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,