def test_atomic_conv_variable(self):
"""A simple test that initializes and fits an AtomicConvModel on variable input size."""
# For simplicity, let's assume both molecules have same number of
# atoms.
frag1_num_atoms = 1000
frag2_num_atoms = 1200
complex_num_atoms = frag1_num_atoms + frag2_num_atoms
batch_size = 1
atomic_convnet = atomic_conv.AtomicConvModel(
batch_size=batch_size,
frag1_num_atoms=frag1_num_atoms,
frag2_num_atoms=frag2_num_atoms,
complex_num_atoms=complex_num_atoms)
# Creates a set of dummy features that contain the coordinate and
# neighbor-list features required by the AtomicConvModel.
features = []
frag1_coords = np.random.rand(frag1_num_atoms, 3)
frag1_nbr_list = {i: [] for i in range(frag1_num_atoms)}
frag1_z = np.random.randint(10, size=(frag1_num_atoms))
frag2_coords = np.random.rand(frag2_num_atoms, 3)
frag2_nbr_list = {i: [] for i in range(frag2_num_atoms)}
frag2_z = np.random.randint(10, size=(frag2_num_atoms))
system_coords = np.random.rand(complex_num_atoms, 3)
system_nbr_list = {i: [] for i in range(complex_num_atoms)}
system_z = np.random.randint(10, size=(complex_num_atoms))
features.append(
(frag1_coords, frag1_nbr_list, frag1_z, frag2_coords, frag2_nbr_list,
frag2_z, system_coords, system_nbr_list, system_z))
features = np.asarray(features)
labels = np.zeros(batch_size)
train = NumpyDataset(features, labels)
atomic_convnet.fit(train, nb_epoch=1)
def test_atomic_conv_with_feat(self):
"""A simple test for running an atomic convolution on featurized data."""
dir_path = os.path.dirname(os.path.realpath(__file__))
ligand_file = os.path.join(dir_path,
"../../feat/tests/data/3zso_ligand_hyd.pdb")
protein_file = os.path.join(dir_path,
"../../feat/tests/data/3zso_protein.pdb")
# Pulled from PDB files. For larger datasets with more PDBs, would use
# max num atoms instead of exact.
frag1_num_atoms = 44 # for ligand atoms
frag2_num_atoms = 2336 # for protein atoms
complex_num_atoms = 2380 # in total
max_num_neighbors = 4
# Cutoff in angstroms
neighbor_cutoff = 4
complex_featurizer = ComplexNeighborListFragmentAtomicCoordinates(
frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors,
neighbor_cutoff)
# arbitrary label
labels = np.array([0])
features, _ = complex_featurizer.featurize([ligand_file], [protein_file])
dataset = deepchem.data.DiskDataset.from_numpy(features, labels)
batch_size = 1
print("Constructing Atomic Conv model")
atomic_convnet = atomic_conv.AtomicConvModel(
batch_size=batch_size,
frag1_num_atoms=frag1_num_atoms,
frag2_num_atoms=frag2_num_atoms,
complex_num_atoms=complex_num_atoms)
print("About to call fit")
# Run a fitting operation
atomic_convnet.fit(dataset)
def test_atomic_conv_initialize():
"""Quick test of AtomicConv."""
acm = atomic_conv.AtomicConvModel(n_tasks=1,
batch_size=1,
layer_sizes=[
1,
],
frag1_num_atoms=5,
frag2_num_atoms=5,
complex_num_atoms=10)
assert acm.complex_num_atoms == 10
assert len(acm.atom_types) == 15
def test_atomic_conv():
"""A simple test that initializes and fits an AtomicConvModel."""
# For simplicity, let's assume both molecules have same number of
# atoms.
N_atoms = 5
batch_size = 1
atomic_convnet = atomic_conv.AtomicConvModel(
n_tasks=1,
batch_size=batch_size,
layer_sizes=[10],
frag1_num_atoms=5,
frag2_num_atoms=5,
complex_num_atoms=10,
dropouts=0.0,
learning_rate=0.003)
# Creates a set of dummy features that contain the coordinate and
# neighbor-list features required by the AtomicConvModel.
features = []
frag1_coords = np.random.rand(N_atoms, 3)
frag1_nbr_list = {0: [], 1: [], 2: [], 3: [], 4: []}
frag1_z = np.random.randint(10, size=(N_atoms))
frag2_coords = np.random.rand(N_atoms, 3)
frag2_nbr_list = {0: [], 1: [], 2: [], 3: [], 4: []}
frag2_z = np.random.randint(10, size=(N_atoms))
system_coords = np.random.rand(2 * N_atoms, 3)
system_nbr_list = {
0: [],
1: [],
2: [],
3: [],
4: [],
5: [],
6: [],
7: [],
8: [],
9: []
}
system_z = np.random.randint(10, size=(2 * N_atoms))
features.append(
(frag1_coords, frag1_nbr_list, frag1_z, frag2_coords, frag2_nbr_list,
frag2_z, system_coords, system_nbr_list, system_z))
features = np.asarray(features)
labels = np.random.rand(batch_size)
train = NumpyDataset(features, labels)
atomic_convnet.fit(train, nb_epoch=150)
assert np.allclose(labels, atomic_convnet.predict(train), atol=0.01)
def test_atomic_conv_with_feat():
"""A simple test for running an atomic convolution on featurized data."""
dir_path = os.path.dirname(os.path.realpath(__file__))
ligand_file = os.path.join(dir_path,
"../../feat/tests/data/3zso_ligand_hyd.pdb")
protein_file = os.path.join(dir_path,
"../../feat/tests/data/3zso_protein_noH.pdb")
# Pulled from PDB files. For larger datasets with more PDBs, would use
# max num atoms instead of exact.
frag1_num_atoms = 44 # for ligand atoms
frag2_num_atoms = 2334 # for protein atoms
complex_num_atoms = 2378 # in total
max_num_neighbors = 4
# Cutoff in angstroms
neighbor_cutoff = 4
complex_featurizer = AtomicConvFeaturizer(frag1_num_atoms, frag2_num_atoms,
complex_num_atoms,
max_num_neighbors,
neighbor_cutoff)
# arbitrary label
labels = np.array([0])
features = complex_featurizer.featurize([(ligand_file, protein_file)])
dataset = NumpyDataset(features, labels)
batch_size = 1
print("Constructing Atomic Conv model")
atomic_convnet = atomic_conv.AtomicConvModel(
n_tasks=1,
batch_size=batch_size,
layer_sizes=[10],
frag1_num_atoms=frag1_num_atoms,
frag2_num_atoms=frag2_num_atoms,
complex_num_atoms=complex_num_atoms)
print("About to call fit")
# Run a fitting operation
atomic_convnet.fit(dataset)
preds = atomic_convnet.predict(dataset)
assert preds.shape == (1, 1, 1)
assert np.count_nonzero(preds) > 0
def test_atomic_conv_variable():
"""A simple test that initializes and fits an AtomicConvModel on variable input size."""
frag1_num_atoms = 1000
frag2_num_atoms = 1200
complex_num_atoms = frag1_num_atoms + frag2_num_atoms
batch_size = 1
atomic_convnet = atomic_conv.AtomicConvModel(
n_tasks=1,
batch_size=batch_size,
layer_sizes=[
10,
],
frag1_num_atoms=frag1_num_atoms,
frag2_num_atoms=frag2_num_atoms,
complex_num_atoms=complex_num_atoms)
# Creates a set of dummy features that contain the coordinate and
# neighbor-list features required by the AtomicConvModel.
features = []
frag1_coords = np.random.rand(frag1_num_atoms, 3)
frag1_nbr_list = {i: [] for i in range(frag1_num_atoms)}
frag1_z = np.random.randint(10, size=(frag1_num_atoms))
frag2_coords = np.random.rand(frag2_num_atoms, 3)
frag2_nbr_list = {i: [] for i in range(frag2_num_atoms)}
frag2_z = np.random.randint(10, size=(frag2_num_atoms))
system_coords = np.random.rand(complex_num_atoms, 3)
system_nbr_list = {i: [] for i in range(complex_num_atoms)}
system_z = np.random.randint(10, size=(complex_num_atoms))
features.append(
(frag1_coords, frag1_nbr_list, frag1_z, frag2_coords, frag2_nbr_list,
frag2_z, system_coords, system_nbr_list, system_z))
features = np.asarray(features)
labels = np.zeros(batch_size)
train = NumpyDataset(features, labels)
atomic_convnet.fit(train, nb_epoch=1)
preds = atomic_convnet.predict(train)
assert preds.shape == (1, 1, 1)
assert np.count_nonzero(preds) > 0
