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BGSR.py
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BGSR.py
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# Main function of BGSR framework for brain graph super-resolution.
# This code requires Python 3 to run.
# Details can be found in the original paper:Brain Graph Super-Resolution for Boosting Neurological
# Disorder Diagnosis using Unsupervised Multi-Topology Residual Graph Manifold Learning.
#
#
# ---------------------------------------------------------------------
#
# This file contains the implementation of the key steps of our BGSR framework:
# (1) Estimation of a connectional brain template (CBT)
# (2) Proposed CBT-guided graph super-resolution :
#
# [pHR] = BGSR(train_data,train_Labels,HR_features,kn)
#
# Inputs:
#
# train_data: ((n-1) × m × m) tensor stacking the symmetric matrices of the training subjects (LR
# graphs)
# n the total number of subjects
# m the number of nodes
#
# train_Labels: ((n-1) × 1) vector of training labels (e.g., -1, 1)
#
# HR_features: (n × (m × m)) matrix stacking the source HR brain graph.
#
# Kn: Number of most similar LR training subjects.
#
#
# Outputs:
# pHR: (1 × (m × m)) vector stacking the predicted features of the testing subject.
#
#
#
# To evaluate our framework we used Leave-One-Out cross validation strategy.
#
#
#
# To test BGSR on random data, we defined the function 'simulateData_LR_HR' where the size of the dataset is chosen by the user.
# ---------------------------------------------------------------------
# Copyright 2020 Busra Asan ([email protected]), Istanbul Technical University.
# Please cite the above paper if you use this code.
# All rights reserved.
# """
#
# ------------------------------------------------------------------------------
import numpy as np
import snf
import SIMLR_PY.SIMLR as SIMLR
from atlas import atlas
import networkx as nx
def BGSR(train_data,train_labels,HR_features,kn, K1, K2):
#These are a reproduction of the closeness, degrees and isDirected functions of aeolianine since I couldn't find a compatible functions in python.
def isDirected(adj):
adj_transpose = np.transpose(adj)
for i in range(len(adj)):
for j in range(len(adj[0])):
if adj[j][i] != adj_transpose[j][i]:
return True
return False
def degrees(adj):
indeg = np.sum(adj, axis=1)
outdeg = np.sum(np.transpose(adj), axis=0)
if isDirected(adj):
deg = indeg + outdeg #total degree
else: #undirected graph: indeg=outdeg
deg = indeg + np.transpose(np.diag(adj)) #add self-loops twice, if any
return deg
def closeness(G, adj):
c = np.zeros((len(adj),1))
all_sum = np.zeros((len(adj[1]),1))
spl = nx.all_pairs_dijkstra_path_length(G, weight="weight")
spl_list = list(spl)
for i in range(len(adj[1])):
spl_dict = spl_list[i][1]
c[i] = 1 / sum(spl_dict.values())
return c
sz1, sz2, sz3 = train_data.shape
# (1) Estimation of a connectional brain template (CBT)
CBT = atlas(train_data, train_labels)
# (2) Proposed CBT-guided graph super-resolution
# Initialization
c_degree = np.zeros((sz1, sz2))
c_closeness = np.zeros((sz1, sz2))
c_betweenness = np.zeros((sz1, sz2))
residual = np.zeros((len(train_data), len(train_data[1]), len(train_data[1])))
for i in range(sz1):
residual[i][:][:] = np.abs(train_data[i][:][:] - CBT) #Residual brain graph
G = nx.from_numpy_matrix(np.array(residual[i][:][:]))
for j in range(0, sz2):
c_degree[i][j] = degrees(residual[i][:][:])[j] #Degree matrix
c_closeness[i][j] = closeness(G, residual[i][:][:])[j] #Closeness matrix
c_betweenness[i][j] = nx.betweenness_centrality(G, weight=True)[j] #Betweenness matrix
# Degree similarity matrix
simlr1 = SIMLR.SIMLR_LARGE(1,K1,0) #The first input is number of rank (clusters) and the second input is number of neighbors.The third one is an binary indicator whether to use memory-saving mode.You can turn it on when the number of cells are extremely large to save some memory but with the cost of efficiency.
S1, F1, val1, ind1 = simlr1.fit(c_degree)
y_pred_X1 = simlr1.fast_minibatch_kmeans(F1,1)
# Closeness similarity matrix
simlr2 = SIMLR.SIMLR_LARGE(1,K1,0)
S2, F2, val2, ind2 = simlr2.fit(c_closeness)
y_pred_X2 = simlr2.fast_minibatch_kmeans(F2,1)
# Betweenness similarity matrix
if not np.count_nonzero(c_betweenness):
S3 = np.zeros((len(c_betweenness), len(c_betweenness)))
else:
simlr3 = SIMLR.SIMLR_LARGE(1,K1,0)
S3, F3, val3, ind3 = simlr3.fit(c_betweenness)
y_pred_X3 = simlr3.fast_minibatch_kmeans(F3,1)
alpha = 0.5 # hyperparameter, usually (0.3~0.8)
T = 20 # Number of Iterations, usually (10~20)
wp1 = snf.make_affinity(S1.toarray(), K=K2, mu=alpha)
wp2 = snf.make_affinity(S2.toarray(), K=K2, mu=alpha)
wp3 = snf.make_affinity(S3, K=K2, mu=alpha)
FSM = snf.snf([wp1, wp2, wp3], K=K2, alpha=alpha, t=T) #Fused similarity matrix
FSM_sorted = np.sort(FSM, axis=0)
ind = np.zeros((kn,1))
HR_ind = np.zeros((kn, len(HR_features[1])))
for i in range(1, kn+1):
a,b,pos = np.intersect1d(FSM_sorted[len(FSM_sorted)-i][0],FSM, return_indices=True)
ind[i-1] = pos
for j in range(len(HR_features[1])):
HR_ind[i-1][j] = HR_features[int(ind[i-1][0])][j]
pHR = np.mean(HR_ind, axis=0) # Predicted features of the testing subject
return pHR