Loading deepchem/models/molgan.py +2 −1 Original line number Diff line number Diff line Loading @@ -64,8 +64,9 @@ class BasicMolGANModel(WGAN): self.nodes = nodes self.embedding_dim = embedding_dim self.dropout_rate = dropout_rate self.name = name super(BasicMolGANModel, self).__init__(name=name, **kwargs) super(BasicMolGANModel, self).__init__(name=self.name, **kwargs) def get_noise_input_shape(self) -> Tuple[int]: """ Loading deepchem/models/tests/test_molgan_model.py 0 → 100644 +73 −0 Original line number Diff line number Diff line import unittest from deepchem.models import BasicMolGANModel as MolGAN class test_molgan_model(unittest.TestCase): """ Unit testing for MolGAN basic layers """ def __init__(self, *args, **kwargs): super(test_molgan_model, self).__init__(*args, **kwargs) self.vertices = 9 self.nodes = 5 self.edges = 5 self.embedding_dim = 10 self.dropout_rate = 0.0 self.batch_size = 100 self.name = 'test_name' self.first_convolution_unit = 128 self.second_convolution_unit = 64 self.aggregation_unit = 128 self.model = MolGAN( edges=self.edges, vertices=self.vertices, nodes=self.nodes, embedding_dim=self.embedding_dim, dropout_rate=self.dropout_rate, name=self.name) def test_build(self): """ Test if initialization data is set-up correctly """ model = self.model assert model.batch_size == self.batch_size assert model.edges == self.edges assert model.nodes == self.nodes assert model.vertices == self.vertices assert model.dropout_rate == self.dropout_rate assert model.name == self.name assert len(model.generators) == 1 assert len(model.discriminators) == 1 def test_shapes(self): """ Check if input and output shapes are correct """ model = self.model # test if adjacency matrix input is correctly set assert model.discriminators[0].input_shape[0] == (None, self.vertices, self.vertices, self.edges) # test if nodes features matrix input is correctly set assert model.discriminators[0].input_shape[1] == (None, self.vertices, self.edges) # check discriminator shape assert model.discriminators[0].output_shape == (None, 1) # check training edges logits shape assert model.generators[0].output_shape[0] == (None, self.vertices, self.vertices, self.edges) # check training nodes logits shapes assert model.generators[0].output_shape[1] == (None, self.vertices, self.nodes) # check molecule generation edges logits shapes assert model.generators[0].output_shape[2] == (None, self.vertices, self.vertices) # check molecule generation nodes logits shapes assert model.generators[0].output_shape[3] == (None, self.vertices) if __name__ == '__main__': unittest.main() Loading
deepchem/models/molgan.py +2 −1 Original line number Diff line number Diff line Loading @@ -64,8 +64,9 @@ class BasicMolGANModel(WGAN): self.nodes = nodes self.embedding_dim = embedding_dim self.dropout_rate = dropout_rate self.name = name super(BasicMolGANModel, self).__init__(name=name, **kwargs) super(BasicMolGANModel, self).__init__(name=self.name, **kwargs) def get_noise_input_shape(self) -> Tuple[int]: """ Loading
deepchem/models/tests/test_molgan_model.py 0 → 100644 +73 −0 Original line number Diff line number Diff line import unittest from deepchem.models import BasicMolGANModel as MolGAN class test_molgan_model(unittest.TestCase): """ Unit testing for MolGAN basic layers """ def __init__(self, *args, **kwargs): super(test_molgan_model, self).__init__(*args, **kwargs) self.vertices = 9 self.nodes = 5 self.edges = 5 self.embedding_dim = 10 self.dropout_rate = 0.0 self.batch_size = 100 self.name = 'test_name' self.first_convolution_unit = 128 self.second_convolution_unit = 64 self.aggregation_unit = 128 self.model = MolGAN( edges=self.edges, vertices=self.vertices, nodes=self.nodes, embedding_dim=self.embedding_dim, dropout_rate=self.dropout_rate, name=self.name) def test_build(self): """ Test if initialization data is set-up correctly """ model = self.model assert model.batch_size == self.batch_size assert model.edges == self.edges assert model.nodes == self.nodes assert model.vertices == self.vertices assert model.dropout_rate == self.dropout_rate assert model.name == self.name assert len(model.generators) == 1 assert len(model.discriminators) == 1 def test_shapes(self): """ Check if input and output shapes are correct """ model = self.model # test if adjacency matrix input is correctly set assert model.discriminators[0].input_shape[0] == (None, self.vertices, self.vertices, self.edges) # test if nodes features matrix input is correctly set assert model.discriminators[0].input_shape[1] == (None, self.vertices, self.edges) # check discriminator shape assert model.discriminators[0].output_shape == (None, 1) # check training edges logits shape assert model.generators[0].output_shape[0] == (None, self.vertices, self.vertices, self.edges) # check training nodes logits shapes assert model.generators[0].output_shape[1] == (None, self.vertices, self.nodes) # check molecule generation edges logits shapes assert model.generators[0].output_shape[2] == (None, self.vertices, self.vertices) # check molecule generation nodes logits shapes assert model.generators[0].output_shape[3] == (None, self.vertices) if __name__ == '__main__': unittest.main()
