def make_laplacian_directory(fname_input):
levs = 3
A = genfromtxt(fname_input, delimiter=',')
A_ = sparse.csr_matrix(A).astype(np.float32)
graphs, perm = coarsening.coarsen(A_, levels=levs, self_connections=False)
L_list = [graph.laplacian(g, normalized=True) for g in graphs]
L = L_list[0].astype(np.float32)
#pdb.set_trace()
return L, perm, levs, L_list
def _coarsen(self, dense_node_inputs, adj):
assert ('metis_' in FLAGS.coarsening)
self.num_level = get_coarsen_level()
assert (self.num_level >= 1)
graphs, perm = coarsen(sp.csr_matrix(adj), levels=self.num_level,
self_connections=False)
permuted_padded_dense_node_inputs = perm_data(
dense_node_inputs.T, perm).T
self.sparse_permuted_padded_dense_node_inputs = self._preprocess_inputs(
sp.csr_matrix(permuted_padded_dense_node_inputs))
self.coarsened_laplacians = []
for g in graphs:
self.coarsened_laplacians.append([self._preprocess_adj(g.todense())])
assert (len(self.coarsened_laplacians) == self.num_laplacians * self.num_level + 1)
def __coarsen(self, at_depth):
assert(at_depth > 0)
if not self.is_leaf() and at_depth > 1:
self.__depth -= 1
for c in self.get_children():
if c.get_depth() is at_depth-1:
c.__coarsen(at_depth-1)
elif not self.is_leaf() and at_depth == 1:
vertex_set = self.get_dataset()
coarsened_set = coarsen(vertex_set)
self.__depth = 0
self.__children = None
self.__data = coarsened_set
else:
raise Exception("something went wrong...")
def first_coarsen(node_features, n):
"""
Returns a graph object based on the coordinates of the tract
Input:
node_features - An array (n x 3) with the 3D coordinates of each point
n - The number of points sampled on each tract
"""
row = np.array(list(range(n - 1)) +
list(range(1, n))) # row[i] connects to col[i]
col = np.array(list(range(1, n)) + list(range(n - 1)))
data = np.array([float(1) for i in range(2 * (n - 1))]) # Unit weights
A = scipy.sparse.csr_matrix((data, (row, col)),
shape=(n, n)).astype(np.float32)
coarsening_levels = 2
L, perm = coarsen(A, coarsening_levels)
vert = perm_data(node_features.transpose(), perm)
return vert.transpose().astype('float16'), L, perm
def prepare(self):
# split train and test
self.training_events = self.dataset[:self.n_training_events]
self.test_events = self.dataset[self.n_training_events:]
# set up model
self.model = Graph_ConvNet(self.options)
self.grid_side = self.options['grid_side']
self.number_edges = self.options['number_edges']
self.metric = self.options['metric']
# create graph of Euclidean grid
self.Grid = grid_graph(self.grid_side, self.number_edges, self.metric)
# Compute coarsened graphs
self.L, self.perm = coarsen(self.Grid, self.options['coarsening'])
lmax = []
for i in range(self.options['coarsening']):
lmax.append(lmax_L(self.L[i]))
self.options['D'] = max(perm) + 1
self.options['FC1Fin'] = self.options['CL2_F'] * \
(self.options['D'] // 16)
def build_coarse_graphs(mesh_face, joint_num, skeleton, flip_pairs, levels=9):
joint_adj = build_adj(joint_num, skeleton, flip_pairs)
# Build graph
mesh_adj = build_graph(mesh_face, mesh_face.max() + 1)
graph_Adj, graph_L, graph_perm = coarsen(mesh_adj, levels=levels)
input_Adj = sp.csr_matrix(joint_adj)
input_Adj.eliminate_zeros()
input_L = laplacian(input_Adj, normalized=True)
graph_L[-1] = input_L
graph_Adj[-1] = input_Adj
# Compute max eigenvalue of graph Laplacians, rescale Laplacian
graph_lmax = []
renewed_lmax = []
for i in range(levels):
graph_lmax.append(lmax_L(graph_L[i]))
graph_L[i] = rescale_L(graph_L[i], graph_lmax[i])
# renewed_lmax.append(lmax_L(graph_L[i]))
return graph_Adj, graph_L, graph_perm, perm_index_reverse(graph_perm[0])
from coarsening import lmax_L
from coarsening import perm_data
from coarsening import rescale_L
# Construct graph
t_start = time.time()
grid_side = 28
number_edges = 4
metric = 'euclidean'
A = grid_graph(grid_side, number_edges,
metric) # create graph of Euclidean grid
# Compute coarsened graphs
coarsening_levels = 4
L, perm = coarsen(A, coarsening_levels)
# Compute max eigenvalue of graph Laplacians
lmax = []
for i in range(coarsening_levels):
lmax.append(lmax_L(L[i]))
print('lmax: ' + str([lmax[i] for i in range(coarsening_levels)]))
# Reindex nodes to satisfy a binary tree structure
train_data = perm_data(train_data, perm)
val_data = perm_data(val_data, perm)
test_data = perm_data(test_data, perm)
print(train_data.shape)
print(val_data.shape)
print(test_data.shape)
def __init__(self,
sname,
mode,
target,
sample_rate,
win_size,
hist_range,
s_month,
s_date,
e_month,
e_date,
data_rm,
batch_size=-1,
coarsening_level=4,
conv_mode='gcnn',
is_coarsen=True):
base_dir = os.path.join('.', 'data', sname)
if target == 'avg':
data_dir = os.path.join(
base_dir, '{}_{}'.format(sample_rate, win_size), mode, target,
'{}_{}-{}_{}'.format(s_date, s_month, e_date,
e_month), 'rm{}'.format(data_rm))
else:
data_dir = os.path.join(
base_dir, '{}_{}'.format(sample_rate, win_size), mode, target,
'{}_{}_{}'.format(hist_range[0], hist_range[-1] + 1,
hist_range[1] - hist_range[0]),
'{}_{}-{}_{}'.format(s_date, s_month, e_date,
e_month), 'rm{}'.format(data_rm))
dict_normal_fname = os.path.join(data_dir, 'dict_normal.pickle')
train_data_dict_fname = os.path.join(data_dir,
'train_data_dict.pickle')
validate_data_dict_fname = os.path.join(data_dir,
'validate_data_dict.pickle')
Adj_fname = os.path.join(base_dir, 'edge_adj.pickle')
if not os.path.exists(dict_normal_fname) or \
not os.path.exists(train_data_dict_fname) or \
not os.path.exists(validate_data_dict_fname) or \
not os.path.exists(Adj_fname):
print("Creating Data...")
self.data_generator(base_dir, data_dir, sname, mode, target,
sample_rate, win_size, hist_range, s_month,
s_date, e_month, e_date, data_rm)
print("Loading data...")
adj = pklLoad(Adj_fname)
dict_normal = pklLoad(dict_normal_fname)
train_data_dict = pklLoad(train_data_dict_fname)
validate_data_dict = pklLoad(validate_data_dict_fname)
if target == 'avg':
self.y_scaler = dict_normal['velocity']
else:
self.y_scaler = None
train_data = train_data_dict['velocity_x']
train_labels = train_data_dict['velocity_y']
train_label_weight = train_data_dict['weight_y']
train_counts = train_data_dict['count_y']
train_vel_lists = train_data_dict['vel_list']
cat_train = train_data_dict['cat']
con_train = train_data_dict['con']
test_data = validate_data_dict['velocity_x']
test_labels = validate_data_dict['velocity_y']
test_labels_weight = validate_data_dict['weight_y']
test_counts = validate_data_dict['count_y']
test_vel_lists = validate_data_dict['vel_list']
cat_test = validate_data_dict['cat']
con_test = validate_data_dict['con']
# self.mean_y = mean_gt(train_data)
# self.mean_y[np.isnan(self.mean_y)] = 0.0
# train_data = fill_mean(train_data, self.mean_y)
# train_labels = fill_mean(train_labels, self.mean_y)
# train_label_weight = np.ones(train_label_weight.shape)
#
# test_data = fill_mean(test_data, self.mean_y)
if conv_mode == 'gcnn':
perm_file = os.path.join(base_dir, 'adj_perm.pickle')
graph_file = os.path.join(base_dir, 'perm_graphs.pickle')
if os.path.exists(perm_file) and os.path.exists(graph_file):
self.perm = pklLoad(perm_file)
self.graphs = pklLoad(graph_file)
else:
self.graphs, self.perm = coarsening.coarsen(
adj, levels=coarsening_level, self_connections=False)
pklSave(perm_file, self.perm)
pklSave(graph_file, self.graphs)
if is_coarsen:
train_data = coarsening.perm_data_hist(train_data, self.perm)
test_data = coarsening.perm_data_hist(test_data, self.perm)
val_x, test_x, val_y, test_y, \
val_y_weight, test_y_weight, \
val_con, test_con, val_cat, \
test_cat, val_count, test_count, \
val_vel_list, test_vel_list = \
train_test_split(test_data, test_labels, test_labels_weight,
con_test, cat_test, test_counts,
test_vel_lists, test_size=0.8)
print("Reshaping tensors...")
self.all_batches = []
self.sizes = []
# Split train, val, test data into batches
self.construct_batches(train_data, train_labels, train_label_weight,
cat_train, con_train, train_counts,
train_vel_lists, batch_size)
self.construct_batches(val_x, val_y, val_y_weight, val_cat, val_con,
val_count, val_vel_list, batch_size)
self.construct_batches(test_x, test_y, test_y_weight, test_cat,
test_con, test_count, test_vel_list, batch_size)
self.adj = adj
self.batch_idx = [0, 0, 0]
print(
"data load done. Number of batches in train: %d, val: %d, test: %d"
% (self.sizes[0], self.sizes[1], self.sizes[2]))
def prepare_graphs():
p = os.path.join(os.getcwd(), '../survivalnet/data/Brain_Integ.mat')
D = sio.loadmat(p)
T = np.asarray([t[0] for t in D['Survival']])
O = 1 - np.asarray([c[0] for c in D['Censored']])
X = D['Integ_X'] #[:,1855:]
X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
fold_size = int(10 * len(X) / 100)
X_train, T_train, O_train = X[2 * fold_size:], T[2 *
fold_size:], O[2 *
fold_size:]
X_test, T_test, O_test = X[:fold_size], T[:fold_size], O[:fold_size]
X_val, T_val, O_val = X[fold_size:2 *
fold_size], T[fold_size:2 *
fold_size], O[fold_size:2 *
fold_size]
print_log('train and test shapes:' + str(X_train.shape) +
str(X_test.shape))
if not LOAD_A:
start = time.time()
dist, idx = graph.distance_scipy_spatial(X_train.T,
k=4,
metric='euclidean')
print_log('graph constructed:' + str(dist.shape) + str(idx.shape) +
' in ' + str(time.time() - start))
A = graph.adjacency(dist, idx).astype(np.float32)
d = X.shape[1]
assert A.shape == (d, d)
np.savez('A_sml',
data=A.data,
indices=A.indices,
indptr=A.indptr,
shape=A.shape)
print('d = |V| = {}, k|V| < |E| = {}'.format(d, A.nnz))
#plt.spy(A, markersize=2, color='black');
#plt.savefig('tmp.png')
else:
start = time.time()
loader = np.load('A.npz')
A = csr_matrix((loader['data'], loader['indices'], loader['indptr']),
shape=loader['shape'])
print_log('graph loaded:' + ' in ' + str(time.time() - start))
print('adjacency matrix type and shape: ', A.__class__, A.shape)
start = time.time()
graphs, perm = coarsening.coarsen(A, levels=CL, self_connections=False)
print_log('graph coarsened:' + ' in ' + str(time.time() - start))
X_train = coarsening.perm_data(X_train, perm)
X_val = coarsening.perm_data(X_val, perm)
X_test = coarsening.perm_data(X_test, perm)
print_log('train and test shapes:' + str(X_train.shape) +
str(X_test.shape))
L = [graph.laplacian(A, normalized=True) for A in graphs]
#graph.plot_spectrum(L)
n_train = len(X_train)
params = dict()
params['dir_name'] = 'demo'
params['num_epochs'] = 2000
params['batch_size'] = int(len(X_train) / 1.0)
params['eval_frequency'] = 10
# Building blocks.
params['filter'] = 'chebyshev5'
params['brelu'] = 'b1relu'
params['pool'] = 'apool1'
# Architecture.
params['F'] = [8, 8, 8] # Number of graph convolutional filters.
params['K'] = [9, 9, 9] # Polynomial orders.
params['p'] = [2, 2, 2] # Pooling sizes.
params['M'] = [128, 1] # Output dimensionality of fully connected layers.
# Optimization.
params['regularization'] = 0
params['dropout'] = 1
params['learning_rate'] = 1e-4
params['decay_rate'] = 0.999
params['momentum'] = 0
params['decay_steps'] = n_train / params['batch_size']
model = models.cgcnn(L, **params)
accuracy, loss, t_step = model.cox_fit(X_train, T_train, O_train, X_val,
T_val, O_val)
def train(method, view_com, n_views, k, m, n_epoch, batch_size, pairs, labels,
coords, subj, data, data_type, i_fold):
str_params = view_com + '_k' + str(k) + '_m' + str(m) + '_'
obj_params = 'softmax'
print(str_params)
print('Construct ROI graphs...')
t_start = time.process_time()
# A = grid_graph(86, corners=False)
# A = graph.replace_random_edges(A, 0)
coo1, coo2, coo3 = coords.shape # coo2 is the roi dimension
features = np.zeros([coo1 * coo3, coo2])
for i in range(coo3):
features[coo1 * i:coo1 * (i + 1), :] = coords[:, :, i]
dist, idx = graph.distance_scipy_spatial(np.transpose(features),
k=10,
metric='euclidean')
A = graph.adjacency(dist, idx).astype(np.float32)
if method == '2gcn':
graphs, perm = coarsening.coarsen(A,
levels=FLAGS.coarsening_levels,
self_connections=False)
L = [graph.laplacian(A, normalized=True) for A in graphs]
data = coarsening.perm_data1(data, perm, True)
else:
graphs = list()
graphs.append(A)
L = [graph.laplacian(A, normalized=True)]
print('Execution time: {:.2f}s'.format(time.process_time() - t_start))
# graph.plot_spectrum(L)
del A
print('Set parameters...')
mp = model_perf()
# Architecture.
common = {}
common['dir_name'] = 'ppmi/'
common['num_epochs'] = n_epoch
common['batch_size'] = batch_size
common['eval_frequency'] = 5 * common['num_epochs']
common['patience'] = 5
common['regularization'] = 5e-3
common['dropout'] = 1
common['learning_rate'] = 1e-2
common['decay_rate'] = 0.95
common['momentum'] = 0.9
common['n_views'] = n_views
common['view_com'] = view_com
# common['brelu'] = 'b1relu'
# common['pool'] = 'mpool1'
print('Get feed pairs and labels...')
train_pairs, train_y, val_x, val_y, test_pairs, test_y = get_feed_data(
data, subj, pairs, labels, method)
C = max(train_y) + 1
common['decay_steps'] = train_pairs.shape[0] / (common['batch_size'] * 5)
if method == 'fnn':
str_params += 'siamese_'
name = 'mvfnn'
params = common.copy()
params['method'] = 'fnn'
params['fin'] = 1
params['F'] = [m]
params['K'] = [1]
params['p'] = [1]
params['M'] = [C]
params['dir_name'] += name
mp.test(models.siamese_fnn(L, **params), name, params, data,
train_pairs, train_y, val_x, val_y, test_pairs, test_y)
if method == '2fnn':
str_params += 'siamese_layer2_'
name = 'mvfnn2'
params = common.copy()
params['method'] = 'fnn'
params['fin'] = 1
params['F'] = [m]
params['K'] = [1]
params['p'] = [1]
params['M'] = [64, C]
params['dir_name'] += name
mp.test(models.siamese_fnn(L, **params), name, params, data,
train_pairs, train_y, val_x, val_y, test_pairs, test_y)
if method == 'gcn':
# str_params += 'b_max_eu_'
name = 'mvgcn'
params = common.copy()
params['method'] = 'gcn'
params['F'] = [m] # filters
params['K'] = [k] # supports
params['p'] = [1]
params['M'] = [C]
params['fin'] = val_x.shape[3]
params['dir_name'] += name
params['filter'] = 'chebyshev5'
params['brelu'] = 'b2relu'
params['pool'] = 'apool1'
mp.test(models.siamese_m_cgcnn(L, **params), name, params, data,
train_pairs, train_y, val_x, val_y, test_pairs, test_y)
# Common hyper-parameters for LeNet5-like networks.
if method == '2gcn':
str_params += 'p4_fc64_'
name = 'mvgcn2'
params = common.copy()
params['method'] = '2gcn'
params['F'] = [m, 64] # filters
params['K'] = [k, k] # supports
params['p'] = [4, 4]
params['M'] = [512, C]
params['fin'] = val_x.shape[3]
params['dir_name'] += name
params['filter'] = 'chebyshev5'
params['brelu'] = 'b2relu'
params['pool'] = 'apool1'
mp.test(models.siamese_m_cgcnn(L, **params), name, params, data,
train_pairs, train_y, val_x, val_y, test_pairs, test_y)
# mp.save(data_type)
method_type = method + '_'
mp.fin_result(method_type + data_type + str_params + obj_params, i_fold)
def train(modality, method, data_type, distance, k, fdim, nhops, mem_size, code_size, n_words, edim, n_epoch, batch_size, pairs, labels, coords, data, records, i_fold):
str_params = '_' + modality + '_' + distance + '_k' + str(k) + '_fdim' + str(fdim) + '_nhops' + str(nhops) + '_memsize' + str(mem_size) + '_codesize' + str(code_size) + '_nwords' + str(n_words) + '_edim' + str(edim)
print ('Construct ROI graphs...')
t_start = time.process_time()
coo1, coo2, coo3 = coords.shape # coo2 is the roi dimension
features = np.zeros([coo1*coo3, coo2])
for i in range(coo3):
features[coo1*i:coo1*(i+1), :] = coords[:, :, i]
dist, idx = graph.distance_scipy_spatial(np.transpose(features), k=10, metric='euclidean')
A = graph.adjacency(dist, idx).astype(np.float32)
if method == '2gcn':
graphs, perm = coarsening.coarsen(A, levels=FLAGS.coarsening_levels, self_connections=False)
L = [graph.laplacian(A, normalized=True) for A in graphs]
data = coarsening.perm_data1(data, perm)
else:
graphs = list()
graphs.append(A)
L = [graph.laplacian(A, normalized=True)]
print ('The number of GCN layers: ', len(L))
print('Execution time: {:.2f}s'.format(time.process_time() - t_start))
# graph.plot_spectrum(L)
del A
print ('Set parameters...')
mp = model_perf(i_fold, get_pair_label(pairs, labels))
# Architecture.
common = {}
common['dir_name'] = 'ppmi/'
common['num_epochs'] = n_epoch
common['batch_size'] = batch_size
common['eval_frequency'] = 5 * common['num_epochs']
common['patience'] = 5
common['regularization'] = 1e-2
common['dropout'] = 1
common['learning_rate'] = 5e-3
common['decay_rate'] = 0.95
common['momentum'] = 0.9
common['init_std'] = 5e-2
print ('Get feed pairs and labels...')
train_pairs, val_pairs, test_pairs = pairs
train_x, train_y, val_x, val_y, test_x, test_y = get_feed_data(data, pairs, labels, method)
train_r, val_r, test_r = get_feed_records(records, pairs, mem_size, code_size, method)
C = max(train_y)+1
common['decay_steps'] = train_x.shape[0] / common['batch_size']
if method == 'MemGCN':
# str_params += ''
name = 'cgconv_softmax'
params = common.copy()
params['method'] = method
params['p'] = [1] # pooling size
params['M'] = [C]
params['K'] = k # support number
params['nhops'] = nhops # hop number
params['fdim'] = fdim # filters dimension
params['edim'] = edim # embeddings dimension
params['mem_size'] = mem_size # the length of sequential records
params['code_size'] = code_size # the size of one record
params['n_words'] = n_words # feature dimension
params['distance'] = distance
params['fin'] = train_x.shape[2]
params['dir_name'] += name
params['filter'] = 'chebyshev5'
params['brelu'] = 'b2relu'
params['pool'] = 'apool1'
mp.test(models.siamese_cgcnn_mem(L, **params), name, params,
data, records, train_x, train_r, train_y,
val_x, val_r, val_y, test_x, test_r, test_y,
train_pairs, test_pairs)
# mp.save(data_type)
method_type = method + '_'
mp.fin_result(method_type + data_type + str_params, i_fold)
# Connections are only vertical or horizontal on the grid.
# Corner vertices are connected to 2 neightbors only.
if corners:
import scipy.sparse
A = A.toarray()
A[A < A.max()/1.5] = 0
A = scipy.sparse.csr_matrix(A)
print('{} edges'.format(A.nnz))
print("{} > {} edges".format(A.nnz//2, FLAGS.number_edges*m**2//2))
return A
t_start = timeit.default_timer()
A = grid_graph(58, corners=False)
A = graph.replace_random_edges(A, 0)
graphs, perm = coarsening.coarsen(A, levels=FLAGS.coarsening_levels, self_connections=False)
L = [graph.laplacian(A, normalized=True) for A in graphs]
print('Execution time: {:.2f}s'.format(timeit.default_timer() - t_start))
graph.plot_spectrum(L)
del A
# # Data
with gzip.open('../data/syn_adhd_train_data_quater_scale.pkl.gz', 'rb') as f:
X_train = pickle.load(f)
X = zip(*X_train)[0]
labels = zip(*X_train)[1]
X = np.asarray(X)
y = np.asarray(labels)
import coarsening
import tensorflow as tf
import numpy as np
import scipy as spy
# A=[[(0,1),0.3],
# [(0,2),0.4],
# [(0,3),0.3],
# [(1,2),0.3],
# [(1,3),0.3],
# [(1,4),0.4],
# [(2,0),0.4],
# [(2,3),0.3],
# [(2,4),0.3],
# [(3,0),0.3],
# [(3,1),0.4],
# [(3,4),0.3],
# [(4,0),0.3],
# [(4,2),0.4],
# [(4,3),0.3]]
A = spy.sparse.rand(5, 5, 0.3, 'coo', None)
print(A)
graphs, perm = coarsening.coarsen(A, 1, False)
print('graphs:')
print(graphs)
print('perm:')
print(perm)
