def test_Embedding2D():
bond = layers.Input(name='bond', shape=(1, ), dtype='int32')
sbond = Squeeze()(bond)
embedding = Embedding2D(3, 5)
o = embedding(sbond)
assert o._keras_shape == (None, 5, 5)
model = GraphModel([bond], o)
x1 = np.array([1, 1, 2, 2, 0])
out = model.predict_on_batch([x1])
assert_allclose(out[0], out[1])
assert_allclose(out[2], out[3])
assert not (out[0] == out[-1]).all()
def test_set2set():
atom = layers.Input(name='atom', shape=(5, ), dtype='float32')
node_graph_indices = layers.Input(name='node_graph_indices',
shape=(1, ),
dtype='int32')
snode = Squeeze()(node_graph_indices)
reduce_layer = Set2Set()
o = reduce_layer([atom, snode])
assert o._keras_shape == (None, 10)
model = GraphModel([atom, node_graph_indices], o)
x1 = np.random.rand(5, 5)
x2 = np.array([0, 0, 0, 1, 1])
out = model.predict_on_batch([x1, x2])
assert out.shape == (2, 10)
def test_ReduceAtomToMol():
atom = layers.Input(name='atom', shape=(5, ), dtype='float32')
node_graph_indices = layers.Input(name='node_graph_indices',
shape=(1, ),
dtype='int32')
snode = Squeeze()(node_graph_indices)
reduce_layer = ReduceAtomToMol()
o = reduce_layer([atom, snode])
assert o._keras_shape == (None, 5)
model = GraphModel([atom, node_graph_indices], o)
x1 = np.random.rand(5, 5)
x2 = np.array([0, 0, 0, 1, 1])
out = model.predict_on_batch([x1, x2])
assert_allclose(x1[:3].sum(0), out[0])
assert_allclose(x1[3:].sum(0), out[1])
def test_GatherMolToAtomOrBond():
global_state = layers.Input(name='global_state',
shape=(5, ),
dtype='float32')
node_graph_indices = layers.Input(name='node_graph_indices',
shape=(1, ),
dtype='int32')
snode = Squeeze()(node_graph_indices)
layer = GatherMolToAtomOrBond()
o = layer([global_state, snode])
assert o._keras_shape == (None, 5)
model = GraphModel([global_state, node_graph_indices], o)
x1 = np.random.rand(2, 5)
x2 = np.array([0, 0, 0, 1, 1])
out = model.predict_on_batch([x1, x2])
assert_allclose(out, x1[x2])
# atom_contributions = pd.Series(model.coef_.flatten(), index=X.columns)
# atom_contributions = atom_contributions.reindex(np.arange(preprocessor.atom_classes)).fillna(0)
# Construct input sequences
batch_size = 32
train_sequence = GraphSequence(train_inputs, y_train_scaled, batch_size, final_batch=False)
valid_sequence = GraphSequence(valid_inputs, y_valid_scaled, batch_size, final_batch=False)
# Raw (integer) graph inputs
node_graph_indices = Input(shape=(1,), name='node_graph_indices', dtype='int32')
atom_types = Input(shape=(1,), name='atom', dtype='int32')
distance_rbf = Input(shape=(150,), name='distance_rbf', dtype='float32')
connectivity = Input(shape=(2,), name='connectivity', dtype='int32')
squeeze = Squeeze()
snode_graph_indices = squeeze(node_graph_indices)
satom_types = squeeze(atom_types)
# Initialize RNN and MessageLayer instances
atom_features = 64
# Initialize the atom states
atom_state = Embedding(
preprocessor.atom_classes,
atom_features, name='atom_embedding')(satom_types)
atomwise_energy = Embedding(
preprocessor.atom_classes, 1, name='atomwise_energy',
)(satom_types)
def test_save_and_load_model(get_2d_sequence, tmpdir):
preprocessor, sequence = get_2d_sequence
node_graph_indices = Input(shape=(1, ),
name='node_graph_indices',
dtype='int32')
atom_types = Input(shape=(1, ), name='atom', dtype='int32')
bond_types = Input(shape=(1, ), name='bond', dtype='int32')
connectivity = Input(shape=(2, ), name='connectivity', dtype='int32')
squeeze = Squeeze()
snode_graph_indices = squeeze(node_graph_indices)
satom_types = squeeze(atom_types)
sbond_types = squeeze(bond_types)
atom_features = 5
atom_state = Embedding(preprocessor.atom_classes,
atom_features,
name='atom_embedding')(satom_types)
bond_matrix = Embedding2D(preprocessor.bond_classes,
atom_features,
name='bond_embedding')(sbond_types)
atom_rnn_layer = GRUStep(atom_features)
message_layer = MessageLayer(reducer='sum', dropout=0.1)
# Perform the message passing
for _ in range(2):
# Get the message updates to each atom
message = message_layer([atom_state, bond_matrix, connectivity])
# Update memory and atom states
atom_state = atom_rnn_layer([message, atom_state])
atom_fingerprint = Dense(64, activation='sigmoid')(atom_state)
mol_fingerprint = ReduceAtomToMol(reducer='sum')(
[atom_fingerprint, snode_graph_indices])
out = Dense(1)(mol_fingerprint)
model = GraphModel(
[node_graph_indices, atom_types, bond_types, connectivity], [out])
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model.compile(optimizer=keras.optimizers.Adam(lr=1E-4), loss='mse')
hist = model.fit_generator(sequence, epochs=1)
loss = model.evaluate_generator(sequence)
_, fname = tempfile.mkstemp('.h5')
model.save(fname)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
model = load_model(fname, custom_objects=custom_layers)
loss2 = model.evaluate_generator(sequence)
assert_allclose(loss, loss2)
