def __call__(self, atoms1, atoms2):
args = dict(use_charge=self.use_charge, adjacency=self.adjacency)
g1 = Graph.from_ase(atoms1, **args)
g2 = Graph.from_ase(atoms2, **args)
R1 = self._mlgk(g1, g1).diagonal()**-0.5
R2 = self._mlgk(g2, g2).diagonal()**-0.5
R12 = self._mlgk(g1, g2)
K = R1[:, None] * R12 * R2[None, :]
D = np.sqrt(np.maximum(2 - 2 * K, 0))
return max(D.min(axis=1).max(), D.min(axis=0).max())
def test_molecular_kernel_on_organics(benchmark):
graphs = [
Graph.from_ase(molecule(name)) for name in g2.names
if len(molecule(name)) > 1
]
kernel = Tang2019MolecularKernel()
def fun(kernel, graphs):
return kernel(graphs, nodal=True)
benchmark.pedantic(fun,
args=(kernel, graphs),
iterations=3,
rounds=3,
warmup_rounds=1)
def test_ase(atoms):
g = Graph.from_ase(atoms)
assert(len(g.nodes) == len(atoms))
assert(len(g.edges) > 0)
def test_ase_pbc2(atoms):
adj = AtomicAdjacency(shape='tent1', length_scale=1.0, zoom=1)
graph_pbc = Graph.from_ase(atoms, use_pbc=True, adjacency=adj)
graph_nopbc = Graph.from_ase(atoms, use_pbc=False, adjacency=adj)
assert(len(graph_pbc.edges) > len(graph_nopbc.edges))
def test_ase_pbc1(atoms, adj):
graph_pbc = Graph.from_ase(atoms, use_pbc=True, adjacency=adj)
graph_nopbc = Graph.from_ase(atoms, use_pbc=False, adjacency=adj)
assert(len(graph_pbc.edges) == len(graph_nopbc.edges))
def test_ase_one():
atoms = molecule('H2')
graph = Graph.from_ase(atoms)
assert(len(graph.nodes) == 2)
assert(len(graph.edges) == 1)
sys.stderr.write('Unrecognized argument: %s\n' % arg)
sys.exit(1)
print(arg_dict)
formula = arg_dict.pop('formula', 'CH3COOH')
zoom = arg_dict.pop('zoom', 1.5)
repeat = arg_dict.pop('repeat', 4)
nlaunch = arg_dict.pop('nlaunch', 1)
block_per_sm = arg_dict.pop('block_per_sm', 8)
block_size = arg_dict.pop('block_size', 128)
device = pycuda.driver.Device(arg_dict.pop('device', 0))
njobs = device.MULTIPROCESSOR_COUNT * block_per_sm * repeat
g = Graph.from_ase(molecule(formula), adjacency=dict(h=zoom))
kernel = Kernel()
''' generate jobs '''
jobs = [Job(0, 0, GPUArray(len(g.nodes)**2, np.float32)) for i in range(njobs)]
''' call GPU kernel '''
for i in range(nlaunch):
kernel.kernel._launch_kernel([g], jobs, nodal=False, lmin=0)
R = jobs[0].vr_gpu.get().reshape(len(g.nodes), -1)
r = R.sum()
print('Nodal similarity:\n', R, sep='')
print('Overall similarity:\n', r, sep='')
for job in jobs:
file = arg_dict.pop('file', 'pdb-3kDa-1324.json')
active = json.loads('[' + arg_dict.pop('active', 1) + ']')
zoom = arg_dict.pop('zoom', 1.5)
# reorder = arg_dict['reorder'] if 'reorder' in arg_dict else 'natural'
sys.stderr.write('Loading file %s\n' % file)
try:
pdb_json = json.loads(open(file).read())
except FileNotFoundError:
pdb_json = json.loads(
open(os.path.join(os.path.dirname(__file__), file)).read()
)
graph_list = []
for i in active:
mol = pdb_json[i]
sys.stderr.write(
'%5d: %s, %d atoms\n' % (i, mol['pdb_id'], len(mol['sym']))
)
atoms = Atoms(mol['sym'], mol['xyz'])
graph_list.append(Graph.from_ase(atoms, adjacency=dict(h=zoom)))
kernel = Tang2019MolecularKernel()
print(kernel(graph_list))
def test_ase_pbc2(atoms):
graph_pbc = Graph.from_ase(atoms, use_pbc=True, adjacency=simple_adj)
graph_nopbc = Graph.from_ase(atoms, use_pbc=False, adjacency=simple_adj)
assert (len(graph_pbc.edges) > len(graph_nopbc.edges))
def fun(atoms):
return Graph.from_ase(atoms)