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coarsen_pooling_with_last_eigen_padding.py
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coarsen_pooling_with_last_eigen_padding.py
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import graph
import networkx
import matplotlib.pyplot as plt
from networkx.algorithms import community
import community as cm
import numpy as np
from sklearn.cluster import SpectralClustering
from scipy import sparse as sp
import scipy
import torch
from utils import sparse_mx_to_torch_sparse_tensor
def adj2edgeindex(adj):
adj = adj.tocoo().astype(np.float32)
row = adj.row
col = adj.col
edge_index = torch.LongTensor([list(row),list(col)])
return edge_index
class Graphs():
def __init__(self, adjacency_matrix, pooling_sizes):
self.adjacency_matrix = adjacency_matrix
self.num_nodes = adjacency_matrix[:,0].shape[0]
self.pooling_sizes = pooling_sizes
self.graphs = [scipy.sparse.csr_matrix(adjacency_matrix)]
self.layer2pooling_matrices=dict()
def coarsening_pooling(self, normalize = True):
adj = scipy.sparse.csr_matrix(self.adjacency_matrix)
for i in range(len(self.pooling_sizes)):
adj_coarsened, pooling_matrices = self._coarserning_pooling_(adj, self.pooling_sizes[i],normalize)
self.graphs.append(adj_coarsened)
self.layer2pooling_matrices[i] = pooling_matrices
adj = scipy.sparse.csr_matrix(adj_coarsened)
num_nodes_before_final = adj_coarsened.shape[0]
if num_nodes_before_final < 4:
num_nodes_before_final = 4
num_nodes_before_final = 4
pooling_matrices_final = [sp.lil_matrix((adj_coarsened.shape[0],1)) for i in range(num_nodes_before_final)]
if adj_coarsened.shape[0]>1:
L_i = graph.laplacian(adj_coarsened, normalize)
lamb_i, U_i = graph.fourier(L_i)
for j in range(num_nodes_before_final):
if j < adj_coarsened.shape[0]:
if U_i[0,j] < 0:
pooling_matrices_final[j][:,0] = -U_i[:,j].reshape(-1,1)
else:
pooling_matrices_final[j][:,0] = U_i[:,j].reshape(-1,1)
else:
if U_i[0, adj_coarsened.shape[0]-1] < 0:
pooling_matrices_final[j][:,0] = -U_i[:, adj_coarsened.shape[0]-1].reshape(-1,1)
else:
pooling_matrices_final[j][:,0] = U_i[:, adj_coarsened.shape[0]-1].reshape(-1,1)
else:
for j in range(num_nodes_before_final):
pooling_matrices_final[j][:,0] = adj_coarsened.reshape(-1,1)
self.layer2pooling_matrices[i+1] = pooling_matrices_final
def prepare_for_pytorch(self):
self.edge_index_lists = [0]*len(self.graphs)
for i in range(len(self.graphs)):
self.edge_index_lists[i] = adj2edgeindex(self.graphs[i])
for i in self.layer2pooling_matrices:
self.layer2pooling_matrices[i] = [sparse_mx_to_torch_sparse_tensor(spmat).t() for spmat in self.layer2pooling_matrices[i]]
def _coarserning_pooling_(self, adjacency_matrix, pooling_size, normalize=False):
num_nodes = adjacency_matrix[:,0].shape[0]
A_dense = adjacency_matrix.todense()
num_clusters = int(num_nodes/pooling_size)
if num_clusters == 0:
num_clusters = num_clusters + 1
sc = SpectralClustering(n_clusters = num_clusters, affinity= 'precomputed', n_init=10)
sc.fit(A_dense)
clusters = dict()
for inx, label in enumerate(sc.labels_):
if label not in clusters:
clusters[label] = []
clusters[label].append(inx)
num_clusters = len(clusters)
num_nodes_in_largest_clusters = 0
for label in clusters:
if len(clusters[label])>=num_nodes_in_largest_clusters:
num_nodes_in_largest_clusters = len(clusters[label])
if num_nodes_in_largest_clusters <=5:
num_nodes_in_largest_clusters = 5
num_nodes_in_largest_clusters = 5
Adjacencies_per_cluster = [adjacency_matrix[clusters[label],:][:,clusters[label]] for label in range(len(clusters))]
######## Get inter matrix
A_int = sp.lil_matrix(adjacency_matrix)
for i in range(len(clusters)):
zero_list = list(set(range(num_nodes)) - set(clusters[i]))
for j in clusters[i]:
A_int[j,zero_list] = 0
A_int[zero_list,j] = 0
######## Getting adjacenccy matrix wuith only external links
A_ext = adjacency_matrix - A_int
######## Getting cluster vertex indicate matrix
row_inds = []
col_inds = []
data = []
for i in clusters:
for j in clusters[i]:
row_inds.append(j)
col_inds.append(i)
data.append(1)
Omega = sp.coo_matrix((data,(row_inds,col_inds)))
A_coarsened = np.dot( np.dot(np.transpose(Omega),A_ext), Omega)
########## Constructing pooling matrix
pooling_matrices = [sp.lil_matrix((num_nodes,num_clusters)) for i in range(num_nodes_in_largest_clusters)]
for i in clusters:
adj = Adjacencies_per_cluster[i]
if len(clusters[i])>1:
L_i = graph.laplacian(adj, normalize)
lamb_i, U_i = graph.fourier(L_i)
for j in range(num_nodes_in_largest_clusters):
if j<len(clusters[i]):
if U_i[0,j] <0:
pooling_matrices[j][clusters[i],i] = - U_i[:,j].reshape(-1,1)
else:
pooling_matrices[j][clusters[i],i] = U_i[:,j].reshape(-1,1)
else:
if U_i[0, len(clusters[i])-1] <0:
pooling_matrices[j][clusters[i],i] = - U_i[:, len(clusters[i])-1].reshape(-1,1)
else:
pooling_matrices[j][clusters[i],i] = U_i[:, len(clusters[i])-1].reshape(-1,1)
else:
for j in range(num_nodes_in_largest_clusters):
pooling_matrices[j][clusters[i],i] = adj.reshape(-1,1)
return A_coarsened, pooling_matrices