def encode(self, chunk, use_random=False):
'''
Args:
chunk: a list of `n` strings, each being a SMILES.
Returns:
A numpy array of dtype np.float32, of shape (n, latent_dim)
Note: Each row should be the *mean* of the latent space distrubtion rather than a sampled point from that distribution.
(It can be anythin as long as it fits what self.decode expects)
'''
'''
cfg_tree_list = []
for smiles in chunk:
ts = parser.parse(smiles, self.grammar)
assert isinstance(ts, list) and len(ts) == 1
n = AnnotatedTree2MolTree(ts[0])
cfg_tree_list.append(n)
'''
if type(chunk[0]) is str:
cfg_tree_list = parse(chunk, self.grammar)
else:
cfg_tree_list = chunk
onehot, _ = batch_make_att_masks(cfg_tree_list, self.tree_decoder, self.onehot_walker, dtype=np.float32)
x_inputs = np.transpose(onehot, [0, 2, 1])
if use_random:
self.ae.train()
else:
self.ae.eval()
z_mean, _ = self.ae.encoder(x_inputs)
return z_mean.data.cpu().numpy()
def process_chunk(smiles_list):
grammar = parser.Grammar(cmd_args.grammar_file)
cfg_tree_list = []
for smiles in smiles_list:
ts = parser.parse(smiles, grammar)
assert isinstance(ts, list) and len(ts) == 1
n = AnnotatedTree2MolTree(ts[0])
cfg_tree_list.append(n)
walker = OnehotBuilder()
tree_decoder = create_tree_decoder()
onehot, masks = batch_make_att_masks(cfg_tree_list, tree_decoder, walker, dtype=np.byte)
return (onehot, masks)
def parse_smiles_with_cfg(smiles_file, grammar_file):
grammar = parser.Grammar(cmd_args.grammar_file)
cfg_tree_list = []
with open(smiles_file, 'r') as f:
for row in tqdm(f):
smiles = row.strip()
ts = parser.parse(smiles, grammar)
assert isinstance(ts, list) and len(ts) == 1
n = AnnotatedTree2MolTree(ts[0])
cfg_tree_list.append(n)
return cfg_tree_list
if __name__ == '__main__':
cfg_tree_list = parse_smiles_with_cfg(cmd_args.smiles_file, cmd_args.grammar_file)
all_true_binary, all_rule_masks = batch_make_att_masks(cfg_tree_list)
print(all_true_binary.shape, all_rule_masks.shape)
f_smiles = '.'.join(cmd_args.smiles_file.split('/')[-1].split('.')[0:-1])
out_file = '%s/%s.h5' % (cmd_args.save_dir, f_smiles)
h5f = h5py.File(out_file, 'w')
h5f.create_dataset('x', data=all_true_binary)
h5f.create_dataset('masks', data=all_rule_masks)
h5f.close()
cfg_tree_list = []
with open(smiles_file, 'r') as f:
for row in tqdm(f):
smiles = row.strip()
ts = parser.parse(smiles, grammar)
assert isinstance(ts, list) and len(ts) == 1
n = AnnotatedTree2MolTree(ts[0])
cfg_tree_list.append(n)
return cfg_tree_list
if __name__ == '__main__':
cfg_tree_list = parse_smiles_with_cfg(cmd_args.smiles_file,
cmd_args.grammar_file)
all_true_binary, all_rule_masks = batch_make_att_masks(cfg_tree_list)
print(all_true_binary.shape, all_rule_masks.shape)
f_smiles = '.'.join(cmd_args.smiles_file.split('/')[-1].split('.')[0:-1])
out_file = '%s/%s.h5' % (cmd_args.save_dir, f_smiles)
h5f = h5py.File(out_file, 'w')
h5f.create_dataset('x', data=all_true_binary)
h5f.create_dataset('masks', data=all_rule_masks)
h5f.close()