = osp.join(path, args.dataset)
dataset = get_dataset(path, args.dataset)
data = dataset[0]
# print(min(data.y))
data = data.to(device)
# A = to_dense_adj(data.edge_index)
# A = A.view([param['instance_number'], param['instance_number']])
criterion_instance = contrastive_loss.InstanceLoss(param['instance_number'], param['tau'], device).to(
device)
encoder = Encoder(dataset.num_features, param['num_hidden'], get_activation(param['activation']),
base_model=get_base_model(param['base_model']), k=param['num_layers']).to(device)
decoder = Decoder(param['num_hidden'], dataset.num_features, get_activation(param['activation']),
base_model=get_base_model(param['base_model']), k=param['num_layers']).to(device)
model = Model(encoder, decoder, param['instance_number']).to(device)
# H, CH, Coefficient = model(data.x, data.edge_index)
optimizer = torch.optim.Adam(
model.parameters(),
lr=param['learning_rate'],
weight_decay=param['weight_decay']
)
K = len(np.unique(data.y.cpu().numpy()))
# dim_subspace = 12
# alpha = 0.04
alpha = max(0.4 - (K - 1) / 10 * 0.1, 0.1)
# ro = 8
# num_subspaces = K
# spectral_dim = K
acclist = []
nmilist = []
arilist = []
for epoch in range(1, param['num_epochs']+1): #param['num_epochs'] + 1
loss_instance, loss_c, loss_knbrs, rec_loss, loss, C = train()
# get C
C = C.detach().to('cpu').numpy()
commonZ = thrC(C, alpha)
y_pred, _ = post_proC(commonZ, K)
# y_pred = spectral_clustering_1(C, K, dim_subspace, alpha, ro)
# Evalue
# rows = list(range(param['instance_number']))
# cols = list(range(param['instance_number']))
# C[rows, cols] = 0.0
# C_normalized = normalize(C).astype(np.float32)
# Aff = 0.5 * (np.abs(C) + np.abs(C).T) # get Aff
# y_pred = spectral_clustering_2(Aff, num_subspaces, spectral_dim) # use spectral_clustering get predicted label
acc, nmi, ari = clustering_evaluation(y_pred, data.y.cpu().numpy()) # get acc nmi ari
acclist.append(acc)
nmilist.append(nmi)
arilist.append(ari)
print(f'Epoch={epoch:03d}, loss={loss:.4f}, loss_instance = {loss_instance:.4f}, loss_knbrs = {loss_knbrs:.4f}, rec_loss = {rec_loss:.4f}, loss_c={loss_c:.4f}')
print('ACC = {:.4f}, NMI = {:.4f} ARI = {:.4f} '.format(acc, nmi, ari))
# print(C)
print("=== Final ===")
print('best_acc: {}, best_nmi: {}, best_adj: {}'.format(max(acclist), max(nmilist), max(arilist)))
x = np.arange(param['num_epochs'])
plt.plot(x, acclist, label='accdsc')
plt.plot(x, nmilist, label='nmidsc')
plt.plot(x, arilist, label='aridsc')
# plt.plot(x, list_loss, label='list_loss')
plt.legend(['ACC', 'NMI', 'ARI'])
plt.title("Cora_Performance")
plt.show()