def test_model(id, signals, nn_params, model_params):
Gx, Gy = data_sets.perturbated_graphs(signals['g_params'], signals['eps1'], signals['eps2'],
pct=signals['pct'], perm=signals['perm'])
# Define the data model
data = data_sets.LinearDS2GSLinksPert(Gx, Gy,
signals['N_samples'],
signals['L_filter'], signals['g_params']['k'], # k is n_delts
median=signals['median'])
data.to_unit_norm()
data.add_noise(signals['noise'], test_only=signals['test_only'])
data.to_tensor()
Gx.compute_laplacian('normalized')
Gy.compute_laplacian('normalized')
archit = GIGOArch(Gx.L.todense(), Gy.L.todense(),
nn_params['Fi'], nn_params['Fo'], nn_params['Ki'], nn_params['Ko'], nn_params['C'],
nn_params['nonlin'], nn_params['last_act_fn'], nn_params['batch_norm'],
nn_params['arch_info'])
model_params['arch'] = archit
model = Model(**model_params)
t_init = time.time()
epochs, _, _ = model.fit(data.train_X, data.train_Y, data.val_X, data.val_Y)
t_conv = time.time() - t_init
mean_err, med_err, mse = model.test(data.test_X, data.test_Y)
print("DONE {}: MSE={} - Mean Err={} - Median Err={} - Params={} - t_conv={} - epochs={}".format(
id, mse, mean_err, med_err, model.count_params(), round(t_conv, 4), epochs
))
return mse, mean_err, med_err, model.count_params(), t_conv, epochs
def test_model(id, signals, nn_params, model_params):
Gx, Gy = data_sets.perturbated_graphs(signals['g_params'],
signals['eps1'],
signals['eps2'],
pct=signals['pct'],
perm=signals['perm'])
# Define the data model
data = data_sets.LinearDS2GSLinksPert(
Gx,
Gy,
signals['N_samples'],
signals['L_filter'],
signals['g_params']['k'], # k is n_delts
median=signals['median'])
data.to_unit_norm()
data.add_noise(signals['noise'], test_only=signals['test_only'])
data.to_tensor()
if nn_params['arch_type'] == "basic":
Gx.compute_laplacian('normalized')
archit = BasicArch(Gx.L.todense(), nn_params['F'], nn_params['K'],
nn_params['M'], nn_params['nonlin'], ARCH_INFO)
elif nn_params['arch_type'] == "mlp":
archit = MLP(nn_params['F'], nn_params['nonlin'], ARCH_INFO)
elif nn_params['arch_type'] == "conv":
archit = ConvNN(N, nn_params['F'], nn_params['K'], nn_params['nonlin'],
nn_params['M'], ARCH_INFO)
elif nn_params['arch_type'] == "linear":
archit = MLP(nn_params['F'], nn_params['nonlin'], ARCH_INFO)
else:
raise RuntimeError("arch_type has to be either basic, mlp or conv")
model_params['arch'] = archit
model = Model(**model_params)
t_init = time.time()
epochs, _, _ = model.fit(data.train_X, data.train_Y, data.val_X,
data.val_Y)
t_conv = time.time() - t_init
mean_err, med_err, mse = model.test(data.test_X, data.test_Y)
print(
"DONE {}: MSE={} - Mean Err={} - Median Err={} - Params={} - t_conv={} - epochs={}"
.format(id, mse, mean_err, med_err, model.count_params(),
round(t_conv, 4), epochs),
flush=True)
return mse, mean_err, med_err, model.count_params(), t_conv, epochs
def estimate_signals(i, G_params, eps_c, eps_d, n_samples, L, nodes_enc,
nodes_dec, ups, feat_enc, feat_dec, feat_only_conv):
# Create graphs
Gx, Gy = data_sets.perturbated_graphs(G_params, eps_c, eps_d, seed=SEED)
# Create graph signals
data = data_sets.LinearDS2GS(Gx,
Gy,
n_samples,
L,
3 * G_params['k'],
median=True)
data.to_unit_norm()
print('Median Diff between Y and X:',
np.median(np.linalg.norm((data.train_X - data.train_Y)**2, 1)))
X = data.train_X
Beta = np.linalg.pinv(X.T.dot(X)).dot(X.T).dot(data.train_Y)
test_Y = data.test_Y
est_Y_test = data.test_X.dot(Beta)
test_err = np.sum(
(est_Y_test - test_Y)**2, axis=1) / np.linalg.norm(data.test_Y)**2
print('Linear model: mean err: {} - median: {}'.format(
np.mean(test_err), np.median(test_err)))
data.to_tensor()
# Obtein clusters
cluster_x = gc.MultiResGraphClustering(Gx, nodes_enc, k=4, up_method=None)
cluster_y = gc.MultiResGraphClustering(Gy, nodes_dec, k=4, up_method=ups)
# Standar ConvAutoenc
net = architecture.GraphEncoderDecoder(feat_enc, [Gx.N] * 7,
cluster_x.Ds,
feat_dec, [Gx.N] * 7,
cluster_y.Us,
feat_only_conv,
As_dec=cluster_y.As,
last_act_fn=nn.Tanh(),
act_fn=nn.Tanh())
model = Model(net,
decay_rate=.9,
epochs=25,
batch_size=100,
learning_rate=0.05,
verbose=True,
eval_freq=1,
max_non_dec=5)
print('Model parameters: ', model.count_params())
model.fit(data.train_X, data.train_Y, data.val_X, data.val_Y)
mean_err, median_err, mse = model.test(data.test_X, data.test_Y)
print(
'Autoencoder: Graph {}: N: {} Mean MSE: {} - Mean Err: {} - Median Err: {}'
.format(i, Gx.N, mse, mean_err, median_err))
# Graph Autoenc
net = architecture.GraphEncoderDecoder(feat_enc,
cluster_x.sizes,
cluster_x.Ds,
feat_dec,
cluster_y.sizes,
cluster_y.Us,
feat_only_conv,
As_dec=cluster_y.As,
last_act_fn=nn.Tanh(),
act_fn=nn.Tanh())
model = Model(net,
decay_rate=.9,
epochs=25,
batch_size=100,
learning_rate=0.05,
verbose=True,
eval_freq=1,
max_non_dec=5)
print('Model parameters: ', model.count_params())
model.fit(data.train_X, data.train_Y, data.val_X, data.val_Y)
mean_err, median_err, mse = model.test(data.test_X, data.test_Y)
print(
'GRAPH ENC-DEC Graph {}: N: {} Mean MSE: {} - Mean Err: {} - Median Err: {}'
.format(i, Gx.N, mse, mean_err, median_err))
return mean_err, mse, model.count_params()
As_enc=clust_x.As, act_fn=lrn['af'],
last_act_fn=lrn['laf'], ups=exp['ups'],
downs=exp['downs'])
elif exp['type'] == 'AutoConv':
net = ConvAutoencoder(exp['f_enc'], exp['kernel_enc'],
exp['f_dec'], exp['kernel_dec'])
elif exp['type'] == 'AutoFC':
net = FCAutoencoder(exp['n_enc'], exp['n_dec'], bias=exp['bias'])
else:
raise RuntimeError('Unknown experiment type')
if exp['type'] != 'Linear':
model = Model(net, learning_rate=lrn['lr'], decay_rate=lrn['dr'],
batch_size=lrn['batch'], epochs=lrn['epochs'],
eval_freq=EVAL_F, max_non_dec=lrn['non_dec'],
verbose=VERBOSE)
epochs, _, _ = model.fit(data.train_X, data.train_Y, data.val_X, data.val_Y)
mean_err[i], med_err[i], mse[i] = model.test(data.test_X, data.test_Y)
print('G: {}, {}-{} ({}): epochs {} - mse {} - MedianErr: {}'
.format(id, i, exp['type'], model.count_params(), epochs,
mse[i], med_err[i]))
return mean_err, med_err, mse
if __name__ == '__main__':
# Set seeds
np.random.seed(SEED)
manual_seed(SEED)
# Graphs parameters
Gs = {}
Gs['n_graphs'] = 15
def run(id, Gs, signals, lrn, p_n):
Gx, Gy = ds.perturbated_graphs(Gs['params'],
Gs['pct_val'][0],
Gs['pct_val'][1],
pct=Gs['pct'],
seed=SEED)
data = ds.LinearDS2GS(Gx,
Gy,
signals['samples'],
signals['L'],
signals['deltas'],
median=signals['median'],
same_coeffs=signals['same_coeffs'])
# data = ds.NonLinearDS2GS(Gx, Gy, signals['samples'], signals['L'],
# signals['deltas'], median=signals['median'],
# same_coeffs=signals['same_coeffs'])
data.to_unit_norm()
data.add_noise(p_n, test_only=signals['test_only'])
median_dist = np.median(np.linalg.norm(data.train_X - data.train_Y,
axis=1))
print('Signal {}: distance {}'.format(id, median_dist))
data.to_tensor()
med_err = np.zeros(N_EXPS)
mse = np.zeros(N_EXPS)
epochs = np.zeros(N_EXPS)
for i, exp in enumerate(EXPS):
clust_x = gc.MultiResGraphClustering(Gx,
exp['n_enc'],
k=exp['n_enc'][-1],
up_method=exp['downs'])
clust_y = gc.MultiResGraphClustering(Gy,
exp['n_dec'],
k=exp['n_enc'][-1],
up_method=exp['ups'])
net = GraphEncoderDecoder(exp['f_enc'],
clust_x.sizes,
clust_x.Ds,
exp['f_dec'],
clust_y.sizes,
clust_y.Us,
exp['f_conv'],
As_dec=clust_y.As,
As_enc=clust_x.As,
act_fn=lrn['af'],
last_act_fn=lrn['laf'],
ups=exp['ups'],
downs=exp['downs'])
model = Model(net,
learning_rate=lrn['lr'],
decay_rate=lrn['dr'],
batch_size=lrn['batch'],
epochs=lrn['epochs'],
eval_freq=EVAL_F,
max_non_dec=lrn['non_dec'],
verbose=VERBOSE)
epochs[i], _, _ = model.fit(data.train_X, data.train_Y, data.val_X,
data.val_Y)
_, med_err[i], mse[i] = model.test(data.test_X, data.test_Y)
print('G: {}, {}-{} ({}): epochs {} - mse {} - MedianErr: {}'.format(
id, i, exp['type'], model.count_params(), epochs[i], mse[i],
med_err[i]))
return med_err, mse, epochs