y_test = test_dataset.y
y_test *= -1 * 2.479 / 4.184
test_dataset = dc.data.DiskDataset.from_numpy(
test_dataset.X,
y_test,
test_dataset.w,
test_dataset.ids,
tasks=pdbbind_tasks)
at = [6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]
radial = [[
1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5,
9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0
], [0.0, 4.0, 8.0], [0.4]]
#radial = [[12.0], [0.0, 4.0, 8.0], [0.4]]
rp = create_symmetry_parameters(radial)
layer_sizes = [32, 32, 16]
weight_init_stddevs = [
1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]),
1 / np.sqrt(layer_sizes[2])
]
dropouts = [0.3, 0.3, 0.05]
penalty_type = "l2"
penalty = 0.
model = TensorflowFragmentRegressor(
len(pdbbind_tasks),
rp,
at,
frag1_num_atoms,
frag2_num_atoms,
complex_num_atoms,
max_num_neighbors = 12
neighbor_cutoff = 12.0
train_dataset = dc.data.DiskDataset(train_dir)
test_dataset = dc.data.DiskDataset(test_dir)
pdbbind_tasks = ["-logKd/Ki"]
transformers = [
dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)
]
for transformer in transformers:
train_dataset = transformer.transform(train_dataset)
test_dataset = transformer.transform(test_dataset)
at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]
radial = [[12.0], [0.0, 4.0, 8.0], [4.0]]
rp = create_symmetry_parameters(radial)
layer_sizes = [32, 32, 16]
weight_init_stddevs = [
1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]),
1 / np.sqrt(layer_sizes[2])
]
dropouts = [0., 0., 0.]
penalty_type = "l2"
penalty = 0.
model = TensorflowFragmentRegressor(len(pdbbind_tasks),
rp,
at,
frag1_num_atoms,
frag2_num_atoms,
complex_num_atoms,
max_num_neighbors,