def test_utils_data():
# Load ENZYMES because we use it also in datasets tests
A_list, X_list, y = tud.load_data('ENZYMES', clean=True)
# Test numpy to batch
X_batch, A_batch = numpy_to_batch(X_list, A_list)
assert X_batch.ndim == 3
assert A_batch.ndim == 3
assert X_batch.shape[0] == A_batch.shape[0]
assert X_batch.shape[1] == A_batch.shape[1] == A_batch.shape[2]
# Test numpy to disjoint
X_disj, A_disj, I_disj = numpy_to_disjoint(X_list, A_list)
assert X_disj.ndim == 2
assert A_disj.ndim == 2
assert X_disj.shape[0] == A_disj.shape[0] == A_disj.shape[1]
return loss
################################################################################
# FIT MODEL
################################################################################
current_batch = 0
model_loss = 0
batches_in_epoch = np.ceil(len(A_tr) / batch_size)
print('Fitting model')
batches_train = batch_iterator([X_tr, A_tr, E_tr, y_tr],
batch_size=batch_size,
epochs=epochs)
for b in batches_train:
X_, A_, E_, I_ = numpy_to_disjoint(*b[:-1])
A_ = ops.sp_matrix_to_sp_tensor(A_)
y_ = b[-1]
outs = train_step(X_, A_, E_, I_, y_)
model_loss += outs.numpy()
current_batch += 1
if current_batch == batches_in_epoch:
print('Loss: {}'.format(model_loss / batches_in_epoch))
model_loss = 0
current_batch = 0
################################################################################
# EVALUATE MODEL
################################################################################
print('Testing model')
acc = acc_fn(y_, predictions)
return loss, acc
################################################################################
# FIT MODEL
################################################################################
current_batch = 0
model_lss = model_acc = 0
batches_in_epoch = np.ceil(len(a_train) / batch_size)
print('Fitting model')
batches_train = batch_iterator([x_train, a_train, y_train],
batch_size=batch_size, epochs=epochs)
for b in batches_train:
x_, a_, i_ = numpy_to_disjoint(*b[:-1])
a_ = ops.sp_matrix_to_sp_tensor(a_)
y_ = b[-1]
lss, acc = train_step(x_, a_, i_, y_)
model_lss += lss.numpy()
model_acc += acc.numpy()
current_batch += 1
if current_batch == batches_in_epoch:
model_lss /= batches_in_epoch
model_acc /= batches_in_epoch
print('Loss: {}. Acc: {}'.format(model_lss, model_acc))
model_lss = model_acc = 0
current_batch = 0
################################################################################