def __init__(self, vocab, args):
super(DiffVAE, self).__init__()
self.vocab = vocab
self.hidden_size = hidden_size = args.hidden_size
self.rand_size = rand_size = args.rand_size
self.jtmpn = JTMPN(hidden_size, args.depthG)
self.mpn = MPN(hidden_size, args.depthG)
if args.share_embedding:
self.embedding = nn.Embedding(vocab.size(), hidden_size)
self.jtnn = JTNNEncoder(hidden_size, args.depthT, self.embedding)
self.decoder = JTNNDecoder(vocab, hidden_size, self.embedding, args.use_molatt)
else:
self.jtnn = JTNNEncoder(hidden_size, args.depthT, nn.Embedding(vocab.size(), hidden_size))
self.decoder = JTNNDecoder(vocab, hidden_size, nn.Embedding(vocab.size(), hidden_size), args.use_molatt)
self.A_assm = nn.Linear(hidden_size, hidden_size, bias=False)
self.assm_loss = nn.CrossEntropyLoss(size_average=False)
self.T_mean = nn.Linear(hidden_size, rand_size / 2)
self.T_var = nn.Linear(hidden_size, rand_size / 2)
self.G_mean = nn.Linear(hidden_size, rand_size / 2)
self.G_var = nn.Linear(hidden_size, rand_size / 2)
self.B_t = nn.Sequential(nn.Linear(hidden_size + rand_size / 2, hidden_size), nn.ReLU())
self.B_g = nn.Sequential(nn.Linear(hidden_size + rand_size / 2, hidden_size), nn.ReLU())
def __init__(self, vocab, hidden_size, latent_size, depth):
super(JTPropVAE, self).__init__()
self.vocab = vocab
self.hidden_size = hidden_size
self.latent_size = latent_size
self.depth = depth
self.embedding = nn.Embedding(vocab.size(), hidden_size)
self.jtnn = JTNNEncoder(vocab, hidden_size, self.embedding)
self.jtmpn = JTMPN(hidden_size, depth)
self.mpn = MPN(hidden_size, depth)
self.decoder = JTNNDecoder(vocab, hidden_size, latent_size / 2,
self.embedding)
self.T_mean = nn.Linear(hidden_size, latent_size / 2)
self.T_var = nn.Linear(hidden_size, latent_size / 2)
self.G_mean = nn.Linear(hidden_size, latent_size / 2)
self.G_var = nn.Linear(hidden_size, latent_size / 2)
self.propNN = nn.Sequential(
nn.Linear(self.latent_size, self.hidden_size), nn.Tanh(),
nn.Linear(self.hidden_size, 1))
self.prop_loss = nn.MSELoss()
self.assm_loss = nn.CrossEntropyLoss(size_average=False)
self.stereo_loss = nn.CrossEntropyLoss(size_average=False)
def __init__(self, vocab, hidden_size, latent_size, depthT, depthG):
super(JTNNVAEMLP, self).__init__()
self.vocab = vocab
self.hidden_size = hidden_size
self.latent_size = latent_size = latent_size / 2 #Tree and Mol has two vectors
self.jtnn = JTNNEncoder(hidden_size, depthT, nn.Embedding(vocab.size(), hidden_size))
self.decoder = JTNNDecoder(vocab, hidden_size, latent_size, nn.Embedding(vocab.size(), hidden_size))
self.jtmpn = JTMPN(hidden_size, depthG)
self.mpn = MPN(hidden_size, depthG)
self.A_assm = nn.Linear(latent_size, hidden_size, bias=False)
self.assm_loss = nn.CrossEntropyLoss(size_average=False)
self.T_mean = nn.Linear(hidden_size, latent_size)
self.T_var = nn.Linear(hidden_size, latent_size)
self.G_mean = nn.Linear(hidden_size, latent_size)
self.G_var = nn.Linear(hidden_size, latent_size)
self.T_hat_mean = nn.Linear(latent_size, latent_size)
self.T_hat_var = nn.Linear(latent_size, latent_size)
#New MLP
self.gene_exp_size = 978
self.gene_mlp = nn.Linear(self.gene_exp_size, latent_size)
self.tree_mlp = nn.Linear(latent_size+latent_size, latent_size)
def decode(self, x_tree_vecs, x_mol_vecs):
#currently do not support batch decoding
assert x_tree_vecs.size(0) == 1 and x_mol_vecs.size(0) == 1
pred_root,pred_nodes = self.decoder.decode(x_tree_vecs, x_mol_vecs)
if len(pred_nodes) == 0: return None
elif len(pred_nodes) == 1: return pred_root.smiles
#Mark nid & is_leaf & atommap
for i,node in enumerate(pred_nodes):
node.nid = i + 1
node.is_leaf = (len(node.neighbors) == 1)
if len(node.neighbors) > 1:
set_atommap(node.mol, node.nid)
scope = [(0, len(pred_nodes))]
jtenc_holder,mess_dict = JTNNEncoder.tensorize_nodes(pred_nodes, scope)
_,tree_mess = self.jtnn(*jtenc_holder)
tree_mess = (tree_mess, mess_dict) #Important: tree_mess is a matrix, mess_dict is a python dict
x_mol_vec_pooled = x_mol_vecs.sum(dim=1) #average pooling?
x_mol_vec_pooled = self.A_assm(x_mol_vec_pooled).squeeze() #bilinear
cur_mol = copy_edit_mol(pred_root.mol)
global_amap = [{}] + [{} for node in pred_nodes]
global_amap[1] = {atom.GetIdx():atom.GetIdx() for atom in cur_mol.GetAtoms()}
cur_mol = self.dfs_assemble(tree_mess, x_mol_vec_pooled, pred_nodes, cur_mol, global_amap, [], pred_root, None)
if cur_mol is None:
return None
cur_mol = cur_mol.GetMol()
set_atommap(cur_mol)
cur_mol = Chem.MolFromSmiles(Chem.MolToSmiles(cur_mol))
return Chem.MolToSmiles(cur_mol) if cur_mol is not None else None
def tensorize(tree_batch, vocab, assm=True, if_need_origin_word = False):
set_batch_nodeID(tree_batch, vocab)
smiles_batch = [tree.smiles for tree in tree_batch]
(jtenc_holder,mess_dict),origin_word = JTNNEncoder.tensorize(tree_batch)
mpn_holder = MPN.tensorize(smiles_batch)
if assm is False:
if if_need_origin_word:
return tree_batch, jtenc_holder, mpn_holder, origin_word
else:
return tree_batch, jtenc_holder, mpn_holder
cands = []
batch_idx = []
for i,mol_tree in enumerate(tree_batch):
for node in mol_tree.nodes:
#Leaf node's attachment is determined by neighboring node's attachment
if node.is_leaf or len(node.cands) == 1: continue
cands.extend( [(cand, mol_tree.nodes, node) for cand in node.cands] )
batch_idx.extend([i] * len(node.cands))
jtmpn_holder = JTMPN.tensorize(cands, mess_dict)
batch_idx = torch.LongTensor(batch_idx)
return tree_batch, jtenc_holder, mpn_holder, (jtmpn_holder,batch_idx)
def __init__(self,
vocab,
hidden_size,
latent_size,
depthT,
depthG,
loss_type='cos'):
super(JTNNMJ, self).__init__()
self.vocab = vocab
self.hidden_size = hidden_size
self.latent_size = latent_size = latent_size / 2 #Tree and Mol has two vectors
self.jtnn = JTNNEncoder(hidden_size, depthT,
nn.Embedding(vocab.size(), hidden_size))
self.decoder = JTNNDecoder(vocab, hidden_size, latent_size,
nn.Embedding(vocab.size(), hidden_size))
self.jtmpn = JTMPN(hidden_size, depthG)
self.mpn = MPN(hidden_size, depthG)
self.A_assm = nn.Linear(latent_size, hidden_size, bias=False)
self.assm_loss = nn.CrossEntropyLoss(size_average=False)
self.T_mean = nn.Linear(hidden_size, latent_size)
self.T_var = nn.Linear(hidden_size, latent_size)
self.G_mean = nn.Linear(hidden_size, latent_size)
self.G_var = nn.Linear(hidden_size, latent_size)
#For MJ
self.gene_exp_size = 978
self.gene_mlp = nn.Linear(self.gene_exp_size, 2 * hidden_size)
self.gene_mlp2 = nn.Linear(2 * hidden_size, 2 * hidden_size)
self.cos = nn.CosineSimilarity()
self.loss_type = loss_type
if loss_type == 'L1':
self.cos_loss = torch.nn.L1Loss(reduction='elementwise_mean')
elif loss_type == 'L2':
self.cos_loss = torch.nn.MSELoss(reduction='elementwise_mean')
elif loss_type == 'cos':
self.cos_loss = torch.nn.CosineEmbeddingLoss()
def __init__(self, vocab, hidden_size, latent_size, depth):
super(JTNNVAE, self).__init__()
self.vocab = vocab
self.hidden_size = int(hidden_size)
self.latent_size = int(latent_size)
self.depth = depth
self.embedding = nn.Embedding(vocab.size(), hidden_size)
self.jtnn = JTNNEncoder(vocab, hidden_size, self.embedding)
self.jtmpn = JTMPN(hidden_size, depth)
self.mpn = MPN(hidden_size, depth)
self.decoder = JTNNDecoder(vocab, hidden_size, latent_size / 2,
self.embedding)
self.T_mean = nn.Linear(hidden_size, int(latent_size / 2))
self.T_var = nn.Linear(hidden_size, int(latent_size / 2))
self.G_mean = nn.Linear(hidden_size, int(latent_size / 2))
self.G_var = nn.Linear(hidden_size, int(latent_size / 2))
self.assm_loss = nn.CrossEntropyLoss(size_average=False)
self.stereo_loss = nn.CrossEntropyLoss(size_average=False)