Loading contrib/one_shot_models/tests/test_vina_model.pydeleted 100644 → 0 +0 −195 Original line number Diff line number Diff line """ Testing construction of Vina models. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "MIT" import os import unittest import tensorflow as tf import deepchem as dc import numpy as np from tensorflow.python.framework import test_util from deepchem.models.tf_new_models.vina_model import VinaModel from deepchem.models.tf_new_models.vina_model import get_cells from deepchem.models.tf_new_models.vina_model import put_atoms_in_cells from deepchem.models.tf_new_models.vina_model import compute_neighbor_cells from deepchem.models.tf_new_models.vina_model import compute_closest_neighbors from deepchem.models.tf_new_models.vina_model import get_cells_for_atoms from deepchem.models.tf_new_models.vina_model import compute_neighbor_list import deepchem.utils.rdkit_util as rdkit_util from deepchem.utils.save import load_sdf_files from deepchem.utils import pad_array class TestVinaModel(test_util.TensorFlowTestCase): """ Test Container usage. """ def setUp(self): super(TestVinaModel, self).setUp() self.root = '/tmp' def test_vina_model(self): """Simple test that a vina model can be initialized.""" vina_model = VinaModel() def test_get_cells(self): """Test that tensorflow can compute grid cells.""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 with self.test_session() as sess: ndim = 3 cells = get_cells(start, stop, nbr_cutoff, ndim=ndim).eval() assert len(cells.shape) == 2 assert cells.shape[0] == 4**ndim ndim = 2 cells = get_cells(start, stop, nbr_cutoff, ndim=ndim).eval() assert len(cells.shape) == 2 assert cells.shape[0] == 4**ndim # TODO(rbharath): Check that this operation is differentiable. def test_compute_neighbor_list(self): """Test that neighbor list can be computed with tensorflow""" N = 10 start = 0 stop = 12 nbr_cutoff = 3 ndim = 3 M = 6 k = 5 # The number of cells which we should theoretically have n_cells = int(((stop - start) / nbr_cutoff)**ndim) with self.test_session() as sess: coords = start + np.random.rand(N, ndim) * (stop - start) coords = tf.stack(coords) nbr_list = compute_neighbor_list( coords, nbr_cutoff, N, M, n_cells, ndim=ndim, k=k) nbr_list = nbr_list.eval() assert nbr_list.shape == (N, M) def test_put_atoms_in_cells(self): """Test that atoms can be partitioned into spatial cells.""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 ndim = 3 k = 5 # The number of cells which we should theoretically have n_cells = ((stop - start) / nbr_cutoff)**ndim with self.test_session() as sess: cells = get_cells(start, stop, nbr_cutoff, ndim=ndim) coords = np.random.rand(N, ndim) _, atoms_in_cells = put_atoms_in_cells(coords, cells, N, n_cells, ndim, k) atoms_in_cells = atoms_in_cells.eval() assert len(atoms_in_cells) == n_cells # Each atom neighbors tensor should be (k, ndim) shaped. for atoms in atoms_in_cells: assert atoms.shape == (k, ndim) def test_compute_neighbor_cells(self): """Test that indices of neighboring cells can be computed.""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 ndim = 3 # The number of cells which we should theoretically have n_cells = ((stop - start) / nbr_cutoff)**ndim # TODO(rbharath): The test below only checks that shapes work out. # Need to do a correctness implementation vs. a simple CPU impl. with self.test_session() as sess: cells = get_cells(start, stop, nbr_cutoff, ndim=ndim) nbr_cells = compute_neighbor_cells(cells, ndim, n_cells) nbr_cells = nbr_cells.eval() assert len(nbr_cells) == n_cells nbr_cells = [nbr_cell for nbr_cell in nbr_cells] for nbr_cell in nbr_cells: assert nbr_cell.shape == (26,) def test_compute_closest_neighbors(self): """Test that closest neighbors can be computed properly""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 ndim = 3 k = 5 # The number of cells which we should theoretically have n_cells = ((stop - start) / nbr_cutoff)**ndim # TODO(rbharath): The test below only checks that shapes work out. # Need to do a correctness implementation vs. a simple CPU impl. with self.test_session() as sess: cells = get_cells(start, stop, nbr_cutoff, ndim=ndim) nbr_cells = compute_neighbor_cells(cells, ndim, n_cells) coords = np.random.rand(N, ndim) _, atoms_in_cells = put_atoms_in_cells(coords, cells, N, n_cells, ndim, k) nbrs = compute_closest_neighbors(coords, cells, atoms_in_cells, nbr_cells, N, n_cells) def test_get_cells_for_atoms(self): """Test that atoms are placed in the correct cells.""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 ndim = 3 k = 5 # The number of cells which we should theoretically have n_cells = ((stop - start) / nbr_cutoff)**ndim # TODO(rbharath): The test below only checks that shapes work out. # Need to do a correctness implementation vs. a simple CPU impl. with self.test_session() as sess: cells = get_cells(start, stop, nbr_cutoff, ndim=ndim) coords = np.random.rand(N, ndim) cells_for_atoms = get_cells_for_atoms(coords, cells, N, n_cells, ndim) cells_for_atoms = cells_for_atoms.eval() assert cells_for_atoms.shape == (N, 1) def test_vina_construct_graph(self): """Test that vina model graph can be constructed.""" data_dir = os.path.dirname(os.path.realpath(__file__)) protein_file = os.path.join(data_dir, "1jld_protein.pdb") ligand_file = os.path.join(data_dir, "1jld_ligand.pdb") vina_model = VinaModel() # TODO(rbharath): Commenting this out due to weird segfaults #def test_vina_generate_conformers(self): # """Test that Vina Model can generate conformers""" # data_dir = os.path.dirname(os.path.realpath(__file__)) # protein_file = os.path.join(data_dir, "1jld_protein.pdb") # ligand_file = os.path.join(data_dir, "1jld_ligand.pdb") # max_protein_atoms = 3500 # max_ligand_atoms = 100 # print("Loading protein file") # protein_xyz, protein_mol = rdkit_util.load_molecule(protein_file) # protein_Z = pad_array( # np.array([atom.GetAtomicNum() for atom in protein_mol.GetAtoms()]), # max_protein_atoms) # print("Loading ligand file") # ligand_xyz, ligand_mol = rdkit_util.load_molecule(ligand_file) # ligand_Z = pad_array( # np.array([atom.GetAtomicNum() for atom in ligand_mol.GetAtoms()]), # max_ligand_atoms) Loading
contrib/one_shot_models/tests/test_vina_model.pydeleted 100644 → 0 +0 −195 Original line number Diff line number Diff line """ Testing construction of Vina models. """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Bharath Ramsundar" __copyright__ = "Copyright 2016, Stanford University" __license__ = "MIT" import os import unittest import tensorflow as tf import deepchem as dc import numpy as np from tensorflow.python.framework import test_util from deepchem.models.tf_new_models.vina_model import VinaModel from deepchem.models.tf_new_models.vina_model import get_cells from deepchem.models.tf_new_models.vina_model import put_atoms_in_cells from deepchem.models.tf_new_models.vina_model import compute_neighbor_cells from deepchem.models.tf_new_models.vina_model import compute_closest_neighbors from deepchem.models.tf_new_models.vina_model import get_cells_for_atoms from deepchem.models.tf_new_models.vina_model import compute_neighbor_list import deepchem.utils.rdkit_util as rdkit_util from deepchem.utils.save import load_sdf_files from deepchem.utils import pad_array class TestVinaModel(test_util.TensorFlowTestCase): """ Test Container usage. """ def setUp(self): super(TestVinaModel, self).setUp() self.root = '/tmp' def test_vina_model(self): """Simple test that a vina model can be initialized.""" vina_model = VinaModel() def test_get_cells(self): """Test that tensorflow can compute grid cells.""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 with self.test_session() as sess: ndim = 3 cells = get_cells(start, stop, nbr_cutoff, ndim=ndim).eval() assert len(cells.shape) == 2 assert cells.shape[0] == 4**ndim ndim = 2 cells = get_cells(start, stop, nbr_cutoff, ndim=ndim).eval() assert len(cells.shape) == 2 assert cells.shape[0] == 4**ndim # TODO(rbharath): Check that this operation is differentiable. def test_compute_neighbor_list(self): """Test that neighbor list can be computed with tensorflow""" N = 10 start = 0 stop = 12 nbr_cutoff = 3 ndim = 3 M = 6 k = 5 # The number of cells which we should theoretically have n_cells = int(((stop - start) / nbr_cutoff)**ndim) with self.test_session() as sess: coords = start + np.random.rand(N, ndim) * (stop - start) coords = tf.stack(coords) nbr_list = compute_neighbor_list( coords, nbr_cutoff, N, M, n_cells, ndim=ndim, k=k) nbr_list = nbr_list.eval() assert nbr_list.shape == (N, M) def test_put_atoms_in_cells(self): """Test that atoms can be partitioned into spatial cells.""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 ndim = 3 k = 5 # The number of cells which we should theoretically have n_cells = ((stop - start) / nbr_cutoff)**ndim with self.test_session() as sess: cells = get_cells(start, stop, nbr_cutoff, ndim=ndim) coords = np.random.rand(N, ndim) _, atoms_in_cells = put_atoms_in_cells(coords, cells, N, n_cells, ndim, k) atoms_in_cells = atoms_in_cells.eval() assert len(atoms_in_cells) == n_cells # Each atom neighbors tensor should be (k, ndim) shaped. for atoms in atoms_in_cells: assert atoms.shape == (k, ndim) def test_compute_neighbor_cells(self): """Test that indices of neighboring cells can be computed.""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 ndim = 3 # The number of cells which we should theoretically have n_cells = ((stop - start) / nbr_cutoff)**ndim # TODO(rbharath): The test below only checks that shapes work out. # Need to do a correctness implementation vs. a simple CPU impl. with self.test_session() as sess: cells = get_cells(start, stop, nbr_cutoff, ndim=ndim) nbr_cells = compute_neighbor_cells(cells, ndim, n_cells) nbr_cells = nbr_cells.eval() assert len(nbr_cells) == n_cells nbr_cells = [nbr_cell for nbr_cell in nbr_cells] for nbr_cell in nbr_cells: assert nbr_cell.shape == (26,) def test_compute_closest_neighbors(self): """Test that closest neighbors can be computed properly""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 ndim = 3 k = 5 # The number of cells which we should theoretically have n_cells = ((stop - start) / nbr_cutoff)**ndim # TODO(rbharath): The test below only checks that shapes work out. # Need to do a correctness implementation vs. a simple CPU impl. with self.test_session() as sess: cells = get_cells(start, stop, nbr_cutoff, ndim=ndim) nbr_cells = compute_neighbor_cells(cells, ndim, n_cells) coords = np.random.rand(N, ndim) _, atoms_in_cells = put_atoms_in_cells(coords, cells, N, n_cells, ndim, k) nbrs = compute_closest_neighbors(coords, cells, atoms_in_cells, nbr_cells, N, n_cells) def test_get_cells_for_atoms(self): """Test that atoms are placed in the correct cells.""" N = 10 start = 0 stop = 4 nbr_cutoff = 1 ndim = 3 k = 5 # The number of cells which we should theoretically have n_cells = ((stop - start) / nbr_cutoff)**ndim # TODO(rbharath): The test below only checks that shapes work out. # Need to do a correctness implementation vs. a simple CPU impl. with self.test_session() as sess: cells = get_cells(start, stop, nbr_cutoff, ndim=ndim) coords = np.random.rand(N, ndim) cells_for_atoms = get_cells_for_atoms(coords, cells, N, n_cells, ndim) cells_for_atoms = cells_for_atoms.eval() assert cells_for_atoms.shape == (N, 1) def test_vina_construct_graph(self): """Test that vina model graph can be constructed.""" data_dir = os.path.dirname(os.path.realpath(__file__)) protein_file = os.path.join(data_dir, "1jld_protein.pdb") ligand_file = os.path.join(data_dir, "1jld_ligand.pdb") vina_model = VinaModel() # TODO(rbharath): Commenting this out due to weird segfaults #def test_vina_generate_conformers(self): # """Test that Vina Model can generate conformers""" # data_dir = os.path.dirname(os.path.realpath(__file__)) # protein_file = os.path.join(data_dir, "1jld_protein.pdb") # ligand_file = os.path.join(data_dir, "1jld_ligand.pdb") # max_protein_atoms = 3500 # max_ligand_atoms = 100 # print("Loading protein file") # protein_xyz, protein_mol = rdkit_util.load_molecule(protein_file) # protein_Z = pad_array( # np.array([atom.GetAtomicNum() for atom in protein_mol.GetAtoms()]), # max_protein_atoms) # print("Loading ligand file") # ligand_xyz, ligand_mol = rdkit_util.load_molecule(ligand_file) # ligand_Z = pad_array( # np.array([atom.GetAtomicNum() for atom in ligand_mol.GetAtoms()]), # max_ligand_atoms)
